Information processing device, information processing method, and program

Abstract

To provide an information processing device, an information processing method, and a program that can provide information for issuing appropriate a coupon to a user.SOLUTION: An information processing device includes: an acquisition unit that acquires first information indicating a coupon acquisition record for a coupon issued by a member store of an electronic payment service and available for use by a user through an acquisition operation, second information indicating an attribute of the user, and third information indicating a use record of the user at the member store; and a coupon acquisition rate prediction unit that learns a relationship between the attribute information of the user, the use record of the user, and the acquisition record of the coupon by machine learning based on the first information, the second information, and the third information, and predicts, on the basis of a learning result of the relationship, a coupon acquisition rate, which is a probability that the user performs the acquisition operation for the coupon.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The present invention relates to an information processing device, an information processing method, and a program. [Background technology]

[0002] Conventionally, attempts have been made to promote sales at stores by issuing discount coupons to users. In relation to such attempts, a technology has been proposed that makes it possible to grasp the effect of a discount and automatically determine an appropriate discount amount (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2010-218483 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, even if it was possible to grasp the effect of discounts, the appropriate coupons might not be issued to the appropriate users, which could reduce the likelihood that users would use the coupons.

[0005] The present invention has been made in consideration of the above circumstances, and one of its objects is to provide an information processing device, an information processing method, and a program that can provide information for issuing appropriate coupons to users. [Means for solving the problem]

[0006] One aspect of the present invention is an information processing device that includes an acquisition unit that acquires first information indicating the acquisition history of a coupon issued by a member store of an electronic payment service and that can be used by a user when the user performs an acquisition operation, second information indicating attributes of the user, and third information indicating the user's history of use of the member store, and a coupon acquisition rate prediction unit that learns the relationship between the user's attribute information and the user's usage history, and the acquisition history of the coupon through machine learning based on the first information, the second information, and the third information, and predicts a coupon acquisition rate, which is the probability that the user will perform the acquisition operation for the coupon, based on the learning results of the relationship. Effect of the Invention

[0007] According to one aspect of the present invention, it is possible to provide an information processing device, an information processing method, and a program capable of providing information for issuing an appropriate coupon to a user. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram showing an example of a configuration for realizing an electronic payment service. [Diagram 2] This is a sequence diagram (part 1) illustrating the general flow of electronic payment. [Diagram 3] This is a sequence diagram (part 2) illustrating the general flow of electronic payment. [Figure 4] 1 is a configuration diagram of a payment server 100 according to a first embodiment. [Diagram 5] FIG. 13 is a diagram showing an example of the contents of user information 172. [Figure 6] FIG. 13 is a diagram showing an example of the contents of affiliated store / store information 176. [Figure 7] FIG. 2 is a diagram illustrating an example of a configuration of data used for learning a coupon acquisition rate prediction model. [Figure 8] FIG. 13 is a diagram showing an example of the contribution rates of input parameters analyzed during learning of a coupon acquisition rate prediction model (part 1). [Figure 9] FIG. 2 is a diagram (part 2) showing an example of the contribution rates of input parameters analyzed during learning of the coupon acquisition rate prediction model. [Figure 10] FIG. 11 is a diagram (part 3) showing an example of the contribution rates of the input parameters analyzed during learning of the coupon acquisition rate prediction model. [Figure 11] FIG. 4 is a diagram showing an example of the contribution rate of the input parameters analyzed during learning of the coupon acquisition rate prediction model (part 4). [Figure 12] FIG. 13 is a diagram illustrating an example of an analysis result of the contribution degree of an input parameter. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, with reference to the drawings, an embodiment of an information processing device, an information processing method, and a program of the present invention will be described. Various devices for providing services to users and performing internal analysis, such as a "server," "management device," and "information providing device" that will appear below, may be realized by a group of distributed devices, and each device may be operated by a different business operator. Furthermore, the owner of the hardware of the device (the provider of the cloud server) and the business that actually operates the device may also be different. An application program and a payment server work together to provide an electronic payment service. In the following description, the application program is referred to as a payment application. An electronic payment service is a service that supports payments related to the purchase of goods and services at a store. A store is, for example, a physical store (real store) that exists in the real world, but may also include a virtual store for electronic commerce. A virtual store may include a store provided by an entity different from the operator of the electronic payment service. In that case, when making a payment for shopping at a virtual store, the screen is controlled to transition to an interface screen of the electronic payment service. In an electronic payment service, a store is treated as belonging to, for example, an affiliated store (brand), and processing such as payment when a purchase is made at a store is mainly performed between the user and the affiliated store. Alternatively, a process such as a settlement may be performed between a user and a store. Hereinafter, a case where the information processing device of the present invention is configured as the settlement server will be described, but the information processing device may be configured as a device separate from the settlement server.

