CN111178954A - Advertisement putting method and system and electronic equipment - Google Patents
Advertisement putting method and system and electronic equipment Download PDFInfo
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- CN111178954A CN111178954A CN201911329774.8A CN201911329774A CN111178954A CN 111178954 A CN111178954 A CN 111178954A CN 201911329774 A CN201911329774 A CN 201911329774A CN 111178954 A CN111178954 A CN 111178954A
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Abstract
The invention provides an advertisement delivery method, an advertisement delivery system and electronic equipment based on user behavior data feedback. The method comprises the following steps: acquiring user behavior data of a user on the advertisement from a third party, synchronizing the user behavior data to a data processing platform in real time, extracting user characteristic data from the user behavior data, and screening the user characteristic data; training a marketing optimization model using training data, the training data including user characteristic data, financial performance data, and advertising information data of historical users; inputting the screened user characteristic data of the target user into the marketing optimization model, calculating a user evaluation value, and determining advertisement putting rule data according to the user characteristic data and the calculated user evaluation value; and advertising based on the advertising rule data. The invention optimizes the target data, improves the accuracy of advertisement putting and also saves the cost of advertisement putting.
Description
Technical Field
The invention relates to the technical field of internet, in particular to an advertisement delivery method, an advertisement delivery system, electronic equipment and a computer readable medium based on user behavior data feedback.
Background
The traditional advertisement is in the form of mass media and advertisement agents, advertisement display is achieved by purchasing advertisement positions, and the static advertisement enables consumers to receive only one piece of information, or establish simple impression on the brand, or know the related activities of the brand, interested people pay attention to the static advertisement, but people who cannot know the interest of the static advertisement are not interested, and the pertinence and the effect are not strong.
Currently, internet advertising has been developed into a new model, which has the following characteristics: firstly, the coverage rate of the internet advertisement is wide, and the accuracy is high. The intelligent and accurate marketing is realized by means of technical means, and accurate orientation in the aspects of time, regions, frequency, interest, population characteristics and the like can be carried out on audience groups through tracking, mining and analyzing netizen data. Secondly, the number of people spreading internet advertisements is huge, and according to the report released by Forrester Research of Research company, the number of global netizens in 2013 reaches 22 hundred million, 17% of the world netizens come from China, and the audience population is wide. Thirdly, the cost advantage of the internet advertisement in distribution and propagation is greatly reduced. Fourth, the diversification of the internet advertisement forms, the internet advertisement forms can be spread through web advertisements, text texts, electronic mail advertisements, button advertisements, sponsorship advertisements, interstitial advertisements, homepage advertisements, keyword advertisements, rich media advertisements, video advertisements and other various forms, and in the face of the internet advertisements with many advantages, a reliable method and a system for accurately delivering the advertisements do not exist at present, the Internet advertisement field also exists that the delivery is not accurate, the mode mainly depends on a pop-up window, the interest and hobbies of users cannot be mastered, and the timeliness of the advertisements is poor.
However, one technical problem that needs to be urgently solved by those skilled in the art is: how to creatively provide an effective measure, and the accurate delivery of the internet advertisement is realized by fully considering mass internet users and a big data mining technology.
Therefore, it is necessary to provide a more accurate advertisement delivery method.
Disclosure of Invention
In order to solve the above problems, the present invention provides an advertisement delivery method based on user behavior data feedback, including: acquiring user behavior data of a user on the advertisement from a third party, and synchronizing the user behavior data to the data processing platform in real time; extracting user characteristic data from the user behavior data, and screening the user characteristic data; training a marketing optimization model using training data, the training data including user characteristic data, financial performance data, and advertising information data of historical users; inputting the screened user characteristic data of the target user into the marketing optimization model, and calculating a user evaluation value; determining advertisement putting rule data according to the user characteristic data and the calculated user evaluation value; and advertising based on the advertising rule data.
