RU2774604C1 - Method for collecting and processing data with measuring effectiveness of advertising materials and advertising campaigns for automated selection of online advertising platforms for the purpose of placing advertising materials - Google Patents

Abstract

FIELD: electrical engineering.

SUBSTANCE: invention relates to the field of electrical engineering, information technology and computer technology. The method and system of automated configuration of placement of advertising materials of companies on advertising platforms on which presentation of advertising materials is carried out, with changes in the settings of placement of advertising materials and advertising campaigns and presentation of advertising materials of advertising campaigns with changed settings to users are proposed.

EFFECT: proposed is the automated configuration of the placement of advertising materials of companies on advertising platforms.

14 cl, 6 dwg, 11 tbl

Description

FIELD OF TECHNOLOGY

The invention relates to the field of electrical engineering and information technology and computer technology, and, in particular, to methods and systems for collecting and processing data with measuring the effectiveness of placing online advertising materials with changing the settings of advertising materials and advertising campaigns and presenting advertising materials with changed presentation settings to users, including methods for managing systems for automated settings of advertising materials, advertising campaigns, placement of advertising materials, selection of online advertising sites in order to place advertising materials.

BACKGROUND OF THE INVENTION

Currently, a method for ranking one or more advertisements (see US7739708B2, publ. 06/15/2010, "System and method for revenue based advertisement placement"), including: receiving a search query that includes one or more search terms; obtaining a result set containing one or more advertisements in response to the search query; for one or more advertisements that make up the result set, calculating the click through rate; for one or more advertisements, calculating the expected revenue for a given advertisement using the click-through rate of that advertisement and indicating the revenue associated with the given advertisement, indicating the revenue, including the expected revenue from the offer; ordering one or more advertisements in accordance with the expected income from one or more advertisements; and calculating an actual price value for at least one of the advertisements using the "bubble popping" algorithm, wherein the actual price is the price that the advertiser will pay to retain that position in the order of one or more advertisements for at least one of the advertisements in the result set.

The disadvantages of this solution are at least insufficiently efficient use of computing resources, including for determining the effectiveness of placing online advertising materials with an increase in the effectiveness of advertising campaigns and placing advertising materials with changes to the settings of advertising campaigns and placement of advertising materials and using changed settings for advertising campaigns and placement of advertising materials, and increasing the speed, insufficient efficiency in determining the effectiveness of advertising campaigns, advertising platforms, placement of advertising materials, insufficient amount of information collected to effectively determine the effectiveness of advertising campaigns, advertising platforms, placement of advertising materials, insufficient efficiency in managing advertising materials, advertising campaigns.

Also known is a method for measuring the effectiveness of placing online advertising and / or targeted advertising (see RU2659475C1, publ. 07/02/2018, "METHOD FOR MEASURING THE EFFICIENCY OF PLACING ONLINE ADVERTISING CAMPAIGNS (OPTIONS)"), in which it is sequentially carried out in offline mode by at least one recording device, collecting and recognizing offline data located in the area of the advertised location or location with the advertised product of mobile and/or wearable devices by at least one of the offline identifiers of each of these devices, including at least the WiFi MAC address, forming array of offline location data; transmitting the array of offline location data to the data warehouse, where they register and mark offline location data, at least by the name of the location and the time of registration of each device in the location, compare the array of offline location data obtained over a predetermined period of time with the array of online data of mobile and wearable devices of the online advertising platform, containing at least one recognized online identifier of mobile and/or wearable devices on which online advertising was displayed, mobile and/or wearable devices on which online advertising was displayed are identified in the location by comparing arrays of offline location data and online data of the online advertising placement platform and identifying at least one pair of offline and online identifiers of a mobile and/or wearable device in them for a predetermined period of time, and measuring the effectiveness of online advertising placement and/or targeted th advertising is carried out by calculating the conversion rate from online to offline, as the ratio of the array of mobile and/or wearable devices identified in the location, on which advertising was displayed online, to the total array of mobile and/or wearable devices, on which advertising was displayed online.

The disadvantages of this solution are at least insufficiently efficient use of computing resources, including for determining the effectiveness of placing online advertising materials with an increase in the effectiveness of advertising campaigns and placing advertising materials with changes to the settings of advertising campaigns and placement of advertising materials and using changed settings for advertising campaigns and placement of advertising materials, and increasing the speed, insufficient efficiency in determining the effectiveness of advertising campaigns, advertising platforms, placement of advertising materials, insufficient amount of information collected to effectively determine the effectiveness of advertising campaigns, advertising platforms, placement of advertising materials, insufficient efficiency in managing advertising materials, advertising campaigns.

Also known is a method for placing promotional and informational materials on IT devices (see RU2617556C2, publ. 04/25/2017, "METHOD FOR PLACING AND PRESENTING ADVERTISING AND INFORMATIONAL MATERIALS ON IT DEVICES"), which consists in creating an information and reference system, including at least at least one switching subsystem, preparation of advertising space by project participants, advertisers, advertising agencies, owners of IT devices, website operator, creation by the website operator in the switching subsystem of sections of common use for system users and personal virtual cabinets of suppliers of advertising and information materials, registration of IT devices in the general information field with specified parameters, placement of advertising and information materials in the general information field of the switching subsystem, preparation and permission to present advertising and information materials on IT devices selected by suppliers of advertising and information mats rials, the operator of the switching subsystem, characterized in that IT devices retrieve advertising and information materials placed by suppliers in the switching subsystem in the form of separate information blocks, store them on IT devices, provide the user when there is no connection to the Internet, and in normal mode users of IT devices communicate with suppliers of advertising and information materials, who independently choose the type of communication with the user using the communication subsystem.

The disadvantages of this solution are at least insufficiently efficient use of computing resources, including for determining the effectiveness of placing online advertising materials with an increase in the effectiveness of advertising campaigns and placing advertising materials with changes to the settings of advertising campaigns and placement of advertising materials and using changed settings for advertising campaigns and placement of advertising materials, and increasing the speed, insufficient efficiency in determining the effectiveness of advertising campaigns, advertising platforms, placement of advertising materials, insufficient amount of information collected to effectively determine the effectiveness of advertising campaigns, advertising platforms, placement of advertising materials, insufficient efficiency in managing advertising materials, advertising campaigns.

The present invention overcomes at least part of the above disadvantages or all of these disadvantages, as well as realizing the advantages of the present invention, as described within the scope of the present invention.

SUMMARY OF THE INVENTION

The technical result achieved by the proposed invention is the automated setting of the placement of advertising materials of companies on advertising sites.

According to one of the implementation options, a method is proposed for measuring the effectiveness of placing online advertising materials and advertising platforms on which advertising materials are presented, with changing the settings for placing advertising materials and advertising campaigns and presenting advertising materials of advertising campaigns with changed settings to users, in which: statistics data by a server for receiving, processing data and generating processing results from at least one server of services for collecting, storing and processing user statistical data using the API provided by such services, where the received statistics data contain at least those related to users, location and presentation of advertising materials and placed advertising materials data obtained at least as a result of the interaction of users with elements, including advertising materials, b-sites, as well as data obtained as a result of presenting advertising materials, including information about at least the user's reactions to the presented advertising materials, expressed in the user's actions using at least one user's computing device and being the user's response to advertising materials, including registered and transmitted by at least one computing device and transmitted to servers of services for collecting, storing and processing user statistical data and / or generated by servers of services for collecting, storing and processing user statistical data; the received statistics data is stored in at least one statistical data database by the server for receiving, processing data and generating processing results; the first data sample is formed by the server for receiving, processing data and generating processing results from statistics data stored in the statistical data database to form a data set of advertising campaigns effectiveness indicators, where each formed sample contains statistics data for each of the users for each of the advertising campaigns, created, configured and launched by the user using the service for placing advertising materials on advertising sites containing at least a web page for placing advertising materials as part of advertising campaigns, including a unique user name, name of the advertising campaign, identifier of the advertising material placement site in the format of a unified website resource index, the number of presentations of advertising materials, the number of user interactions with the presented advertising materials, the cost of placing advertising materials, the name of the advertising group materials that combines advertising materials within one campaign, and the data sampling is carried out according to two or more preset parameters, where such parameters are a unique user name, the name of the advertising campaign, the ID of the advertising material placement site, belonging to a group of advertisements by the name of the group; the second sample of data is formed from the statistics data stored in the statistical data database by the server for receiving, processing data and generating processing results, which contains statistics data for each user for each of the advertising campaigns created, configured and launched by the user using the advertising placement service materials on advertising sites at the cost of advertising campaigns attracted by the advertisement of clients, including the date and time of visiting the web page on which the advertising material is placed, whether the visit to the website was carried out without using user interaction with the placed advertising materials, the number of target customers, on which the presentation of advertising materials is directed, the number of pre-set goals of advertising campaigns for one visit to the website by the user, the cost for each preset goal of each advertising campaign for one visit to the website according to by the user, the cost of visiting the website by the user, the name of the advertising campaign, the description of the category for the recommendation, the unique username; the formation of a data set of advertising campaigns performance indicators is carried out from the data of the first data sample and the data of the second data sample, by forming a multidimensional data array, the dimensions of which are the days of the week, hours and names of advertising sites, containing data for each of the users for a predetermined time interval for which a multidimensional array of data has been formed and which includes data on the effectiveness of advertising campaigns, and containing data on the sequence of user actions from the moment the advertising materials are presented, the interactions of users with the presented advertising materials to go to the advertised websites, to the completion of targeted actions on the websites by users , set by setting the parameters of advertising campaigns of the service for placing advertising materials by the parameters of the presentation of advertising materials, made with the possibility of setting the parameters of the presentation of advertising materials; in the generated multidimensional data array within a predetermined time interval by the server for receiving, processing data and generating processing results: at least one time interval for presenting advertising material is detected, specified by the parameters for presenting advertisements of the advertising material placement service, for each advertising material belonging to the corresponding advertising campaign , within which user interactions with the presented advertising material did not lead to the achievement of the pre-set goals of advertising campaigns or during which the user did not interact with the presented advertising material, and ineffective advertising sites are identified, where the value of the probability of user interaction with the advertising material on the website of the advertising site in such a given time interval for the presentation of advertising material is less than a given value, where the probability of user interaction is calculated, to as the ratio of the number of user interactions with advertising material to the total number of presentations of advertising materials on websites for each advertising site, or the value of the probability of achieving a given goal in the settings of an advertising campaign is less than a given value, where the probability of achieving a given goal is calculated as the ratio of the number of achievements of a given goal to the total number of presentations of advertising materials on websites for each advertising platform; the server for receiving, processing data and generating processing results performs: the formation of a list of advertising materials with predetermined time intervals for presenting advertising materials for each advertising campaign, for which time intervals for presenting advertising material were identified, within which user interactions with the presented advertising did not lead to the achievement of preset goals of advertising campaigns or during which the user did not interact with the presented advertising material, and the formation of a list of ineffective advertising sites; the server for receiving, processing data and generating processing results generates a set of values for parameters of advertising sites, containing values for disabling advertising sites from the list of ineffective advertising sites, and a set of values for parameters of the presentation of advertising materials for placement services of advertising materials to stop the presentation of advertising materials contained in the list advertising materials with predetermined time intervals for the presentation of advertising materials for each advertising campaign, for which time intervals for the presentation of advertising material were identified, within which user interactions with the presented advertising did not lead to the achievement of the preset goals of advertising campaigns or during which the user did not interact with the presented advertising material, and such data sets are transmitted to at least one of the servers of advertising material placement services. in; at least one of the advertising material placement service servers applies the values contained in the set of advertising material presentation parameter values, and applies the advertising space parameter values containing values to exclude advertising spaces from the list of inefficient advertising spaces to stop displaying advertising materials on them; placement with the presentation of at least one advertising material on at least one advertising site is carried out by at least one server of services for placing advertising materials with changed parameter settings for advertising materials and advertising sites with the launch of at least one advertising campaign.

In one of the private implementation options, the statistical data received by the server for receiving, processing data and generating processing results include user interaction parameters containing at least the time spent on the web page and / or the frequency of visiting websites, and / or showing interest in the product , product categories, and/or user-used computing devices, data from such computing devices, containing at least information about making purchases, identifiers of a computing device, on the Internet, in services, applications, and/or registration data in them, as well as data at least from cash registers and / or cameras, and / or face recognition systems, and / or sound recording devices, and / or access control systems, and / or user location tracking tools, as well as data from personal accounts of collection, storage and processing user statistics and data on the effectiveness of advertising channels, campaigns and keywords, scoring, conversions from advertising materials, costs, impressions and user interactions with web links, target audience, advertising campaign budget, call sources, their dates, times, keywords, including those received after recognition and analysis user speech.

In one of the private implementation options, the data contained in the first data sample and the second data sample are updated by sending requests by the receiving server, processing data and generating processing results to at least one of the servers of the services for collecting, storing and processing statistical user data and receiving from him the requested data by collating and updating data on at least the time the user visited the website, the duration of the visit to the website, the name of the advertising site, belonging to the advertising campaign, counter identifiers in the format of the program code that collects statistical data on the website , with checking the correctness and relevance of statistical data, for each user.

In one of the private implementation options, the parameters of users visiting websites are the time the user spent on the web page, the frequency of visits by users to the web page, the manifestation of interest in a particular product or product categories, as well as data recorded and processed by cash registers, access control systems , tracking the user's location.

In one of the private implementation options related to the placement of advertising materials, data includes data on the effectiveness of advertising channels, advertising campaigns and keywords, scoring, conversions from the presentation of advertising materials, costs, impressions and user interactions with web links, target audience, advertising campaign budget , call sources, their dates, time, keywords, including those obtained after recognition and analysis of the user's speech.

In one particular implementation, when saving data to a statistical database, the stored data is checked for the validity of data types using the conversion of a value from one data type to another or determining the type of a variable.

In one of the private implementation options, the user is presented with a list of advertising sites to disable and a set of parameter values for advertising sites and values for disabling advertising sites from the list of ineffective advertising sites, and stopping the presentation of advertising materials and a list of advertising materials for stopping the presentation and a set of values for displaying advertising parameters. materials to stop the presentation of advertising materials contained in the list of advertising materials with predetermined time intervals for the presentation of advertising materials for each advertising campaign, for which time intervals for the presentation of advertising material were identified, within which user interactions with the presented advertising material did not lead to the achievement of pre-set goals of advertising campaigns or during which the user has not interacted with the presented advertising material.

In one of the private implementation options, the services for collecting, storing and processing user statistics are contextual advertising services on web pages or services for assessing website traffic and analyzing user behavior.

In one of the private implementation options, data is transferred to services for collecting, storing and processing user statistics from servers of contextual advertising services on web pages or servers of services for assessing website traffic and analyzing user behavior.

In one particular implementation, the data set of advertising campaign performance indicators from the first data sample data and the second data sample data contains data on behavioral changes in user actions.

In one of the private implementation options, after disabling advertising sites from the list of ineffective advertising sites, other advertising sites are set for related advertising materials, after identifying effective advertising sites in the generated multidimensional data array within a predetermined time interval, on which the probability value of user interaction with advertising material on the website of the advertising site in such a given time interval for the presentation of advertising material is maximum, where the probability of user interaction is calculated as the ratio of the number of user interactions with the advertising material to the total number of presentations of advertising materials on websites for each advertising site, or the value of the probability of reaching specified goal in the settings of the advertising campaign is maximum, where the probability of achieving a given goal is calculated as the ratio of the number of achievements of a given goal to the total number of presentations of re promotional materials on websites for each advertising platform.