[0010] [Electronic payment service] Fig. 1 is a diagram showing an example of a configuration for realizing an electronic payment service. The electronic payment service is realized mainly by a payment server 100. The payment server 100 communicates with, for example, one or more user terminal devices 10, one or more first store terminal devices 50, and one or more second store terminal devices 70 via a network NW. The network NW includes, for example, the Internet, a LAN (Local Area Network), a wireless base station, a provider device, and the like.

[0011] The user terminal device 10 is, for example, a portable terminal device such as a smartphone or a tablet terminal. The user terminal device 10 is a computer device having at least an optical reading function, a communication function, a display function, an input reception function, and a program execution function. In the following description, components for realizing these functions are referred to as a camera, a communication device, a touch panel, a CPU (Central Processing Unit), etc. In the user terminal device 10, a processor such as a CPU executes a payment application 20, thereby operating to provide an electronic payment service to a user in cooperation with a payment server 100. The payment application 20 is installed in the user terminal device 10 from, for example, an application store, and controls the camera, communication device, touch panel, etc.

[0012] The first store terminal device 50 is installed, for example, in a store. The first store terminal device 50 is a computer device having at least a product price acquisition function, an optical reading function, a program execution function, and a communication function. The first store terminal device 50 includes a so-called POS (Point of Sale) device, and the product price acquisition function and the optical reading function may be realized by the POS device. The store code image 60 is placed in the store, and is a code image such as a QR code (registered trademark) printed on a paper or plastic medium. The store code image 60 may be displayed on a display placed in the store (which may be the display of a terminal device such as a smartphone).

[0013] The second store terminal device 70 is used by the operator of the affiliated store. The second store terminal device 70 is a smartphone, a tablet terminal, a personal computer, or the like. An interface 72 for affiliated stores runs on the second store terminal device 70. The interface 72 for affiliated stores may be an app for affiliated stores or a browser. The interface 72 for affiliated stores accepts coupon settings and the like made by the operator of the affiliated store and transmits them to the payment server 100. The second store terminal device 70, which is a smartphone, has the function of displaying a code image corresponding to a store code image and reading a code image displayed by the user terminal device 10 by executing the app for affiliated stores.

[0014] The payment server 100 realizes electronic payment based on payment information received from the user terminal device 10 or the first store terminal device 50. The first store terminal device 50 may include a POS device and an affiliated store server, in which case the payment information is sent from the POS device via the affiliated store server to the payment server 100. In the following explanation, no distinction is made between these two and it is assumed that the payment information is sent from the first store terminal device 50.

[0015] 2 and 3 are sequence diagrams illustrating the general flow of electronic payment. There may be two patterns of electronic payment: pattern 1 and pattern 2.

