CN115409528A - Advertisement putting analysis method and device, electronic equipment and storage medium - Google Patents
Advertisement putting analysis method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention provides an advertisement putting analysis method, an advertisement putting analysis device, electronic equipment and a storage medium, wherein the method comprises the following steps: determining potential user information of various advertisements launched by a site to be launched in each time period, wherein the potential user information is determined based on at least one of mobile signaling data, store transaction data, passenger basic information and passenger tariff information of the site to be launched in the corresponding time period; calculating advertising value scores of various advertisements in each time interval based on the pedestrian volume of the site to be delivered in each time interval and the information of each potential user, wherein the pedestrian volume is determined based on the mobile signaling data; and determining the advertisement putting strategy of the site to be put based on the advertisement value score. The method, the device, the electronic equipment and the storage medium provided by the invention can scientifically and accurately launch the advertisement content to the target audience, so that the advertisement space of the site is more efficiently utilized, and the same site can display richer advertisement content.
Description
Technical Field
The invention relates to the technical field of big data, in particular to an advertisement putting analysis method and device, electronic equipment and a storage medium.
Background
The advertisement is used as a specific information which is scientifically refined and artistic and is transmitted to a target audience through a transmission medium, so that public and non-face-to-face information transmission activities which change or strengthen the concept and behavior of people are achieved, people can see everywhere in modern cities, but the advertisement target audience of subways and bus stops is undoubtedly the most in large and medium-sized cities, and advertisers are trained to use up the brain juice to analyze and optimize advertisement putting strategies in order to better and fully utilize advertisement resources of the subway and bus stops.
Currently, advertisements are mainly delivered by roughly estimating the traffic of statistical sites. However, under the advertisement delivery analysis scheme, the value of advertisement delivery is greatly reduced, no pertinence is provided, the target of audience groups is not strong, the delivered content is simple and random, and cannot be scientifically and accurately delivered to the target audience by the aid of feeling.
Disclosure of Invention
The invention provides an advertisement delivery analysis method, an advertisement delivery analysis device, electronic equipment and a storage medium, which are used for overcoming the defect that the advertisement delivery in the prior art is not targeted and realizing the scientific and accurate delivery of advertisement contents to target audiences.
In a first aspect, the present invention provides an advertisement delivery analysis method, including:
determining potential user information of various advertisements launched by a site to be launched in each time interval, wherein the potential user information is determined on the basis of at least one of mobile signaling data, store transaction data, passenger basic information and passenger tariff information of the site to be launched in a corresponding time interval;
calculating advertising value scores of various advertisements in each time interval based on the pedestrian volume of the site to be delivered in each time interval and the information of each potential user, wherein the pedestrian volume is determined based on the mobile signaling data;
and determining the advertisement putting strategy of the site to be put based on the advertisement value score.
In one embodiment, the potential user information includes advertising potential user information;
the determining of the potential user information of the sites to be delivered for delivering various advertisements in each time interval includes:
establishing a population model based on the mobile signaling data and the passenger basic information;
establishing a consumption model based on the mobile signaling data, the store transaction data and the passenger tariff information;
and matching the advertisement potential user information corresponding to various advertisements in each time interval based on the population model and the consumption model.
In one embodiment, the population model includes at least one of a total population flow model, a weekday population flow model, a holiday population flow model, a population flow variation model, a population classification model, and an age group population model;
the consumption model comprises at least one of a consumption level model, a consumption type model, a consumption period model, a consumption population proportion model, a potential consumption capability model and a potential consumption preference model.
In one embodiment, the potential user information includes application preference potential user information;
the determining the potential user information of the sites to be delivered with various advertisements in each time interval includes:
determining the application preference potential user information based on the mobile signaling data.
In one embodiment, the calculating the advertisement value score of each type of advertisement in each time period based on the traffic of the site to be delivered in each time period and each potential user information includes:
and calculating the advertisement value score of any single type advertisement in any time period based on the total pedestrian volume of the site to be delivered in any time period, the maximum pedestrian volume of the site to be delivered, the advertisement potential user traffic and the application preference potential user traffic of any single type advertisement in any time period.