Preferably, the advertisement delivery method further includes: and feeding back the data analysis result of the data processing platform to the third party in real time, wherein the third party displays the advertisement information related to the advertisement putting rule data.
Preferably, the screening the user feature data includes: and matching the extracted user characteristic data with user characteristic data in a pre-stored database of the data processing platform to screen out target users and non-target users, wherein the target users are used for representing users who want to carry out advertisement putting, and the non-target users are used for representing users who do not carry out advertisement putting.
Preferably, the extracting user feature data from the user behavior data includes: and extracting at least one characteristic data of the mobile phone type, the mobile phone number, the mobile phone positioning position information, the IP address, the mailbox, the age and the gender of the user from the user behavior data.
Preferably, the financial performance data includes data relating to registration, credit or completion.
Preferably, the data analysis result includes at least one of a user conversion rate, an access amount, an access user, a click ID, a click time, and user characteristic data related to the presentation of the advertisement.
Preferably, the obtaining of the user behavior data of the user on the advertisement from the third party includes: and acquiring at least one characteristic data of the number of clicks, the click time, whether the financial products of the displayed advertisement are registered, whether the financial products are trusted and whether the financial products are finished of the displayed advertisement by the user in a preset time period.
Preferably, the predetermined period of time comprises seven days, ten days, twelve days, fifteen days or a month.
Preferably, a rule corresponding to the advertisement placement rule and the user having the user characteristic data is established by the calculated user evaluation value and the user characteristic data.
In addition, the invention also provides an advertisement delivery system based on user behavior data feedback, which comprises: the data acquisition module acquires user behavior data of the user on the advertisement from a third party and synchronizes the user behavior data to the data processing platform in real time; the data processing module extracts user characteristic data from the user behavior data and performs screening processing on the user characteristic data; a training module to train a marketing optimization model using training data, the training data including user characteristic data, financial performance data, and advertising information data of historical users; the calculation module is used for inputting the marketing optimization model by using the screened user characteristic data of the target user and calculating a user evaluation value; a determination module which determines advertisement putting rule data according to the user characteristic data and the calculated user evaluation value; and the delivery module is used for delivering the advertisements based on the advertisement delivery rule data.
Preferably, the advertisement delivery system further comprises a feedback module, the feedback module feeds back the data analysis result of the data processing platform to the third party in real time, and the third party displays the advertisement information related to the advertisement delivery rule data.
Preferably, the advertisement delivery system further includes a screening module, where the screening module matches the extracted user feature data with user feature data in a pre-stored database of the data processing platform to screen out a target user and a non-target user, where the target user is used to represent a user who wants to deliver an advertisement, and the non-target user is used to represent a user who does not deliver an advertisement.
Preferably, the data processing module further comprises: and extracting at least one characteristic data of the mobile phone type, the mobile phone number, the mobile phone positioning position information, the IP address, the mailbox, the age and the gender of the user from the user behavior data.
Preferably, the financial performance data includes data relating to registration, credit or completion.
Preferably, the data analysis result includes at least one of a user conversion rate, an access amount, an access user, a click ID, a click time, and user characteristic data related to the presentation of the advertisement.
Preferably, the obtaining of the user behavior data of the user on the advertisement from the third party includes: and acquiring at least one characteristic data of the number of clicks, the click time, whether the financial products of the displayed advertisement are registered, whether the financial products are trusted and whether the financial products are finished of the displayed advertisement by the user in a preset time period.
Preferably, the predetermined period of time comprises seven days, ten days, twelve days, fifteen days or a month.
Preferably, a rule corresponding to the advertisement placement rule and the user having the user characteristic data is established by the calculated user evaluation value and the user characteristic data.
In addition, the present invention also provides an electronic device, wherein the electronic device includes: a processor; and a memory storing computer-executable instructions that, when executed, cause the processor to perform a method of advertising based on user behavior data feedback according to the present invention.
Furthermore, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs which, when executed by a processor, implement the advertisement delivery method based on user behavior data feedback according to the present invention.
