Analysis server, advertisement distribution system, terminal device, analysis method and program

Abstract

A technology is provided that improves the accuracy of extracting users to whom advertisements are to be delivered. [Solution] The analysis server is equipped with: a means for calculating the contribution that the user's features make to the magnitude of a difference between an index value indicating the level of interest the user has in the target of the advertisement when an advertisement is delivered and an index value indicating the level of interest the user has in the target of the advertisement when the advertisement is not delivered, based on the user's features and a means for extracting the features whose contribution satisfies a predetermined condition.

Description

[Technical field]

[0001] The present invention relates to an analysis server, an advertisement distribution system, a terminal device, an analysis method, and a program. [Background technology]

[0002] Advertisements play an important role in providing products and services to consumers. Patent Document 1 discloses an advertising effectiveness prediction system that builds a prediction model by learning training data that associates known advertisements with consumer response information to the advertisements, inputs unknown advertisements into the prediction model, and predicts consumer response information corresponding to the unknown advertisements. This system can be used to predict the effectiveness of an advertisement before it is distributed, and by making corrections or the like so as to obtain the desired effect, it is possible to create an advertisement that enhances consumers' purchasing motivation. In order to increase the effectiveness of an advertisement, in addition to improving the content of the advertisement, it is important to consider to what target the advertisement is distributed. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2019-144916 A Summary of the Invention [Problem to be solved by the invention]

[0004] The problem to be solved by the present disclosure is to provide an analysis server, an advertisement delivery system, a terminal device, an analysis method, and a program that can improve the accuracy of extracting users who are targets of advertisement delivery.

[0005] Therefore, an object of the present invention is to provide an analysis server, an advertisement distribution system, a terminal device, an analysis method, and a program that solve at least part of the above-mentioned problems. [Means for solving the problem]

[0006] According to one aspect of the present invention, the analysis server includes: a means for calculating, based on a user's feature values ​​and a difference between an index value indicating the level of interest the user has in the target of the advertisement when an advertisement is delivered and an index value indicating the level of interest the user has in the target of the advertisement when the advertisement is not delivered, a contribution degree that the user's feature values ​​make to the magnitude of the difference; and a means for extracting the feature values ​​whose contribution degree satisfies a predetermined condition.

[0007] According to one aspect of the present invention, an advertising delivery system includes the above-described analysis server, a delivery server including a means for receiving an advertising delivery request, a means for identifying advertising targets having at least one matching feature based on the feature extracted for each of the candidate targets of the advertising to be delivered in response to the delivery request, the feature having a contribution level that satisfies a predetermined condition, as the advertising targets to be delivered to the user, and a means for delivering the advertisement identified by the identification means.

[0008] According to one aspect of the present invention, a terminal device receives an advertisement transmitted by the above-described advertisement distribution system.

[0009] According to one aspect of the present invention, an analysis method is an analysis method executed by a computer, and includes the steps of: calculating a contribution of a user's feature to the magnitude of a difference between an index value indicating the level of interest the user has in the target of the advertisement when an advertisement is delivered and an index value indicating the level of interest the user has in the target of the advertisement when the advertisement is not delivered, based on the user's feature; and extracting the feature whose contribution satisfies a predetermined condition.

[0010] According to one aspect of the present invention, the program causes a computer to function as a means for calculating the contribution of a user's features to the magnitude of a difference between an index value indicating the level of interest the user has in the target of the advertisement when an advertisement is delivered and an index value indicating the level of interest the user has in the target of the advertisement when the advertisement is not delivered, based on the user's features and a means for extracting the features whose contribution satisfies a predetermined condition. Effect of the Invention

[0011] According to the present invention, it is possible to improve the accuracy of extracting users who are targets of advertisement delivery. [Brief description of the drawings]

[0012] [Figure 1] 1 is a block diagram illustrating an example of an advertisement delivery system according to an embodiment. [Diagram 2] FIG. 13 is a diagram illustrating an example of a purchase record table according to the embodiment. [Diagram 3] FIG. 2 is a diagram illustrating an example of a purchase prediction model according to the embodiment. [Figure 4] FIG. 13 is a diagram showing an example of a calculation result of a purchase probability according to the embodiment. [Diagram 5] FIG. 11 is a diagram illustrating an example of a difference in purchase probability between a case where an advertisement is delivered and a case where an advertisement is not delivered according to the embodiment. [Figure 6] FIG. 13 is a diagram illustrating an example of a contribution model according to the embodiment. [Figure 7] 11 is a flowchart illustrating an example of a process for analyzing a feature amount that contributes to an advertising effect according to the embodiment. [Figure 8A] 11 is a flowchart illustrating an example of an advertisement delivery process according to the embodiment. [Figure 8B] 11 is a flowchart illustrating an example of a process for delivering a point-added advertisement according to the embodiment. [Figure 9] FIG. 13 is a diagram illustrating an example of a purchase probability table according to the embodiment. [Figure 10]FIG. 2 is a diagram illustrating an example of a hardware configuration of an advertisement delivery system according to an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0013] <Embodiment> (System Configuration) FIG. 1 is a block diagram illustrating an example of an advertisement delivery system according to an embodiment. The advertisement delivery system 1 includes a user terminal 10, a DB server 20, an advertising medium server 30, a purchase management server 40, and an analysis server 50. The user terminal 10, the DB server 20, the advertising medium server 30, the purchase management server 40, and the analysis server 50 are communicatively connected via a network NW.

[0014] The user terminal 10 is a terminal device such as a smartphone or a PC (personal computer). The user terminal 10 is used by a user who can purchase products and services by using an online shopping mall provided by the purchase management server 40 or an advertisement provided by the advertising media server 30. For example, when a user operates the user terminal 10 and logs into the advertising media server 30 or the purchase management server 40 using his / her account information, an advertisement suitable for the user is delivered to the user terminal 10. When the user views or clicks on an advertisement displayed on the user terminal 10, the history is recorded in the advertising media server 30 in association with the user ID of the user. In addition, when a user operates the user terminal 10 to purchase a product at an online shopping mall, the purchase history is recorded in the purchase management server 40 in association with the user ID.