[0016] In the case of pattern 1 (hereinafter referred to as user scan) shown in FIG. 2, the user terminal device 10 with the payment application 20 activated decodes the store code image 60 by the optical reading function (S1). The store code image 60 includes store URL (Uniform Resource Locator) information. This store URL is an electronic payment service domain to which store-identifying information is added, and is associated with an affiliated store ID, a store ID, etc. in the payment server 100 (described later). The payment application 20 transmits the first payment information including the store URL and the account ID to the payment server 100 (S2). The payment server 100 searches for store information (described later) from the affiliated store ID and the store ID corresponding to the store URL, acquires the affiliated store name and the store name information (S3), and transmits it to the payment application 20 (S4). The user inputs the payment amount into the user terminal device 10 on the screen on which the affiliated store name and the store name are displayed (S5). Then, the user terminal device 10 generates second payment information including at least the payment amount, and transmits it to the payment server 100 (S6). The payment server 100 performs electronic payment based on the received second payment information (S7). The payment server 100 then transmits a payment completion notice (information for displaying a payment completion screen) to the payment application 20 (S8), and the payment application 20 displays the payment completion screen (S9). Note that when the store code image 60 is displayed on a display installed in the store, the store code image 60 may include not only the store URL but also information on the payment amount. In this case, the step of the user inputting the payment amount is omitted, and the information on the payment amount is included in the first payment information and transmitted to the payment server 100. Information on the affiliated store name and the store name may be included in the payment completion screen and displayed.

[0017] In the case of pattern 2 (hereinafter referred to as store scan) shown in FIG. 3, when the payment application 20 is started, when a payment operation is performed in the payment application 20, when an automatic update timing (e.g., every minute) occurs, and at other timings, the payment application 20 transmits a request for issuing a one-time code to the payment server 100 (S11). The payment server 100 generates a one-time code (S12) and transmits it to the payment application 20 (S13). The payment application 20 displays a code image such as a QR code or a barcode generated based on the one-time code (S14). The user holds (presents) the display surface of the user terminal device 10 over the first store terminal device 50, and the first store terminal device 50 decodes the code image by an optical reading function and obtains the one-time code, etc. (S15). The first store terminal device 50 then generates payment information including the one-time code, payment amount, affiliated store ID, store ID, etc., and transmits it to the payment server 100 (S16). The payment amount information is acquired in advance by reading a barcode or manually entering it. The payment server 100 identifies the user corresponding to the one-time code based on the received information and performs electronic payment (S17). The payment server 100 then transmits a payment completion notice to the payment application 20 (S18), and the payment application 20 displays a payment completion screen (S19).

[0018] Note that electronic payment may be performed using only one of the above patterns. Furthermore, the "account ID" described in FIG. 2 may be other information (e.g., a phone number) that can be used as user identification information. Furthermore, issuance of a one-time code may be omitted in the store scan, and the payment application 20 may display a code image generated based on the user's account ID. In this case, the payment server 100 identifies the user corresponding to the account ID instead of identifying the user corresponding to the one-time code.

[0019] [Payment server] FIG. 4 is a configuration diagram of the payment server 100 according to the first embodiment. The payment server 100 includes, for example, a communication unit 110, a payment content providing unit 120, a payment processing unit 130, an information management unit 140, a coupon issuing unit 150, a coupon acquisition rate analysis unit 160, and a storage unit 170. The components other than the communication unit 110 and the storage unit 170 are realized by, for example, a hardware processor such as a CPU executing a program (software). Some or all of these components may be realized by hardware (including circuitry) such as an LSI (Large Scale Integration), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), or a GPU (Graphics Processing Unit), or may be realized by cooperation between software and hardware. The program may be stored in advance in a storage device (a storage device with a non-transient storage medium) such as an HDD (Hard Disk Drive) or flash memory, or may be stored in a removable storage medium (non-transient storage medium) such as a DVD or CD-ROM, and installed in the storage device by inserting the storage medium into a drive device.

[0020] The storage unit 170 is a HDD, a flash memory, a RAM (Random Access Memory), etc. The storage unit 170 may be a NAS (Network Attached Storage) device that the payment server 100 can access via a network. The storage unit 170 stores information such as user information 172, payment content information 174, and affiliated store / shop information 176.

