Information processing device and information processing method

Abstract

An information processing device according to one embodiment of the present invention comprises: an acquisition unit that acquires log information indicating whether a user has participated in a measure implemented for a store group belonging to a target platform; and an output unit that outputs an index that indicates the effect of the measure on business performance of the store group, and that results from evaluation based on the log information.

Description

Information processing device and information processing method

[0001] This invention relates to a technology for estimating the impact of measures implemented on a platform on stores.

[0002] Technologies for evaluating the effectiveness of advertising measures are known. For example, Patent Document 1 discloses an invention for evaluating the effectiveness of advertising based on location information obtained from a customer's terminal within the area surrounding a physical store and browsing information indicating whether or not the customer viewed the advertisement.

[0003] Patent No. 7144788

[0004] The invention described in Patent Document 1 was merely limited to estimating the effectiveness of advertising measures based on customer location information and browsing information detected around the target store.

[0005] In contrast, the present invention provides an improved technology for evaluating the impact of measures implemented on a platform.

[0006] An information processing device according to one aspect of this disclosure includes an acquisition unit that acquires log information indicating whether a user participated in a measure in a group of stores belonging to a target platform, and an output unit that outputs an index indicating the effect of the measure on the sales performance of the group of stores, which is evaluated based on the log information.

[0007] An information processing method relating to another aspect of this disclosure includes the steps of: a computer acquiring log information indicating whether a user participated in a measure in a group of stores belonging to a target platform; and outputting an index indicating the effect of the measure on the sales performance of the group of stores, the index being evaluated based on the log information.

[0008] According to the present invention, it is possible to evaluate the impact of measures implemented on a platform.

[0009] A diagram illustrating the system configuration of an information processing system 1 according to one embodiment. A diagram illustrating the functional configuration of the information processing system 1. A diagram illustrating the hardware configuration of the information processing device 10. A sequence chart illustrating a method for estimating the effectiveness of measures on a platform. A diagram illustrating a platform database 1000. A flowchart illustrating a method for calculating the degree of effectiveness of a target measure. A diagram illustrating a measure effectiveness list 2000.

[0010] 1. Diagram 1 illustrates the system configuration of an information processing system 1 according to one embodiment. In this example, the information processing system 1 (or simply the system) is a system for evaluating the impact of measures implemented on a platform, according to the stores belonging to that platform. In this example, the platform is a general term for a place that provides a common environmental infrastructure to businesses and users with specific purposes (in this case, purposes related to sales). The platform includes, for example, real-world shopping malls or shopping streets, as well as online stores represented by e-commerce, various point programs, or online web services such as electronic payment services. Measures are activities introduced for purposes such as sales promotion, and include, for example, advertisements, campaigns, coupons, or promotions conducted for customers (an example of users). In such a platform, multiple companies or tenants (hereinafter collectively referred to as "stores") belong as member stores, and for example, the impact of implemented measures may extend to stores other than the store targeted by the measure. Therefore, it is necessary to evaluate the impact of measures on individual, partial, or all stores belonging to the platform with greater accuracy. The term "business operator" here refers to, for example, a retail business operator or a service provider operator.

[0011] Conventional related technologies typically estimate the store-specific effects of a measure based on individual store POS (Point of Sales) systems or point programs for a single company. In this case, it is difficult to evaluate the impact of the measure on individual, some, or all stores belonging to the aforementioned platform. Therefore, with regard to the ripple effect of a measure on the platform, the inventors focused on the trends and usage patterns of users who utilize the platform. In particular, this invention proposes a technology to solve the above problem by combining whether or not each user participates in a measure with a method using Fuzzy DiD, which is useful for estimating the effects of measures. Fuzzy DiD will now be explained.