In one embodiment, the calculating the advertisement value score of each type of advertisement in each time period based on the traffic of the site to be delivered in each time period and each potential user information includes:
and calculating the advertisement value score of the comprehensive type advertisement in any time period based on the total pedestrian volume of the site to be delivered in any time period, the maximum pedestrian volume of the site to be delivered and the advertisement potential user traffic of the comprehensive type advertisement in any time period.
In one embodiment, the determining an advertisement placement strategy for the to-be-placed site based on the advertisement value score further includes:
and if the pedestrian volume of the site to be launched in any time period is lower than a preset threshold value, closing the advertisement of the site to be launched.
In a second aspect, the present invention provides an advertisement placement analysis apparatus, including:
the system comprises a determining module, a judging module and a display module, wherein the determining module is used for determining potential user information of various advertisements launched by a site to be launched in each time interval, and the potential user information is determined on the basis of at least one of mobile signaling data, shop transaction data, passenger basic information and passenger tariff information of the site to be launched in a corresponding time interval;
the calculation module is used for calculating the advertising value scores of various advertisements in each time interval based on the pedestrian volume of the site to be launched in each time interval and the information of each potential user, wherein the pedestrian volume is determined based on the mobile signaling data;
and the delivery module is used for determining the advertisement delivery strategy of the site to be delivered based on the advertisement value score.
In a third aspect, the present invention provides an electronic device, comprising a memory and a memory storing a computer program, wherein the processor implements the steps of the advertisement impression analysis method of the first aspect when executing the program.
In a fourth aspect, the present invention provides a processor-readable storage medium storing a computer program for causing a processor to perform the steps of the advertisement placement analysis method of the first aspect.
According to the advertisement putting analysis method, the device, the electronic equipment and the storage medium, the potential user information of various advertisements put in each time interval by the site to be put is obtained through big data technology analysis, the advertisement value score is determined according to the potential user information so as to formulate the advertisement putting strategy, the advertisement content putting to the target audience is scientifically and accurately realized, the advertisement position of the site is more efficiently utilized, the same site can display richer advertisement content, and the time interval of the advertisement putting strategy is more accurate and reasonable through analyzing the people flow of the site to be put and the potential user information of various advertisements in time intervals.
Drawings
In order to more clearly illustrate the technical solutions of the present invention or the prior art, the drawings needed for the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and those skilled in the art can also obtain other drawings according to the drawings without creative efforts.
FIG. 1 is a flow chart of an advertisement placement analysis method provided by the present invention;
FIG. 2 is a second schematic flow chart of an advertisement placement analysis method provided by the present invention;
fig. 3 is a schematic structural diagram of an advertisement placement analysis apparatus provided by the present invention;
fig. 4 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
An embodiment of the present invention provides an advertisement delivery analysis method, and fig. 1 is a schematic flow diagram of the advertisement delivery analysis method provided by the present invention, and as shown in fig. 1, the method includes:
and step 110, determining potential user information of various advertisements launched by the site to be launched in each time interval, wherein the potential user information is determined based on at least one of mobile signaling data, store transaction data, passenger basic information and passenger tariff information of the site to be launched in the corresponding time interval.
The station to be launched is a station needing advertisement launching analysis, and the station can be a bus station, a subway station or other stations capable of launching advertisements. In addition, the length of the time interval and the type of the advertisement are not specifically limited in the embodiment of the present invention, each time interval may be month granularity, day granularity, hour granularity, and the like, and each type of advertisement may include a single type of advertisement, such as music, games, animation, and the like, and may also include a comprehensive type of advertisement. The passenger tariff information is communication tariff data of the passenger, such as an opened package tariff, an opened polyphonic ringtone, a cartoon service and the like, and information of the passenger such as consumption capability, consumption preference and the like can be analyzed.
Specifically, in the existing advertisement delivery analysis method, the advertisement is delivered by roughly estimating the pedestrian volume of the statistical site, so that the value of advertisement delivery is greatly reduced, no pertinence exists, and the target of audience groups is not strong. Aiming at the problem, the embodiment of the invention firstly collects one or more data of mobile signaling data of the site to be delivered in each time period, transaction data of shops near the site to be delivered, basic information of the passenger such as sex, age, school calendar, native place and the like, and tariff information of the passenger, and then comprehensively analyzes and processes the data through a big data technology, thereby obtaining potential user information of various advertisements delivered by the site to be delivered in each time period.