[0021] The communication unit 110 is a communication interface for connecting to the network NW. The communication unit 110 is, for example, a network interface card.

[0022] The payment content providing unit 120 has, for example, a function of a Web server, and provides information (content) for displaying various screens of the electronic payment service to the user terminal device 10. The payment content providing unit 120 appropriately reads necessary content from the payment content information 174 and provides it to the user terminal device 10. The user terminal device 10 accepts various inputs by the user while the content is being played by the payment application 20, and transmits the above-mentioned payment information and the like to the payment server 100.

[0023] The payment processing unit 130 performs payment processing based on the payment information transmitted by the user terminal device 10 or the first store terminal device 50. The payment processing unit 130 performs payment processing while referring to the user information 172.

[0024] FIG. 5 is a diagram showing an example of the contents of the user information 172. The user information 172 is an example of the registration information of a user. The user information 172 is, for example, a user URL, an account ID, a telephone number, a password, as well as information associated with an email address, a user ID, a name, an address, a date of birth, a registration date, a charge balance, a post-payment setting, a post-payment limit, a post-payment usage amount, a post-payment available amount, a payment method setting, a bank account, a credit card number, a charge history information, and a payment history information. The user URL is used for a remittance process between users. When registering for the electronic payment service, it is necessary to register a telephone number and a password. The account ID is issued to the user by the payment server 100, and the user ID is an ID that can be set by the user at will (does not have to be set). Similarly, the email address, and the name, address, and date of birth are information that can be set by the user at will (does not have to be set). The registration date is the date on which the user registered for the electronic payment service (the date on which the account was created). Hereinafter, the user's instance (electronic payment account) to which this information is associated will be referred to as an account.

[0025] The charge balance is information indicating the balance of electronic money that is set by a user by transferring money to an account in advance. The means of transfer include transfer from an ATM (Automatic Teller Machine) of a designated company (bank) and transfer from a registered bank account. The deferred payment setting is information indicating whether or not the setting for enabling electronic payment by deferred payment has been completed, and is set to either "completed" or "not completed." The deferred payment limit is the limit of deferred payment that can be used each month, the deferred payment usage amount is the amount of deferred payment that has already been used in the current month, and the deferred payment available amount is the amount of deferred payment that can be used in the current month, which is calculated by subtracting the deferred payment usage amount from the deferred payment limit. Although only one deferred payment limit is shown in the figure, in reality, there is also a daily upper limit, and the lower of these may be set as the deferred payment limit. Further details of deferred payment will be described later. The payment method setting is setting information indicating whether the user will perform electronic payment using the charge balance or payment by deferred payment at that time. The bank account and credit card number are information on a bank account or credit card number (account number, card number) that can be used to deposit funds into an electronic payment service. Charge history information is a history of the user's previous transfers to the electronic payment service to increase the charge balance. Payment history information is information that indicates the details of payments made by the user for each payment (date and time, store ID of the store where the purchase was made, payment amount, payment method, etc.).

[0026] 6 is a diagram showing an example of the contents of affiliated store / store information 176. The affiliated store / store information 176 includes, for example, a first table 176A in which an affiliated store ID and a store ID are associated with a store URL, a second table 176B in which an affiliated store name and sales amount (described above) are associated with an affiliated store ID, and a third table 176C in which a store ID is associated with a store name. In addition to this information, the affiliated store / store information 176 may also include information such as the category of the affiliated store or store, the location of the store, and payment patterns.

[0027] The information management unit 140 manages user information 172 and affiliated store / store information 176 based on information acquired from the user terminal device 10 and the second store terminal device 70. The information management unit 140 adds new records to, edits, and deletes the user information 172 and affiliated store / store information 176.