[0015] The DB server 20 (DB is an abbreviation of database) includes a user information management DB 21. The DB server 20 writes various information to the user information management DB 21 and reads various information from the user information management DB 21. The DB server 20 transmits the read information to the analysis server 50 and the like. The user information management DB 21 stores personal information such as the name, age, sex, address, email address, and telephone number of the user who uses the advertising medium server 30 and the purchase management server 40, and various feature quantities that represent the characteristics of the user, in association with the user ID. The user feature quantities include demographic information such as the age and sex of each user, and user attribute information such as hobbies, preferences, and tendencies. The user attribute information includes factual attributes and estimated attributes estimated from other user attribute information. In addition, the user information management DB 21 may store advertisement delivery history for each user ID and each product, the number of times an advertisement is displayed, the number of times an advertisement is clicked, and the purchase history of the product. Also, different user IDs may be assigned to the same user for each advertising media server 30, purchase management server 40, and store in a shopping mall provided by purchase management server 40, and the association of these multiple user IDs may be set in user information management DB 21. In the following description, the user ID is used to refer to the identification information of one user collectively, rather than to each of these different IDs individually.

[0016] The advertising medium server 30 is, for example, a web server that provides a place where advertisements for various products and services are posted. The place where advertisements are posted is, for example, a web page such as news or blog, which is called an advertising medium. The advertising medium server 30 transmits a user ID of a user who logs in to the advertising medium server 30 and is viewing a medium and various advertisements contained in the medium to the analysis server 50, and requests the distribution of an advertisement. In response to this request, the analysis server 50 selects an advertisement to be distributed to the user ID, and distributes the selected advertisement. Then, the advertising medium server 30 displays the distributed advertisement at a predetermined position on the advertising medium. In addition, the advertising medium server 30 may store log information (advertising distribution history) such as when an advertisement was distributed and how many times it was distributed, and advertisement impression information such as whether a user viewed or clicked an advertisement, for each user ID. Note that among advertisements, there are advertisements with points that are given to users who view the advertisement, and the advertising medium server 30 can display the advertisements with points on the advertising medium. For example, when a user logs in to the advertising medium server 30, the analysis server 50 judges whether an advertisement with points is effective for the user, and delivers the advertisement with points if it is judged to be effective. When the user views the advertisement with points, the advertising medium server 30 may perform point management, such as awarding points to the user.

[0017] The purchase management server 40 manages an online shopping mall. For example, the purchase management server 40 transmits web pages on which products are posted and processes the purchase of products in response to requests from the user terminal 10. The purchase management server 40 manages log information (purchase history) that records which web pages of products the user has accessed and the purchase history of the products. Based on this log information, the purchase management server 40 can analyze the number of times a web page of a product has been accessed for each user ID, and the number of times the product was purchased. The purchase management server 40 also transmits the user ID of a user who is logging in to the purchase management server 40 and considering purchasing a product to the analysis server 50, and requests the delivery of an advertisement. In response to this request, the analysis server 50 selects an advertisement to be delivered to the user ID and delivers the selected advertisement. The purchase management server 40 then displays the delivered advertisement at a predetermined position on the web page on which the product is posted.

[0018] The analysis server 50 analyzes the feature amount of the user that contributes to the magnitude of the advertisement effect. The advertisement effect means that the advertisement arouses interest or curiosity in the product by the advertisement, such as when the user who received the advertisement purchases the advertised product or accesses the product purchase page, and the magnitude of the advertisement effect means the degree of increase in the interest level in that case. In the following description, as an example, the difference between the purchase probability when the advertisement is delivered and the purchase probability when the advertisement is not delivered is used as an index value of the advertisement effect. For example, the analysis server 50 analyzes what feature amount a user has in an advertisement for a certain product, and whether the user purchases the product or becomes interested in the product through the advertisement. In addition, based on the analysis result of the feature amount of the user that contributes to the magnitude of the advertisement effect, the analysis server 50 determines (selects) a product that matches the feature amount of the user who has sent a request for advertisement delivery by logging in to the advertisement management server 30, etc., and delivers the advertisement for the product. In other words, the analysis server 50 determines the user to whom the advertisement is to be delivered based on the analysis result. The analysis server 50 includes an input receiving unit 51 , a control unit 52 , and a storage unit 53 .

[0019] The input reception unit 51 receives information input to the analysis server 50 using an input device such as a keyboard or a mouse, or information transmitted from another device through the communication unit 54. The input reception unit 51 outputs the received information to the control unit 52, or records it in the memory unit 53. For example, the input reception unit 51 receives designation of a product or the like for analyzing user features that contribute to the magnitude of advertising effectiveness. The input reception unit 51 also receives a request for advertisement delivery from the advertising medium server 30. The input reception unit 51 also receives user IDs of users to whom advertisements are to be delivered from the advertising medium server 30 or the purchase management server 40.

[0020] The control unit 52 controls the analysis of the user features that contribute to the magnitude of the advertising effect and the distribution of the advertisement based on the analysis result. The control unit 52 includes a purchase probability prediction unit 521, a contribution analysis unit 522, a distribution decision unit 523, and an advertisement distribution unit 524.

[0021] The purchase probability prediction unit 521 predicts the probability that a user will purchase a product. The purchase probability prediction unit 521 predicts the probability that a user will purchase a product based on the past purchase records of a product of multiple users in the case where an advertisement for the product was delivered and the case where an advertisement was not delivered. An example of a purchase record table for a product is shown in FIG. 2. As shown in the figure, the purchase record table 200 has items such as a user ID, multiple feature amounts such as age, whether or not an advertisement was delivered, and whether or not a product was purchased. For example, a user with a user ID of 001 is 21 years old, has an advertisement delivered, and has purchased a product. The feature amounts include age, sex, area of ​​residence, and what products have been purchased in the past. The purchase record table 200 is stored in the storage unit 53. For example, the purchase probability prediction unit 521 may request the feature amounts, the number of times an advertisement for the target product was delivered, and a product purchase history for all users stored in the user information management DB 21 from the DB server 20, and store this information in the purchase record table 200 of the storage unit 53. Alternatively, the purchase probability prediction unit 521 may obtain information from the advertising media server 30 that can determine whether or not an advertisement has been delivered for the target product for each user ID (for example, advertisement delivery history, delivery count, advertisement impression information, etc.), obtain information on the purchase history of the target product for each user ID from the purchase management server 40, determine whether or not an advertisement has been delivered and whether or not a purchase record has been made based on the obtained information, and store the determination result in the purchase record table 200. At this time, a user who does not have advertisement impression information or the like may be classified as having no advertisement delivery. The purchase probability prediction unit 521 uses the information stored in the purchase record table 200 as learning data, and creates a purchase prediction model in which the user's feature amount and the presence or absence of advertisement delivery are explanatory variables, and the product purchase probability is an objective variable. An example of the purchase prediction model is shown in FIG. 3. The purchase probability prediction unit 521 constructs a purchase prediction model 531 that outputs the probability of purchasing the product when the user's feature amount and the presence or absence of advertisement delivery are inputted, by machine learning or the like. The purchase probability prediction unit 521 calculates, for example, the purchase probability with and without advertisement delivery for all users stored in the purchase history table 200, using the purchase prediction model 531. An example of the calculation result is shown in FIG.Figure 4(a) shows the purchase probability of a certain product with and without ad delivery, and Figure 4(b) shows the purchase probability without ad delivery. For example, for a user with user ID 101, the purchase probability with and without ad delivery is 0.81 and 0.54, respectively.