[0012] Generally, Fuzzy DiD (Fuzzy Differences-in-Differences) is a method used in various fields such as education, healthcare, and economic policy to evaluate the impact of specific elements on a given matter. In Fuzzy DiD, the effect is estimated based on the difference, or DID, between an intervention group (an example of a first user group) in which a specific element is introduced and a comparison group (an example of a second user group) in which it is not introduced. This method allows for a pure comparison that eliminates factors other than whether or not each user participates in the measures implemented in this invention. Therefore, regardless of whether the stores being evaluated are individual, partial, or all of the stores belonging to the platform (or regardless of which stores the measures apply to), an appropriate evaluation of the impact of the implemented measures can be performed. In this invention, the following system is provided to effectively evaluate the impact of measures implemented on the platform.

[0013] The information processing system 1 includes an information processing device 10, a user terminal 20, a platform server 30, and a store terminal 40. In this example, each component of the system is connected via a network 9 as shown in Figure 1. In this example, the network 9 is a computer network such as the Internet or a mobile network.

[0014] The information processing device 10 is an information processing device or server device in the information processing system 1. In this example, the information processing device 10 evaluates the impact of a measure on a group of stores belonging to the target platform. The information processing device 10 obtains log information from the platform server 30 indicating whether a user participated in the measure. Based on the log information, the information processing device 10 identifies a first user group and a second user group. In this example, the first user group is the group of users who participated in the measure. The second user group is the group of users who did not participate in the measure (hereinafter referred to as the "non-participating user group"), extracted based on predetermined conditions. The second user group will be described later. The information processing device 10 can evaluate the impact of the measure by applying a method using Fuzzy DiD to the first user group and the second user group.

[0015] The user terminal 20 is a terminal used by a user in the information processing system 1. The user terminal 20 includes, for example, a smartphone, tablet, or personal computer. In this example, the user terminal 20 can provide the user with various services related to sales, etc., by accessing the platform server 30. The platform server 30 records various logs (an example of log information) from the user terminal 20 in a database or the like. In this example, the user terminal 20 outputs data related to the user's logs on the platform to the platform server 30 as appropriate.

[0016] The platform server 30 is a server device that manages information related to the platform. In this example, the platform is a common infrastructure consisting of stores that provide services or policies, and users that utilize them. Therefore, the platform server 30 can collect information about stores and users from various devices. In this example, the platform server 30 acquires information from store terminals 40, such as store sales, business type, category, or policy information. The platform server 30 also acquires log information from user terminals 20, such as user usage information, payment information, or policy participation information. This information is stored as data in the platform server 30's database and shared with the information processing device 10 via the network 9.

[0017] The store terminal 40 is a terminal owned by each store and includes, for example, a smartphone, tablet, or personal computer. In this example, the store terminal 40 provides various services to customers via the platform server 30. The store terminal 40 can also record the sales performance of each store or the purchasing behavior of each customer, and has the function of recording, for example, POS data and point history. As a result, the platform server 30 can obtain various log information from the store terminal 40.

[0018] Figure 2 is a diagram illustrating the functional configuration of the information processing system 1. In this embodiment, the information processing device 10 has functional blocks (or components) consisting of an acquisition unit 11, an output unit 12, a identification unit 13, a calculation unit 14, an estimation unit 15, a storage unit 191, and a control unit 192. In this example, the storage unit 191 stores various types of data, programs, and software, including a database. In this example, the control unit 192 performs various types of control.

[0019] The acquisition unit 11 acquires log information indicating whether a user participated in a measure at a group of stores belonging to the target platform. In this example, the log information is information that shows various histories collected and recorded by the platform server 30, and includes, for example, the operation history of the user terminal 20, the access history of the store terminal 40, or the user's usage history of the platform.

[0020] The output unit 12 outputs an indicator that shows the effect of the measures on the sales performance of the group of stores, and outputs an indicator evaluated based on log information. In this example, sales performance refers to the results related to the sales of the stores, and includes, for example, sales, performance, or various achievements. This indicator is calculated, for example, based on the definition formula in Fuzzy DiD. The output unit 12 outputs the results obtained from the estimation unit 15 as an indicator. The estimation unit 15 will be described later.