[0028] The coupon issuing unit 150 accepts registration of various setting information related to coupon issuing conditions and usage conditions from the operator of the affiliated store, registers it in the storage unit 170 as coupon information 178, and issues a coupon to a user based on the registered coupon information. More specifically, the coupon issuing unit 150 recognizes a coupon that can be newly acquired based on the coupon information, and notifies the user who can acquire the coupon. The user can use the coupon by performing an operation to acquire the notified coupon. The coupon issuing unit 150 manages various information related to coupon issuance and acquisition in the coupon information 178. For example, information such as which coupon was issued to which user and whether the issued coupon was acquired by the user is also managed in the coupon information 178. The coupon information registration function for the payment server 100 is provided, for example, by the affiliated store interface 72 of the second store terminal device 70.

[0029] The coupon acquisition rate analysis unit 160 learns a model (hereinafter referred to as a "coupon acquisition rate prediction model") for predicting the probability that a user will acquire an issued coupon (hereinafter referred to as an "acquisition rate") based on the coupon usage history, payment history, user information 172, etc., by a machine learning technique. In addition, the coupon acquisition rate analysis unit 160 predicts the coupon acquisition rate for a target user using the learned coupon acquisition rate prediction model. The coupon acquisition rate analysis function by the coupon acquisition rate analysis unit 160 is provided to the operator of the affiliated store via the affiliated store interface 72 of the second store terminal device 70, for example. The operator of the affiliated store can check the validity and validity of the coupon that has been issued / will be issued by using the function of the coupon acquisition rate analysis unit 160 of the payment server 100 via the affiliated store interface 72.

[0030] [Electronic payment] When payment information is acquired from the user terminal device 10 or the first store terminal device 50, the payment processing unit 130 refers to the user information 172 to acquire the "payment method setting" of the user. For a user whose "payment method setting" is set to "charge balance", the payment processing unit 130 performs electronic payment as follows. For example, the payment processing unit 130 performs electronic payment by decreasing the charge balance managed in association with the user ID and increasing the item value of the affiliated store's sales. The item value of the affiliated store's sales is not used as electronic money itself, for example, and an amount corresponding to the item value of the sales is transferred to a bank account in a cycle according to an agreement between the affiliated store and the electronic payment service.

[0031] The payment processing unit 130 performs electronic payment for users whose "setting information" is set to "deferred payment" as follows. Deferred payment is set separately from "credit payment" in cooperation with a credit card company, which is a separate entity from the operator of the electronic payment service, and the operator of the electronic payment service acts as a creditor and allows electronic payment that is not dependent on the charge balance within the deferred payment limit. In order to receive the deferred payment service, a credit card provided by the operator of the electronic payment service may be required. The amount used for deferred payment is settled on the payment date of the following month, for example, by debiting from a bank account, for one month. In this case, the payment processing unit 130 performs provisional payment by adding the payment amount to the deferred payment amount and subtracting the same amount from the available deferred payment amount, and when the closing date comes, it performs processing to debit the payment for the current month on the payment date of the following month as described above, or requests the operator of the credit card company to perform such processing. In addition, if the payment amount exceeds the available deferred payment amount at the time of provisional payment, an error notification is returned to the payment application 20.

[0032] [Issue coupon] The coupon issuing unit 150 identifies users to whom a coupon is to be issued based on the coupon issuing conditions included in the coupon information 178. For example, the coupon issuing conditions may include various user attributes such as the user's age, marital history, whether or not the user owns a home, family size, whether or not the user has children, income, product purchase history, coupon acquisition history, and coupon usage history. Based on this information, the coupon issuing unit 150 extracts users to whom a registered coupon can be issued, and notifies the extracted users that the coupon can be acquired.

[0033] [Use coupon] When a user performs an operation to acquire a coupon prior to using the coupon, the coupon issuing unit 150 manages the coupon as having been acquired. The acquired coupon may be applied to the payment by the user selecting it at the time of payment, or when the payment processing unit 130 performs the payment processing for the user, an applicable coupon among the acquired coupons may be automatically applied to the payment. In the latter case, the application of the coupon may be performed fully automatically, or may be performed when a confirmation operation is performed by the user. The coupon issuing unit 150 may manage the usage history of the acquired coupon in the coupon information 178.