[0022] By creating the purchase prediction model 531 illustrated in FIG. 3, it is possible to predict the purchase probability of a user who has no past purchase history of a product based on the feature amount of the user. For a user who has a past purchase history of a product, the purchase probability may be predicted as follows. For example, the purchase probability prediction unit 521 acquires information on the number of times an advertisement for the product was delivered to the user and the number of times the advertisement was delivered at which the user purchased the product. The purchase probability prediction unit 521 acquires the number of times the advertisement was delivered from the advertising medium server 30, and compares it with information on the purchase date and time of the product acquired from the purchase management server 40. For example, if the product was purchased before a predetermined time has elapsed since the advertisement delivery and the advertisement delivery is the third advertisement delivery, it is determined that the user purchased the product at the third advertisement delivery. Alternatively, if there is log information indicating that the advertisement was clicked at the third advertisement delivery and a purchase was made thereafter, it is determined that the user purchased the product at the third advertisement delivery. In this case, the purchase probability prediction unit 521 predicts the purchase probability at the time of advertisement delivery to be 1 / 3. Furthermore, purchase probability prediction unit 521 acquires log information stored by purchase management server 40, and calculates the purchase probability in the absence of advertisement delivery based on information regarding the number of times a user logged in to purchase management server 40 and accessed the product in question at the online shopping mall and the number of accesses at which the user purchased the product. For example, if a user purchased the product on the fifth access, purchase probability prediction unit 521 predicts the purchase probability in the absence of advertisement delivery to be 1 / 5.

[0023] The contribution analysis unit 522 analyzes the feature amount of the user that contributes to the magnitude of the advertising effect. The contribution analysis unit 522 analyzes the feature amount of the user that affects the magnitude of the difference between the purchase probability with and without the advertisement delivery (difference = "purchase probability with advertisement delivery" - "purchase probability without advertisement delivery"), and extracts the feature amount that contributes greatly to the difference. The reason why the difference between the purchase probability with and without the advertisement delivery is large is that the advertisement promotes the purchase. Therefore, the feature amount of the user that contributes to the increase in the difference can be said to be the feature amount of the user that contributes to the magnitude of the advertising effect. For example, the contribution analysis unit 522 calculates the data of the table illustrated in FIG. 5, creates a learning model by machine learning using the feature amount of the user as an explanatory variable and the difference between the purchase probability with and without the advertisement delivery predicted by the purchase probability prediction unit 521 as an objective variable, and calculates the contribution (contribution degree) given to the magnitude of the difference for each user's feature amount by performing SHAP analysis on the created learned model. A model that indicates the feature amount of the user that contributes to the magnitude of the advertising effect is called a contribution model. An example of the contribution model is shown in FIG. 6. The vertical axis of FIG. 6 indicates the feature amount of the user, and the horizontal axis indicates the absolute value of the SHAP value. The contribution analysis unit 522 extracts feature amounts whose contribution value is equal to or greater than a predetermined value, and registers the extracted feature amounts in the storage unit 53. Alternatively, the contribution analysis unit 522 may arrange the feature amounts in order of the greatest contribution amount, extract a predetermined number of feature amounts from the top, and register them in the storage unit 53. The feature amounts of the user that contribute to the magnitude of the advertising effect, extracted by the contribution analysis unit 522, may be registered in the user information management DB 21 for each product. In the case of the contribution model 532 illustrated in FIG. 6, the contribution analysis unit 522 extracts feature amount 1 and feature amount 2 as the feature amounts of the user that contribute to the magnitude of the advertising effect.

[0024] The method of calculating the contribution is not limited to the SHAP analysis. For example, the correlation coefficient between each user feature and the difference may be calculated, and the feature whose correlation coefficient is equal to or greater than a threshold value may be extracted. Alternatively, data whose difference is equal to or greater than a predetermined value may be extracted from the difference data of each user illustrated in FIG. 5, information on the user feature corresponding to the extracted data may be collected, and the frequency of appearance of the user feature may be counted as the contribution. For example, in a data group whose difference is equal to or greater than a predetermined value for a certain product α, if the frequency of appearance of data of "age in 20s" is equal to or greater than a predetermined number of times, or if the frequency of appearance of data having a feature of "purchasing related product β in the past" is equal to or greater than a predetermined number of times, the contribution analysis unit 522 may extract the features of "age in 20s" and "purchasing related product A in the past" as features with high contribution.

[0025] Note that, here, the difference in the probability of purchase between cases with and without ad delivery is calculated. However, instead of calculating the probability of purchase, it is also possible to use other actions that can confirm that the user has become interested in the product due to the advertisement, such as the probability of accessing a web page on which the product is posted, or the probability of downloading a catalogue about the product, requesting information or making an inquiry, as index values ​​to calculate the difference in the probability of accessing a web page for purchasing the product between cases with and without ad delivery, and analyze the feature quantities that have a strong influence on the magnitude of this difference.