Advantageous effects
Compared with the prior art, the method combines big data mining technology with accurate advertisement delivery, solves the problem that a traditional advertisement delivery user is not accurate, records user behavior data of the user on the advertisement in the whole commercial chain, extracts and analyzes the user behavior data, and determines advertisement delivery rule data through data extraction and analysis so that a third party can display advertisement information related to the advertisement delivery rule data, target data is optimized, the advertisement delivery accuracy is improved, and advertisement delivery cost is saved.
Drawings
In order to make the technical problems solved by the present invention, the technical means adopted and the technical effects obtained more clear, the following will describe in detail the embodiments of the present invention with reference to the accompanying drawings. It should be noted, however, that the drawings described below are only illustrations of exemplary embodiments of the invention, from which other embodiments can be derived by those skilled in the art without inventive faculty.
FIG. 1 is a method flow diagram of an example of a method for advertisement delivery based on user behavior data feedback of the present invention.
Fig. 2 is a schematic structural block diagram showing an advertisement delivery method based on user behavior data feedback to which the present invention is applied.
FIG. 3 is a method flow diagram of another example of a method of advertisement delivery based on user behavior data feedback of the present invention.
Fig. 4 is a schematic block diagram of an example of the advertisement delivery system based on user behavior data feedback of the present invention.
Fig. 5 is a schematic block diagram of another example of the advertisement delivery system based on user behavior data feedback of the present invention.
Fig. 6 is a schematic block diagram of another example of the advertisement delivery system based on user behavior data feedback of the present invention.
Fig. 7 is a block diagram of an exemplary embodiment of an electronic device according to the present invention.
Fig. 8 is a block diagram of an exemplary embodiment of a computer-readable medium according to the present invention.
Detailed Description
Exemplary embodiments of the present invention will now be described more fully with reference to the accompanying drawings. The exemplary embodiments, however, may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these exemplary embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of the invention to those skilled in the art. The same reference numerals denote the same or similar elements, components, or parts in the drawings, and thus their repetitive description will be omitted.
Features, structures, characteristics or other details described in a particular embodiment do not preclude the fact that the features, structures, characteristics or other details may be combined in a suitable manner in one or more other embodiments in accordance with the technical idea of the invention.
In describing particular embodiments, the present invention has been described with reference to features, structures, characteristics or other details that are within the purview of one skilled in the art to provide a thorough understanding of the embodiments. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific features, structures, characteristics, or other details.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various elements, components, or sections, these terms should not be construed as limiting. These phrases are used to distinguish one from another. For example, a first device may also be referred to as a second device without departing from the spirit of the present invention.
The term "and/or" and/or "includes any and all combinations of one or more of the associated listed items.
Example 1
Hereinafter, an advertisement delivery method based on user behavior data feedback according to the present invention will be described with reference to fig. 1 to 3, and the advertisement delivery method includes:
step S101, acquiring user behavior data of a user on the advertisement from a third party, and synchronizing the user behavior data to a data processing platform in real time; and extracting user characteristic data from the user behavior data, and screening the user characteristic data.
Step S102, training a marketing optimization model by using training data, wherein the training data comprises user characteristic data, financial performance data and advertisement information data of historical users.
Step S103, inputting the screened user characteristic data of the target user into the marketing optimization model, calculating a user evaluation value, and determining advertisement putting rule data according to the user characteristic data and the calculated user evaluation value.
And step S104, advertising based on the advertising rule data.
In order to more clearly illustrate the sorting method of the present invention, the advertisement placement for the loan products will be specifically described as an example.
First, step S101 is described. In step S101, user behavior data of the user on the advertisement is obtained from a third party, synchronized to the data processing platform in real time, and user feature data is extracted from the user behavior data and is subjected to screening processing, see fig. 1 and fig. 2.