[0034] [Learning a coupon acquisition rate prediction model] The coupon acquisition rate analysis unit 160 constructs (learns) a coupon acquisition rate prediction model by performing machine learning (unsupervised learning) using the coupon issuance record, the acquisition record of the issued coupon, and various attribute information of the user. The coupon acquisition rate prediction model is trained to output the acquisition rate of the coupon by the user by inputting the content of the coupon to be issued and various attribute information of the user. For example, the machine learning model may be a model based on principal component analysis that learns the contribution of each input variable. In addition, when the coupon acquisition rate can be calculated or defined based on the performance data, supervised learning may be performed using training data in which the coupon acquisition rate is associated with the performance data.

[0035] FIG. 7 is a diagram showing an example of the configuration of data used for learning the coupon acquisition rate prediction model. The example in FIG. 7 is an example of the configuration of training data including coupon acquisition record data DS1, campaign data DS2, user statistical data DS3, user purchase record data DS4, product data DS5, and mapping data DS6. The coupon acquisition record data DS1 is a data set showing the record of coupon acquisition status. The campaign data DS2 is a data set showing the contents of campaigns implemented by affiliated stores. The user statistical data DS3 is a data set showing statistical information on user attributes for each user. The user purchase record data DS4 is a data set showing the record of user purchasing activities. The product data DS5 is a data set showing the product attributes of each product. The mapping data DS6 is a data set showing the correspondence (mapping) between products and coupons.

[0036] The coupon acquisition record data DS1, campaign data DS2, user statistical data DS3, user purchase record data DS4, product data DS5, and mapping data DS6 may be stored in advance in the storage unit 170, or may be created when the coupon acquisition rate analysis unit 160 learns the coupon acquisition rate prediction model. Some or all of this data may be created based on various information managed in the storage unit 170, or may be created based on information provided by the operator of the affiliated store via the affiliated store interface 72 or the like.

[0037] For example, the coupon acquisition rate analysis unit 160 performs the above-mentioned machine learning to construct a coupon acquisition rate prediction model that inputs parameter values ​​other than the coupon acquisition record and outputs the coupon acquisition rate. The coupon acquisition rate analysis unit 160 also has a function of analyzing the contribution of each input parameter to the coupon acquisition rate predicted by the coupon acquisition rate prediction model. For example, the coupon acquisition rate analysis unit 160 has a function of calculating and illustrating the SHAP (SHapley Additive exPlanations) value of each input parameter for the prediction result (see Figs. 8 to 11).

[0038] 8 to 11 are diagrams showing an example of the results of analyzing the contribution of each input parameter to the coupon acquisition rate predicted by the coupon acquisition rate prediction model. In FIG. 8 to FIG. 11, the lower horizontal axis represents the SHAP value. The SHAP value is one of the means for fairly distributing the gain obtained by the cooperation of multiple players to each player in the cooperative game theory, and in the machine learning field, it is applied as one of the methods for presenting the grounds for prediction for a specific input to a machine learning model. In addition, in FIG. 8 to FIG. 11, the upper horizontal axis represents the output (coupon acquisition rate) of the coupon acquisition rate prediction model. The value of the graph corresponding to each parameter represents the cumulative value of the SHAP value from the bottom parameter to each parameter. In other words, the sum of the SHAP values ​​is the coupon acquisition rate, and the coupon acquisition rate (prediction result) when the value in parentheses is given to the parameter on the vertical axis is represented by the value of the intersection of the graph and the upper horizontal axis.