[0026] The distribution decision unit 523 decides the product to be distributed for advertisement. For example, when the distribution decision unit 523 receives a request for advertisement distribution from the advertising medium server 30 or the like, the distribution decision unit 523 compares the user feature amount associated with the user ID with the feature amount of the user that contributes to the magnitude of the advertisement effect extracted by the contribution analysis unit 522, selects one or more products for which the feature amount corresponding to the user ID matches the feature amount extracted by the contribution analysis unit 522 as the target for advertisement distribution, and decides to distribute an advertisement for a specific product from among the selected products. In the above example, if the user A is in his twenties and has previously purchased the product β, the distribution decision unit 523 decides to distribute an advertisement for the product α to the user A. If the user A is in his thirties and has not previously purchased the product β, the distribution decision unit 523 decides not to distribute an advertisement for the product α to the user A. Note that, when there are multiple contribution amounts extracted by the contribution analysis unit 522, the decision as to whether or not to distribute the advertisement can be arbitrarily set according to the magnitude of the contribution amount of each feature amount. For example, when the contribution of "has purchased related product A in the past" is particularly high (large SHAP value, large correlation coefficient, high appearance frequency) among the features "age is in 20s" and "has purchased related product A in the past" and the contribution of "has purchased related product A in the past" is not so high compared to that, the advertisement for product α may be delivered to a user who has the feature of "has purchased related product A in the past" even if the user is not in his / her 20s, or the advertisement for product α may be delivered, for example, once in three times to a user who is in his / her 20s and does not have the feature of "has purchased related product A in the past." In addition, when there are multiple candidate products to be delivered in response to a request for advertisement delivery received from the advertising medium server 30, etc., and there are user features that match the multiple products, the delivery determination unit 523 determines to deliver advertisements for the product from which the user feature with the highest or higher contribution level is extracted.For example, in the storage unit 53, feature quantity 1 is registered as a feature quantity with a high contribution degree for product α1, feature quantity 2 is registered as a feature quantity with a high contribution degree for product α2, and feature quantity 3 is registered as a feature quantity with a high contribution degree for product α3, and the relationship of the magnitude of each contribution degree (e.g., SHAP value) is contribution degree of feature quantity 1>contribution degree of feature quantity 2>contribution degree of feature quantity 3. When products α1 to α3 are targets of advertisement distribution and user A1 has all of features 1 to 3, the distribution decision unit 523 decides to distribute an advertisement for product α1 corresponding to feature quantity 1 with the highest contribution degree. In addition, when user A2 has features 2 to 3, the distribution decision unit 523 decides to distribute an advertisement for product α2 corresponding to feature quantity 2 to user A2.

[0027] The advertisement distribution unit 524 distributes, to the user terminal 10, an advertisement for the product that the distribution determination unit 523 has determined to be distributed.

[0028] The storage unit 53 stores various types of information. For example, the storage unit 53 stores a purchase prediction model 531 and a contribution model 532.

[0029] (operation) (1) Feature analysis that contributes to advertising effectiveness Next, the operation of the advertisement delivery system 1 will be described. FIG. 7 is a flowchart illustrating an example of a process for analyzing a feature amount that contributes to an advertising effect according to the embodiment.

[0030] The input receiving unit 51 receives the designation of a product (step S1). The entity that inputs designation information of the product to be analyzed to the analysis server 50 may be an advertiser, a product provider, a requester for product analysis, etc., or may be an analyst of advertising effectiveness, an administrator of the analysis server, etc. Furthermore, multiple products may be designated. When multiple products are designated, the processes of steps S2 to S5 are executed for each product.

[0031] Next, the purchase probability prediction unit 521 creates a purchase prediction model (step S2). The purchase probability prediction unit 521 creates a purchase prediction model 531 (FIG. 3) that predicts the purchase probability of the product specified in step S1 based on the user's feature amount and the presence or absence of advertisement distribution.

[0032] Next, the purchase probability prediction unit 521 performs purchase prediction (step S3). The purchase probability prediction unit 521 inputs the feature quantities of many users and the presence or absence of advertisement delivery to the purchase prediction model 531 created in step S2, and predicts the purchase probability with advertisement delivery and the purchase probability without advertisement delivery for each user (FIG. 4).

[0033] Next, the contribution analysis unit 522 creates a contribution model (step S4). From the prediction result of step S3, the contribution analysis unit 522 calculates the difference between the purchase probability of each user when advertisements are delivered and when advertisements are not delivered, and calculates the data shown in Fig. 5. The contribution analysis unit 522 analyzes the feature amount that contributes to the magnitude of the calculated difference by SHAP analysis or the like, and creates a contribution model 532 (Fig. 6).

[0034] Next, the contribution analysis unit 522 extracts and registers features having a high degree of contribution to the advertising effectiveness (step S5). For example, the contribution analysis unit 522 refers to the contribution model 532, extracts features having a degree of contribution (for example, absolute value of SHAP value) equal to or greater than a predetermined threshold, and registers the extracted features in the storage unit 53, the user information management DB 21, or the like in association with the product specified in step S1. Note that information on the user features having a high degree of contribution to the advertising effectiveness extracted for each product may be provided to the advertiser, the product provider, the product analysis requester, or the like. For example, the contribution analysis unit 522 may output the extraction result of step S5 to an electronic file, or the like, and transmit the electronic file to the advertiser, the product provider, the product analysis requester, or the like.