[0021] The identification unit 13 identifies a group of users who did not participate in the initiative as a second user group, based on the first user group, which represents the group of users who participated in the initiative, and which includes users who meet predetermined conditions. In this example, the conditions are, for example, conditions related to propensity scores corresponding to the usage trends of individual users belonging to the first user group when using the platform. Now, let's explain propensity scores.

[0022] The propensity score is a numerical score that quantifies each user's usage tendency for the platform based on various data (i.e., log information, etc.). The method for calculating the propensity score is defined in advance by the information processing system 1 according to the type of data for each platform obtained from the platform server 30. In this example, the propensity score is calculated comprehensively based on, for example, each user's monthly usage frequency for the platform, monthly payment amount, years of service use, usage time, or past participation history in initiatives. Therefore, it can be considered that users with higher propensity scores are more likely to use the platform. The identification unit 13 identifies the second user group according to whether the propensity scores of the non-participating user group are similar to (i.e., whether the numerical values ​​match) the propensity scores of individual users belonging to the first user group. As a result, the output unit 12 can output evaluations of the first user group and the second user group as indicators.

[0023] The calculation unit 14 calculates the propensity score based on usage logs (an example of log information) acquired according to the platform configuration. In this example, the usage logs are logs related to user usage on the platform. The calculation unit 14 calculates a propensity score for each user based on the usage logs described above and records it in the database.

[0024] The estimation unit 15 estimates the effectiveness of the measures based on log information. In this example, the estimation unit 15 performs estimation using Fuzzy DiD. As a result, the output unit 12 can output the results obtained from the estimation unit 15 as indicators for individual stores, some stores, or all stores in the group of stores. In this example, the indicators are defined in advance in the information processing system 1 based on various perspectives. For example, if the indicators include cost-effectiveness calculated based on the costs incurred in implementing the measures, the output unit 12 can output the cost-effectiveness obtained from the estimation unit 15 as an indicator.

[0025] Figure 3 illustrates the hardware configuration of the information processing device 10. Physically, the information processing device 10 is configured as a computer including a processor 101, memory 102, storage 103, communication device 104, input device (optional), display device (optional), and a bus connecting these. Each of these devices operates on power supplied from a battery (not shown). In the following description, the term "device" can be read as a circuit, device, unit, etc. The hardware configuration of the information processing device 10 may include one or more of the devices shown in Figure 3, or it may be configured without some of the devices. Alternatively, multiple devices with different enclosures may be connected via communication to constitute the information processing device 10.

[0026] Each function in the information processing device 10 is realized by loading predetermined software (programs) onto hardware such as the processor 101 and memory 102, which allows the processor 101 to perform calculations, control communication by the communication device 104, and control at least one of the reading and writing of data in the memory 102 and storage 103.

[0027] The processor 101 controls the entire computer, for example, by running the operating system. The processor 101 may be composed of a central processing unit (CPU) that includes interfaces with peripheral devices, control devices, arithmetic units, registers, etc. Also, for example, a baseband signal processing unit or a call processing unit may be implemented by the processor 101.

[0028] The processor 101 reads programs (program code), software modules, data, etc., from at least one of the storage 103 and the communication device 104 into the memory 102 and executes various processes accordingly. The program used is one that causes the computer to execute at least a part of the operations described later. Functional blocks of the information processing device 10 may be stored in the memory 102 and implemented by control programs that run on the processor 101. Various processes may be executed by one processor 101, or they may be executed simultaneously or sequentially by two or more processors 101. The processor 101 may be implemented by one or more chips. The program may also be transmitted to the information processing device 10 via a telecommunications line.

[0029] Memory 102 is a computer-readable recording medium and may consist of at least one of the following: ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), RAM (Random Access Memory), etc. Memory 102 may also be called a register, cache, main memory, etc. Memory 102 can store executable programs (program code), software modules, etc., for carrying out the method according to this embodiment.

[0030] The storage 103 is a computer-readable recording medium and may consist of at least one of the following: an optical disc such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disc, a digital multipurpose disc, a Blu-ray® disc), a smart card, flash memory (e.g., a card, a stick, a key drive), a floppy® disk, a magnetic strip, etc. The storage 103 may also be called an auxiliary storage device.