According to another implementation variant, a system is provided for collecting data for measuring the effectiveness of placing online advertising materials and advertising platforms where advertising materials are presented, with changing the settings for placing advertising materials and advertising campaigns and presenting advertising materials of advertising campaigns with changed settings to users, which contains : at least one server of at least one service for collecting, storing and processing statistical data, configured to register, collect, receive, transmit, store and / or process data obtained as a result of at least actions performed by users or actions recorded and processed by electronic computing devices, and configured to transmit data to at least a statistics acquisition unit; a statistics acquisition unit configured to receive data from the servers of services for collecting, storing and processing statistical data using the API, and at least one table is created in which the data is stored by the data storage unit obtained from the corresponding API, and with the possibility of transferring them to block of management and organization; a task block configured to manage tasks in the queue and distribute the load using the task queue block and execute scheduled tasks, including tasks for obtaining statistics from external sources via the API, and receiving task messages from the sender and transmitting them to the addressee, where the sender and recipient messages is a block of the task queue, with the formation of requests for obtaining statistics data on the specified parameters for the selected time intervals, where the task is formed by the block of tasks in the format of a function call; a task queue block configured to generate a task queue and manage task schedules using an asynchronous messaging system and configured to schedule a selection from the queue of tasks that must be completed at a specific point in time and that are launched at the time specified for them, and also configured to run programs for collecting, processing and presenting data to the user, and includes at least one executor that provides the code execution flow and the allocation of resources and an isolated environment in memory and the competitive execution of several tasks simultaneously; a control and administration unit, configured to manage tasks for collecting data of their analysis, including creating and editing scheduled tasks with setting the schedule to the user using the user interface; a control and organization unit configured to receive data from other units and distribute them between other units and ensure the differentiation of the functionality of each of the units through means for exchanging data using a protocol and a data interface with other units; a statistics database, configured to receive data from the statistics acquisition unit and the statistics acquisition unit, and configured to store statistics data, first data sampling data, second data sampling data, data sets of advertising campaign performance indicators, generated lists of promotional materials with time intervals , lists of inefficient advertising sites, data sets of values for parameters of advertising sites; a predictive block configured to generate a first data sample from statistics data stored in the statistics database to generate a data set of advertising campaigns performance indicators, where each generated sample contains statistics data for each of the users for each of the advertising campaigns created, configured and launched by the user using the service for placing advertising materials on advertising sites containing at least a web page for placing advertising materials as part of advertising campaigns, including a unique user name, name of the advertising campaign, identifier of the advertising material placement site in the format of a website uniform resource locator , the number of presentations of advertising materials, the number of user interactions with the presented advertising materials, the cost of placing advertising materials, the name of the group of advertising materials that combines advertising materials within one campaign, and the data sampling is carried out according to two or more preset parameters, where such parameters are a unique user name, the name of the advertising campaign, the identifier of the site for placing advertising materials, belonging to a group of advertisements by the name of the group, and made with the possibility of generating statistics from data, stored in the statistics database of the second data sample, which contains statistics data for each of the users for each of the advertising campaigns created, configured and launched by the user using the service for placing advertising materials on advertising sites at the cost of advertising campaigns attracted by the advertisement of customers, including date and time of the visit to the web page on which the advertising material is placed, whether the visit to the website was carried out without using user interaction with the placed advertising materials, the number of target customers, per of which the presentation of advertising materials is directed, the number of pre-set goals of advertising campaigns per visit to the website by the user, the cost for each preset goal of each advertising campaign per visit to the website by the user, the cost of visiting the website by the user, the name of the advertising campaign, the description of the category for the recommendation , a unique user name, and configured to generate a data set of advertising campaign performance indicators from the data of the first data sample and the data of the second data sample, by forming a multidimensional data array, the dimensions of which are days of the week, hours and names of advertising sites, containing data for each of users for a predetermined time interval for which a multidimensional data array has been generated and to which the data on the effectiveness of advertising campaigns relate, and containing data on the sequence of user actions from the moment they present of advertising materials, user interactions with the presented advertising materials to go to the advertised websites, before the users perform targeted actions on the websites, set by setting the parameters of advertising campaigns of the service for placing advertising materials with the parameters for presenting advertising materials, made with the ability to set the parameters for presenting advertising materials , and in the generated multidimensional data array within a predetermined time interval: at least one time interval for presenting an advertising material is detected, specified by the parameters for presenting advertisements of the advertising materials placement service, for each advertising material belonging to the corresponding advertising campaign, within which user interactions with presented advertising material did not lead to the achievement of the pre-set goals of advertising campaigns or during which the user did not interact with the subject which is an advertising material, and ineffective advertising sites are identified where the probability value of user interaction with advertising material on the website of the advertising site in such a given time interval for presenting advertising material is less than a given value, where the probability of user interaction is calculated as the ratio of the number of user interactions with advertising material to the total number of presentations of promotional materials on websites for each advertising platform, or the value of the probability of achieving a given goal in the settings of an advertising campaign is less than a given value, where the probability of achieving a given goal is calculated as the ratio of the number of achievements of a given goal to the total number of presentations of advertising materials on websites for each advertising platform, as well as made with the possibility of generating a list of advertising materials with predetermined time intervals for presenting advertising materials for each advertising campaign, for which the time intervals for the presentation of advertising material were identified, within which user interactions with the presented advertising material did not lead to the achievement of the pre-set goals of advertising campaigns or during which the user did not interact with the presented advertising material, and the formation of a list of ineffective advertising sites, and with the possibility of generating a set of values for parameters of advertising sites, containing values for disabling advertising sites from the list of ineffective advertising sites, and a set of values for the parameters of the presentation of advertising materials for advertising materials placement services to stop the presentation of advertising materials contained in the list of advertising materials with specified time intervals for the presentation of advertising materials for each advertising campaign, for which the time intervals for the presentation of advertising material were identified, within which the interactions of the field advertisers with the advertised did not lead to the achievement of the pre-set goals of advertising campaigns or during which the user did not interact with the presented advertising material, and such data sets are transmitted to at least one of the servers of the advertising materials placement services; block for automated management of advertising campaigns, configured to create and send requests to the API of at least one service for placing advertising materials of at least one server to adjust the settings of advertising campaigns using the values contained in the set of values for the presentation of advertising materials and the values of the advertising parameters sites containing values for excluding advertising sites from the list of ineffective advertising sites for stopping the presentation of advertising materials on them; at least one server of at least one advertising material placement service configured to obtain values contained in a set of advertising material presentation parameter values and advertising space parameter values containing values for excluding advertising spaces from the list of ineffective advertising spaces to stop serving on of advertising materials and the application of the values contained in the set of values of the parameters for the presentation of advertising materials, and the application of the values of the parameters of advertising sites containing values to exclude advertising sites from the list of ineffective advertising sites to stop the presentation of advertising materials on them, as well as made with the possibility of placing with by presenting at least one advertising material on at least one advertising space with changed parameter settings for advertising materials and advertising spaces, launching at least at least one advertising campaign.

In one of the private implementation options in the proposed system, the data collected, received, transmitted, stored and / or processed are user visits to websites, user interaction with websites and elements of websites, web links, advertising materials, user interaction parameters with them, including at least the time the user spent on the web page, the frequency of visiting websites, the manifestation of interest in the advertised objects, the parameters of the computing devices used by users and the parameters recorded by the user's computing devices, data on the effectiveness of advertising channels, advertising campaigns, key words, scoring, conversions from the presentation of advertising materials, costs, impressions and user interactions, target audience, advertising campaign budget, call sources, their dates, times, keywords.

In one of the private implementation options in the proposed system, the task block is a message broker that uses an open protocol to transfer messages between system components.

In one of the private implementation options in the proposed system, the control and administration unit and the control and organization unit are configured to generate user credentials, including at least tokens, user identifiers.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an exemplary overall diagram of a system implementing the present invention, according to one embodiment of the present invention;

FIG. 2 illustrates one embodiment of a system implementing the present invention, according to one embodiment of the present invention;

FIG. 3 illustrates one of the options for obtaining, processing and storing data by a statistics obtaining unit, according to one embodiment of the present invention;

FIG. 4 illustrates an exemplary block diagram of the operation of a predictive block, according to one embodiment of the present invention;

FIG. 5 illustrates an exemplary block diagram of the operation of an external system connection unit, according to one embodiment of the present invention;

FIG. 6 illustrates another exemplary overall diagram of a system implementing the present invention, according to one embodiment of the present invention;

FIG. 7 illustrates a block diagram of an exemplary embodiment of the present invention;

FIG. 8 illustrates an example of a general purpose computer system.

DESCRIPTION OF EMBODIMENTS

The objects and features of the present invention, methods for achieving these objects and features will become apparent by reference to exemplary embodiments. However, the present invention is not limited to the exemplary embodiments disclosed below, but may be embodied in various forms. The gist of the description is nothing but specific details provided to assist a person skilled in the art in a thorough understanding of the invention, and the present invention is defined only within the scope of the appended claims.

As used in this specification, the terms "module", "component", "element", "part", "unit", "component" and the like are used to refer to computer entities that may be hardware, such as a device or part of a device. , in particular, including at least one processor, microcontroller, etc., or software, for example, a computer program, "firmware" (from English firmware), etc., allowing the computer system hardware to perform calculations or control functions that are a combination of instructions and data.

FIG. 1 shows an exemplary overall diagram of a system implementing the present invention, according to one embodiment of the present invention.

The invention relates to the field of information technology and computer technology will allow and can be used to manage online and / or targeted advertising materials (in a particular case, advertising and information content) with the possibility of evaluating the effectiveness of placing online advertising materials and advertising platforms on which promotional materials are presented , with changing the settings for placing advertising materials and advertising campaigns and presenting advertising materials of advertising campaigns with the changed settings to users.

The present invention allows and can be used to quantify and/or qualitatively determine the effectiveness of the placement of advertising materials and the effectiveness of advertising campaigns, as well as to plan the details of advertising campaigns using the results obtained by implementing the present invention.

The present invention also allows and can be used to collect data to calculate the effectiveness of placing advertising materials, advertising sites where advertising materials are placed, ongoing advertising campaigns, where advertising materials are presented to users, including within the framework of advertising campaigns and advertise objects.

The present invention also allows and can be used to reduce the time of obtaining information about placed advertising materials, advertising campaigns and advertising sites, and also allows you to select the most effective advertising campaigns, advertising sites and advertising materials.

The present invention also provides management of a system for collecting, in particular automated, information for collecting data, managing advertising materials, advertising campaigns and determining the effectiveness of placing advertising materials online, increasing the effectiveness of advertising campaigns and placing advertising materials, making changes to the settings of advertising campaigns and advertising placement. materials and using modified settings for advertising campaigns and the placement of advertising materials.

The present invention makes it possible to at least improve the accuracy of determining (measuring, calculating) the effectiveness of advertising materials, advertising platforms and advertising campaigns, increasing the efficiency of using computing resources (used in the present invention) to determine the effectiveness of placing advertising materials, advertising platforms and advertising campaigns online, reducing the time of obtaining information to determine the effectiveness of placing online advertising materials, advertising platforms and advertising campaigns, as well as to increase the reliability of data processing, including data on at least advertising materials, advertising platforms and advertising campaigns, users, and at least data used to determine the effectiveness of advertising materials, advertising platforms and advertising campaigns, and allows you to automatically (or semi-automated) manage at least advertising campaigns, placement of advertising materials, select advertising sites for placement of advertising materials using the parameter settings generated in the process of analyzing the effectiveness of advertising materials, advertising sites and advertising campaigns, and also simplifies navigation through the array of data on advertising materials, advertising sites, advertising campaigns and their effectiveness.

The present invention makes it possible at least to enable users, including advertisers, including companies and organizations, to more effectively manage advertising materials, advertising campaigns, place advertising campaigns and advertising materials on the Internet (online), and quickly adjust and manage changes at least advertising campaign, increase the transparency of the costs of advertising campaigns, with the ability to track the effectiveness of advertising materials, advertising campaigns, advertising platforms in the dynamics of the process of their implementation.

A system that implements the method (or at least one part of the method) described in the framework of the present invention, an exemplary version of which is shown in FIG. 1 contains servers of services for collecting, storing and processing user statistics 144 . Servers of services for collecting, storing and processing user statistical data 144 , for example, servers of such services (systems, platforms) as Yandex.Metrica 105 ( FIG. 6 ), Yandex.Direct 106 ( FIG. 6 ), Calltouch 107 ( FIG. 6 ), Comagic 108 ( FIG. 6 ), Smartcallback 109 ( FIG. 6 ), Google Ads 111 ( FIG. 6 ), Google Analytics 112 ( FIG. 6 ), collect, store and/or process user data resulting from the performance of actions by such users, including using electronic computing devices (for example, visiting sites on the Internet, interacting with sites and site elements, for example, web links, advertising materials, etc., including the parameters of user interaction with them, e.g., time spent on a web page, frequency of visits to certain sites, expression of interest in certain goods and categories of goods, computing devices used by the user, purchases made, etc.) or actions that register captured and processed by electronic computing devices, such as cash registers, cameras, facial recognition systems, sound recorders, access control systems, user location tracking, etc.

So, for example, servers of services for collecting, storing and processing user statistical data 144 can receive, process and store data such as data on the effectiveness of advertising channels, campaigns and keywords, scoring, conversions from advertising presentations (advertising materials), costs, impressions and clicks (user interactions), target audience, advertising campaign budget, call sources, their dates, times, keywords, including those obtained after processing, for example, conversion (in particular, recognition) and analysis of the user's speech, etc.

Shown in FIG. 1 system also includes a server for receiving, processing data and generating processing results 133 , which receives data from at least servers of services for collecting, storing and processing user statistics 144 , from databases ( 177 , 155 ) a data storage unit 166 , with servers of CRM-systems (from the English CRM - Customer Relationship Management, customer relationship management system) 113 , and data transfer to them, as described in the framework of the present invention. Also, the server for receiving, processing data and generating processing results 133 processes the received data (from other servers, services, from the statistics database (DB) 177 , server database 155 and other data sources, as described in the framework of the present invention), and also previously processed and stored, for example, in the databases of the data storage unit 166 .

The server for receiving, processing data and generating processing results 133 transfers the processed data to at least one database (DB), at least one data storage unit 166 .

In the statistics database177 stores at least the data received from the statistics getter122 for further processing by the server for receiving, processing data and generating processing results133. So, in the statistics database177 can store statistics data received by the statistics obtaining block122 using various APIs. So, for each connector (which are described in more detail below) API, a separate table can be created, into which saving is carried out (by means of the statistics database177 and / or server for receiving, processing data and generating processing results133 etc.) statistics obtained from the corresponding API. In a particular case, to communicate with the statistics database177, in particular, for writing or reading data by means of presenting results115 and / or a server for receiving, processing data and generating processing results133, means (for example, a utility, application, etc.) can be used to send (transmit) HTTP requests, and such a request is sent to the IP address of the server on which the database is deployed with authorization, which is carried out using credentials ( for example, login/password, etc.), in particular, stored in the server database155. In a particular case, said query is a SQL query, in particular, a standard SQL query, for example, "SELECT * FROM datatable". So, the server for receiving, processing data and generating processing results133 saves data (in particular, "raw" - unprocessed data) received (including through the control unit and organizations101) from the block for obtaining statistics122, and also saves data received (including through the control unit and organization101) from the predictive block102and resulting from data processing by the predictive block102. The mentioned token can be, for example, an OAuth token (Oauth token) that uses the open protocol (scheme) of OAuth authorization, which allows you to provide a third party with limited access to the user's protected resources without the need to transfer to it (the third party) the login and password, which is the code ( in particular, a special code) allowing access to the data of a specific user. Represents a unique identifier string in hexadecimal format, for example: "token": "ea135929105c4f29a0f5117d2960926f". This token is passed in the query string to the API of a third-party system. Unlike the login and password for identification, this method is more secure, since the password is not transmitted in clear text in the request. In addition, the token is more difficult to pick up, and the token management system is more flexible, since time and event limits are set - for example, when changing the client password for the API, the token is automatically revoked. REST (from the English. Representational State Transfer - "representation state transfer") - is an architectural style of interaction between the components of a distributed application in a network. For example, the interaction between the frontend and the backend, which can run on different computing devices (computers, "machines") on the network. In this case, the interaction is carried out through ordinary HTTP requests (GET or POST).

The server database 155 stores at least the resulting data processing, including data processing by the predictive block 102 as described in the framework of the present invention. So, the server database 155 can store service tables that ensure the operation of the control unit and organization 101 , credentials (logins, tokens, user identifiers, generated, for example, by the control and administration unit 188 and / or control unit and organization 101 , and assigned users with the corresponding blocks), including for connecting to third-party APIs (from the English application programming interface - application programming interface, application programming interface), including servers, services, etc., described in the framework of the present invention, and the data can be stored in encrypted form, including using secure hash algorithms, for example, SHA-2 (Eng. Secure Hash Algorithm Version 2 - secure hashing algorithm, version 2), including SHA-224, SHA-256, SHA-384 , SHA-512, SHA-512/256, SHA-512/224.

So, in the server database 155 , credentials created by users using the control and administration block (module) 188 are stored, as well as credentials from the server database 155 , at least tokens are transferred to the statistics obtaining block 122 , including to establish a connection with the servers of services for collecting, storing and processing user statistics 144 .

The system shown in FIG. 1 also includes a data storage unit 166 . The data storage unit 166 includes a data warehouse (Data WareHouse DWH) using ETL technology (from the English. Extract, Transform, Load - literally "extract, transform, load").

The data storage unit 166 includes at least one DBMS (database management system) and software, for example, in the format of executable scripts, in particular in Python and SQL, for preprocessing and cleaning data, including statistics obtained from control and organization block 101 (from connectors to third-party APIs). When collecting statistics data in a database (in particular, in the statistics database 177 ) all the necessary fields are collected for each advertisement. In a particular case, basically such content of such fields contains certain parameters - numeric values and/or parameters, for example, in text form (format). The mentioned fields can also contain (within) a data structure. An example is UTM tag data, which can contain multiple fields with values: `cid_{campaign_id}|gid_{gbid}|adid_{ad_id}|phid_{phrase_id}|placement_{source}|device_type_{device_type}|retargeting_id_{retargeting_id} `. The set of these fields may vary depending on the settings of advertising campaigns and ads. Such fields (their value, as in the above example) need to be cleared, since parsing (from the English. Parsing - parsing, parsing) of the value in string form is carried out to form a key: value structure. The field with keywords is also collected, which also contains negative keywords that are excluded from the search: "real estate in Moscow -industrial -auction" - the entire phrase is obtained from the API only in its entirety, while negative keywords are discarded, since in a particular case they are not used for further processing and presentation, including in the format of reports.