[0035] (2) Ad delivery operations FIG. 8A is a flowchart illustrating an example of an advertisement delivery process according to the embodiment. It is assumed that a user to whom an advertisement is to be delivered is logged in to the advertising media server 30 or the purchase management server 40. In addition, it is assumed that, for the advertisement to be delivered, user features that contribute highly to the advertising effect are analyzed in advance by the process illustrated in FIG. The input reception unit 51 receives a request for advertisement delivery from the advertisement medium server 30 or the like (step S11). The request for advertisement delivery may be transmitted, for example, when the user logs in to the advertisement medium server 30 or the purchase management server 40, or when an advertisement space is displayed on a web page accessed by the user operating the user terminal 10. The request for advertisement delivery includes a user ID and a type of advertisement medium. The input reception unit 51 outputs to the control unit 52 that the advertisement delivery request has been received. In the control unit 52, the delivery determination unit 523 determines a product to be advertisement delivered (step S12). The delivery determination unit 523 refers to the user information management DB 21 using the user ID included in the request for advertisement delivery, and acquires various features registered in association with the user ID. In addition, the delivery determination unit 523 reads out from the storage unit 53 user features that have a high contribution to the advertisement effect and are registered in association with the product to be advertisement delivered. Each product registered in the storage unit 53 in association with a user feature that has a high contribution to the advertisement effect is a candidate for an advertisement delivery target. The distribution decision unit 523 compares the feature acquired from the user information management DB21 with the feature read from the storage unit 53, and if there is a feature that matches the two, it decides to distribute an advertisement for the product. If there is no advertisement for a product with a matching feature, the distribution decision unit 523 decides not to distribute the advertisement. If it is decided not to distribute the advertisement (there is no product for which advertisement is distributed) (step S13; No), the processing of FIG. 8A is terminated. If it is decided to distribute the advertisement (step S13; Yes), the distribution decision unit 523 instructs the advertisement distribution unit 524 to distribute an advertisement for the determined product. If there is one product whose feature matches the user's feature and the feature extracted for the product to be advertised, the distribution decision unit 523 instructs the advertisement distribution unit 524 to distribute an advertisement for that product. When there are multiple products whose features match the user's features extracted for the products, the distribution decision unit 523 identifies a product for which an advertisement is to be distributed from among the multiple products, and instructs the advertisement distribution unit 524 to distribute an advertisement for the identified product.For example, the distribution determination unit 523 may specify a product corresponding to the feature with the highest contribution as the product to be distributed for advertisement, or may specify a product having the most features matching the user's features (a product having the most matching features when comparing multiple features with high advertising effectiveness associated with the product and multiple features owned by the logged-in user) as the product to be distributed for advertisement. The advertisement distribution unit 524 distributes the advertisement (step S14). The advertisement distribution unit 524 distributes the advertisement for the product specified by the distribution determination unit 523. The user terminal 10 receives the transmitted advertisement and outputs it to the display unit.

[0036] (2´) Delivery process of advertising with points In the process of Fig. 8A, only a determination is made as to whether or not to transmit an advertisement, but if it is determined that an advertisement is to be delivered, a further determination may be made as to whether an advertisement with points or an advertisement without points is to be delivered. The process in this case will be described with reference to Fig. 8B. It is assumed that a user to whom an advertisement is to be delivered is logged in to the advertising media server 30 or the purchase management server 40. For the advertisement to be delivered, user features that contribute highly to the advertising effectiveness are analyzed in advance by the process illustrated in FIG. 7, and the results are registered in the storage unit 53. Furthermore, it is assumed that a purchase probability table 201 (FIG. 9), which will be described later, is registered in the storage unit 53. Note that steps S11 to S13 are similar to the process described in FIG. 8A, and will be described briefly.

[0037] The input reception unit 51 receives a request for advertisement distribution from the advertising media server 30 (step S11). Next, the distribution determination unit 523 determines a product for which the advertisement is to be distributed (step S12). If it is determined that the advertisement is not to be distributed (there is no product for which the advertisement is to be distributed) (step S13; No), the process of FIG. 8B is terminated. If it is determined that the advertisement is to be distributed (step S13; Yes), the distribution determination unit 523 determines whether to distribute an advertisement with points or an advertisement without points (step S141). For example, the memory unit 53 stores a purchase probability table 201 illustrated in FIG. 9. As illustrated, the purchase probability table 201 registers, for a certain product, the purchase probability without advertisement distribution, the purchase probability with advertisement distribution with points, and the purchase probability with advertisement distribution without points for each user ID. For example, instead of the purchase history table 200 illustrated in FIG. 2, in the column for whether or not advertisement is delivered in the purchase history table 200, an option of "2: Ad delivery with points" is added in addition to "0: No advertisement delivery" and "1: Ad delivery", and a table is prepared which records for each user whether the product was purchased in the case of "0: No advertisement delivery", in the case of "1: Ad delivery" (corresponding to "Ad delivery without points"), or in the case of "2: Ad delivery with points", and the purchase probability prediction unit 521 creates a purchase prediction model 531' with the user's features and whether or not advertisement is delivered (the above flags 0 to 2) as explanatory variables and the purchase probability as a target variable. The purchase probability prediction unit 521 inputs the characteristics of the user with user ID=101 and the above flag "0" into this purchase prediction model 531' to calculate the purchase probability (1.0%) when no advertisement is delivered, inputs the characteristics of the user with user ID=101 and the above flag "1" to calculate the purchase probability (1.5%) when advertisements without points are delivered, inputs the characteristics of the user with user ID=101 and the above flag "2" to calculate the purchase probability (2.5%) when advertisements with points are delivered, and registers these values ​​in the purchase probability table 201.Similarly, for other users with user IDs 102, 103, etc., the purchase probability prediction unit 521 predicts the purchase probability in the cases of no advertisement delivery, no point advertisement delivery, and point advertisement delivery, using the purchase prediction model 531', and registers the predicted purchase rates in the purchase probability table 201. The purchase probability table 201 illustrated in Fig. 9 is a table created in advance in this manner.

[0038] The distribution decision unit 523 refers to the purchase probability table 201 and determines whether to distribute an advertisement with points or without points (step S141). For example, if the purchase probability when the advertisement with points is distributed is greater than the purchase probability when the advertisement without points is distributed by a predetermined value or more, the distribution decision unit 523 decides to distribute an advertisement with points, and otherwise decides to distribute an advertisement without points. For example, in the case of the example of FIG. 9, if the user to whom the advertisement is distributed is a user with user ID=101, the distribution decision unit 523 decides to distribute an advertisement with points, and if the user to whom the advertisement is distributed is a user with user ID=102, the distribution decision unit 523 decides to distribute an advertisement without points. When it is decided to distribute an advertisement with points (step S141; Yes), the distribution decision unit 523 instructs the advertisement distribution unit 524 to distribute an advertisement with points related to the product determined in step S13. The advertisement distribution unit 524 distributes an advertisement with points for the instructed product (step S15). When it is determined that an advertisement without points is to be distributed (step S141; No), the distribution determination unit 523 instructs the advertisement distribution unit 524 to distribute an advertisement without points for the product determined in step S13. The advertisement distribution unit 524 distributes the advertisement without points for the instructed product (step S16). The user terminal 10 receives the transmitted advertisement and outputs it to the display unit.