In this example, user behavior data of the user for the advertisement over a predetermined period of time including seven days, ten days, twelve days, fifteen days or one month is obtained from, for example, a third party, wherein the user behavior data includes at least one of the feature data of the number of clicks on the displayed advertisement, the time of the click, whether a financial product (in this example, a loan-type product) for the displayed advertisement is registered, whether credit is granted, and whether the piece is finished.
Further, user information is acquired from a third party, and the user information includes, for example, age, sex, occupation, monthly income/annual income, loan information, payment information, overdue information, the number of times of use of loan-type products, and the like.
Specifically, the acquired user behavior data are synchronized to a Kafka distributed data processing platform in real time, and user characteristic data are extracted from the user behavior data, wherein the user characteristic data comprise at least one characteristic data of the mobile phone type, the mobile phone number, the mobile phone positioning position information, the IP address, the mailbox, the age and the gender of the user.
Preferably, the extracted user characteristic data is matched with user characteristic data in a pre-stored database of the data processing platform to screen out target users and non-target users, wherein the target users are used for representing users who want to perform advertisement delivery, and the non-target users are used for representing users who do not perform advertisement delivery.
In addition, in the invention, PHP is used as a service development language, a Kafka distributed data processing platform is combined, advertisement data return clicked or displayed by a user in App, such as a headline, a fast hand and the like, is received in real time, and the return data is used for data analysis.
Next, step S102 is described. In step S102, the marketing optimization model is trained using the training data.
In this example, a marketing optimization model is established, for example using an algorithmic model such as a funnel model, an electrical path model, a channel conversion verification model, logistic regression, gradient boosting tree (GBDT), and so forth. The foregoing examples are provided for the purpose of illustration only and are not to be construed as limiting the present invention.
Specifically, the training data includes user characteristic data, financial performance data, and advertising information data of the historical user.
In this example, the financial performance data includes data relating to registration, credit or completion. Specifically, the financial performance data user generates interest or non-interest information data after clicking or accessing the advertisement displayed on the page, such as multiple click unregistered, clicking or accessing, registering, granting credit, moving payment, and the like.
Preferably, the advertisement information data includes advertisement click or access time, advertisement content information, the number of times of advertisement click at the same address, click ID, advertisement distribution platform information.
It should be noted that the specific meaning and the obtaining manner of the user feature data are the same as those of the user feature data in step S101, and therefore, the description thereof is omitted.
Further, the input features are user feature information and advertisement information data of the user. The output characteristic is an evaluation value indicating that the user has converted by clicking or accessing the presentation advertisement, in other words, the evaluation value is associated with the following information data: the probability of registering, granting credit, moving support and the like of the user due to clicking or accessing the displayed advertisement, the user clicking or accessing time, the user clicking ID, the user characteristic data and the like.
The above description is only given as a preferred example, and the present invention is not limited thereto.
In addition, training the marketing optimization model using the training data further includes defining good and bad samples. As a specific example, a "transformation of the user by clicking on or accessing the exposed advertisement" may be used to define a good-bad sample, i.e., a label value of "whether the user transforms by clicking on or accessing the exposed advertisement" is specified to be 0 or 1, where 1 indicates that the user transforms by clicking on or accessing the exposed advertisement, and 0 indicates that the user does not transform by clicking on or accessing the exposed advertisement.
Preferably, the user is monitored for the occurrence of a conversion within 1 to 12 days after clicking on the product advertisement. Further, the conversion includes user registration information, credit granting information, dynamic support information, and the like.
Specifically, for example, if a user is monitored to click on or visit the same advertisement page multiple times within 1 to 12 days, but no conversion behavior occurs, i.e., no registration occurs, such a user is marked as a non-target user for the user screening process.
For each user, the evaluation value output by the pin optimization model is typically a value between 0 and 1. A closer to 1 indicates that the user is more prone to conversion by clicking on or accessing the exposed advertisement.