Specifically, the advertisement value score is an index for evaluating the value of the site to be delivered for delivering any type of advertisement in any time period. Considering the limitation of the content delivered by the site advertisement space, the value of each advertisement in each time interval can be evaluated according to the determined target audience groups of each advertisement in each time interval, so that the type of the advertisement delivered by the site to be delivered in any time interval is determined, and an advertisement delivery strategy is obtained. The advertisement delivery strategy can be specifically obtained by the following method:
firstly, analyzing a mobile phone signal of each user according to collected mobile signaling data of a site to be launched in each time period to obtain an activity track of the user, and further counting the pedestrian volume of the site to be launched in each time period; on the basis, the advertising value score of various advertisements launched in each time interval of the site can be calculated according to the pedestrian volume of the site to be launched in each time interval and the information of each potential user;
then, comparing the advertisement value scores of the advertisements in each time interval to determine an advertisement placement strategy of the to-be-placed site, where the advertisement placement strategy may be that the to-be-placed site places one type of advertisement in any time interval, for example, the advertisement value score of the to-be-placed site is the highest in 18-00 pm.
According to the method provided by the embodiment of the invention, the potential user information of various advertisements launched by the site to be launched in each time interval is obtained through big data technology analysis, the advertisement value score is determined according to the potential user information so as to formulate an advertisement launching strategy, and the advertisement content launching is scientifically and accurately carried out on the target audience, so that the advertisement space of the site is more efficiently utilized, the same site can display richer advertisement content, and the time interval of the advertisement launching strategy is more accurate and reasonable through analyzing the pedestrian volume of the site to be launched and the potential user information of various advertisements in different time intervals.
According to any of the above embodiments, the potential user information includes advertisement potential user information;
establishing a population model based on the mobile signaling data and the passenger basic information;
establishing a consumption model based on the mobile signaling data, the store transaction data and the passenger tariff information;
and matching the advertisement potential user information corresponding to various advertisements in each time interval based on the population model and the consumption model.
Specifically, the advertisement potential user information is obtained by analyzing consumption preference of each passenger in a corresponding time slot, for example, according to the consumption preference analysis of each passenger in the morning at 7-00-8.
In order to accurately obtain the information of the potential advertisement users and further enable the advertisement putting strategy to be more accurate and scientific, the information of the potential advertisement users can be obtained specifically through the following modes: modeling analysis is carried out on population conditions of station passengers according to the mobile signaling data and the passenger basic information so as to obtain a population model, then modeling analysis is carried out on consumption preferences of the station passengers according to the mobile signaling data, the shop transaction data and the passenger tariff information so as to obtain a consumption model, and then accurate operation matching is carried out on advertisement putting contents of the to-be-put-in station through an artificial intelligence algorithm according to the population model and the consumption model so as to obtain advertisement potential user information corresponding to various advertisements in each time period.
Based on any embodiment, the population model comprises at least one of a general population flow model, a working day population flow model, a holiday population flow model, a population flow variation model, a population classification model and an age group population model;
the consumption model includes at least one of a consumption level model, a consumption type model, a consumption period model, a consumption population proportion model, a potential consumption capability model and a potential consumption preference model.
Here, the total population flow model may reflect the total population flow condition in each time period of the site to be launched; respectively establishing a workday population flow model and a holiday population flow model which can reflect population flow conditions of sites to be released on different dates in consideration of the difference between the population flow of the sites on the workday and the population flow of the sites on the holidays; the population flow change model can reflect the change situation of the flow of people 24 hours per day at the site to be released; the population classification model can reflect the proportion of working population, resident population and floating population in each time period of the site to be launched; the age group population model can reflect the proportion of middle school students, college students, middle-aged people, old people and other different age groups in each time period of the site to be released; the consumer group proportion module can reflect the proportion of the consumer group to the total number in each time period of the site to be released.