[0039] The example in Figure 8 shows the "user's income bracket (income_bracket)", "selling price of the product to which the coupon is applied (selling_price)", "user's marital status (marital_status)", "user's family size (family_size)", "user's age (age_range)", "product ID (iten_id)", "product category (category)", "discount rate by coupon (discount)", "product quantity (quantity)", and "whether the user has children (no_of_children)". The figure shows the predicted coupon acquisition rate values ​​when the input parameters are ", "type of product brand (brand_type)", "whether the user owns a home (rented)", and "whether there are other discounts (other_discount)", and the parameter values ​​are 5, 21.37 dollars, married (Married), 2 people, 46-55 years old, 23256, daily necessities (Grocery), 10.33% off, 2 items, no children (0), long-established store (Established), no home owned (0), no other discounts (0). FIG. 8 shows an example where the coupon acquisition rate is predicted to be 84% when a coupon is issued with each of the parameter values ​​shown in the figure.

[0040] In contrast, FIG. 9 shows an analysis result in the case where the types of input parameters are the same as those in FIG. 8, but the parameter values ​​are different. Specifically, the marital history (marital_status) of the user, the discount rate by the coupon (discount), the family size (family_size) of the user, the product ID (iten_id), the selling price (selling_price) of the product to which the coupon is applied, and the presence or absence of children (no_of_children) of the user are different from those in the example of FIG. 8, and the predicted result of the coupon acquisition rate in this case is 81%. According to such an analysis result, it can be inferred that the coupon acquisition rate will be lower with the parameter settings in FIG. 9 than with the parameter settings in FIG. 8. Also, according to the analysis result in FIG. 9, it can be seen that the SHAP value of the parameter with respect to the coupon acquisition rate changes when the parameter value is different (i.e., the arrangement of the parameters on the vertical axis changes). Furthermore, it can be seen that the SHAP value of the parameter other than the parameter with respect to the coupon acquisition rate also changes when the parameter value is different. Note that in the examples of FIG. 8 and FIG. 9, the input parameters are arranged from the top in descending order of SHAP value. For this reason, the higher the parameter, the steeper the slope of the graph.

[0041] On the other hand, Fig. 10 and Fig. 11 are examples of cases where some parameters are predicted to reduce the coupon acquisition rate. In the example of Fig. 10, the SHAP value monotonically increases to about 0.2 for the parameters "other discount" to "income bracket", but decreases to about 0.1 for "product category". That is, according to the analysis result of Fig. 10, it can be seen that the parameter "product category = Meat" for the coupon to be issued in Fig. 10 is not appropriate for targeting users. That is, in this case, the parameter "product category = Meat", which is a factor in reducing the coupon acquisition rate, can be considered to have a negative contribution to the coupon acquisition rate of the coupon to be issued. When such an analysis result is obtained, for example, the operator of the affiliated store can consider measures such as targeting products such as health care, which are expected to have a higher conversion rate for the relevant age group. Similarly, according to the example of the analysis result in FIG. 11, it can be seen that the "selling price of the product to which the coupon is applied (selling_price)" and the "marital history of the user (marital_status)" have negative contributions.

[0042] Fig. 12 is a diagram showing an example of the analysis result of the contribution of input parameters. In Fig. 12, class 0 represents the contribution to a user not acquiring a coupon (i.e., a negative contribution), and class 1 represents the contribution to a user acquiring a coupon (i.e., a positive contribution). Since a user not acquiring a coupon and a user acquiring a coupon are in an inverse relationship, the strength of influence (absolute value of the contribution) is the same for the contribution of class 0 and the contribution of class 1 for each parameter.

[0043] As shown in Figure 12, the applicant of the present application has discovered that when the input parameters are "user's income bracket (income_bracket)," "product category (category)," "user's marital status (marital_status)," "selling price of the product to which the coupon is applied (selling_price)," "user's age (age_range)," "product ID (iten_id)," "discount rate due to the coupon (discount)," "user's family size (family_size)," "whether the user has children (no_of_children)," "product quantity (quantity)," "product brand type (brand_type)," "whether the user owns a home (rented)," and "whether other discounts are available (other_discount)," the contribution of these parameters to the coupon acquisition rate prediction model increases in this order.