[0031] The communication device 104 is hardware (transceiver / receiver device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as a network device, network controller, network card, communication module, etc.

[0032] Each device, such as the processor 101 and memory 102, is connected by a bus for communicating information. The bus may be configured using a single bus, or different buses may be used for each device.

[0033] The information processing device 10 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), and an FPGA (Field Programmable Gate Array), and some or all of each functional block may be realized by this hardware. For example, the processor 101 may be implemented using at least one of these hardware components.

[0034] In this example, the program stored in the storage 103 includes a program (hereinafter referred to as the "server program") that causes the computer to function as a server in the information processing system 1. When the processor 101 is executing the server program, the processor 101, memory 102, storage 103, and communication device 104 are examples of functional blocks for operating the information processing device 10. The processor 101 is an example of a specific unit 13, a calculation unit 14, an estimation unit 15, and a control unit 192. At least one of the memory 102 and the storage 103 is an example of a storage unit 191. The communication device 104 is an example of an acquisition unit 11 and an output unit 12.

[0035] Although a detailed explanation will be omitted, the user terminal 20 and the store terminal 40 are computers having a processor, memory, storage, communication device, display device, input device, and output device; specifically, they are, for example, smartphones, tablet terminals, or personal computers. In this example, the programs stored in the storage of the user terminal 20 and the store terminal 40 include programs that cause the computers to function as clients in the information processing system 1 (hereinafter referred to as "client programs").

[0036] The platform server 30 is a computer having a processor, memory, storage, and communication devices. In this example, the programs stored in the platform server 30's storage include programs that cause the computer to function as a platform server in the information processing system 1 (hereinafter referred to as the "platform server program"). The configuration of the information processing system 1 has now been described. Next, the operation of the information processing system 1 will be described.

[0037] 2. Operation Diagram 4 is a sequence chart illustrating a method for estimating the effects of a policy on a platform. Here, we will explain an example of how to estimate the effects of a target policy (hereinafter referred to as "target policy") on various platforms. Here, we will explain an example in which the processing according to the present invention is applied to a platform such as a web service on the internet.

[0038] In step S101, the platform server 30 acquires log information from the user terminal 20 or the store terminal 40. In this example, the platform server 30 has a function of automatically recording a history (an example of log information) regarding the processes executed within the platform. In addition, the platform server 30 can periodically make a data request to the user terminal 20 or the store terminal 40 to acquire log information. As a result, the platform server 30 can acquire various log information such as the participation log of the user regarding the target measure, the usage log of the users using the platform, or the POS data (i.e., sales log) in the store terminal 40. The acquired log information is recorded in a database. The database will be described later.

[0039] In step S102, the information processing device 10 receives a designation regarding the target measure from various devices. The designation regarding the target measure is made by an individual, a company, or an organization (hereinafter referred to as "system user") using the information processing system 1. In this example, the information processing device 10 acquires, for example, information for specifying the measure and information regarding the designation of the store to be evaluated (hereinafter referred to as "target store") from, for example, the administrator of the system, the operator of the platform, or the device (or employee) of a company operating multiple stores. Note that the target store includes individual, part, or all of the stores belonging to the platform. This process includes a process for evaluating the influence exerted by the target measure on the target stores belonging to the platform. Therefore, the following processes are started when the information processing device 10 receives the designation regarding the target measure and the target store from the system user.

[0040] In step S103, the information processing device 10 makes a data request to the platform server 30. In this example, the data request includes various requests such as acquisition of data used for evaluating the influence of the measure, access to the database, or statistical processing of data. Here, the database managed by the platform server 30 will be described.