[0039] In the process of step S141 in FIG. 8B, a determination is made as to whether to deliver an advertisement with points or an advertisement without points based on the purchase probability table 201 illustrated in FIG. 9. However, for example, a difference 1 (1.5%) obtained by subtracting the purchase probability when an advertisement with points is delivered in the purchase probability table 201 (2.5% when user ID=101) from the purchase probability when no advertisement is delivered (1.0% when user ID=101) and a difference 2 (0.5%) obtained by subtracting the purchase probability when an advertisement without points is delivered (1.5% when user ID=101) from the purchase probability when no advertisement is delivered (1.0% when user ID=101) may be calculated for all users, and the contribution analysis unit 522 may analyze the user feature amount that contributes to the magnitude of difference 1 and the user feature amount that contributes to the magnitude of difference 2 by SHAP analysis or the like, and determine whether to deliver an advertisement with points or an advertisement without points based on the analysis results. For example, the contribution analysis unit 522 has determined through analysis that the features that contribute to the magnitude of difference 1 for the product (advertising effectiveness of the ad with points) are feature 1 and feature 2, and the features that contribute to the magnitude of difference 2 (advertising effectiveness of the ad without points) are feature 1 and feature 3. If a user to whom the ad is to be delivered has feature 1 and feature 2, the delivery decision unit 523 decides to deliver an ad with points.

[0040] (effect) As described above, according to this embodiment, it is possible to calculate the characteristic amounts of a user that contribute to the magnitude of advertising effectiveness. This makes it possible to improve the accuracy of extracting users to whom advertisements are to be delivered when delivering advertisements. Specifically, users having the characteristic amounts of a user that contribute to the magnitude of advertising effectiveness are extracted as users to whom advertisements are to be delivered. This makes it possible to deliver advertisements to users for whom the advertising effect is high. Since advertisements are matched with users based on the user's characteristic amounts, it is possible to provide advertisements that are suitable for new users even for whom there is no data such as past product purchase history.

[0041] In the above embodiment, the feature amount of the user that contributes to the magnitude of the advertising effect for each product or service is analyzed, but the feature amount of the user that contributes to the magnitude of the advertising effect for each product or service category may be calculated. For example, the feature amount of the user that contributes to the magnitude of the advertising effect when an advertisement related to food is delivered may be analyzed from the data of the purchase record when an advertisement for a product belonging to the food category is delivered and the purchase record when no advertisement is delivered. Alternatively, the feature amount of the user that contributes to the magnitude of the advertising effect may be analyzed in units of a store or manufacturer that provides a product or service. In the above embodiment, when a request for advertisement delivery is received from the advertising medium server 30 or the like, the analysis server 50 determines a specific product from at least one or more candidates of products to be delivered to the medium based on the received information on the medium and user, and delivers an advertisement for the product. However, the embodiment may be such that the advertising medium server 30 specifies a product, requests the analysis server 50 to deliver the advertisement for the product, and the analysis server 50 determines whether or not to deliver the advertisement for the product. More specifically, the delivery decision unit 523 may refer to the user information management DB21 using the user ID included in the request for advertisement delivery, acquire various feature amounts registered in association with the user ID, read from the storage unit 53 user feature amounts that contribute highly to the advertisement effect registered in association with the product for which advertisement is delivered, compare the feature amounts acquired from the user information management DB21 with the feature amounts read from the storage unit 53, and if the two match (for example, if at least one feature amount matches, or if a predetermined number or more of the feature amounts match), decide to deliver the advertisement, and if the feature amounts do not match, decide not to deliver the advertisement for the requested product. If it is decided to deliver the advertisement, the delivery decision unit 523 instructs the advertisement delivery unit 524 to deliver the advertisement for the requested product.

[0042] In the above embodiment, the feature of the user that contributes to the magnitude of the advertising effect for each product or service is analyzed, but when there are multiple types of advertisements for one product, the feature of the user that contributes to the magnitude of the advertising effect of the advertisement may be analyzed for each advertisement, and an advertisement whose feature of the advertisement delivery target user matches the feature of the advertisement with a large advertising effect may be delivered. For example, assume that there are three types of advertisements γ1 to γ3 for a product α, and the feature of advertisement γ1 with a large advertising effect is feature 1, the feature of advertisement γ2 with a large advertising effect is feature 2, and the feature of advertisement γ3 with a large advertising effect is feature 3. In addition, if the feature of advertisement delivery target user A1 is feature 1 and the feature of advertisement delivery target user A2 is feature 2, the delivery decision unit 523 decides to deliver advertisement γ1 to user A1 and to deliver advertisement γ2 to user A2.

[0043] 1, the analysis server 50 includes the advertisement delivery unit 524, but the advertisement delivery server may be provided as a server device separate from the analysis server 50. In this configuration, the function of the delivery determination unit 523 may be implemented in the advertisement delivery server, and the user feature with a high contribution to the advertising effect extracted for each product by the contribution analysis unit 522 may be registered in the advertisement delivery server.

[0044] FIG. 10 is a diagram showing an example of a hardware configuration of an advertisement delivery system according to an embodiment. The computer 900 includes a CPU 901, a main storage device 902, an auxiliary storage device 903, an input / output interface 904, and a communication interface 905. The above-mentioned user terminal 10, DB server 20, advertising media server 30, purchase management server 40, and analysis server 50 are implemented in the computer 900. The above-mentioned processes are stored in the auxiliary storage device 903 in the form of a program. The CPU 901 reads the program from the auxiliary storage device 903, expands it in the main storage device 902, and executes the above-mentioned processes according to the program. The CPU 901 also secures a memory area in the main storage device 902 according to the program. The CPU 901 also secures a memory area in the auxiliary storage device 903 for storing data being processed according to the program.

[0045] In at least one embodiment, the auxiliary storage device 903 is an example of a non-transient tangible medium. Other examples of non-transient tangible media include a magnetic disk, a magneto-optical disk, a CD-ROM, a DVD-ROM, and a semiconductor memory connected via the input / output interface 904. In addition, when this program is distributed to the computer 900 via a communication line, the computer 900 that receives the program may deploy the program in the main storage device 902 and execute the above-mentioned process. In addition, the program may be for realizing part of the above-mentioned functions. Furthermore, the program may be a program that realizes the above-mentioned functions in combination with other programs already stored in the auxiliary storage device 903, that is, a so-called difference file (difference program).