It can be understood that the population models and consumption models described above have certain guidance on the classification of the target population of the advertisement, for example, generally, the demands for the advertisement are different in different age groups, the elderly prefer daily advertisement, and the college prefer game advertisement, so the population model in the age group has certain guidance, and for example, the consumption type model finds that 18-25 year old girls 18. Thus, one or more of the population models and one or more of the consumption models may be selected for determination of advertisement potential user information for placement of various types of advertisements over various time periods.
Based on any of the above embodiments, the potential user information comprises application preference potential user information;
step 110 comprises:
based on the mobile signaling data, application preference potential user information is determined.
Specifically, considering that the mobile signaling data may analyze the preferences of the user for different applications APP (applications), the preferences of the user for different applications APP have a great correlation with the tendency for various types of advertisements, for example, the user frequently opens a music APP, which may indicate that the user is interested in music type advertisements to a great extent. Therefore, the potential user information of the site to be delivered for delivering various advertisements in each time interval determined by the embodiment of the present invention may include application preference potential user information, and the application preference potential user information may be obtained specifically in the following manner: and analyzing the preference of each user for different applications APP according to the mobile signaling data of the site to be launched in each time period, and determining the potential user information of the application preference for launching various advertisements in each time period according to the corresponding relation between the applications APP and the advertisement types.
According to the method provided by the embodiment of the invention, online passenger behavior preference information is combined with information such as commodity transaction information and passenger basic information of a station passenger online by utilizing a big data technology, so that the passenger label portrait is more accurate, more accurate target audience groups can be determined for various advertisements, and the advertisement putting strategy is more scientific and reasonable.
Based on any of the above embodiments, step 120 includes:
and calculating the advertisement value score of any single type advertisement in any time period based on the total pedestrian volume of the site to be delivered in any time period, the maximum pedestrian volume of the site to be delivered, the advertisement potential user traffic volume and the application preference potential user traffic volume of any single type advertisement in any time period.
Specifically, aiming at the evaluation of the advertising value score of each single type of advertisement in each time interval of the site to be delivered, the embodiment of the invention adopts the people flow standard deviation combined with the normal distribution principle, adds different advertising potential user flow rates and different application preference potential user flow rates, distributes different weights, and comprehensively considers and calculates the advertising value score condition of each site. Specifically, the following formula can be used for calculation:
wherein, V Alone Advertising value score, S, for any individual type of advertisement k As the total flow of people in the corresponding time period, S max Is the maximum traffic of the site to be launched, P k Is the total traffic weight for the sites to be dropped,
favoring potential user traffic for applications of this type of advertising, M k In order to apply the preferred potential user weights,
advertisement potential user traffic for this type of advertisement, A k Weights potential users for the advertisement.
It should be noted that if the advertisement potential user information of any type of advertisement is determined to be a certain type of age group user, the advertisement potential user traffic of the type of advertisement may be calculated according to the age group. In addition, different weight values may be set for the calculation of different types of advertisement value scores.
Based on any of the above embodiments, step 120 includes:
and calculating the advertisement value score of the comprehensive type advertisement in any time period based on the total pedestrian volume of the site to be delivered in any time period, the maximum pedestrian volume of the site to be delivered and the advertisement potential user volume of the comprehensive type advertisement in any time period.
Specifically, considering that the advertisement types include an individual type advertisement and a comprehensive type advertisement, and there may be a case where the value generated by delivering the comprehensive type advertisement is higher than the value generated by the individual type advertisement, the advertisement value score of the comprehensive type advertisement in each time slot is calculated by the embodiment of the present invention, and specifically, the following formula may be used to calculate the advertisement value score:
wherein, V Synthesis of An advertisement value score for the composite type advertisement, S k As the total flow of people in the corresponding time period, S max Is the maximum traffic of the site to be launched, P k Is the total traffic weight for the sites to be dropped,
for comprehensive types of advertising potential user traffic, A k Weights potential users for the advertisement.
Based on any of the above embodiments, step 130 further includes:
and if the pedestrian volume of the site to be launched in any time period is lower than a preset threshold value, closing the advertisement of the site to be launched.