The data storage unit 166 performs preliminary cleaning and validation of data types. As described within the scope of the present invention, statistics data is obtained from the API and stored, for example, in columns with string format or date format. In a particular case, data can be transferred to a column with some id (in a special case, id is represented by decimal numbers, in particular, all id) and if there is no value, the characters "-" or "--" can be transferred. When aggregating (including processing) data, a data type error may occur (be received) because a number or zero is expected to be received. To avoid such situations, a check (by a validator, validation module) for the validity of data types is carried out using one of the known methods using one of the known algorithms, for example, using the conversion of a value of one type to another (for example, text to numeric), determining the type of a variable, etc. .d. The cleaned data by means of the data storage unit 166 (including the means of the DBMS) are distributed (stored) according to the corresponding tables in the statistics database 177 for further use. Since the cleared and saved data are “raw”, in particular, they are presented as is, and for the means of presenting results 115 , for example, the means of presenting results 115 (including those for generating dashboards), including those implemented by BI systems, for example, Power BI, and third-party CRM systems (hosted on servers of CRM systems 113 ) require aggregated and related data from different sources, then further the data storage unit 166 , in particular, using scripts, aggregates, in particular, forms, data sets for end user (client, consumer), in particular, for the formation of dashboards for the purpose of presenting to the user, BI systems (from the English Business Intelligence - a set of tools and technologies for collecting, analyzing and processing data), etc., CRM systems and stored in separate tables of the statistics database 177 . In a particular case, the mentioned data sets are formed for each client by sites (in particular, sites) on which advertisements are placed. One example of a dataset table schema: “User (customer) name; Date and time; site name; site URL; ID (identifier) of the advertising campaign; ad ID; clicks; Impressions; Price". Thus, the data storage unit 166 generates datasets in a user-relevant presentation and volume, in particular, "data marts" can be formed. So, for example, the generated data sets can be presented in the following format and contain the following data [{"Total number of sites": 158166, "All costs": 212977579.88000005, "Lead-site pair": 191684.0, "Average costs per lead": 1111.0868923853845}, {"Features with little information": 145831, "Costs": [3898188, 1.8], "Leads": [5362.0, 2.8], "Costs per lead": 727.0026948899665}, {"Features that are recommended ban": 7045, "Costs": [14691808, 6.9], "Leads": [22749.0, 11.9], "Cost per lead": 645.8221240494089}, {"Sites that can be banned for a small number of leads": 1142, "Costs": [3404400, 1.6], "Leads": [2462.0, 1.3], "Costs per lead": 1382.7783671811535}, {"Positions that are good": 4148, "Costs": [190983184, 89.7], "Leads ": [161111.0, 84.1], "Costs per lead": 1185.4136811887463}, {"Last month's costs": 814051, "Last month's costs for sites that are recommended to be banned": [136513, 16.8, "%)"] , "Expenses for the last month by sites, cat Some attract little leads": [24982, 3.1, "%)"], "Last month's spending on good sites": [575498, 70.7, "%)"]}].

In a particular case, the data storage unit 166 includes at least one data store, which is a means, in particular a device, for storing information (data) and can be implemented by at least one hard disk drive (HDD /HDD, eng. hard (magnetic) disk drive), solid-state drive (SSD, eng. solid-state drive), hybrid hard drive (SSHD, eng. solid-state hybrid drive), storage area network (SHD / SAN, eng. . Storage Area Network), network storage system / network storage (NAS, English Network Attached Storage) and / or any other means (device) that allows at least recording and / or storage of data. The data in said data stores may be stored in any known format, such as in a database (DB), for example, in the form of at least one table or a set of related or unrelated database tables. It is worth noting that at least one of said database may be a hierarchical, object, object-oriented, object-relational, relational, network and/or functional database, each of which may be centralized, concentrated, distributed, heterogeneous, homogeneous, fragmented/partitioned, replicated, spatial, temporal, space-time, cyclic, extra-large database, etc., and various database management systems (DBMS) can be used to manage, create and use databases.

Also, the data in said data stores may be stored in at least one file, in particular in the form of a text file, or the data may be stored in any of at least one other currently known data/information storage format, or in a data format invented later.

The data storage unit 166 may be part of the server for receiving, processing data and generating the results of processing 133 or may be associated with it by any known wired or wireless communication (connection) method.

It is worth noting that the components (parts) depicted in FIG. 1 systems can be interconnected (and any other components, devices and services (services) capable of receiving information from the components (parts) of the described system and / or transmitting data to the described system components) through various types of communication, for example, through local computing network (LAN), the Internet 114 , (means of) mobile communication, satellite communication and / or through any other type or method of wired communication (for example, via USB interface, RS-232 / COM port interface, etc. ) and/or wireless communications, such as Bluetooth, Wi-Fi, mobile cellular communications (GSM), including 3G, 4G, LTE, in particular in the 850/900/1800/1900 MHz bands, satellite communications, trunked communications and data transmission channels with ultra-low power consumption, forming complex wireless networks with mesh topology (ZigBee), etc.

It is worth noting that at least one part of the method described in the framework of the present invention (or the whole method) may be carried out on the server for receiving, processing data and generating results of processing 133 , which may be a web server or include a web server. It should be noted that the connection between the server for receiving, processing data and generating processing results 133 and the servers for collecting, storing and processing user statistical data 144 , data storage unit 166 , servers of CRM systems 113 can be carried out through at least one transfer protocol data, including network protocol, for example, TCP/IP, HTTP/HTTPs (Hyper Text Transfer Protocol, Hypertext Transfer Protocol), FTP (File Transfer Protocol), POP3 (Post Office Protocol) , a standard mail connection protocol), SMTP (English Simple Mail Transfer Protocol, a protocol that specifies a set of rules for transferring mail), TELNET (remote access protocol), XMPP (from English Extensible Messaging and Presence Protocol - an extensible messaging protocol and presence information, formerly known as Jabber, is an open, XML-based protocol for instant messaging and presence information. AI near real time), DTN, etc.

Shown in FIG. 1 , the system, in particular, the server for receiving, processing data and generating processing results 133 , also includes a block for obtaining statistics 122 , which receives data transmitted by servers of services for collecting, storing and processing user statistical data 144 , including upon request sent by the block (module ) obtaining statistics 122 , and the connection of the block for obtaining statistics 122 with the servers of services for collecting, storing and processing user statistical data 144 is carried out using the API provided by such servers for collecting, storing and processing user statistical data 144 . So, for example, statistics can be obtained (statistical data, statistical data) from the Yandex.Metrics service 105 ( FIG. 6 ), the Yandex.Direct service 106 ( FIG. 6 ), the Calltouch service 107 ( FIG. 6 ), the Comagic service 108 ( FIG. 6 ), Smartcallback service 109 ( FIG. 6 ), Google Ads service 111 ( FIG. 6 ), Google Analytics service 112 ( FIG. in which users can at least create advertising campaigns) and call tracking cabinets (from the English call-tracking - call tracking, call tracking), allowing at least to link the number of incoming calls with the effectiveness of the advertising channel, and also providing additional information about phone calls. Obtaining statistics (statistics data, statistical data) can be carried out, for example, in text format, in particular, in JSON (JavaScript Object Notation), TSV (tab separated values), etc. d.

Thus, the block for obtaining statistics 122 may contain (or, in particular, may be implemented) connectors (a set of connectors) to the APIs of the specified services (Yandex.Metrika 105 ( FIG. 6 ), Yandex.Direct 106 ( FIG. 6 ), Calltouch 107 ( FIG. 6 ), Comagic 108 ( FIG. 6 ), Smartcallback 109 ( FIG. 6 ), Google Ads 111 ( FIG. 6 ), Google Analytics 112 ( FIG. 6 )), configured to add new connections ( connections). Mentioned services Yandex.Metrika 105 ( FIG. 6 ), Yandex.Direct service 106 ( FIG. 6 ), Calltouch service 107 ( FIG. 6 ), Comagic service 108 ( FIG. 6 ), Smartcallback service 109 ( FIG. 6 ), Google Ads service 111 ( FIG. 6 ), Google Analytics service 112 ( FIG. 6 ) are systems, in particular, implemented by at least one server, in particular, operating on at least one server.

The block for obtaining statistics 122 allows you to receive data, including statistics data, at least from personal accounts of services (systems) for collecting, storing and processing user statistical data, in particular, from the corresponding servers for collecting, storing and processing user statistical data 144 . One of the options for receiving, processing and saving data by the block for obtaining statistics 122 , in particular, at least connectors (a set of connectors) to the APIs of these services (Yandex.Metrica 105 ( FIG. 6 ), Yandex.Direct 106 ( FIG. 6 ), Calltouch 107 ( FIG. 6 ), Comagic 108 ( FIG. 6 ), Smartcallback 109 ( FIG. 6 ), Google Ads 111 ( FIG. 6 ), Google Analytics 112 ( FIG. 6 )), is shown in FIG. 3 .

In the process of obtaining data, including through (using) the API, data can be divided and ordered, for example, by users (in particular, customers). So, the data collected from different third-party APIs is divided into separate tables (for example, in the statistics database177, including by means of such a database), for example, a table is used (including formed) for each data source. Data is sorted by periods (for example, by months), including to increase the performance of the statistics database177. In a particular case, thus, when sampling data, only clusters (sections of the table united by one period and having an appropriate index) of the data table corresponding to the selected time interval can be searched. So, in the process of receiving data by the block for obtaining statistics122 data can be collected for all users with further filtering of the generated list (list of users) that contains data identifying users (for example, associated name and user ID, for example, in the format (["User name1", "User ID1"], [" Username2", "UserID2"]))) for which statistics are obtained from the API. Said filtering can be carried out to separate information, including due to the fact that the system implementing the present invention is multi-user. Thus, in a particular case, the first user (User1) does not have access to the data of the second user (User2) and vice versa, unless otherwise provided. The data is filtered by the user ID (id) in the system - a unique identifier.

In a particular case, statistics data can be obtained using HTTP requests, the data comes in JSON format.

Below is the API request body code (in JSON format):

"body = { /**/

params: {

"SelectionCriteria": {

"DateFrom": "2018-01-01",

"DateTo": "2020-10-01"

},

"FieldNames": [

"Date",

"CampaignName",

"adgroupname",

"AdId",

"AdNetworkType",

"AvgClickPosition",

"AvgImpressionPosition",

"Bounce"

"impressions",

"Clicks"

cost,

"Placement",

"Sessions"

],

"ReportType": "AD_PERFORMANCE_REPORT",

"DateRangeType": "CUSTOM_DATE",

"Format": "TSV",

"Include VAT": "NO",

"IncludeDiscount": "NO"

}

}",

where - "body" - the body of the request - is a dictionary containing the necessary parameters for receiving data from the API;

- "params" - a dictionary with query parameters;

- "SelectionCriteria" - contains data selection criteria, in this case, the start date and end date of the period, and a list of user IDs or their campaigns for which statistics are obtained can also be specified, and, in a particular case, statistics are collected for all by default users and their campaigns available in the personal account of the advertising platform.

- "FieldNames" - a list of fields to be obtained, where:

- “Date” – campaign date,

- "CampaignName" - the name of the campaign,

- "AdGroupName" - the name of the ad group,

- "AdId" - ad ID,

- "AdNetworkType" - type of advertising platform,

- "AvgClickPosition" - average clicks,

- "AvgImpressionPosition" - the average value of impressions,

- "Bounces" - the number of bounces,

- "Impressions" - the number of impressions,

- "Clicks" - the number of clicks,

- "Cost" - cost,

- "Placement" - a platform for placing an ad,

- "Sessions" - the number of sessions;

- "ReportType" – report type in the advertising platform API;

- “DateRangeType” – type of date interval, and automatic intervals can be used, for example, AUTO (automatically) or LAST_7_DAYS (last seven days);

- “Format” – data format received from API (TSV, CSV);

- «IncludeVAT» – include value added tax (VAT), yes/no;

- "IncludeDiscount" - include the discount (yes/no).

The described system (an exemplary version of which is shown in FIG. 1 ) that implements the present invention also includes a predictive block 102 that processes the collected statistical data stored in the statistics database 177 with the formation of a data set including at least predicted efficiency data advertising sites, including user-selected advertising sites, and data can be stored in the format of table(s), graph(s), report(s) containing tables and graphs, as described in the framework of the present invention. So, the predictive block 102 in the process of processing (analysis) generates a data set, including in the format of at least one report, from the data received from the control unit and the organization 101 , including from pre-selected (selected) for processing by predictive block 102 . The preliminary selection (selection) of data for processing by the predictive block 102 can be carried out by the predictive block 102 or the control and organization block 101 . An exemplary block diagram of the operation of the predictive block 102 is shown in FIG. 4 .

Shown in FIG. 1 , the system, in particular, the server for receiving, processing data and generating processing results 133 , also includes a block (module) for automated management of advertising campaigns 118 , which (automatically or semi-automatically, and / or by the user using such a block (module) launches an advertising campaigns using the received parameters from the control unit and the organization 101 , as described in the framework of the present invention.The automated advertising campaign management unit 118 interacts with the predictive unit 102 , through which it receives reports on the effectiveness of advertising campaigns, and uses the data from these reports to automatically adjust advertising campaigns in personal accounts of advertising platforms, through the management API.

In a particular case, to the control and organization unit101 (which in a particular case functions as a controller) data is transmitted and distributed among other units of the system implementing the present invention, an exemplary version of which is shown inFIG. one. In a particular case, the predictive block102 interacts with the block of automated management of advertising campaigns118 through the control and organization unit101, in particular, through it the block of automated management of advertising campaigns118 receives data sets, for example, in report format, from a predictive block102, which in a particular case makes it possible to delimit the functionality of the system blocks, an approximate version of which is shown inFIG. one, e.g. predictive block102 in a particular case, performs forecasting (in a particular case, only forecasting), a block of automated management of advertising campaigns118 manages advertising campaigns (in particular, only management of advertising campaigns), including (and in particular) sending requests to the API to update the parameters (settings) of advertising campaigns, as described in the framework of the present invention, etc., and in a particular case, due to the delimitation of duties of the system blocks, an approximate version of which is shown inFIG. one.

After the predictive block 102 receives data (for example, in a report format), the control and organization block 101 generates (creates) a task for the automated advertising campaign management block 118 _by sending data from data sets (in particular, a report) to it. Further, the block of automated management of advertising campaigns 118 sends a corresponding request to the API of at least one service, for example, the service for placing (presenting and managing) advertising materials of at least one server (for example, the server for placing advertising materials) to adjust the settings of advertising campaigns.

In a particular case, the parameters for advertising campaigns are divided into several types. One of them is the time of the advertising campaign, i.e. on which days of the week and at what time of the day the advertisement will be shown, and therefore the budget will be spent. The next type of parameters is on which sites advertising is placed (advertising materials). By default, for example, the Yandex advertising network or Google Ads are a set of websites connected to the advertising network, and placed ads are displayed on all websites without exception. As a result of the operation of the predictive block 102 , an efficiency parameter is generated for each website, which is used to disable placement on those websites where the return will be unreasonably expensive or will not bring leads.

Shown in FIG. 1 , the system, in particular, the server for receiving, processing data and generating processing results 133 , also includes a control and organization unit 101 , which combines the described blocks (in particular, it may include the described blocks) that collect data from API sources and transmit, by to at least one database, task schedulers that are responsible for scheduling regularly executed algorithms, for example, the data collection algorithm (algorithms) that receives data from the API and saves them to the statistics database 177 , executed in the format of executable scripts, predictive block 102 , which at least performs data sampling and processes the selected data using an analytical algorithm, a means (in particular, a block, a module) for presenting results 115 , in particular, statistical data (statistics) that plot charts using data obtained as a result of the operation of the predictive block 102 , and also includes a user control panel (“admin panel”), which can be used to manage data collection and processing tasks, manage task schedules, and edit credentials for connecting to services for collecting, storing and processing user statistical data, for example, Yandex.Metrica 105 ( FIG. 6 ), Yandex.Direct 106 ( FIG. 6 ), Calltouch 107 ( FIG. 6 ), Comagic 108 ( FIG. 6 ), Smartcallback 109 ( FIG. 6 ), Google Ads 111 ( FIG. 6 ), Google Analytics 112 ( FIG. 6 ). Said analytic algorithm may be or may be based on a mathematical model of the flow of events with variable intensity, improved and automated using tools, for example, DataScience - software libraries that support multidimensional arrays (including matrices) and high-level mathematical functions designed to work with multidimensional arrays and providing implementations of computational algorithms (in the form of functions and operators) optimized for working with multidimensional arrays, in particular, with the execution of algorithms that can be expressed as a sequence of operations on arrays (matrices) and implemented using such software libraries, for example, NumPy, and software libraries for data processing and analysis that provide special data structures and operations for manipulating numerical tables and time series, for example, Pandas in Python, and Pandas work with data is built on top of the library NumPy, which is a lower level tool.

In a particular case, the control and organization block 101 may include at least one block and / or may include means for exchanging data (via one of the known protocols and data transfer interfaces) with other blocks, for example, a predictive block 102 , a block for obtaining statistics 122 , etc., as well as other parts of the described system, for example, a data storage unit 166 , servers of CRM systems 113 , including those using an external systems connection unit 103 , and others. The management and organization unit 101 may include or be a web application framework such as, for example, Django. The mentioned tools for web applications may include (or use) the MVC design pattern (from the English. Model-View-Controller - “Model-View-Controller”, “Model-View-Controller”), which is a scheme for separating application data, user interface and control logic into three separate components: model, view and controller. Also, the control and organization unit 101 may be (or may include, in particular contain) a site using the mentioned tools for web applications, for example, it may be a website on a framework, for example, a website on Django, and the web the site (website) may function using (in particular, may be created from) one or more applications, which may be alienable and pluggable. Handlers URL (from the English. Uniform Resource Locator - Uniform Resource Locator) control unit and organization 101 can be configured using regular expressions, including explicitly. The interaction of the control unit and the organization 101 can be carried out using programming technology that links databases with the concepts of object-oriented programming languages, creating a virtual object database, for example, using ORM technology (from the English. Object Role Model - object role model).