[0046] In addition, the components in the above-described embodiment may be replaced with known components as appropriate without departing from the spirit of the present invention. Furthermore, the technical scope of the present invention is not limited to the above-described embodiment, and various modifications may be made without departing from the spirit of the present invention.

[0047] A part or all of the above-described embodiments can be described as, but are not limited to, the following supplementary notes.

[0048] (Appendix 1) The analysis server is equipped with: a means (contribution analysis unit 522) for calculating the contribution that the user's features make to the magnitude of a difference between an index value indicating the level of interest the user has in the target of the advertisement when an advertisement is delivered and an index value indicating the level of interest the user has in the target of the advertisement when the advertisement is not delivered, based on the user's features and a means (contribution analysis unit 522) for extracting the features whose contribution satisfies a predetermined condition.

[0049] (Appendix 2) The analysis server described in Appendix (1) further comprises: means for receiving an advertisement delivery request (input receiving unit 51); means for identifying a target of the advertisement to be delivered to the user based on characteristics of the user to be the target of the advertisement based on the delivery request and the characteristics extracted for each of the candidate targets of the advertisement to be delivered in response to the delivery request, the contribution level of which satisfies a predetermined condition (delivery decision unit 523); and means for delivering the advertisement identified by the identification means (advertisement delivery unit 524). The means for identifying the advertisement target may identify the advertisement target having at least one matching feature amount as the advertisement target to be delivered to the user.

[0050] (Appendix 3) When the identification means identifies a plurality of targets of the advertisement having at least one feature that matches the feature of the user, the means for delivering the advertisement (advertising delivery unit 524) is the analysis server described in Appendix (2), which delivers an advertisement related to the target of the advertisement from which the feature having the greatest contribution degree was extracted among the matching features.

[0051] (Appendix 4) The analysis server according to claim 2, further comprising: a means (purchase probability prediction unit 521) for acquiring, in the case where an advertisement with points is provided for awarding points to a user who has received the advertisement, and an advertisement without points is provided, a first index value indicating the level of interest in the target of the advertisement held by the user to whom the advertisement is to be delivered when the advertisement with points is delivered, and a second index value indicating the level of interest in the target of the advertisement held by the user to whom the advertisement is to be delivered when the advertisement without points is delivered; and when a target of the advertisement to be delivered to the user is identified by the identifying means, the delivering means (advertising delivery unit 524) delivers the advertisement with points if the first index value is greater than the second index value, and delivers the advertisement without points if the second index value is greater than the first index value.

[0052] (Appendix 5) The analysis server according to appendix (1) to (4) further comprises a means for accepting designation of a target of the advertisement (input accepting unit 51), a means for acquiring purchase history information of the target for each user as an index value indicating the level of interest in the target (purchase probability prediction unit 521), a means for acquiring information on whether an advertisement for the target has been delivered to each user (purchase probability prediction unit 521), and a means for calculating, based on the purchase history information and the delivery presence / absence information, a purchase probability of the target for each user when the advertisement is delivered as the index value when the advertisement is delivered, and a means for calculating a purchase probability of the target when the advertisement is not delivered as the index value when the advertisement is not delivered (purchase probability prediction unit 521).

[0053] (Appendix 6) The analysis server according to any one of appendices (1) to (5) further includes a means (purchase probability prediction unit 521) for calculating, based on a prediction model in which the user's features and whether or not the advertisement is delivered are explanatory variables and the index value is a target variable, an index value indicating the level of interest the user has in the target of the advertisement when the advertisement is delivered and an index value indicating the level of interest the user has in the target of the advertisement when the advertisement is not delivered.

[0054] (Appendix 7) The means for calculating the degree of contribution (contribution analysis unit 522) is an analysis server described in Supplementary Notes (1) to (6), which calculates the degree of contribution by performing SHAP analysis on a machine learning model that uses the user's features as explanatory variables and the difference as a target variable.

[0055] (Appendix 8) The means for calculating the degree of contribution (contribution analysis unit 522) is an analysis server described in Supplementary Notes (1) to (7), which extracts the users whose difference is equal to or greater than a predetermined value, aggregates the extracted features of the users, and regards the frequency of appearance of the features of the users in the aggregated result as the degree of contribution.

[0056] (Appendix 9) The means for calculating the degree of contribution (contribution degree analysis unit 522) is an analysis server described in Supplementary Notes (1) to (8), which extracts features whose contribution degree is equal to or greater than a predetermined value, or arranges the features in order of the greatest degree of contribution, extracts a predetermined number of the features from the top, and regards the extracted features as the features of the user that contribute to the magnitude of the advertising effect.

[0057] (Appendix 10) The means for calculating the degree of contribution (contribution analysis unit 522) is the analysis server described in Supplementary Notes (1) to (9) that calculates the feature amount for each product, service, product or service category, and company that provides a product or service.

[0058] (Appendix 11) The analysis server according to any one of appendices (1) to (10), wherein the index value is any one of the following: a probability of delivering the target of the advertisement, a probability of accessing a web page in which the target of the advertisement is described, and a probability of requesting information or making an inquiry about the target of the advertisement.

[0059] (Appendix 12) An advertisement delivery system comprising: an analysis server as described in Supplementary Notes (1) to (11); a delivery server comprising: a means for receiving an advertisement delivery request; a means for identifying a target of the advertisement to be delivered to the user based on characteristics of a user to be a target of the advertisement based on the delivery request and the characteristics extracted for each of the candidate targets of the advertisement to be delivered in response to the delivery request, the contribution level of which satisfies a predetermined condition; and a means for delivering the advertisement identified by the identification means.

[0060] (Appendix 13) A terminal device that receives an advertisement transmitted by the analysis server citing supplementary note (2) or the advertisement distribution system described in supplementary note 12.

[0061] (Appendix 14) An analysis method executed by a computer, comprising: a step of calculating, based on a user's feature values ​​and a difference between an index value indicating the level of interest the user has in the target of the advertisement when an advertisement is delivered and an index value indicating the level of interest the user has in the target of the advertisement when the advertisement is not delivered, a degree of contribution that the user's feature values ​​make to the magnitude of the difference; and a step of extracting the feature values ​​whose degree of contribution satisfies a predetermined condition.