Specifically, in consideration of the fact that the existing advertisement putting technical scheme causes resource waste to a certain extent, for example, the advertisement of a general site is displayed by lighting for 24 hours, videos are played, and the like, so that electric energy resources are wasted. Aiming at the problem, the embodiment of the invention can count the pedestrian volume of the site to be released in each time interval by collecting the mobile signaling data of the site to be released in each time interval, and can close the advertisement content display of the site if the pedestrian volume in any time interval is judged to be lower than the preset threshold value, thereby reducing unnecessary energy consumption. Here, the preset threshold may be set arbitrarily as needed, and this is not particularly limited in the embodiment of the present invention.
Further, after the advertisement of the site to be delivered is closed, if it is detected that the pedestrian volume exceeds the preset threshold value in a certain period, that is, the pedestrian volume is recovered, the advertisement content display of the site can be automatically started.
According to the method provided by the embodiment of the invention, the energy-saving advertisement strategy of the site to be launched is formulated based on the people flow statistical data of each time interval, so that the advertisement position electrification display equipment of the site is more energy-saving.
Based on any of the above embodiments, fig. 2 is a second schematic flow chart of the advertisement delivery analysis method provided by the present invention, and as shown in fig. 2, taking an urban subway bus station as an example, the specific flow of the method is as follows:
s1, data acquisition, including:
(1) Acquiring big data of urban subway and bus passenger mobile signaling;
(2) Collecting commodity transaction data of shops near a city subway bus station;
(3) Basic information and tariff information data of urban public transport subway passengers;
s2, population model creation, wherein the modeling analysis of passenger population of the subway and bus station is carried out according to the collected signaling data and the passenger basic data, and comprises the following steps:
(1) A total population flow model;
(2) A weekday population flow model;
(3) A holiday population flow model;
(4) The people flow change model is a 24-hour people flow change model in the graph every day;
(5) The population classification model is a working population model, a resident population model and a floating population model in the graph;
(6) The age group population model is a model of middle school students, college students, middle aged and old people in the figure;
s3, creating a consumption model, modeling and analyzing the consumption preference of passengers at subway and bus stations according to the collected passenger tariff information, commodity transaction information of nearby shops and mobile signaling data, wherein the consumption model comprises the following steps:
(1) A consumption level model;
(2) A consumption type model;
(3) A consumption period model;
(4) A consumer proportion model;
(5) A potential consumption capability model;
(6) A potential consumption preference model;
s4, establishing a subway and bus advertisement delivery index model, carrying out accurate operation matching on advertisement delivery of subway and bus stations through an artificial intelligence algorithm according to a population model and a consumption model to obtain the advertisement value scores of various advertisements in each time interval, and finally establishing an accurate advertisement delivery index model according to the advertisement value score condition of the stations, wherein the model comprises the following steps:
(1) A site integrated advertising index model;
(2) A site game class advertisement index model;
(3) Site animation advertisement index model;
(4) A site reading type advertisement index model;
(5) A site music advertisement index model;
(6) A site secondary meta-class advertisement index model;
(7) Site household appliances, daily use, clothes, travel, learning and other advertisement index models and the like.
S5, establishing a subway and bus advertisement strategy model, performing accurate operation matching on advertisement delivery of subway and bus stations through an artificial intelligence algorithm according to a population model and a consumption model to obtain an advertisement delivery index model, performing operation matching on when each station displays the best type of advertisement according to the advertisement value score condition in the advertisement delivery index model, and therefore performing advertisement delivery management on each station in a city and outputting an accurate advertisement delivery strategy, wherein the method comprises the following steps:
(1) Different types of product advertisement delivery site strategies, such as game advertisement best delivery sites, video advertisement best delivery sites, beverage advertisement best delivery sites, and the like;
(2) Advertisement putting strategies for sites with different dates, such as best advertisement putting sites in working days, best advertisement putting sites in festivals and holidays, best advertisement putting sites in major festivals and holidays and the like;
(3) The advertisement delivery strategies of different time slot sites, for example, the advertisement delivery strategy of 6;
s6, outputting an energy-saving advertisement strategy, comprising the following steps:
when the people flow less, closing the electrified advertisement content display;
when the people flow is recovered, the electrified advertisement content display is automatically started.