Shown in FIG. 1 , the system, in particular, the server for receiving, processing data and generating processing results 133 , can also include a task block (task broker, messages) 120 that manages tasks in the queue, in particular, the execution order, and load distribution using the task queue block 104 , in particular, performs scheduled tasks, for example, obtaining statistics from external sources via API. In a particular case, task block 120 can be a message broker using the AMQP (Advanced Message Queuing Protocol) standard, for example, use RabbitMQ technology. Task block 120 , which is, in particular, a message broker, receives messages (what needs to be done) from the sender and transfers them to the addressee, where the sender and recipient of messages is the task queue block 104 , configured to create jobs and schedule the execution of these jobs (in in particular, the functionality of creating tasks and the functionality of scheduling the execution of such tasks can be implemented). Thus, a request can be generated to obtain statistics data for certain clients for selected time intervals, for example, automatically or by the user in the user's control panel ("admin panel"). As a result of the mentioned request, the task queue block creates a task for executing the corresponding piece of code and sends it to the task block queue 120 . Next, the task queue block 104 is scheduled to select from the queue those tasks that must be completed at a particular point in time and it is they who “start” (run) at the time specified for them. In a particular case, due to the fact that all tasks first enter the queue, there is no situation when all tasks are launched simultaneously, regardless of free resources, each task is launched only at the right time. High load causes a delayed start after the resources necessary for execution are freed.

The task queue block 104 performs task queuing and task scheduling, for example, using an asynchronous messaging system, and can be implemented, for example, using Celery ("distributed task queue") technology. The task queue block 104 in a particular case may represent (be) a block of a distributed, in particular, asynchronous, job queue (in a particular case, be a distributed asynchronous job queue (including a module for processing, executing, etc. of a distributed asynchronous job queue) The task queue block 104 launches scripts (for example, various algorithms for collecting, processing and presenting data to the user), which can work asynchronously (in parallel) or synchronously (in series).The task queue block 104 includes (contains) at least one executor (Worker), providing the flow of code execution and the allocation of resources and an isolated environment in memory (in particular, in RAM) of the server for receiving, processing data and generating processing results 133 (or at least one of its blocks, for example, a task queue block ) for it. Worker can provide concurrent execution of several tasks at the same time. designed with the possibility of distributed execution on multiple servers, periodic jobs (replacing cron, a special case of which is crond used in Red Hat Linux systems) , re-executing a job if an exception is thrown, limiting the number of jobs per unit of time, distributing jobs between workers' tasks (of which there may be many, in particular more than one), monitoring the execution of tasks - in particular, the status of the execution of a task (in particular, a request) can be monitored in real time, for example, the user can be presented (in particular, displayed) the status of the task ( in particular, the request). In combination (particularly jointly) with task block 120 , an automated system for managing all tasks within the system is formed, an exemplary version of which is shown in FIG. 1 . Thus, task block 120 and task queue block 104 may form an automated subsystem for managing the described jobs (in particular, requests).

The server for receiving, processing data and generating processing results 133 may also include a control and administration unit (module) 188 , which may be part of the control and organization unit 101 or may be associated with it, and the control and administration unit (module) includes a user interface in particular the graphical user interface. The control and administration unit 188 allows the user, using the user interface, at least to manage tasks for collecting data from their analysis, for example, create and edit scheduled tasks, set up schedules.

To present the results to at least one user, including receiving reports, displaying results, including on a website, transferring, saving processed data to a file, for example, to a report file, including diagrams, the user's personal account is used, which may contain means (module, unit, device, including a computing device with installed software, for example, a server, a personal computer, etc.) for presenting results 115 or be means for presenting results 115 , including, at least presentation (in a particular case, storage) with data that is at least the results of data processing by the predictive block 102 , including, but not limited to, indicators (main indicators) in the resulting data, in particular, indicators of the effectiveness of advertising sites , expressed in quantitative terms in the fields: "Clicks", "Cost", "Impressions", "Leads" (attracted with the help of rivers customer ads), “Goals Achieved” (Goals), which form the effectiveness (efficiency value) depending on time - for certain periods of time (for example, hours, days, months), in particular, display a picture of performance over time, and with the possibility of loading (downloading), saving, etc. by the user, including in the format of digital entities, for example, a report document, including in file format (for example, in table format, text formats, image formats, etc., including in xlsx, pdf, etc. formats) d.). Results presentation means 115 may contain datasets containing the results of data processing by at least predictive block 102 , including reports (including real-time reports) including indicators and groups of indicators. Means for presenting results 115 can generate and present sets in the format of digital entities, for example, dashboards (dashboards, dashboards). Results presentation means 115 can also receive data from data storage unit 166 and/or from control and organization unit 101 and at least visualize representations of the received data arrays, as well as process the received data and store the received data and processing results. Means for presenting results 115 may include an analytical panel with an interface for interactive interaction with indicators, including those that change over time. In a particular case, the results presentation means 115 may be located on at least one server, for example, on the server for serving advertising materials, in which the presentation of advertising campaigns and the presentation of advertising materials are configured. So, the means of presenting results 115 are connected (a connection is established) to the statistics database 177 via the HTTP protocol, for authorization of which the login and password of the account (stored in the server data database 155 ) with read-only rights to tables with statistics data can be used. So, data, in particular, data for the formation (in particular, construction) of dashboards, graphs, charts, are obtained (aggregated) by means of presenting results 115 by sending (transmitting) SQL queries 166 to the data storage unit. SQL query example:

SELECT

login,

Placement,

Campaign Name,

date,

bounce,

impressions,

clicks,

cost,

sessions

FROM direct data.

The result of the above query is shown in Table 1 below.

Table 1

Login Placement Campaign-Name Date Bounces Impressions clicks cost Sessions tektanow site.
name competition_n 2020-01-18 00:00:00 0 ten 5 99.65 5

, where

- Login - login of the agency client, consists of the name in Latin letters, for example bigtimemoscow;

- Placement - ad placement site;

- CampaignName - campaign name;

- Date - campaign date;

- Bounces - number of bounces;

- Impressions - number of impressions;

- Clicks - number of clicks;

- Cost - cost;

- Sessions - number of sessions.

Also shown in FIG. 1 , the system may include a block for connecting external systems 103 , which is used at least by the control and organization unit 101 and the means of presenting results 115 to exchange data with the servers of CRM systems 113 , and can also be used to exchange data with the means of presenting results 115 , in particular, can be used to connect third-party systems, such as CRM systems, systems that form dashboards, etc. The external systems connection block 103 is configured to provide third-party systems with the ability to connect to the server for receiving, processing data and generating processing results 133 (establish communication with it for data exchange) and at least receive statistics data (both before processing and after processing, including primary processing), as well as data on the result of data processing by the predictive block 102 , in particular, performing, at least the execution of the predictive algorithm. Said connection can be made using a token (generated, for example, according to the Oauth standard, in particular, as well as in third-party APIs, and also represents a unique identifier string, for example, in hexadecimal format), for example, using a protocol (scheme) authorization, including open authorization, which allows to provide servers of CRM systems 113 (including CRM systems and users of CRM systems) with access, including limited access, to resources, including protected resources (data), including including data of the result of data processing by the predictive block 102 , statistics data, including without transferring credentials to the servers of CRM systems 113 , for example, login and password. Thus, the connection can be made via the OAuth protocol using the mentioned token.

In a particular case, the block for connecting external systems 103 can use the REST API, in particular, it can be a REST API application, implemented, for example, using the Django REST Framework (framework) and allows you to create APIs for connecting via HTTP requests (the data format is JSON) of third-party services to the server for receiving, processing data and generating processing results 133 .

Means for presenting results 115 , for example, forming dashboards, including those using Power BI technology or being in a particular case Power BI applications, connect to the server for receiving, processing data and generating processing results 133 using an external systems connection block 103 , aggregating the data stored in statistics database tables 177. Data aggregation can be performed using SQL queries sent by means of presenting results 115 . Connecting the means of presenting results 115 can be carried out via HTTP requests, and data can be transmitted to the means of presenting results 115 as strings in CSV format (from the English. Comma-Separated Values - comma-separated values).

Servers of CRM systems 113 (in particular, CRM systems), as well as means of presenting results 115 , can connect to the server for receiving, processing data and generating processing results 133 through the external systems connection block 103 via HTTP requests, are authorized using a token and receive statistics datasets in JSON format, limited by the access level of a particular token.

In the data warehouse of the servers of CRM systems 113 , the data transmitted by the connection unit of external systems 103 is stored, at least the server for receiving, processing data and generating processing results 133 , for example, service tables that ensure the operation of at least the control unit and organization ( in a special case, the Django framework) 101 , credentials (logins, tokens, IDs (unique identifiers) of users) for connecting to a third-party API, and the data is stored in encrypted form.

FIG. 2 shows one of the options for the operation of one of the variants of the system that implements the present invention, according to one of the embodiments of the present invention.

In step 210 , statistics data is received by the server for receiving, processing data and generating processing results 133 , in particular, by the block for obtaining statistics 122 with further transmission, at least to the control unit and organizations 101 , from the servers of services for collecting, storing and processing statistical data users 144 . So, in a particular case, statistical data is collected from analytics systems (in particular, from personal accounts of systems) using connectors to the corresponding APIs provided by such systems, implemented in the external systems connection block 103 and / or in the control and organization block 101 , and statistics on advertising campaigns from several sources is obtained, which is generated by the external systems connection unit 103 and/or the control and organization unit 101 into statistics data sets, in particular, on advertising campaigns from at least one source of statistics data.

In step 215 , the statistics acquisition unit 122 , including through the control and organization unit 101 (using the means of the control unit and organization 101 ), transfers the received data to at least one database, in particular, to the statistics database 177 , with their storage, at least in the statistics database 177 , which in a particular case contains many tables, for example, one table for each third-party analytics system, in particular, a CRM system.

In step 220 , the predictive block 102 performs data sampling, including for predictive analysis (predictive processing, at least by the predictive block 102 ) and the formation of processed data sets, including the formation of digital entities, for example, reports containing such data . The mentioned data selection being formed contains statistics data on advertising campaigns, including the values of such fields (fields are presented) as the name of the advertising campaign, client login, advertising platform (website url), impressions, clicks, cost, name of the ad group ( combines ads within one campaign, usually by the name of the site and the date the ad was placed), and the data sampling is carried out according to several parameters (including several parameters at once): client, advertising campaign, placement site, belonging to an ad group.

In step 225 , the predictive block 102 generates a data sample to form digital entities, for example, dashboards, which contains statistics on the cost of attracting leads (achieving the goals of advertising campaigns), including the values of such fields (represented by fields), such as the date / time of the visit , visit property (whether the visit is a direct visit to the website), the number of target customers, the number of leads per visit, the cost of the lead, the cost of the visit, the name of the advertising campaign, the category description for the recommendation, the user (client) login. The specified data is grouped by visits, leads and days, i.е. a data set is formed (with the formation of a picture of that) about how many original visits and leads were, for example, on each day of the week.

In step 235 , the predictive block 102 generates data sets, including in the format of digital entities, for example, in a report format, which may contain at least the performance indicators of advertising sites when placing advertisements (materials) on them and recommendations for further use (application), as well as parameters (settings), including recommended ones, of advertising campaigns. In a particular case, a report is generated in the form of performance indicators, which contains recommendations for use and the necessary settings for advertising campaigns. Thus, recommendations are formed, for example, in the form (in the format) of a report consisting of data, for example, in the format of tables, with quantitative indicators for cost, clicks and impressions, diagrams showing behavioral changes depending on the time of day, days of the week, and also in the form of standard recommendations (and / or instructions, for example, for at least one block, module of the system shown in FIG. 1 , for execution, including in an automated mode) for optimizing an advertising campaign, including by eliminating placing on those sites that bring little or no deals and focusing the budget on sites that bring the most leads. Advertising companies contain many settings (parameters), for example, settings for displaying ads at certain times of the day and on certain days of the week.

So, for example, parameters (settings) are transmitted by a command to the API server of the contextual advertising service on web pages (for example, Yandex.Direct), for example, in the dictionary format, which may include at least the following parameters or part of them:

- Id - identifier of the campaign whose parameters need to be changed;

- Name - name of the campaign (in a particular case, up to 255 characters);

- StartDate - start date of ad impressions, for example, in the format YYYY-MM-DD and in a particular case not less than the current date;

In a particular case, ad impressions start at 00:00 Moscow time (regardless of the value of the TimeZone parameter). The start time of impressions is affected by the time targeting settings (the TimeTargeting parameter).

Ad impressions are possible provided that at least one ad has been accepted by moderation and funds have been deposited into the campaign or to the general account. In a particular case, it is an optional parameter;

- EndDate - end date for ad impressions (for example, in YYYY-MM-DD format). In a particular case, ad impressions stop at 24:00 Moscow time (regardless of the value of the TimeZone parameter). In a particular case, it is an optional parameter;

- TimeZone - time zone at the location of the advertiser. A dictionary of time zones can be obtained using the Dictionaries.get method. Default Europe/Moscow;

- DailyBudget - campaign daily budget settings. In a particular case, daily budget management is available if a manual display strategy is selected in the campaign, otherwise an error is returned when trying to set a daily budget. In a particular case, it is an optional parameter;

- "ExcludedSites": { /* array (list) of strings */

"Items": [(string), ... ] /* lists the domains of sites to be disabled */

}

An array of display locations where ads should not be shown:

- domain names of sites;

- mobile application identifiers (bundle ID for iOS, package name for Android);

- names of external networks (SSP). The list of titles can be obtained using the Dictionaries.get method.

In a particular case, no more than 1000 elements in the array and no more than 255 characters in each element of the array. In a particular case, it is one of the key parameters; it receives a list of sites that need to be excluded from showing ads;

- "TimeTargeting": {

"Schedule": { /* Array of numbers */

"Items": [(number), ... ]

},

"ConsiderWorkingWeekends": ( "YES" | "NO" ), /* required */

}

, where "TimeTargeting" - Time targeting and hourly bid adjustment settings;

- "Schedule" - settings for time targeting and hourly bid adjustments. In a particular case, no more than 7 elements in the array.

Each element of the array contains, in a special case, 25 numbers separated by commas. The first number is the number of the day of the week: from 1 (Monday) to 7 (Sunday). The next 24 numbers are the sequence of coefficients to the bet for displaying ads during the corresponding hours. Coefficients are specified as a percentage from 0 to 200, the value must be a multiple of 10. Coefficient 0 means that ads are not shown during this hour. Array element example: 1, 0, 0, 50, 50, 100, 100, 150, 200, 200, 150, 100, 100, 80, 70, 100, 100, 100, 50, 50, 40, 30, 0, 0, 0

Thus, the parameters are passed in numerical form with an accuracy of up to an hour in a day - if the value is zero, the ad is not shown at the specified hour, so the budget is not wasted during inefficient times.

- ConsiderWorkingWeekends – determines (sets) whether to change the show schedule when the working day is moved to Saturday or Sunday. For example, if the working day is moved from Monday to Saturday, if set to "YES", impressions will be shown on a working Saturday according to the Monday schedule, and on a non-working Monday, according to the Saturday schedule.

Thus, in the advertising campaign, the list of advertising sites can be excluded as ineffective in terms of attracting leads (transactions).

In step 240 , the means of presenting results 115 present at least one data set (to the user), for example, in the form of a report in the form of diagrams on a website with the ability to save (including upload) the report, means of presenting results 115 . So, the report data (in real time) can be displayed to the user, for example, in the form of diagrams, at least on one page of the website with the ability to upload the report in the form of at least one text, graphic, etc. pdf, xlsx file. So that users can monitor real-time data on advertising materials, sites, etc., as well as after changes have been made to the settings of parameters of advertising sites, presentation of advertising materials, etc.

In step 245 , the control and organization unit 101 transfers data, including parameters (settings), including advertising campaign parameters, to the automated advertising campaign management unit 118 , for example, in JSON format. So, in a particular case, the block of automated management of advertising campaigns 118 interacts with the predictive block 102 , through which it receives reports on the effectiveness of advertising campaigns, and uses the data from these reports to automatically set up advertising campaigns in personal accounts of advertising platforms, through the management API.

In step 250 , the automated campaign management unit 118 launches an advertising campaign using the received parameters, in particular, the adjusted parameters. Thus, the parameters for advertising campaigns are divided into several types. One of them is the time of the advertising campaign, i.e. on which days of the week and at what time of the day the advertisement will be shown, and therefore the budget will be spent. The next type of parameters is on which sites advertising is placed (advertising materials). By default, for example, the Yandex advertising network or Google Ads are a set of websites connected to the advertising network, and placed ads are displayed on all websites without exception. As a result of the operation of the predictive block 102 , an efficiency parameter is generated for each website, which is used to disable placement on those websites where the return (effectiveness) will be unreasonably expensive or will not bring leads.

If, as a result of the operation of the predictive block 102 , a list of websites (domains, sites) to disable is received, the check is triggered - i.e. the presence of the list is a trigger for sending a request to update the corresponding advertising campaigns. In this case, a request is generated to update the relevant advertising campaigns (those whose ads are placed on the specified sites). Disabling sites occurs selectively within this update request, i.e. according to the list and time intervals, all other sites remain unchanged and participate in ad serving, using the above options to disable.