[0062] (Appendix 15) This program causes a computer to function as a means for calculating the contribution that a user's feature values ​​makes to the magnitude of a difference between an index value indicating the level of interest the user has in the target of the advertisement when an advertisement is delivered and an index value indicating the level of interest the user has in the target of the advertisement when the advertisement is not delivered, based on the user's feature values ​​and a means for extracting the feature values ​​whose contribution level satisfies a predetermined condition. [Explanation of symbols]

[0063] 1. Advertising distribution system 10. User terminal 20. Database Server 21 User information management DB 30...Advertising media server 40 Purchasing management server 50...Analysis Server 51 Input reception unit 52 Control section 521 Purchase Probability Prediction Unit 522 Contribution Analysis Section 523 Distribution Decision Department 524···Advertising Distribution Department 53...Storage section 531 Purchase prediction model 532...Contribution model 900...Computer 901···CPU 902...Main memory 903...Auxiliary storage device 904 Input / Output Interface 905 Communication Interface

Claims

1. a means for calculating a contribution of the user's feature amount to the magnitude of a difference between an index value indicating a probability that the user will take an action on the target of the advertisement when the advertisement is delivered and an index value indicating a probability that the user will take an action on the target of the advertisement when the advertisement is not delivered, based on the feature amount including demographic information of the user and user attribute information indicating a hobby, preference or tendency of the user; and means for extracting the feature amount whose contribution degree satisfies a predetermined condition; An analysis server comprising:

2. A means for accepting requests to deliver advertisements; a means for identifying, as a target of the advertisement to be delivered to the user, a target of the advertisement having at least one feature that matches the feature of the user, based on a feature of a user to which the advertisement related to the delivery request is to be delivered and the feature extracted for each candidate target of the advertisement to be delivered in response to the delivery request, the contribution degree of which satisfies a predetermined condition; a means for delivering the advertisement identified by the identifying means; The analysis server of claim 1 , further comprising:

3. When the specifying means specifies a plurality of targets of the advertisement having at least one feature value matching the feature value of the user, the means for delivering the advertisement delivers an advertisement related to the advertisement target from which the feature quantity having the greatest contribution degree was extracted among the matching feature quantities. The analysis server according to claim 2 .

4. The advertisement further comprises a means for acquiring, when a point-bearing advertisement for awarding points to a user to whom the advertisement is delivered and a non-point-bearing advertisement are present, a first index value indicating the probability that a user to whom the advertisement is delivered will take an action on the target of the advertisement when the point-bearing advertisement is delivered, and a second index value indicating the probability that a user to whom the advertisement is delivered will take an action on the target of the advertisement when the non-point-bearing advertisement is delivered, When a target of the advertisement to be delivered to the user is identified by the identifying means, the distribution means distributes the point-added advertisement when the first index value is greater than the second index value; When the second index value is greater than the first index value, the no-point advertisement is delivered. The analysis server according to claim 2 .

5. A means for accepting designation of a target of the advertisement; A means for acquiring purchase history information of the target of the advertisement for each user; A means for acquiring information on whether or not an advertisement is delivered to the target of the advertisement for each user; a means for calculating, for each user, a purchase probability of the target of the advertisement when the advertisement is delivered as the index value when the advertisement is delivered, based on the purchase record information and the delivery / non-delivery information, and calculating a purchase probability of the target of the advertisement when the advertisement is not delivered as the index value when the advertisement is not delivered; The analysis server according to claim 1 or 2, further comprising:

6. a means for calculating the index value that the user has when the advertisement is delivered and the index value that the user has when the advertisement is not delivered based on a prediction model in which the feature amount of the user and whether or not the advertisement is delivered are explanatory variables and the index value is a response variable; The analysis server according to claim 1 or 2, further comprising:

7. The means for calculating the degree of contribution calculates the degree of contribution by performing a SHAP analysis on a machine learning model in which the feature amount of the user is an explanatory variable and the difference is an objective variable. The analysis server according to claim 1 or 2.

8. the means for calculating the degree of contribution extracts the users whose difference is equal to or greater than a predetermined value, aggregates the extracted features of the users, and determines the frequency of appearance of the features of the users in the aggregated result as the degree of contribution. The analysis server according to claim 1 or 2.

9. the means for calculating the degree of contribution extracts a feature quantity whose degree of contribution is equal to or greater than a predetermined value, or arranges the feature quantities in order of the degree of contribution, extracts a predetermined number of the feature quantities from the top, and regards the extracted feature quantities as the feature quantities of the user that contribute to the magnitude of the advertising effect. The analysis server according to claim 1 or 2.

10. the means for calculating the degree of contribution calculates the feature amount for each product, service, product or service category, and company providing the product or service; The analysis server according to claim 1 or 2.

11. The index value is any one of the following: a probability of delivering the target of the advertisement, a probability of accessing a web page on which the target of the advertisement is posted, and a probability of making a request for information or an inquiry about the target of the advertisement. The analysis server according to claim 1 or 2.

12. The analysis server according to claim 1 ; means for receiving an advertisement delivery request; means for identifying, as an advertisement target to be delivered to the user, an advertisement target having at least one feature that matches the user's feature, based on a feature of a user to be a delivery target of the advertisement related to the delivery request and the feature extracted for each candidate target of the advertisement to be delivered in response to the delivery request, the contribution degree of which satisfies a predetermined condition; and means for delivering the advertisement identified by the identifying means; A distribution server comprising: An advertisement delivery system comprising:

13. 1. A computer-implemented analysis method comprising: a step of calculating a degree of contribution of the user's feature amount to the magnitude of the difference based on a feature amount including demographic information of the user and user attribute information indicating a user's hobbies, preferences, or tendencies, and an index value indicating a probability that the user will take an action on the target of the advertisement when the advertisement is delivered and a difference between the index value indicating a probability that the user will take an action on the target of the advertisement when the advertisement is not delivered; extracting the feature amount whose contribution degree satisfies a predetermined condition; The analytical method has the following features:

14. Computer, a means for calculating a contribution of the user's feature amount to the magnitude of a difference between an index value indicating a probability that the user will take an action on the target of the advertisement when the advertisement is delivered and an index value indicating a probability that the user will take an action on the target of the advertisement when the advertisement is not delivered, based on the feature amount including the user's demographic information and user attribute information indicating a hobby, preference or tendency; means for extracting the feature quantity whose contribution degree satisfies a predetermined condition; A program to function as a

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