The method provided by the embodiment of the invention aims to solve the problems of the existing advertisement putting analysis, provides intelligent urban subway and public transport advertisement putting strategy analysis, collects the on-line and off-line consumption and behavior preference information of passengers at the urban subway and public transport station in various modes, creates a population model, a consumption model and a station advertisement index model of the subway and public transport station by using big data and artificial intelligence AI technology, and finally outputs the advertisement putting strategy and the energy-saving advertisement strategy of a scientific system according to the comprehensive analysis of the models and the information of advertisement positions and advertisers so as to assist the construction of the intelligent city.
The following describes the advertisement delivery analysis apparatus provided by the present invention, and the advertisement delivery analysis apparatus described below and the advertisement delivery analysis method described above may be referred to in correspondence with each other.
Based on any of the above embodiments, fig. 3 is a schematic structural diagram of an advertisement delivery analysis apparatus provided by the present invention, and as shown in fig. 3, the apparatus includes:
a determining module 310, configured to determine potential user information of various advertisements delivered by a site to be delivered in each time interval, where the potential user information is determined based on at least one of mobile signaling data, store transaction data, passenger basic information, and passenger tariff information of the site to be delivered in a corresponding time interval;
a calculating module 320, configured to calculate an advertisement value score of each type of advertisement in each time period based on the traffic of the site to be delivered in each time period and information of each potential user, where the traffic is determined based on the mobile signaling data;
and the delivery module 330 is configured to determine an advertisement delivery policy of the site to be delivered based on the advertisement value score.
According to the device provided by the embodiment of the invention, the potential user information of various advertisements launched by the site to be launched in each time interval is obtained through big data technology analysis, the advertisement value score is determined according to the potential user information so as to formulate an advertisement launching strategy, and the advertisement content launching is scientifically and accurately carried out on the target audience, so that the advertisement space of the site is more efficiently utilized, the same site can display richer advertisement content, and the time interval of the advertisement launching strategy is more accurate and reasonable through analyzing the pedestrian volume of the site to be launched and the potential user information of various advertisements in different time intervals.
According to any of the above embodiments, the potential user information includes advertisement potential user information;
the determination module 310 includes an advertisement potential user information determination unit for:
establishing a population model based on the mobile signaling data and the passenger basic information;
establishing a consumption model based on the mobile signaling data, the store transaction data and the passenger tariff information;
and matching the advertisement potential user information corresponding to various advertisements in each time interval based on the population model and the consumption model.
Based on any embodiment, the population model comprises at least one of a general population flow model, a working day population flow model, a holiday population flow model, a population flow variation model, a population classification model and an age group population model;
the consumption model includes at least one of a consumption level model, a consumption type model, a consumption period model, a consumption population proportion model, a potential consumption capability model and a potential consumption preference model.
Based on any of the above embodiments, the potential user information comprises application preference potential user information;
the determination module 310 comprises an application preference potential user information determination unit for:
based on the mobile signaling data, application preference potential user information is determined.
Based on any of the above embodiments, the calculation module 320 includes an individual type advertisement calculation unit for:
and calculating the advertisement value score of any single type advertisement in any time period based on the total pedestrian volume of the site to be delivered in any time period, the maximum pedestrian volume of the site to be delivered, the advertisement potential user traffic volume and the application preference potential user traffic volume of any single type advertisement in any time period.
Based on any of the above embodiments, the calculation module 320 includes an integrated type advertisement calculation unit, configured to:
and calculating the advertisement value score of the comprehensive type advertisement in any time period based on the total pedestrian volume of the site to be delivered in any time period, the maximum pedestrian volume of the site to be delivered and the advertisement potential user volume of the comprehensive type advertisement in any time period.
Based on any of the above embodiments, the apparatus further includes an energy saving module, configured to:
and if the pedestrian volume of the site to be launched in any time period is lower than a preset threshold value, closing the advertisement of the site to be launched.
Fig. 4 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 4: a processor (processor) 410, a Communication Interface (Communication Interface) 420, a memory (memory) 430 and a Communication bus 440, wherein the processor 410, the Communication Interface 420 and the memory 430 are communicated with each other via the Communication bus 440. Processor 410 may invoke computer programs in memory 430 to perform the steps of the ad placement analysis method, including, for example: determining potential user information of various advertisements launched by the site to be launched in each time period, wherein the potential user information is determined based on at least one of mobile signaling data, store transaction data, passenger basic information and passenger tariff information of the site to be launched in the corresponding time period; calculating advertising value scores of various advertisements in each time interval based on the pedestrian volume of the site to be delivered in each time interval and the information of each potential user, wherein the pedestrian volume is determined based on mobile signaling data; and determining an advertisement putting strategy of the site to be put based on the advertisement value score.