The following is a general view of the request for managing advertising campaigns via the API. The “update” method is used to make changes to campaign settings.

curl \

-H 'Authorization: Bearer TOKEN' \

-H 'Client-Login: CLIENT_LOGIN' \

-d '{ "method": "update",

params: {

"Campaigns": [{ (List of ad campaigns)

"Id": CAMPAIGN_ID,

"Name": CAMPAIGN_NAME,

"StartDate": START DATE,

"EndDate": END DATE,

"TimeZone": TIME ZONE,

"DailyBudget": DAILY BUDGET,

"ExcludedSites": { /* array (list) of strings */

"Items": [(string), ... ] /* lists the domains of sites to be disabled */

},

"TimeTargeting": {

"Schedule": { /* Array of numbers */

"Items": [(number), ... ]

},

"ConsiderWorkingWeekends": ( "YES" | "NO" )

}

}]

}

}'https://api.direct.yandex.com/json/v5/campaigns

The predictive block 102 processes the statistics data stored in the statistics database (statistics data database) 177 using a predictive algorithm to generate at least one data set containing predicted performance data of (by the user) selected advertising sites, for example, in the format of the generated a report with tables and graphs of the predicted effectiveness of selected advertising sites. Thus, the mentioned predictive block 102 (including using a predictive algorithm) receives (including upon request) data from the statistics database table 177 transferred to it earlier from the API, according to parameters, for example: the amount of information on the site (non-zero number clicks and cost of clicks), the ratio of the number of leads to the number of visits (the influence of the site on audience engagement), the percentage of visits that are not zero in time (the number of bounces as an indicator of the site’s compliance with the site’s theme), the percentage of lost clicks (when there was a visit and an ad click, but a click not recorded in the statistics). Next, the predictive block 102 checks data from the server of the system (service) of contextual advertising on web pages (for example, Yandex.Direct, which is used in the present invention as an example of a contextual advertising service on web pages) and from the server of the web traffic assessment service. sites and user behavior analysis (for example, Yandex.Metrica, which is used in the present invention as an example of a service for evaluating website traffic and analyzing user behavior), for example, such data are checked (in particular, verified), in particular, parameters such as visit time, duration, advertising platform, belonging to an advertising campaign, and advertising campaign data is obtained from the server of the contextual advertising service on web pages (for example, Yandex.Direct), to which data is transmitted, in particular, statistics from the server of the web traffic assessment service -sites and analysis of user behavior (for example, Yandex.Metrica), including (but not limited to ing) information about the id of the counter that collects statistics on the site, and this field can be used to check the correctness and relevance of statistics, since the information is updated constantly (with a given, in particular, configured, frequency, for example, once every one or several seconds, one or several minutes, etc.) and, in particular, is not transmitted instantly between the mentioned services (for example, from Yandex.Metrica to Yandex.Direct), for which, in a particular case, such a check is carried out, the id of visits, advertising campaigns and platforms for generating information on a specific user (client), for example, the time of visiting the site (when he came to the site), from where, what actions he performed, whether he returned again and from where he returned, for example, from advertising materials or a "direct" visit was made to the site, etc., since the server of the contextual advertising service on web pages (for example, Yandex.Direct) sends summary data, for example, the amount of expenses per day, amounts clicks per day and the amount of sessions of users (clients) per day. As a result of the comparison, a data set is formed, in particular, combined statistics, from the server of the contextual advertising service on web pages (for example, Yandex.Direct), supplemented with data, in particular, statistics, from the server of the website traffic assessment and behavior analysis service users (for example, Yandex.Metrica).

Further, using the predictive block 102 , statistics from the server of the service for evaluating website traffic and analyzing user behavior (for example, Yandex.Metrica) is reduced to days by original visits and leads, where the said reduction includes combining several tables with statistics data for several common fields. So, for example, after combining Table 5, Table 6, Table 7, Table 8, tables of combined data are formed (Table 9, Table 10 and Table 11). Next, the predictive block 102 connects data from the server of the service for evaluating website traffic and analyzing user behavior (for example, Yandex.Metrica), in particular, connects statistics of visits from the server for evaluating website traffic and analyzing user behavior (for example, Yandex .Metrica), in particular, given in Table 5, Table 6, Table 7, Table 8, to data from the server of the system (service) of contextual advertising on web pages (for example, Yandex.Direct), in particular, statistics data are combined according to common fields: Campaign, Site, as shown in Table 10 and Table 11.

Further, from this data (combined by common fields), the predictive block 102 , using relational algebra operations (selection, product, union, intersection, join, subtraction), for example, implemented in the pandas library, forms a multidimensional data array - a dataframe (DataFrame) - in whose dimensions are days of the week, hours, and sites. The dataframe is formed for further sorting of sites and time intervals by category, as described below, Table 9, Table 10, Table 11 are formed from it. , including statistics data, and perform mathematical operations, without loss of performance.

The predictive block 102 fetches statistics data for the current user (in particular, generates a data sample) at specified (defined) time intervals, and the time intervals can be set by the user in the personal account interface, for example, using the well-known (generally accepted) element - the datepicker ( from English Datepicker), which allows you to select the interval (period) between two dates. Time intervals can be almost any (day, week, ten, twenty days, month, year, etc.), in particular, those available in the datepicker.

The mentioned time intervals are passed to the task as start ("start_date") and end ("end_date") parameters. Thus, the task is formed by task block 120 in the form (format) of a function call, for example, implemented in Python. The function itself (its code) is obtained by the task block 120 from the control and organization block 101 . For example, in the function(start_date, end_date) function, the start date and end date of the period for which you want to get statistics data are passed as parameters. The statistics data stored in the statistics database (statistics data DB) 177 contains information about visits to the site, "clicks" (interactions) on advertisements (selection of advertisements by the user, means of computing devices using software, etc., for example, scripts), the cost of each "click" (from the English click) as a result of the advertising campaign (during (continuing) the period of time when the advertising campaign is active, in particular, the ads that relate to it are displayed (presented) website users with all accompanying events, such as, for example, clicks, budget expenses for clicks, etc.; the period of time is the period from the moment the advertising campaign is activated (turned on) in the personal account until the statistics data is received, in case when the ad campaign is still active, or until the ad campaign stops - in this case, this date will be before the date of receiving yes data from the API), as well as other parameters, such as, for example, the percentage of clicks (from English. bounces), as well as other parameters that are directly related to those listed and are relative indicators between them. The mentioned click-through rate is obtained from the API and calculated on the side of the advertising platform.

During data processing by the predictive block102 data is used from several sources at once, and the sources are API services (Yandex.Metrika105 (FIG. 6), Yandex.Direct106 (FIG. 6), Google Ads111 (FIG. 6), Google Analytics112 (FIG. 6), Calltouch call tracking107 (FIG. 6), Comagic108 (FIG. 6), smart callback109 (FIG. 6)). Data in all sources can be data of the same type, and only the names of the fields can make a difference. In call tracking, a phone number is added, since it can be a label (a kind of label), as, for example, when clicking on a link, utm-tags carry information from where the user went to the site, and the phone number helps to identify users (in particular, to separate one user from another). The received data is matched by unique identifiers. Data matching can be done by searching for similar (in particular, identical) identifiers in the data tables being matched. Further, when a match is found, the columns of these tables are added to one resulting table. In a particular case, matching can be done using JOIN methods (for example, when using Pandas), similar to the corresponding methods in SQL queries. In a particular case, matching can be done using SQL queries. Statistical data (statistics) from call trackings are combined (merged, "glued") with statistics from Yandex.Metrica105(FIG. 6), Yandex.Direct106(FIG. 6) by unique identifiers (ID) of advertising campaigns, counter IDs on websites. To track activity (in particular, any activity) on a website, a software component can be added (installed) to the website, for example, a script that is a counter that transmits data to an advertising service (for example, Yandex.Metrica).

Yandex.Metrica 105 statistics ( FIG. 6 ) are "glued" with statistics from Yandex.Direct by utm-tags, in particular by ClientId. So, there are matches in the campaign id field, for example, in Yandex.Direct, and the same field in the Yandex.Metrica utm tag. The mentioned ClientId is added to the data table, for example, Yandex.Direct, when collecting statistics data, and is used to distinguish data between several users of the system, an exemplary version of which is shown in FIG. 1 , and corresponds to the current user (in fact, means the current user). In call tracking, utm tags contain the identifier(s) of the advertising campaign. Using the mentioned identifiers, a search is made for matches in data, for example, Yandex.Direct and Yandex.Metrica, then these data are combined by a matching identifier.

In a particular case, in the process of processing (analyzing) data by the predictive block 102 , two complementary methods are used:

1. Setting the time intervals for displaying advertising materials (ads, advertisements), in particular, finding (calculating) such time intervals during which attracting leads for each Yandex.Direct 106 campaign ( FIG. 6 ) is unlikely. Time intervals are checked for the number of clicks and goals achieved. In the process of data processing, the sequence of user actions is tracked: he saw an advertisement, then followed it to a website, then performed a target action (reached the goal set in the advertising campaign settings, for example, pressed the "Buy" button or the "Call me" button ). Thus, those time intervals in which clicks did not lead to the achievement of goals can be avoided (in particular, they can be excluded from processing). During these intervals, the probability of getting leads (users who have reached the goal) is less than other intervals that bring the maximum number of leads at comparable or lower costs. Thus, the advertising budget can be reduced (or the advertising budget can be completely avoided) for clicks that do not lead to the achievement of goals (committing targeted actions on the site), by limiting the time intervals.

2. setting up the lists of used advertising sites (setting up lists of advertising sites) that fake clicks from ads and are ineffective sites, in particular, detection (search) of advertising sites that fake clicks from ads, and ineffective sites, with the formation of a list. According to the analysis and study of the collected statistics data, a pattern was empirically calculated (for a given number of agency clients, for example, for all clients or part of them, for example, for 200 clients, and the number may change with the growth of clients), which is no later than after 235 views (ads), the first visit (to the website) occurs with a probability of 86%. Thus, screening (exclusion) of those sites that have received more than 235 views and not a single visit is carried out. Among such sites may be those that deliberately fake transitions. In addition, among such inefficient sites there may be those that do not bring the required number of views, and hence further visits. In a particular case, the indicated value of 235 views can be changed, in particular, adjusted, for example, reduced or increased (by the user or using the tools of the described system, including on the basis of ongoing data analysis, including including at least the number of views and number of visits) including to achieve higher rates.

The input data are: the interval of the study, passed as parameters (for example, from 2018-01-01 to 2019-04-25) , the website address (in particular, the visit is the user's stay on one specific site) , the number of visits received from Yandex.Direct, the amount of funds spent on the advertising campaign (calculated by summing up the value of the “Cost” column in the statistics data for the corresponding campaign), the number of target ClientIDs for which the transaction was made (where the transaction is the target action performed by the user, in this case, a website client, for example, clicked the "order" button on the website. The mentioned input data (in particular, parameters) can be used to form a combined data array (hereinafter referred to as "OMD" or in formulas - "df"), combined from different (different) sources Data from Yandex.Direct and Yandex.Metrica are combined by target ClientID by forming a field based on common values calculated by the formula: CampaignName + Pla cement + Date.

So, in a particular case, campaign settings are defined, for example, Yandex.Direct, which allows you to save advertising budgets without reducing the volume of attracted leads.

Thus, the input information is linked by identifiers, email addresses, phone numbers into a single visitor history, and both direct leads, that is, those who completed the target action in the current visit, and pending leads, who performed the target action later in another time interval, are traced. In a particular case, to exclude time intervals, only those time intervals are selected in which not only in the entire previous history there was not a single lead (direct or delayed), but the probability of a lead appearing at these moments after calculations turned out to be minimal. The described invention allows you to trace the entire path of the lead using data received, at least from the server of CRM systems, with the formation of a chain of transformations from the moment of "click" to the target call, accurately checking the quality of the lead and accurately determining which advertisement is which advertising system attracted the client. The accuracy of the lead quality check is determined by the exact match of the click and the subsequent target call (belonging to the same user and the same conversion chain), even in the absence of some statistics. For example, a user makes a click and a transition, while the click itself is not taken into account immediately or is not taken into account at all (for example, due to technical problems of the service, for example, Yandex.Metrika, Yandex.Direct, etc.), while he goes to a website, makes (makes) a targeted call and becomes a lead, and this whole sequence is tracked by combining data from different sources. For example, an advertising campaign may not be indicated in a utm tag, but there is a time, a client id, a platform, which are compared, for which advertising campaign actions from the specified chain were (occurred).

Also, the described invention (by means of at least the predictive block 102 ) monitors complex cases when, for example, with a user (website visitor, web page) who clicked on an advertisement from a service, for example, Yandex.Direct 106 ( FIG. 6 ), fails to contact promptly, but he calls the company himself, ahead of the operator's call the next morning. In this case, the operator who received the call (or the automatic system automatically determines, including using voice recognition, analysis, etc. tools, voice messages, etc., means and methods of polling users using user feedback, for example , by choosing one of the answer options by entering numbers, messages using a keyboard, including a numeric one, in particular, a virtual one, etc.), indicates which advertising campaign it belongs to, so it still happens (is carried out) call identification and ownership.

table 2

Number of Yandex.Direct visits The amount of costs for the entire interval Number of target ClientIDs Website 1 721693 37101372 11309 Website 2 543068 29575132 6917 Website 3 354588 25983006 2911

Table 2 shows an example of input data for a given (set, selected, defined) study interval (for example, from 2018-01-01 to 2019-04-25).

In the process of the mentioned (first) method of setting the time intervals for displaying advertising materials, for each campaign, such settings for displaying ads are determined so that the probability of a lead coming in remains the same, intervals when extra clicks were made on ads are excluded. The aforementioned invariability of the lead arrival probability in a particular case is achieved due to the fact that for displaying ads, exactly those time intervals (by time of day, by days of the week) are selected, in which the intensity of target clicks, which then turned into leads, was higher. Thus, statistically, in these time intervals, the probability of obtaining leads remains. In other time intervals, the probability is much less, since they include single clicks that did not bring leads, and even if they did, they are disproportionately smaller in comparison with the funds spent on the advertising budget - fractions of a percentage that can actually be neglected, including within cost optimization .

The time interval for advertising campaign activity (time for displaying ads and, accordingly, budget spending) is limited by the recommended days/hours. Recommendations are formed into data sets (including those formed in a report), an example of which (in particular, a report) is given below (the first in each paragraph is the name of the campaign in Latin):

brand_poisk_mmo

Considered interval from 2018-01-03 to 2019-04-26

Interval costs 4121504.78

Average costs per month 260319.55

It is better not to apply restrictions;

locaciya_vc_poisk_mmo

Considered interval from 2018-01-03 to 2019-04-26

Interval costs 384285.93

Average costs per month 29527.96

It is better to limit the show time from 9:00 to 21:59

Possible savings per month 3653.03 ( 12.37% );

zhk_lesnoe_poisk_mmo

Considered interval from 2018-01-03 to 2019-04-26

Interval costs 563116.02

Average costs per month 35498.73

It is better to limit the show time from 9:00 to 21:59

Possible savings per month 1058.08 ( 2.98% );

retargeting_poisk_mmo

Considered interval from 2018-01-01 to 2019-04-26

Interval costs 10727720.91

Average costs per month 688763.29

It is better to limit the show time from 9:00 to 21:59

Possible savings per month 3949.09 ( 0.57 % )}}}.

The result of the implementation are indicators of average savings per month and savings for the entire period, in particular Savings per month = costs per month / costs per month after optimization, in % (i.e. savings per month in percent is equal to the ratio of costs per month to costs in month after optimization); Lifetime savings = Lifetime cost / Lifetime optimization cost, in % (i.e. Lifetime savings in percent equals the ratio of Lifetime cost to Lifetime optimization cost). . This method uses a mathematical model of the flow of events with variable intensity, improved and automated using DataScience tools (English data science, data science).

As described above, mathematical models of the flow of events with variable intensity can be used, improved and automated using tools such as DataScience, using algorithms that can be expressed as a sequence of operations on arrays (matrices) and implemented using such software libraries, such as NumPy, and software libraries for data processing and analysis, providing special data structures and operations for manipulating numerical tables and time series, such as Pandas, and using software libraries for data processing and analysis, providing special data structures and operations to manipulate numerical tables and time series, for example, Pandas, relational algebra operations can be performed on arrays (tables) of data - selection, product, union, intersection, join, subtraction, for example:

def df = self.df.groupby(['CampaignName', 'Placement']).agg({target: 'sum', 'Leads': 'sum'})

df = df[df['Leads'] > 0]

df['enum_delta'] = df[target] / df['Leads'] .

Table 3

Average savings per month As a percentage of consumption Savings over the entire period As a percentage of consumption website 1 155209 5.46% 2025741 5.46% website 2 150616 6.08% 1578168 5.33% website 3 235974 17.91% 2494368 9.6%

Table 3 shows an example of calculated savings: average savings per month (Savings per month = cost per month / cost per month after optimization, in numerical terms; i.e. Savings per month equals the ratio of costs per month to costs per month after optimization) , average savings per month as a percentage of consumption (Savings per month = costs per month / costs per month after optimization, in %; i.e. savings per month equals the ratio of costs per month to costs per month after optimization), savings for the entire period (Lifetime savings = full-period cost / full-time optimization cost, in numerical terms; i.e., Lifetime savings equals the ratio of full-cycle costs to full-time optimization costs), savings for the whole period as a percentage of consumption (Lifetime savings = fulltime cost / fulltime optimization cost, in %; i.e. Lifetime savings equals the ratio of fulltime cost to fulltime optimization cost).

The (second) way to set up lists of advertising sites. In a particular case, not all advertising sites included in the network of advertising sites of advertising networks, for example, the Yandex advertising network, are equally effective for each website. In addition, there are sites that "cheat" clicks on ads using robots that can fulfill goals in Yandex.Metrica 105 and fill out feedback forms.