Next, step S103 will be described. In step S103, the marketing optimization model is input using the user characteristic data of the screened target user, a user evaluation value is calculated, and advertisement delivery rule data is determined according to the user characteristic data and the calculated user evaluation value.
In this example, the data processing platform performs data analysis on the user characteristic data of the target user and the calculated user evaluation value, feeds back a data analysis result to the third party in real time, and the third party displays advertisement information related to the advertisement putting rule data to realize accurate advertisement putting, so that the advertisement putting cost is saved.
Specifically, the data analysis result includes at least one of a user conversion rate, a visit amount (PV), a visiting User (UV), a click ID, a click time, and user characteristic data related to the presentation of the advertisement.
Preferably, the advertisement putting rule data includes a display plan, advertisement putting times within a predetermined time, advertisement putting cities, and advertisement content information.
Next, step S104 will be described. In step S104, advertisement placement is performed based on the advertisement placement rule data.
Specifically, a rule corresponding to the user having the user feature data and the advertisement placement rule is established by the calculated user evaluation value and the user feature data.
In the embodiment, the third party returns the relevant information of the user accessing the advertisement page to the data processing platform in real time in the form of log, and updates the user characteristic data, the advertisement delivery rule data and the like in real time, thereby optimizing the target data.
It should be noted that the above description is only given by way of example, and the present invention is not limited thereto. In other embodiments, both steps S102 and S103 may be split into two steps, for example, step S103 may be split into step S103 and step S301, see fig. 3 specifically.
Compared with the prior art, the method combines big data mining technology with accurate advertisement delivery, solves the problem that a traditional advertisement delivery user is not accurate, records user behavior data of the user on the advertisement in the whole commercial chain, extracts and analyzes the user behavior data, and determines advertisement delivery rule data through data extraction and analysis so that a third party can display advertisement information related to the advertisement delivery rule data, target data is optimized, the advertisement delivery accuracy is improved, and advertisement delivery cost is saved.
Those skilled in the art will appreciate that all or part of the steps to implement the above-described embodiments are implemented as programs (computer programs) executed by a computer data processing apparatus. When the computer program is executed, the method provided by the invention can be realized. Furthermore, the computer program may be stored in a computer readable storage medium, which may be a readable storage medium such as a magnetic disk, an optical disk, a ROM, a RAM, or a storage array composed of a plurality of storage media, such as a magnetic disk or a magnetic tape storage array. The storage medium is not limited to centralized storage, but may be distributed storage, such as cloud storage based on cloud computing.
Embodiments of a data warehouse building apparatus of the present invention are described below, which may be used to perform method embodiments of the present invention. The details described in the device embodiments of the invention should be regarded as complementary to the above-described method embodiments; reference is made to the above-described method embodiments for details not disclosed in the apparatus embodiments of the invention.
Example 2
Referring to fig. 4, 5 and 6, the present invention further provides an advertisement delivery system 400 based on user behavior data feedback, where the advertisement delivery system 400 includes: the data acquisition module 401 acquires user behavior data of the user on the advertisement from a third party, and synchronizes the user behavior data to the data processing platform in real time; a data processing module 402, where the data processing module 402 extracts user feature data from the user behavior data and performs screening processing on the user feature data; a training module 403, the training module 403 training a marketing optimization model using training data, the training data including user characteristic data, financial performance data, and advertising information data of historical users; a calculation module 404, wherein the calculation module 404 inputs the marketing optimization model by using the screened user characteristic data of the target user, and calculates a user evaluation value; a determination module 405, wherein the determination module 405 determines advertisement delivery rule data according to the user feature data and the calculated user evaluation value; a placement module 406, the placement module 406 performing advertisement placement based on the advertisement placement rule data.
Preferably, as shown in fig. 5, the advertisement delivery system further includes a feedback module 501, where the feedback module 501 feeds back the data analysis result of the data processing platform to the third party in real time, and the third party displays advertisement information related to the advertisement delivery rule data.