In addition, the logic instructions in the memory 430 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
In another aspect, the present invention also provides a computer program product comprising a computer program stored on a non-transitory computer readable storage medium, the computer program comprising program instructions which, when executed by a computer, enable the computer to perform the advertisement placement analysis method provided by the above methods, the method comprising: determining potential user information of various advertisements launched by the site to be launched in each time period, wherein the potential user information is determined based on at least one of mobile signaling data, store transaction data, passenger basic information and passenger tariff information of the site to be launched in the corresponding time period; calculating advertising value scores of various advertisements in each time interval based on the pedestrian volume of the site to be delivered in each time interval and the information of each potential user, wherein the pedestrian volume is determined based on mobile signaling data; and determining an advertisement putting strategy of the site to be put based on the advertisement value score.
On the other hand, an embodiment of the present application further provides a processor-readable storage medium, where the processor-readable storage medium stores a computer program, where the computer program is configured to cause the processor to execute the method provided in each of the above embodiments, for example, including: determining potential user information of various advertisements launched by the site to be launched in each time period, wherein the potential user information is determined based on at least one of mobile signaling data, store transaction data, passenger basic information and passenger tariff information of the site to be launched in the corresponding time period; calculating advertising value scores of various advertisements in each time interval based on the pedestrian volume of the site to be delivered in each time interval and the information of each potential user, wherein the pedestrian volume is determined based on mobile signaling data; and determining an advertisement putting strategy of the site to be put based on the advertisement value score.
The processor-readable storage medium can be any available medium or data storage device that can be accessed by a processor, including, but not limited to, magnetic memory (e.g., floppy disks, hard disks, magnetic tape, magneto-optical disks (MOs), etc.), optical memory (e.g., CDs, DVDs, BDs, HVDs, etc.), and semiconductor memory (e.g., ROMs, EPROMs, EEPROMs, non-volatile memory (NAND FLASH), solid State Disks (SSDs)), etc.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware. Based on the understanding, the above technical solutions substantially or otherwise contributing to the prior art may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the various embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. An advertisement placement analysis method, comprising:
determining potential user information of various advertisements launched by a site to be launched in each time interval, wherein the potential user information is determined on the basis of at least one of mobile signaling data, store transaction data, passenger basic information and passenger tariff information of the site to be launched in a corresponding time interval;
calculating advertising value scores of various advertisements in each time interval based on the pedestrian volume of the site to be delivered in each time interval and the information of each potential user, wherein the pedestrian volume is determined based on the mobile signaling data;
and determining the advertisement putting strategy of the site to be put based on the advertisement value score.
2. The advertisement placement analysis method according to claim 1, wherein the potential user information includes advertisement potential user information;
the determining of the potential user information of the sites to be delivered for delivering various advertisements in each time interval includes:
establishing a population model based on the mobile signaling data and the passenger basic information;
establishing a consumption model based on the mobile signaling data, the store transaction data and the passenger tariff information;
and matching the advertisement potential user information corresponding to various advertisements in each time interval based on the population model and the consumption model.
3. The advertisement placement analysis method according to claim 2, wherein the population model comprises at least one of a general population flow model, a weekday population flow model, a holiday population flow model, a population flow variation model, a population classification model, and an age group population model;
the consumption model comprises at least one of a consumption level model, a consumption type model, a consumption period model, a consumption population proportion model, a potential consumption capability model and a potential consumption preference model.
4. The advertisement placement analysis method according to claim 1, wherein the potential user information includes application preference potential user information;
the determining of the potential user information of the sites to be delivered for delivering various advertisements in each time interval includes:
determining the application preference potential user information based on the mobile signaling data.