In a particular case, combining data from at least one service, for example, from Yandex.Direct 106 ( FIG. 6 ), with "raw" data from another service, for example, Yandex.Metrics 105 , and with data from other services, for example, tracking services (systems) (Smartcallback 109 , FIG. 6 and Comagic 108 , FIG. 6 ), a system of four scales can be used to calculate (in a particular case, evaluate) the quality of an advertising platform:

1. Lead Acquisition Costs (['Average Cost per Lead'] = ['All Costs'] / ['Leads'], i.e. Average Cost per Lead equals the ratio of All Costs to Leads),

2. Technical trust in the site (df['Technical trust'] = (df['Clicks'] - df['Visits']) / df['Clicks'], i.e. Technical trust is equal to the ratio of the difference between the Number of Clicks and Number of Visits to Number of Clicks), 3. Relevance of the site to the advertised website (df['Site Relevance'] = 1 - df['Bounces'] / df['Clicks'], where Bounces - bounces, Clicks - number of clicks) , i.e. Compliance with the Site is equal to the ratio of the difference of one and bounces to clicks), 4. Lead Acquisition Rate (df['Lead Acquisition'] = df['UniqueLeads'] / df['Sessions'], where UniqueLeads is the number of unique leads, Sessions is the number of sessions, i.e. Lead Acquisition is calculated as the ratio of the number of unique leads to the number of sessions).

These scales are used to divide advertising sites (all used advertising sites) into seven types (types):

A. Advertising platforms, work (in particular, the interaction of an advertising campaign with a platform - showing ads) with which at the moment did not give a clear idea of their quality (where the quality of an advertising platform is determined by the above four scales). This type is assigned to an advertising platform provided that the number of clicks is less than the coefficient that is found by the formula: '].

C. Advertising platforms that attract very few leads. This type is assigned to an advertising platform under the following conditions: The advertising company does not correspond to type A. For an advertising campaign, the cost of a lead is not defined, which is determined by the formula: B_costs / B_leads, where B_costs - costs for the entire time interval, B_leads - the number of leads for the entire time interval, t .e. The cost of a lead is equal to the ratio of costs for the entire time interval to the number of leads for the entire time interval.

C. Advertising platforms that are likely to fake conversions, as calculated by the ratio of leads to clicks. “Probably”, since sites with technical deficiencies may fall under the same ratio, temporary failures on the site also affect the probability. The “Technical Trust” parameter takes part in the calculations (calculations), which is calculated by the formula: (Clicks - Visits) / Clicks, i.e. the value of technical trust is equal to the ratio of the difference between clicks and visits to clicks. This type is assigned to those advertising campaigns (ACs) that were not assigned types A and B. And it is calculated by the formula T < Tmean - Tstd, where T is the Technical Trust of the current advertising campaign, Tmean is the average value of the “Technical Trust” parameter for all ACs, Tstd - standard deviation of the "Technical trust" parameter for all ACs.

D. Advertising platforms with excessive acquisition costs. This type is assigned to an advertising platform under the following conditions: The Lead Cost parameter is less than or equal to the maximum point of the range. This range contains the distribution percentage from the scipy.stats.lognorm.interval() function, for which the input data is: the scipy.stats.lognorm.fit() function from the Lead Cost value in the LMD not included in types A, B and C.

E. Not bad advertising platforms, but with a large number of non-targeted clicks, in particular, with the number of clicks several times higher than the number of goals (visits) - two or more times. Platforms allow you to get leads, which is the goal of advertising campaigns. This type is assigned to an advertising platform under the following conditions: The Lead Generation parameter is less than or equal to the maximum point of the range. This range contains the distribution percentage from the scipy.stats.lognorm.interval() function, for which the inputs are: the scipy.stats.lognorm.fit() function from the value of Acquiring leads to LDMs that are not included in types A, B, C and D., but with a large number of non-targeted clicks.

F. Sites with a very high number of bounces (bounce rate, Bounces), in particular, the number of visits (sessions) during which only one page was viewed, no action was taken (clicks, registration form opens, button presses). This type is assigned to an advertising platform under the following conditions: The “Site Compliance” parameter is less than or equal to the maximum value from the range. This range contains the distribution percentage from the scipy.stats.lognorm.interval() function, for which the input data is: the scipy.stats.lognorm.fit() function from the Site Compliance value in DMD, not included in types A, B, C, D and E.

G. Good grounds. This category includes sites that have been filtered by all the previous (listed above) categories.

Further, the parameters of the advertising campaign are set, in particular, the setting of advertising campaigns, including depending on the goals of the user, with the exception of placing advertising materials on advertising sites related to one of the listed types, in an automated mode or semi-automated mode, including by the user, by means of at least one part or block of the system, an exemplary version of which is shown in FIG. 1 , for example, by a block of automated management of advertising campaigns 118 and using a block of control and organization 101 , and the block of automated management of advertising campaigns 118 is responsible, at least, for creating a request for the API in the required (desired) format and, in a particular case, generates such a request , and the control and organization unit 101 is responsible, at least, for fulfilling such a request (in particular, fulfills such a request), in particular, sending data to the API of the advertising platform (service). The goals mentioned can be the number of views, page visits, email click, phone click, JavaScript event, compound goal (combines the above types of goals, in particular, except for phone and email clicks). In the settings of advertising campaigns, depending on the chosen goal, its value is indicated in quantitative terms or in monetary terms. For example, if you select both the CPC equal to 20.82 and the target price 41.65, it means that at least one goal was completed in two clicks.

So, for example, the calculation of advertising sites recommended for exclusion (from the list) on which advertising materials can be placed can be carried out, with the disconnection of such advertising sites. So, in a particular case, advertising platforms of types “C” and “D” may be recommended for exclusion.

After calculating (estimating) the costs per lead, they can also be disabled (using parameters, for example, by marking, in particular, by marking the corresponding types of advertising sites) advertising sites of the “E” and “F” types, so that at the expense of these funds additionally to finance type "G" (in particular, to move funds to advertising platforms of such types).

Table 4

C and D Average savings per month As a percentage of consumption Savings over the entire period As a percentage of consumption website 1 20915 0.74% 908072 2.45% website 2 33578 1.35% 849672 2.87% website 3 64001 4.86% 2387940 9.19% E and F Average savings per month As a percentage of consumption Savings over the entire period As a percentage of consumption website 1 40131 1.41% 1306551 3.52% website 2 249034 10.05% 3199814 10.82% website 3 519293 39.41% 8081540 31.1%

Table 4 shows the cash savings achieved by disabling advertising spaces.

The predictive block 102 generates a data set, for example, in a report format, and stores it in a statistics database 177 for further use, including processing, presentation, etc., as described in the framework of the present invention. Thus, the use (application, etc.) can be carried out in manual mode (at least one block) and in automatic (automated) mode or semi-automatic (semi-automated) mode.

So, in manual mode, a request is created (by the user) in a personal account indicating the required period and user logins, for which the specified data sets are (needed) to be generated, in particular, a report.

In automatic mode, statistics acquisition block 122 collects statistics on a schedule for subsequent data sampling (for a report) at a user-defined frequency, for example, daily, once a week, etc. Compilation (creation, formation) of a data set (report) in automatic mode (which collects all data available to the advertising agency and the server for receiving, processing data and generating processing results 133 , data) is carried out similarly to manual mode (in which, in particular, the user specifies what kind of statistics data is required to be obtained from the API), except that already collected data is used to form a data set and the user is invited to select the necessary logins from the drop-down list and select start dates and the end of the available period.

In a particular case, the user, for example, when performing (for example, by an agency analyst and/or by means of the described system in automatic mode, and/or by an agency analyst using the tools provided by the described system) the formation of a media plan for a client, a data set is formed, for example, in the format tables, the effectiveness of a particular advertising campaign for all advertising sites, including all advertising sites, with key indicators, such as, for example, the monthly savings from the advertising budget, the savings for the entire period of the advertising campaign.

In a particular case, at least one data set, which is the result of predictive analysis (in particular, carried out using an algorithm) by predictive block 102 , is formed (by agency specialists and / or by means of the described system in automatic mode, and / or by an agency analyst using the tools provided by the described system) media plan, already with the most effective time intervals, budgets and advertising campaign settings.

FIG. 3 shows one of the options for obtaining, processing and storing data by the block for obtaining statistics 122 , according to one embodiment of the present invention.

In step 310 , the control and organization unit 101 starts the data collection task.

In step 315 , the block for obtaining statistics 122 receives data from the data database of the server 155 , in particular, credentials for connecting to the API: login, token, and the block for obtaining statistics 122 receives the data transmitted by the data database of the server 155 in encrypted form and decrypts them. Thus, credentials for connecting to services that transmit data to the server for receiving, processing data and generating processing results 133 , as well as credentials for external systems (including CRM systems) are logins, identifiers, tokens that are stored for at least at least in one database (database), in particular, in the server data database 155 . For their use, at least, by the server for receiving, processing data and generating processing results 133 , including for connection during data collection, when connecting third-party systems to the server for receiving, processing data and generating processing results 133 , such credentials are obtained (transmitted ), in at least one database (database), in particular, in the server database 155 , are decrypted and transmitted in requests, or they are checked against the data received, for example, from external systems and CRM for validity, as described under of the present invention.

In step 320 , the statistics getter 122 transmits the request. Thus, the block for obtaining statistics 122 sends a request, in particular, an HTTP request to the API (servers of services for collecting, storing and processing user statistics 144 ), for example, using the "requests" module built into Python. The request passes an access token for authorization, as well as data sampling parameters:

- time interval, in particular, the time interval for which data is sampled (produced), in particular, statistics are collected in the selected time interval;

- the name of the required statistics fields (for example: "Cost", "Clicks", "Impressions", "CampaignName"), and the required fields are the fields that determine the parameters of the advertising campaign: identifier, campaign name, clicks, cost, impressions;

- aggregation parameters, for example, such as: format of received data - CSV, TSV; inclusion in the price of VAT or discounts.

In step 325 , the statistics getter 122 checks if a data response has been received. So, depending on the answer, the block for obtaining statistics 122 repeats the request or completes the work with saving the data.

If in step 325 it is determined by the block for obtaining statistics 122 that no response with data has been received, then in step 330 the statuses are saved in the server data database 155 . In particular, if after a specified number of attempts (by a user, system administrator, etc., for example, in a personal account), for example, after five attempts, no response was received from the API (servers of services for collecting, storing and processing statistical data users 144 ), then the data collection task is completed and the statistics acquisition block 122 transmits the HTTP statuses received from the API (servers of services for collecting, storing and processing user statistics 144 ) (for example, "403" - access denied, "405" - method not supported, "500" - internal server error, "503" - service unavailable) in the server data database 155 , for example, in the format in the form of "logs" one of the text data formats, such as JSON.

If it is determined in step 325 that a data response has been received, then in step 333 the statistics getter 122 checks whether a valid (preset) response code has been received or not. In the event that one of the servers of the services for collecting, storing and processing user statistics 144 sends a response other than the code "200" corresponding to a successful request, for example, due to an internal failure, temporary network problems, delayed return of the response when the data set ( report) is generated offline and it takes time to generate, then it returns to step 310 after a predetermined period of time, for example, the application statistics obtaining block 122 waits for three minutes, after which it tries to send the request again.

If the success of the request is set in step 333 (the correct code is received, in particular, the HTTP response code, "200"), then in step 335 the statistics obtaining block 122 receives the data transmitted by the servers of the services for collecting, storing and processing user statistics 144 , in one of the text formats, for example, in JSON or TSV(CSV) format, depending on the type of API, and is carried out (by the block for obtaining statistics 122 and/or by means of the statistics database 177 ) transferring and saving data to the means of the statistics database 177 .

FIG. 4 shows an exemplary block diagram of the operation of the predictive block 102 according to one embodiment of the present invention.

In step 410 , the predictive block 102 launches the task of data analysis and forecasting, and the start and end dates of the period are passed as parameters of the task (to the input to the data analysis function (in the form of parameters) that is executed as part of the task).

In step415 predictive block102data is received from the statistics database177. Yes, predictive block102 from statistics database177statistics data collected from third-party APIs (raw data) is requested, in particular, from servers of services for collecting, storing and processing user statistical data144. In a particular case, statistics data from each API connector is stored in a separate table, and for analysis, data is obtained simultaneously from several tables.

In step 420 , the predictive block 102 performs data matching and merging into a data array. The data obtained from the statistics database 177 is compared by common fields: ClientID (client identifier, in particular, in numerical or decimal form), date (date), siteID (site ID, in particular, website), campaign (advertising campaign ) and are combined into a data set, in particular, into a data array, for processing, as described below, and in the process of matching, Yandex.Direct and Yandex.Metrica data are compared by forming a field (ind3) using common values calculated by the formula: CampaignName + Placement + Date.

An example of data from Yandex.Direct is shown in Table 5 and Table 6.

An example of data from Yandex.Metrica is shown in Table 7 and Table 8..

An example of matching data (results) is shown in Table 9.

An example of a combined data set is shown in Table 10 and Table 11.

Table 5, Data from Yandex.Direct, Part 1.

CampaignName login task_id Placement Date Bounces Impressions clicks autotargeting_poisk_mmo bigtimemoscow 3b7f0ac9-b062-4203-9e5d-1d2cdbba4516 go.mail.ru 2020-01-11 00:00:00 0.0 1269.0 36.0 autotargeting_poisk_mmo bigtimemoscow 3b7f0ac9-b062-4203-9e5d-1d2cdbba4516 go.mail.ru 2020-01-12 00:00:00 0.0 1314.0 18.0 autotargeting_poisk_mmo bigtimemoscow 3b7f0ac9-b062-4203-9e5d-1d2cdbba4516 go.mail.ru 2020-01-13 00:00:00 0.0 594.0 36.0 autotargeting_poisk_mmo bigtimemoscow 3b7f0ac9-b062-4203-9e5d-1d2cdbba4516 Yandex 2020-01-03 00:00:00 0.0 1305.0 27.0 autotargeting_poisk_mmo bigtimemoscow 3b7f0ac9-b062-4203-9e5d-1d2cdbba4516 Yandex 2020-01-04 00:00:00 9.0 7785.0 108.0

where

- CampaignName - campaign name;

- login - login of the agency client, consists of the name in Latin letters, for example bigtimemoscow;

- Task_id - identifier of the currently running task within the system, an exemplary version of which is shown in FIG. 1 , or part thereof, for example, inside the server for receiving, processing data and generating processing results 133 and can be used to separate reports, including for each report (data set) such own unique identifier can be generated and assigned;

- Placement - ad placement site;

- Date - campaign date;

- Bounces - number of bounces;

- Impressions - number of impressions;

- Clicks - number of clicks;

- Cost - cost;

- Sessions - number of sessions;

- ind – field composed of CampaignName and Date (value formed from data from "CampaignName" and "Date"):

- ind2 - field composed of Placement and Date (value formed from data from "Placement" and "Date"):

- Created_at - creation date;

- ind3 - field composed of CampaignName, Placement and Date (value formed from data from "CampaignName", "Placement" and "Date").

Table 6, Data from Yandex.Direct, Part 2.

CampaignName cost sessions ind ind2 Created_at ind3 autotargeting_poisk_mmo 5822.549999999998 27.0 autotargeting_poisk_mmo 2020-01-11 go.mail.ru 2020-01-11 2020-12-29 11:44:57 autotargeting_poisk_mmo_go.mail.ru 2020-01-11 autotargeting_poisk_mmo 2212.47 18.0 autotargeting_poisk_mmo 2020-01-12 go.mail.ru 2020-01-12 2020-12-29 11:44:57 autotargeting_poisk_mmo_go.mail.ru 2020-01-12 autotargeting_poisk_mmo 4972.230000000001 36.0 autotargeting_poisk_mmo 2020-01-13 go.mail.ru 2020-01-13 2020-12-29 11:44:57 autotargeting_poisk_mmo_go.mail.ru 2020-01-13 autotargeting_poisk_mmo 5675.4000000000015 9.0 autotargeting_poisk_mmo 2020-01-03 Yandex 2020-01-03 2020-12-29 11:44:57 autotargeting_poisk_mmo_Yandex 2020-01-03 autotargeting_poisk_mmo 17871.300000000007 99.0 autotargeting_poisk_mmo 2020-01-04 Yandex 2020-01-04 2020-12-29 11:44:57 autotargeting_poisk_mmo_Yandex 2020-01-04

where

- CampaignName - campaign name;

- login - login of the agency client, consists of the name in Latin letters, for example bigtimemoscow;

- Task_id - identifier of the currently running task within the system, an exemplary version of which is shown in FIG. 1 , or part thereof, for example, inside the server for receiving, processing data and generating processing results 133 and can be used to separate reports, including for each report (data set) such own unique identifier can be generated and assigned;

- Placement - ad placement site;

- Date - campaign date;

- Bounces - number of bounces;

- Impressions - number of impressions;

- Clicks - number of clicks;

- Cost - cost;

- Sessions - number of sessions;

- ind - field composed of CampaignName and Date (value formed from data from "CampaignName" and "Date");

- ind2 - field composed of Placement and Date (value formed from data from "Placement" and "Date");

- Created_at - creation date;

- ind3 - field composed of CampaignName, Placement and Date (value formed from data from "CampaignName", "Placement" and "Date").