Preferably, as shown in fig. 6, the advertisement delivery system further includes a screening module 601, where the screening module 601 matches the extracted user feature data with user feature data in a pre-stored database of the data processing platform to screen out a target user and a non-target user, where the target user is used to represent a user who wants to deliver an advertisement, and the non-target user is used to represent a user who does not deliver an advertisement.
Preferably, the data processing module 402 further comprises: and extracting at least one characteristic data of the mobile phone type, the mobile phone number, the mobile phone positioning position information, the IP address, the mailbox, the age and the gender of the user from the user behavior data.
Preferably, the financial performance data includes data relating to registration, credit or completion.
Preferably, the data analysis result includes at least one of a user conversion rate, an access amount, an access user, a click ID, a click time, and user characteristic data related to the presentation of the advertisement.
Preferably, the obtaining of the user behavior data of the user on the advertisement from the third party includes: and acquiring at least one characteristic data of the number of clicks, the click time, whether the financial products of the displayed advertisement are registered, whether the financial products are trusted and whether the financial products are finished of the displayed advertisement by the user in a preset time period.
Preferably, the predetermined period of time comprises seven days, ten days, twelve days, fifteen days or a month.
Preferably, a rule corresponding to the advertisement placement rule and the user having the user characteristic data is established by the calculated user evaluation value and the user characteristic data.
In embodiment 2, the same portions as those in embodiment 1 are not described.
Those skilled in the art will appreciate that the modules in the above-described embodiments of the apparatus may be distributed as described in the apparatus, and may be correspondingly modified and distributed in one or more apparatuses other than the above-described embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Example 3
In the following, embodiments of the electronic device of the present invention are described, which may be regarded as specific physical implementations for the above-described embodiments of the method and apparatus of the present invention. Details described in the embodiments of the electronic device of the invention should be considered supplementary to the embodiments of the method or apparatus described above; for details which are not disclosed in embodiments of the electronic device of the invention, reference may be made to the above-described embodiments of the method or the apparatus.
Fig. 7 is a block diagram of an exemplary embodiment of an electronic device according to the present invention. An electronic apparatus 200 according to this embodiment of the present invention is described below with reference to fig. 7. The electronic device 200 shown in fig. 7 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 7, the electronic device 200 is embodied in the form of a general purpose computing device. The components of the electronic device 200 may include, but are not limited to: at least one processing unit 210, at least one memory unit 220, a bus 230 connecting different system components (including the memory unit 220 and the processing unit 210), a display unit 240, and the like.
Wherein the storage unit stores program code executable by the processing unit 210 to cause the processing unit 210 to perform the steps according to various exemplary embodiments of the present invention described in the above-mentioned electronic prescription flow processing method section of the present specification. For example, the processing unit 210 may perform the steps as shown in fig. 1.
The memory unit 220 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)2201 and/or a cache memory unit 2202, and may further include a read only memory unit (ROM) 2203.
The storage unit 220 may also include a program/utility 2204 having a set (at least one) of program modules 2205, such program modules 2205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 200 may also communicate with one or more external devices 300 (e.g., keyboard, pointing device, bluetooth device, etc.), with one or more devices that enable a user to interact with the electronic device 200, and/or with any devices (e.g., router, modem, etc.) that enable the electronic device 200 to communicate with one or more other computing devices. Such communication may occur via an input/output (I/O) interface 250. Also, the electronic device 200 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet) via the network adapter 260. The network adapter 260 may communicate with other modules of the electronic device 200 via the bus 230. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 200, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments of the present invention described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a computer-readable storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to make a computing device (which can be a personal computer, a server, or a network device, etc.) execute the above-mentioned method according to the present invention. The computer program, when executed by a data processing apparatus, enables the computer readable medium to implement the above-described method of the invention, namely: and training the created user risk control model by using APP download sequence vector data and overdue information of the historical user as training data, and calculating the financial risk prediction value of the target user by using the created user risk control model.