5. The advertisement placement analysis method according to claim 1, wherein the calculating of the advertisement value scores of the advertisements in each time slot based on the traffic of the site to be placed in each time slot and the information of the potential users comprises:
and calculating the advertisement value score of any single type advertisement in any time period based on the total pedestrian volume of the site to be delivered in any time period, the maximum pedestrian volume of the site to be delivered, the advertisement potential user traffic and the application preference potential user traffic of any single type advertisement in any time period.
6. The advertisement placement analysis method according to any one of claims 1 to 5, wherein the calculating of the advertisement value score of each type of advertisement in each time slot based on the traffic of the site to be placed in each time slot and each potential user information comprises:
and calculating the advertisement value score of the comprehensive type advertisement in any time period based on the total pedestrian volume of the site to be delivered in any time period, the maximum pedestrian volume of the site to be delivered and the advertisement potential user traffic of the comprehensive type advertisement in any time period.
7. The advertisement placement analysis method according to any one of claims 1 to 5, wherein the determining an advertisement placement strategy for the site to be placed based on the advertisement value score further comprises:
and if the pedestrian volume of the site to be launched in any time period is lower than a preset threshold value, closing the advertisement of the site to be launched.
8. An advertisement placement analysis device, comprising:
the system comprises a determining module, a judging module and a display module, wherein the determining module is used for determining potential user information of various advertisements launched by a site to be launched in each time interval, and the potential user information is determined on the basis of at least one of mobile signaling data, shop transaction data, passenger basic information and passenger tariff information of the site to be launched in a corresponding time interval;
the calculation module is used for calculating the advertising value scores of various advertisements in each time interval based on the pedestrian volume of the site to be launched in each time interval and the information of each potential user, wherein the pedestrian volume is determined based on the mobile signaling data;
and the delivery module is used for determining the advertisement delivery strategy of the site to be delivered based on the advertisement value score.
9. An electronic device comprising a processor and a memory storing a computer program, wherein the steps of the ad placement analysis method of any one of claims 1 to 7 are implemented when the computer program is executed by the processor.
10. A processor-readable storage medium, characterized in that the processor-readable storage medium stores a computer program for causing a processor to execute the steps of the advertisement placement analysis method according to any one of claims 1 to 7.
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| CN202110587650.0A CN115409528A (en) | 2021-05-27 | 2021-05-27 | Advertisement putting analysis method and device, electronic equipment and storage medium |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116051190A (en) * | 2023-01-13 | 2023-05-02 | 深圳市易售科技有限公司 | Method for converting advertisement putting equipment into commodity, computer equipment and medium |
| CN116245571A (en) * | 2023-02-09 | 2023-06-09 | 杭州凯宇信息技术有限公司 | Accurate advertisement pushing method based on service radius |
| CN116485472A (en) * | 2023-04-24 | 2023-07-25 | 道格元创(福建)文化创意有限公司 | Accurate advertisement and information delivery system based on Internet and artificial intelligence |
| CN117593055A (en) * | 2023-11-21 | 2024-02-23 | 广州吴凡科技服务有限公司 | Advertisement effect intelligent analysis method and system based on big data |
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- 2021-05-27 CN CN202110587650.0A patent/CN115409528A/en active Pending
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116051190A (en) * | 2023-01-13 | 2023-05-02 | 深圳市易售科技有限公司 | Method for converting advertisement putting equipment into commodity, computer equipment and medium |
| CN116245571A (en) * | 2023-02-09 | 2023-06-09 | 杭州凯宇信息技术有限公司 | Accurate advertisement pushing method based on service radius |
| CN116245571B (en) * | 2023-02-09 | 2023-10-27 | 杭州凯宇信息技术有限公司 | Accurate advertisement pushing method based on service radius |
| CN116485472A (en) * | 2023-04-24 | 2023-07-25 | 道格元创(福建)文化创意有限公司 | Accurate advertisement and information delivery system based on Internet and artificial intelligence |
| CN116485472B (en) * | 2023-04-24 | 2024-02-27 | 成都世纪飞扬科技集团有限公司 | Accurate advertisement and information delivery system based on Internet and artificial intelligence |
| CN117593055A (en) * | 2023-11-21 | 2024-02-23 | 广州吴凡科技服务有限公司 | Advertisement effect intelligent analysis method and system based on big data |
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