[0044] The coupon acquisition rate analysis unit 160 provides support information to the operator of the affiliated store, such as the contribution of each input parameter to the coupon acquisition rate analyzed by the coupon acquisition rate prediction model, and the coupon acquisition rate calculated by the coupon acquisition rate prediction model. For example, the support information is provided to the operator of the affiliated store via the affiliated store interface 72. The support information may be provided for coupons that have already been issued, or for coupons before they are issued (in the planning stage). By being provided with such support information, the operator of the affiliated store can check whether the predicted coupon acquisition rate has been achieved for coupons that have already been issued, and adjust various parameters for coupons before they are issued (in the planning stage) so that they are appropriate for users.

[0045] According to the embodiment described above, it is possible to provide an information processing device, an information processing method, and a program capable of providing information for issuing an appropriate coupon to a user.

[0046] The above describes the form for carrying out the present invention using an embodiment, but the present invention is not limited to such an embodiment, and various modifications and substitutions can be made within the scope that does not deviate from the gist of the present invention. [Explanation of symbols]

[0047] 10 User terminal device 20. Payment App 50 First store terminal device 60 Store Code Image 70 Second store terminal device 72 Merchant Interface 100 Payment Server 110 Communications Department 120 Payment Contents Provider 130 Payment processing unit 140 Information Management Department 150 Coupon Issue Department 160 Coupon Acquisition Rate Analysis Section 170 Storage section 172 User information 174 Payment Content Information 176 Affiliated Stores / Store Information 178 Coupon Information

Claims

1. an acquisition unit that acquires, with respect to a coupon issued by a member store of an electronic payment service and which can be used by a user performing an acquisition operation, first information indicating the acquisition record of the coupon, second information indicating attributes of the user, and third information indicating the user's usage record of the member store; a coupon acquisition rate prediction unit that learns a relationship between the attribute information of the user, the usage record of the user, and the acquisition record of the coupon by machine learning based on the first information, the second information, and the third information, and predicts a coupon acquisition rate, which is a probability that the user will perform the acquisition operation for the coupon, based on a learning result of the relationship; An information processing device comprising:

2. The machine learning is to analyze the contribution of a plurality of parameters included in the coupon acquisition record, the attributes of the user, and the usage record of the user to the acquisition of the coupon by the user, and to learn a coupon acquisition rate prediction model that outputs a coupon acquisition rate, which is the probability that the coupon will be acquired by the user, based on the analysis result of the contribution. The information processing device according to claim 1 .

3. the coupon acquisition rate prediction model inputs the first information, the second information, and the third information and outputs the coupon acquisition rate; The information processing device according to claim 2 .

4. the coupon acquisition rate prediction unit displays the contribution rates of the plurality of parameters, the values ​​of the plurality of parameters, and the coupon acquisition rate predicted by the plurality of parameters in association with each other; The information processing device according to claim 2 .

5. The computer With regard to a coupon issued by a member store of an electronic payment service and which can be used by a user performing an acquisition operation, first information indicating a record of acquisition of the coupon, second information indicating attributes of the user, and third information indicating a record of use of the member store by the user are acquired; learning a relationship between the attribute information of the user, the usage record of the user, and the acquisition record of the coupon by machine learning based on the first information, the second information, and the third information, and predicting a coupon acquisition rate, which is a probability that the user will perform the acquisition operation for the coupon, based on a learning result of the relationship; Information processing methods.

6. On the computer, With respect to a coupon issued by a member store of an electronic payment service and which can be used by a user performing an acquisition operation, first information indicating a record of acquisition of the coupon, second information indicating attributes of the user, and third information indicating a record of use of the member store by the user are acquired; a machine learning based on the first information, the second information, and the third information is used to learn a relationship between the attribute information of the user, the usage record of the user, and the acquisition record of the coupon, and a coupon acquisition rate, which is a probability that the user will perform the acquisition operation for the coupon, is predicted based on a learning result of the relationship; Program for.

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