Table 7. Data from Yandex. Metrica, Part 1.

visitID Date isNew User visitDuration login task_id clientID goalsID AdId Campaign Name Placement isDirect 1.813653 2020-01-06 10:17:29 0 51 bigtime moscow 3b7f0ac9-b062-4203-9e5d-1d2cdbba4516 1.5726006 907982566e+18 [] 8519607862 brand_poisk_mmo Yandex one 1.813653 2020-01-06 10:17:29 0 51 bigtime moscow 3b7f0ac9-b062-4203-9e5d-1d2cdbba4516 1.5726006 907982566e+18 [] 8519607862 brand_poisk_mmo Yandex one 1.813653 2020-01-06 10:17:29 0 bigtime moscow 3b7f0ac9-b062-4203-9e5d-1d2cdbba4516 1.5726006 907982566e+18 [] 8519607862 brand_poisk_mmo Yandex one 1.813653 2020-01-06 10:17:29 0 51 bigtime moscow 3b7f0ac9-b062-4203-9e5d-1d2cdbba4516 1.5726006 907982566e+18 [] 8519607862 brand_poisk_mmo Yandex one 1.813653 2020-01-06 10:17:29 0 51 bigtime moscow 3b7f0ac9-b062-4203-9e5d-1d2cdbba4516 1.5726006 907982566e+18 [] 8519607862 brand_poisk_mmo Yandex one

where

- visitID – unique identifier of the user's visit to the service for evaluating website traffic and analyzing user behavior (for example, Yandex.Metrica);

- Date - campaign date;

- isNewUser - whether the visit is the visitor's first visit (can be a boolean variable, for example, "Yes" or "No", "1" or "0", etc.);

- visitDuration - duration of the visit (stay on the site);

- login - login of the agency client, consists of the name in Latin letters, for example bigtimemoscow;

- task_id - ID of the currently running task within the system, an example of which is shown in FIG. 1 , or part thereof, for example, inside the server for receiving, processing data and generating processing results 133 and can be used to separate reports, including for each report (data set) such own unique identifier can be generated and assigned;

- clientID - client identifier;

- goalsID - identifier of the goal being achieved in the service for evaluating website traffic and analyzing user behavior (for example, Yandex.Metrica). The goal can be, for example: the user views three pages on the site. This type of goal can be set in the advertising campaign settings, for example, in the advertising account, in particular, in automatic mode or by the user, in particular, by the specialist leading the advertising campaign, and it is assigned a unique identifier;

- AdId - ad ID;

- CampaignName - campaign name;

- Placement - ad placement site;

- isDirect - indicates that it was a direct visit to the site or a visit from an advertisement;

- ind - field composed of CampaignName and Date (value formed from data from "CampaignName" and "Date");

- ind2 - field composed of Placement and Date (value formed from data from "Placement" and "Date");

- isGoal - whether this visit is targeted or not (in binary form - "0" or "1");

- isGoalClient - is the target client or not (in binary form - "0" or "1");

- isDirectOriginalVisit - whether the visit is a unique direct visit from an advertisement;

- isBounce - means bounce, i.e. after switching from an advertisement to the site, the user immediately closed the page without taking any action;

- lastAdvEngine - the last advertising platform from which the user went to the site;

- created_at - creation date;

- ind3 - field composed of CampaignName, Placement and Date (value formed from data from "CampaignName", "Placement" and "Date").

Table 8. Data from Yandex. Metrics, Part 2.

visitID ind ind2 isGoal isGoal Client isDirectOriginalVisit isBounce lastAdv Engine created_at ind3 1.813653 brand_poisk_mmo_ 2020_01_06 Yandex_2020 _01_06 0 one one 0 ya_direct 2020-12-29 11:44:57 brand_poisk_mmo_Yandex_2020_01_06 1.813653 brand_poisk_mmo_ 2020_01_06 Yandex_2020 _01_06 0 one 0 0 ya_direct 2020-12-29 11:44:57 brand_poisk_mmo_Yandex_2020_01_06 1.813653 brand_poisk_mmo_ 2020_01_06 Yandex_2020 _01_06 0 one 0 0 ya_direct 2020-12-29 11:44:57 brand_poisk_mmo_Yandex_2020_01_06 1.813653 brand_poisk_mmo_ 2020_01_06 Yandex_2020 _01_06 0 one 0 0 ya_direct 2020-12-29 11:44:57 brand_poisk_mmo_Yandex_2020_01_06 1.813653 brand_poisk_mmo_ 2020_01_06 Yandex_2020 _01_06 0 one 0 0 ya_direct 2020-12-29 11:44:57 brand_poisk_mmo_Yandex_2020_01_06

where

- visitID - a unique identifier of a user's visit to the service for evaluating website traffic and analyzing user behavior (for example, Yandex.Metrica);

- Date - campaign date;

- isNewUser - whether the visit is the visitor's first visit (can be a boolean variable, for example, "Yes" or "No", "1" or "0", etc.);

- visitDuration - duration of the visit (stay on the site);

- login - login of the agency client, consists of the name in Latin letters, for example bigtimemoscow;

- task_id - ID of the currently running task within the system, an example of which is shown in FIG. 1 , or part thereof, for example, inside the server for receiving, processing data and generating processing results 133 and can be used to separate reports, including for each report (data set) such own unique identifier can be generated and assigned;

- clientID - client identifier;

- goalsID - identifier of the goal being achieved in the service for evaluating website traffic and analyzing user behavior (for example, Yandex.Metrica). The goal can be, for example: the user views three pages on the site. This type of goal can be set in the advertising campaign settings, for example, in the advertising account, in particular, in automatic mode or by the user, in particular, by the specialist leading the advertising campaign, and it is assigned a unique identifier;

- AdId - ad ID;

- CampaignName - campaign name;

- Placement - ad placement site;

- isDirect - indicates that it was a direct visit to the site or a visit from an advertisement;

- ind - field composed of CampaignName and Date (value formed from data from "CampaignName" and "Date");

- ind2 - field composed of Placement and Date (value formed from data from "Placement" and "Date");

- isGoal - is this a target visit or not (in binary form - "0" or "1");

- isGoalClient - is the target client or not (in binary form - "0" or "1");

- isDirectOriginalVisit - whether the visit is a unique direct visit from an advertisement;

- isBounce - means bounce, i.e. after switching from an advertisement to the site, the user immediately closed the page without taking any action;

- lastAdvEngine - the last advertising platform from which the user went to the site;

- created_at - creation date;

- ind3 - field composed of CampaignName, Placement and Date (value formed from data from "CampaignName", "Placement" and "Date").

Table 9, Matching data.

ind3 Leads autotargeting_retargeting_poisk_mmo_Yandex_2020_01_09 one autotargeting_retargeting_poisk_mmo_Yandex_2020_01_16 one brand_poisk_mmo__2020-01-20 2 brand_poisk_mmo__2020-01-30 2 brand_poisk_mmo_Yandex_2020-01-06 one

, where

- ind3 – field composed of CampaignName, Placement and Date (value formed from data from "CampaignName", "Placement" and "Date");

- Leads - leads.

Table 10. Combined data set, Part. one.

CampaignName login task_id Placement Date Bounces Impressions clicks autotargeting_poisk_mmo bigtimemoscow 3b7f0ac9-b062-4203-9e5d-1d2cdbba4516 Yandex 2020-01-09 00:00:00 72.0 12762.0 180.0 autotargeting_poisk_mmo bigtimemoscow 3b7f0ac9-b062-4203-9e5d-1d2cdbba4516 Yandex 2020-01-16 00:00:00 54.0 11259.0 234.0 brand_search_mmo bigtimemoscow 3b7f0ac9-b062-4203-9e5d-1d2cdbba4516 Yandex 2020-01-06 00:00:00 0.0 153.0 63.0 brand_search_mmo bigtimemoscow 3b7f0ac9-b062-4203-9e5d-1d2cdbba4516 Yandex 2020-01-10 00:00:00 9.0 378.0 378.0 brand_search_mmo bigtimemoscow 3b7f0ac9-b062-4203-9e5d-1d2cdbba4516 Yandex 2020-01-13 00:00:00 27.0 531.0 531.0

Table 11. Combined data set, Part. 2.

CampaignName cost sessions ind ind2 created_at ind3 Leads autotargeting_search_mmo 35149.41 270.0 autotargeting_poisk_mmo_2020-01-09 Yandex_2020-01-09 2020-12-29 11:44:57 autotargeting_poisk_mmo_ Yandex_2020-01-09 1.0 autotargeting_search_mmo 37583.28 243.0 autotargeting_poisk_mmo_2020-01-16 Yandex_2020-01-16 2020-12-29 11:44:57 autotargeting_poisk_mmo_ Yandex_2020-01-16 1.0 brand_search_mmo 3596.1299999999987 63.0 brand_poisk_mmo_ 2020-01-06 Yandex_2020-01-06 2020-12-29 11:44:57 brand_poisk_mmo_Yandex_ 2020-01-06 1.0 brand_search_mmo 35394.390000000014 369.0 brand_poisk_mmo_ 2020-01-10 Yandex_2020-01-10 2020-12-29 11:44:57 brand_poisk_mmo_Yandex_ 2020-01-10 2.0 brand_search_mmo 35675.099999999984 531.0 brand_poisk_mmo_ 2020-01-13 Yandex_2020-01-13 2020-12-29 11:44:57 brand_poisk_mmo_Yandex_ 2020-01-13 1.0

In step 425 , the predictive block 102 finds (calculates) the optimal sites and time intervals of maximum efficiency (calculation result). So, the predictive block 102 by grouping data in the ODM by the campaign name (CampaignName) and by the site (Placement), with clicks and leads being summed up, with further calculation of the ratio of clicks to leads and calculating the average value of this ratio (in particular, using mathematical algorithm) calculates the optimal sites for advertising (which can bring the maximum number of leads - i.e. buyers), as well as the time intervals in which these sites will be most effective and thus allow, at least, to save part of the advertising budget.

In step 430 , if there is a successful request to the API, for example, upon receiving the code "200" from the API, the predictive block 102 receives data (resulting from the operation of the algorithm (algorithms) in the predictive block 102 , i.e., expressed in numerical form quantitative indicators by advertising platforms on which it is planned to place ads), for example, in one of the text data formats, in particular, in the JSON or TSV (SVC) format, depending on the API (servers of services for collecting, storing and processing user statistics 144 ).

In step 435 , the data is stored in the statistics database 177 by the predictive block 102 and/or by means of the statistics database 177 (DBMS tools).

FIG. 5 shows an exemplary block diagram of the operation of an external systems connection unit 103 according to one embodiment of the present invention.

In step 510 , the external systems connection block 103 receives a request (from an external client, in particular, at least one server of CRM systems 113 , it is he who connects to the external systems connection block 103 , which in a particular case may be an API or contain ) to receive statistics data, in particular, an HTTP request for obtaining statistics data is received from at least one of the servers of services for collecting, storing and processing user statistical data 144 . So, in step 515 , a verification (comparison) of the token received from one of the servers of the services for collecting, storing and processing user statistical data 144 is carried out with the stored credentials with access granted according to the data access level and access is provided (to the user to the user data, moreover , in a particular case, a user can be a company with many users with its own hierarchy, and in accordance with such a hierarchy, an access structure is used that includes access levels, where the lower the employee in the hierarchical structure, the less data is available to him within the user's personal account, i.e. e. in particular, the company (client company)) according to the level of data access. Thus, the external systems connection block 103 sends a request to the control and organization block 101 , which accesses the server data database 155 directly, searches for a match and validity of the token in it, and returns a response to the external systems connection block 103 . External systems connection block 103 (and/or control and organization block 101 ) checks whether the token has passed validation, in particular, the token transmitted from one of the servers of the services for collecting, storing and processing user statistics 144 is checked against the statistics available in the database 177 credentials and access is granted to the user (and in particular, to the means of presenting results 115 ) according to the level of access to data (data of collected statistics, in particular to data (in a particular case, to all data) that relate to the current user from the moment data is received from API until the results are provided, and such data is stored in the statistics database 177 and as it passes through the cleaning, transformation procedures using at least the predictive block 102, such data or at least part of it is moved through the corresponding tables, to which in in a particular case, the mentioned database is divided for its efficient operation) using access structure, including access levels, depending on the hierarchy of a company with many users, where the lower the employee in the hierarchical structure, the less data is available to him within the user's personal account, i.e. in particular, the company, and the mechanism that ensures the differentiation of access rights to data (what data a particular user can view) in a particular case is filtering by user ID (which in a particular case is present in all data tables from the moment of collection to the provision of results).

If in step 515 it was found that the token did not pass validation, then in step 520 an appropriate error message is sent to CRM indicating the reason for the access denial (in this case, the access denial is eliminated, including in case of an error, the appropriate specialists take measures to eliminate the error or turn to the developers for help in solving the problem, for example, when the token expires, which is a special case of one of the system's protective mechanisms, the token can be received, including generated, re-created and the system can continue to function , including the elimination of the cause of the error, including the error message, for example, from CRM), in particular, a message about the fixed error is returned to the corresponding server of services for collecting, storing and processing user statistics 144 (in particular, the code errors).

If in step 515 it was found that the token was validated, then in step 525 , the block for connecting external systems 103 sends a request to the control block and organization 101 for data sampling. In a particular case, the external systems connection block 103 (API module), implemented using Django REST, accesses the control block and organization 101 with a request to fetch data.

In step 530 , the management and organization block 101 aggregates data from the statistics database 177 and transmits (returns it) in response to a request for aggregated statistics data, for example, in JSON format, to the external systems connection block 103 .

In step 535 , the external systems connection block 103 transmits the aggregated statistics data in response to a request for statistics data to the corresponding (from which the request was received) server of services for collecting, storing and processing user statistics 144 .

FIG. 6 illustrates another exemplary general scheme of a system implementing the present invention, according to one embodiment of the present invention.

A system that implements the method (or at least one part of the method) described in the framework of the present invention, an exemplary version of which is shown in FIG. 1 , contains servers of services for collecting, storing and processing user statistical data 144 connected at least with a server for receiving, processing data and generating processing results 133 , including using the Internet 114 . Servers of services for collecting, storing and processing user statistical data 144 , for example, servers of such services (systems, platforms) as Yandex.Metrica 105 ( FIG. 6 ), Yandex.Direct 106 ( FIG. 6 ), Calltouch 107 ( FIG. 6 ), Comagic 108 ( FIG. 6 ), Smartcallback 109 ( FIG. 6 ), Google Ads 111 ( FIG. 6 ), Google Analytics 112 ( FIG. 6 ), collect, store and/or process user data resulting from the performance of actions by such users, including using electronic computing devices (for example, visiting sites on the Internet, interacting with sites and site elements, for example, web links, advertising materials, etc., including the parameters of user interaction with them, e.g., time spent on a web page, frequency of visits to certain sites, expression of interest in certain goods and categories of goods, computing devices used by the user, purchases made, etc.) or actions that register captured and processed by electronic computing devices, such as cash registers, cameras, facial recognition systems, sound recorders, access control systems, user location tracking, etc., as described in the framework of the present invention.

FIG. 7 illustrates a block diagram of an exemplary embodiment of the present invention.

In step 701 , the server for receiving, processing data and generating processing results 133 receives statistics data from at least one of the servers of the services for collecting, storing and processing user statistical data 144 using the API provided by such services, where the received statistics data contains at least related to users, the placement and presentation of advertising materials and placed advertising materials, data obtained at least as a result of the interaction of users with elements, including advertising materials, websites, as well as data obtained as a result of the presentation of advertising materials, including information on at least about the user's reactions to the presented advertising materials, expressed in the commission of actions by the user using at least one computing device of the user and being the user's response to the advertising material s, including those registered and transmitted by at least one computing device and transmitted to servers of services for collecting, storing and processing user statistical data 144 and / or generated by servers of services for collecting, storing and processing user statistical data 144 .

The statistical data received by the server for receiving, processing data and generating processing results 133 may include user interaction parameters containing at least the time spent on the web page and / or the frequency of visiting websites, and / or showing interest in a product, product categories, and / or user-used computing devices data from such computing devices containing at least information about making purchases, identifiers of a computing device, on the Internet, in services, applications, and / or registration data in them, as well as data from at least cash registers and / or cameras, and / or face recognition systems, and / or sound recording devices, and / or access control systems, and / or user location tracking tools, as well as data from personal accounts of services for collecting, storing and processing user statistical data and data on the effectiveness of advertising channels, campaigns and keywords, ring, conversions from the presentation of advertising materials, costs, impressions and user interactions with web links, target audience, advertising campaign budget, call sources, their dates, times, keywords, including those obtained after recognition and analysis of the user's speech.

Data related to the placement of advertising materials may include data on the effectiveness of advertising channels, advertising campaigns and keywords, scoring, conversions from the presentation of advertising materials, costs, impressions and user interactions with web links, target audience, advertising campaign budget, call sources, their dates, times, keywords, including those obtained after recognition and analysis of the user's speech.

In step 702 , the server for receiving, processing data and generating processing results 133 stores the received statistics data in at least one statistics database 177 .

When saving data to the statistics database 177 , the stored data can be checked for the validity of data types using the conversion of a value from one data type to another or determining the type of a variable.