As shown in fig. 8, the computer program may be stored on one or more computer readable media. The computer readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
In summary, the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that some or all of the functionality of some or all of the components in embodiments in accordance with the invention may be implemented in practice using a general purpose data processing device such as a microprocessor or a Digital Signal Processor (DSP). The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
While the foregoing embodiments have described the objects, aspects and advantages of the present invention in further detail, it should be understood that the present invention is not inherently related to any particular computer, virtual machine or electronic device, and various general-purpose machines may be used to implement the present invention. The invention is not to be considered as limited to the specific embodiments thereof, but is to be understood as being modified in all respects, all changes and equivalents that come within the spirit and scope of the invention.
Claims (10)
1. An advertisement delivery method based on user behavior data feedback is characterized by comprising the following steps:
acquiring user behavior data of a user on the advertisement from a third party, and synchronizing the user behavior data to the data processing platform in real time;
extracting user characteristic data from the user behavior data, and screening the user characteristic data;
training a marketing optimization model using training data, the training data including user characteristic data, financial performance data, and advertising information data of historical users;
inputting the screened user characteristic data of the target user into the marketing optimization model, and calculating a user evaluation value;
determining advertisement putting rule data according to the user characteristic data and the calculated user evaluation value;
and advertising based on the advertising rule data.
2. The advertisement delivery method according to claim 1, further comprising:
and feeding back the data analysis result of the data processing platform to the third party in real time, wherein the third party displays the advertisement information related to the advertisement putting rule data.
3. The advertisement delivery method according to any one of claims 1-2, wherein the filtering the user characteristic data comprises:
and matching the extracted user characteristic data with user characteristic data in a pre-stored database of the data processing platform to screen out target users and non-target users, wherein the target users are used for representing users who want to carry out advertisement putting, and the non-target users are used for representing users who do not carry out advertisement putting.
4. The advertisement delivery method according to any one of claims 1-3, wherein the extracting user characteristic data from the user behavior data comprises:
and extracting at least one characteristic data of the mobile phone type, the mobile phone number, the mobile phone positioning position information, the IP address, the mailbox, the age and the gender of the user from the user behavior data.
5. The advertising method according to any one of claims 1 to 4, wherein the financial performance data comprises data relating to registration, credit or completion.
6. The advertisement delivery method according to any one of claims 1 to 5, wherein the data analysis result includes at least one of user conversion rate, visit amount, visiting user, click ID, click time, and user characteristic data related to presenting the advertisement.
7. The advertisement delivery method according to any one of claims 1 to 6, wherein the obtaining user behavior data of the user on the advertisement from the third party comprises:
and acquiring at least one characteristic data of the number of clicks, the click time, whether the financial products of the displayed advertisement are registered, whether the financial products are trusted and whether the financial products are finished of the displayed advertisement by the user in a preset time period.
8. An advertisement delivery system based on user behavior data feedback, comprising:
the data acquisition module acquires user behavior data of the user on the advertisement from a third party and synchronizes the user behavior data to the data processing platform in real time;
the data processing module extracts user characteristic data from the user behavior data and performs screening processing on the user characteristic data;
a training module to train a marketing optimization model using training data, the training data including user characteristic data, financial performance data, and advertising information data of historical users;
the calculation module is used for inputting the marketing optimization model by using the screened user characteristic data of the target user and calculating a user evaluation value;
a determination module which determines advertisement putting rule data according to the user characteristic data and the calculated user evaluation value;
and the delivery module is used for delivering the advertisements based on the advertisement delivery rule data.
9. An electronic device, wherein the electronic device comprises:
a processor; and the number of the first and second groups,
a memory storing computer-executable instructions that, when executed, cause the processor to perform the method of advertisement delivery based on user behavior data feedback of any of claims 1-7.
10. A computer readable storage medium, wherein the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method of advertising based on user behavior data feedback of any of claims 1-7.
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