In step 703 , the server for receiving, processing data and generating processing results 133 generates the first data sample from the statistics data stored in the statistics database 177 to form a data set of advertising campaigns performance indicators, where each generated sample contains statistics data for each of the users for each from advertising campaigns created, configured and launched by the user using the service for placing advertising materials on advertising sites containing at least a web page for placing advertising materials within advertising campaigns, including a unique user name, advertising campaign name, advertising placement identifier materials in the format of a uniform resource locator of the website, the number of presentations of advertising materials, the number of user interactions with the presented advertising materials, the cost of placing advertising materials, the name of the group of re advertising materials, which combines advertising materials within one campaign, and data sampling is carried out according to two or more preset parameters, where such parameters are a unique user name, the name of the advertising campaign, the ID of the advertising material placement site, belonging to a group of advertisements by group name.

In step 704 , the server for receiving, processing data and generating processing results 133 generates a second data sample from the statistics data stored in the statistics database 177 , which contains statistics data for each of the users for each of the advertising campaigns created, configured and launched by the user with using the service of placing advertising materials on advertising sites at the cost of advertising campaigns attracted by the advertisement of clients, including the date and time of visiting the web page on which the advertising material is placed, whether the visit to the website was carried out without using user interaction with the placed advertising materials, the number target customers to whom the presentation of advertising materials is directed, the number of pre-set goals of advertising campaigns per visit to the website by the user, the cost for each preset goal of each advertising campaign per visit to the web site site by the user, the cost of visiting the website by the user, the name of the advertising campaign, the description of the category for the recommendation, the unique username.

In a particular case, the data contained in the first data sample and the second data sample are updated by sending requests by the server for receiving, processing data and generating processing results 133 to at least one of the servers of services for collecting, storing and processing statistical user data and receiving from it requested data by collating and updating data on at least the time the user visited the website, the duration of the visit to the website, the name of the advertising site, the affiliation of the advertising campaign, the identifiers of the counters in the format of the program code that collects statistical data on the website, with checking the correctness and relevance of statistical data for each user.

Also, data updating can be carried out before the formation of the first data sample and the second data sample, in particular, after receiving the data stored in the statistics database 177 .

The parameters of users' visits to websites (and in particular web pages) may include the time the user spent on the web page, the frequency of visits by users to the web page, the expression of interest in a particular product or categories of products, as well as data recorded and processed by cash registers. , access control systems, user location tracking.

In step 705 , the server for receiving, processing data and generating processing results 133 generates a data set of advertising campaign performance indicators from the data of the first data sample and the data of the second data sample, by forming a multidimensional data array, the dimensions of which are days of the week, hours and names of advertising sites, containing data for each of the users for a predetermined time interval, for which a multidimensional data array has been formed and to which the data of advertising campaigns effectiveness indicators relate, and containing data on the sequence of user actions from the moment the advertising materials are presented, user interactions with the presented advertising materials to switch to the advertised websites, prior to the commission of targeted actions on websites by users, specified by setting the parameters of advertising campaigns of the service for placing advertising materials by the parameters of presenting advertising materials fishing, made with the possibility of setting the parameters for the presentation of advertising materials.

In a particular case, the generated data set of advertising campaign performance indicators from the data of the first data sample and the data of the second data sample contains data on behavioral changes in user actions.

In step 706 , in the generated multidimensional data array within a predetermined time interval by the server for receiving, processing data and generating processing results 133 :

- at least one time interval for the presentation of advertising material, specified by the parameters for the presentation of advertisements of the advertising material placement service, is detected for each advertising material belonging to the corresponding advertising campaign, within which user interactions with the presented advertising material did not lead to the achievement of predefined goals of advertising campaigns, or during which the user did not interact with the presented advertising material, and

- inefficient advertising sites are identified, where the probability value of user interaction with advertising material on the website of the advertising site in such a given time interval for presenting advertising material is less than a given value, where the probability of user interaction is calculated as the ratio of the number of user interactions with advertising material to the total number presentations of advertising materials on websites for each advertising platform, or the value of the probability of achieving a given goal in the settings of an advertising campaign is less than a given value, where the probability of achieving a given goal is calculated as the ratio of the number of achievements of a given goal to the total number of presentations of advertising materials on websites for each advertising space.

In step 707 , the server for receiving, processing data and generating processing results 133 performs:

- formation of a list of advertising materials with predetermined time intervals for the presentation of advertising materials for each advertising campaign, for which time intervals for the presentation of advertising material were identified, within which user interactions with the presented advertising material did not lead to the achievement of the preset goals of advertising campaigns or during which the user did not interact with the presented advertising material, and

-formation of the list of inefficient advertising platforms.

In step 708 , the server for receiving, processing data and generating the results of processing 133 generates a set of values for the parameters of advertising sites, containing values for disabling advertising sites from the list of ineffective advertising sites, and a set of values for the parameters of the presentation of advertising materials for services for placing advertising materials to stop the presentation of advertising materials , contained in the list of advertising materials with predetermined time intervals for the presentation of advertising materials for each advertising campaign, for which the time intervals for the presentation of advertising material were identified, within which user interactions with the presented advertising material did not lead to the achievement of the preset goals of advertising campaigns or during which the user did not interacted with the presented advertising material, and such data sets are transmitted to at least one of the servers of the advertising placement services amino materials.

In step 709 , at least one of the servers of the advertising materials placement services applies the values contained in the set of values of the parameters of the presentation of advertising materials, and applies the values of the parameters of the advertising spaces containing the values to exclude advertising spaces from the list of ineffective advertising spaces to stop serving them. advertising materials.

At step 710 , at least one advertising material is placed with presentation on at least one advertising space by at least one server of advertising materials placement services with changed parameter settings for advertising materials and advertising spaces with the launch of at least one advertising campaign.

In a particular case, after disabling advertising sites from the list of ineffective advertising sites, other advertising sites are set for related advertising materials, after identifying effective advertising sites in the generated multidimensional data array within a predetermined time interval, on which the value of the probability of user interaction with advertising material on website advertising site in such a given time interval for the presentation of advertising material is maximum, where the probability of user interaction is calculated as the ratio of the number of user interactions with advertising material to the total number of presentations of advertising materials on websites for each advertising site, or the value of the probability of achieving a given goal in the settings of the advertising campaign is maximum, where the probability of achieving a given goal is calculated as the ratio of the number of achievements of a given goal to the total number of presentations of advertising materials on websites for each advertising platform.

In a particular case, the user is presented with a list of advertising sites to disable and a set of parameter values for advertising sites and values for disabling advertising sites from the list of ineffective advertising sites, and stopping the presentation of advertising materials and a list of advertising materials for stopping the presentation and a set of parameter values for presenting advertising materials to stop presentation of advertising materials contained in the list of advertising materials at predetermined time intervals for the presentation of advertising materials for each advertising campaign, for which time intervals for the presentation of advertising material were identified, within which user interactions with the advertising material presented did not lead to the achievement of the pre-set goals of advertising campaigns or during which the user did not interact with the presented advertising material.

Services for collecting, storing and processing statistical data of users may be contextual advertising services on web pages or services for assessing website traffic and analyzing user behavior.

Data to services for collecting, storing and processing user statistics can be transferred from servers of contextual advertising services on web pages or servers of services for assessing website traffic and analyzing user behavior.

FIG. 8 shows an example of a general purpose computer system that includes a multi-purpose computing device in the form of a computer 20 or a server, or a unit (module) of the system described in the present invention, including a processor 21 , system memory 22 and a system bus 23 that connects various system components, including system memory with processor 21 .

System bus 23 may be any of various types of bus structures, including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. The system memory includes read only memory (ROM) 24 and random access memory (RAM) 25 . The ROM 24 stores the Basic Input/Output System (BIOS) 26 , which consists of basic routines that help exchange information between elements within the computer 20 , for example, at startup.

Computer 20 may also include a hard disk drive 27 for reading from and writing to a hard disk, not shown, a magnetic disk drive 28 for reading from or writing to a removable magnetic disk 29 , and an optical disk drive 30 for reading from or recording on a removable optical disc 31 such as CD, digital video disc and other optical media. The hard disk drive 27 , the magnetic disk drive 28 , and the optical disk drive 30 are connected to the system bus 23 via a hard disk drive interface 32 , a magnetic disk drive interface 33 , and an optical drive interface 34 , respectively. The drives and their respective computer-readable media provide non-volatile storage of computer-readable instructions, data structures, program modules, and other data to the computer 20 .

Although the typical configuration described here uses a hard drive, a removable magnetic disk 29 and a removable optical disk 31 , one skilled in the art will appreciate that in a typical operating environment, other types of computer-readable media can also be used that can store data that is accessible by a computer, such as magnetic cassettes, flash memory cards, digital video discs, Bernoulli cartridges, random access memory (RAM), read only memory (ROM) and the like.

Various software modules, including the operating system 35 , can be stored on a hard disk, magnetic disk 29 , optical disk 31 , ROM 24 or RAM 25 . Computer 20 includes a file system 36 associated with or included with operating system 35 , one or more software applications 37 , other program modules 38 , and program data 39. A user may enter commands and information into computer 20 using input devices such as like a keyboard 40 and a pointing device 42 . Other input devices (not shown) may include a microphone, joystick, gamepad, satellite dish, scanner, or any other.

These and other input devices are connected to the processor 21 , often via a serial port interface 46 that is connected to the system bus, but may be connected via other interfaces such as a parallel port, game port, or universal serial bus (USB). A monitor 47 or other type of visual display device is also connected to the system bus 23 via an interface such as a video adapter 48 '. In addition to the monitor 47 , personal computers typically include other peripheral output devices (not shown) such as speakers and printers.

Computer 20 may operate in a networked environment via logical connections to one or more remote computers 49 . The remote computer (or computers) 49 may be another computer, server, router, network PC, peering device, or other node on a single network, and also typically includes most or all of the elements described above with respect to computer 20 , although only shown information storage device 50 . Logical connections include a local area network (LAN) 51 and a global computer network (GCN) 52 . Using the host adapter 55 , an external or remote storage (data) device 57 can be connected. Such network environments are commonly found in institutions, corporate computer networks, and the Internet.

The computer 20 used in the LAN network environment is connected to the local network 51 via a network interface or adapter 53 . The computer 20 used in a GCN network environment typically uses a modem 54 or other means to communicate with a wide area computer network 52 such as the Internet.

The modem 54 , which may be internal or external, is connected to the system bus 23 via a serial port interface 46 . In a networked environment, program modules or portions thereof described in connection with computer 20 may be stored on a remote storage device. It should be appreciated that the network connections shown are typical and other means may be used to establish a communication link between computers.

In conclusion, it should be noted that the information given in the description are examples that do not limit the scope of the present invention defined by the formula. A person skilled in the art will appreciate that there may be other embodiments of the present invention consistent with the spirit and scope of the present invention.

Claims (28)

1. A method for automatically setting up the placement of advertising materials of companies on advertising sites, in which: carry out by the server of the service for collecting, storing and processing statistical data of users the collection, storage and processing of statistical data of users obtained as a result of actions by users using electronic computing devices, consisting in visiting sites on the Internet, interacting with sites and elements of sites: from the web - links, advertising materials, including such parameters of user interaction with them, such as: a unique user name, time spent on a web page, frequency of visiting sites, showing interest in goods and categories of goods, computing devices used by the user, purchases made, as well as actions registered and processed by cash registers, cameras, face recognition systems, sound recording devices, access control systems, user location tracking; at the same time, the specified collection, storage and processing of statistical data includes the definition of keywords obtained, among other things, on the basis of recognition and analysis of the user's speech, costs, impressions and clicks of the user, target audience, call sources, date, time; process the received data by the server for receiving, processing data and generating the results of processing from the server of the service for collecting, storing and processing user statistical data, from the statistics database, from the server database, and also use the previously processed and stored data in the databases of the storage unit, where in this case, the processing of statistical data is: - identification of at least one time interval for the presentation of advertising material, specified by the parameters for the presentation of advertisements of the service for placing advertising materials, for each advertising material belonging to the corresponding advertising campaign, within which user interactions with the presented advertising material did not lead to the achievement of preset advertising goals campaigns or during which the user did not interact with the presented advertising material; - identification of advertising sites where the value of the probability of user interaction with advertising material on the website of the advertising site in such a given time interval for the presentation of advertising material is less than a given value, where the probability of user interaction is calculated as the ratio of the number of user interactions with advertising material to the total number of presentations advertising materials on websites for each advertising platform, or the value of the probability of achieving a given goal in the settings of an advertising campaign is less than a given value, where the probability of achieving a given goal is calculated as the ratio of the number of achievements of a given goal to the total number of presentations of advertising materials on websites for each advertising sites; using the predictive block of the server for receiving, processing data and generating processing results, at least one data set is formed containing data on the predicted effectiveness of selected advertising sites based on statistical data and the processing of statistical data, which includes setting the time intervals for displaying advertising materials and setting up lists of advertising sites that are ineffective sites; the server for receiving, processing data and generating processing results transfers to at least one of the servers of services for placing advertising materials a data set with predicted data, as well as settings for time intervals for displaying advertising materials and settings for lists of advertising sites that are ineffective sites; apply the values contained in the data set, the values of the time intervals for the display of advertising materials, as well as the values of the settings to exclude advertising sites from the list of ineffective advertising sites to stop the presentation of advertising materials on them. 2. The method according to claim 1, in which the statistical data received by the server for receiving, processing data and generating processing results include user interaction parameters containing at least the time spent on the web page and / or the frequency of visiting websites, and / or showing interest in a product, product categories, and / or user-used computing devices data from such computing devices containing at least information about making purchases, identifiers of a computing device, on the Internet, in services, applications, and / or registration data in them , as well as data from at least cash registers and / or cameras, and / or face recognition systems, and / or sound recording devices, and / or access control systems, and / or user location tracking tools, as well as data from personal accounts of services collection, storage and processing of user statistics and data on the effectiveness of advertising channels, campaigns and keywords, scoring, conversions from advertising materials, expenses, impressions and user interactions with web links, target audience, advertising campaign budget, call sources, their dates, times, keywords, including those obtained after recognition and analysis of the user's speech . 3. The method according to claim 1, in which the actualization of the data contained in the first data sample and the second data sample is carried out by sending requests by the server for receiving, processing data and generating processing results to at least one of the servers of services for collecting, storing and processing statistical user data and obtaining the requested data from it by collating and updating data on at least the time the user visited the website, the duration of the visit to the website, the name of the advertising site, belonging to the advertising campaign, counter identifiers in the format of the program code that collects statistical data on the website, with checking the correctness and relevance of statistical data, for each user. 4. The method according to claim 1, in which the parameters of the visit by users of websites are the time the user spent on the web page, the frequency of visits by users of the web page, the manifestation of interest in a particular product or categories of goods, as well as data recorded and processed by cash registers , access control systems, user location tracking. 5. The method according to claim 1, in which data related to the placement of advertising materials includes data on the effectiveness of advertising channels, advertising campaigns and keywords, scoring, conversions from the presentation of advertising materials, costs, impressions and user interactions with web links, target audience , advertising campaign budget, call sources, their dates, times, keywords, including those obtained after recognition and analysis of the user's speech. 6. The method according to claim 1, in which, when saving data to a database of statistical data, the stored data is checked for the validity of data types using the conversion of a value of one data type to another or determining the type of a variable. 7. The method according to claim 1, in which the user is presented with a list of advertising sites to disable and a set of parameter values for advertising sites and values for disabling advertising sites from the list of ineffective advertising sites, and stopping the presentation of advertising materials and a list of advertising materials for stopping the presentation and set values of parameters for presenting advertising materials to stop the presentation of advertising materials contained in the list of advertising materials with specified time intervals for the presentation of advertising materials for each advertising campaign, for which time intervals for the presentation of advertising material were identified, within which user interactions with the presented advertising did not lead to the achievement of pre-set goals of advertising campaigns or during which the user did not interact with the presented advertising material. 8. The method according to claim 1, in which the services for collecting, storing and processing user statistics are contextual advertising services on web pages or services for assessing website traffic and analyzing user behavior. 9. The method according to claim 1, in which data are transmitted to services for collecting, storing and processing user statistics from servers of contextual advertising services on web pages or servers of services for assessing website traffic and analyzing user behavior. 10. The method of claim 1, wherein the advertising campaign performance dataset of the first data sample and the second data sample data contains behavioral changes in user actions. 11. The method according to claim 1, in which, after disabling advertising sites from the list of inefficient advertising sites, other advertising sites are set for related advertising materials, after identifying effective advertising sites in the generated multidimensional data array within a predetermined time interval, on which the probability value user interaction with advertising material on the website of the advertising site in such a given time interval for the presentation of advertising material is maximum, where the probability of user interaction is calculated as the ratio of the number of user interactions with advertising material to the total number of presentations of advertising materials on websites for each advertising site, or the value of the probability of achieving a given goal in the settings of an advertising campaign is maximum, where the probability of achieving a given goal is calculated as the ratio of the number of achievements of a given goal to the total number of presentations of advertisements materials on websites for each advertising platform. 12. A system for automated adjustment of the placement of advertising materials of companies on advertising sites, which contains: - at least one server of at least one service for collecting, storing and processing statistical data, - a server for receiving, processing data and generating processing results, including: a block for obtaining statistics, a task block, a task queue block, a control and administration block, a control and organization block, a predictive block, a block for automated management of advertising campaigns, - statistics database, - at least one server of at least one service for placing advertising materials, at the same time, the system for automated adjustment of the placement of advertising materials of companies on advertising sites is made with the possibility of implementing the method according to any one of paragraphs. 1-11. 13. The system of claim 12, wherein the task block is a message broker using an open protocol to pass messages between system components. 14. The system according to claim 12, in which the control and administration unit and the control and organization unit are configured to generate user credentials, including at least tokens, user identifiers.

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