[0041] Figure 5 illustrates a platform database 1000. In this example, the platform database 1000 includes multiple records related to log information. Each record corresponds to one user. Each record includes a user ID, a policy log, a usage log, and a propensity score. The user ID is unique identification information for each user. The policy log and usage log are examples of log information and include data based on various histories recorded within the platform. In this example, the policy log includes basic information for each policy and information about the logs of users who participated in the policy, such as the policy ID, policy details, implementation period, and target stores. This information, for example, records only policies in which a user participated as policy logs, and in the database, users who participated in a policy and the corresponding policy information are linked and recorded. The usage log is historical information about each user's activities or usage within the platform, such as a timestamp, store information, and payment information. In this example, if the platform provides services related to an online store, for example, user purchase records or service usage information for each store are recorded in the database based on history such as timestamps. The propensity score records a score related to a user's usage patterns on the platform.

[0042] Returning to Figure 4, in step S104, the information processing device 10 identifies the first user group, that is, users who participated in the target measure, based on the designation of the target measure and the information stored in the platform database 1000. For example, if the target measure is measure P1 in the platform database 1000, the information processing device 10 identifies user U21 as a user belonging to the first user group. In this example, the information processing device 10 extracts the first user group that participated in the target measure from among the users using the platform and records them in its own database or elsewhere. Next, the method for identifying the second user group will be explained. The following process is an example of operation related to the calculation of propensity scores.

[0043] In step S105, the information processing apparatus 10 calculates the tendency scores of users who use the platform respectively. In this example, the information processing apparatus 10 calculates the tendency score for each user based on the data acquired from the platform server 30 and a predetermined method. In this process, not limited to the first user group, the tendency scores for all users including the non-participating user group are calculated. As the method used for the calculation, various methods such as, for example, RFM (Recency, Frequency, Monetary) analysis, loyalty score according to the number of years of use, user growth score, or clustering method are applied. In this example, in the RFM analysis, the customer indicators analyzed from three perspectives of recency of use, frequency of use, and amount of use are used as the tendency scores. These methods may be combined in a complex manner, or a method based on other perspectives may be adopted. Further, the information processing apparatus 10 may calculate the tendency score for each user by using a machine learning model or the like. Note that, from the perspective of the total number of all users who use the platform or the total number of parameters used for the calculation, if it is expected that the tendency score calculation process takes a long time, the information processing apparatus 10 may calculate the tendency score in advance and record it in the database. In this case, every time the usage log by the user is updated, the information processing apparatus 10 may execute a process to recalculate the tendency score and update the numerical value.

[0044] In step S106, the information processing device 10 identifies the second user group based on the propensity score. First, the information processing device 10 obtains the propensity score (hereinafter collectively referred to as the "first propensity score") for each user belonging to the first user group, which has been previously recorded in the database. The information processing device 10 then selects users from the non-participating user group who have propensity scores (hereinafter referred to as the "second propensity score") that are similar to the first propensity score. For example, if the number of users belonging to the first user group is 100, the information processing device 10 selects the same number of users, i.e., 100 users, from the non-participating user group and determines them to be the second user group. Note that similarity between the first propensity score and the second propensity score refers to, for example, numerical similarity (or agreement). In other words, the information processing device 10 can identify the second user group by comparing (so-called matching) the propensity score of each user belonging to the first user group with the propensity score of the non-participating user group. More specifically, the information processing device 10 identifies users from the non-participating user group who have propensity scores similar to those of user U21, who belongs to the first user group that participated in policy P1. The information processing device 10 performs this process for all users U belonging to the first user group. The range of similarity and how that range is determined are predetermined in the information processing device 10.

[0045] Here, if the number of users in the first user group is greater than the number of users in the non-participating user group, it may be impossible to select the second user group until the number of users in the first user group (hereinafter referred to as the "specified number") is reached. In that case, the information processing device 10 can select the second user group until the specified number is reached by allowing duplicate selection of users in the non-participating user group. For example, if the number of users belonging to the first user group is 100 and the number of users in the non-participating user group is 70, it becomes possible to extract the second user group up to the specified number by using users who have been selected once as selection targets again (or two or three times). The information processing device 10 treats duplicate users in the second user group after such processing as individual users with the same usage history.

[0046] In step S107, the information processing device 10 estimates the effectiveness of the target measure on the platform. In this example, the information processing device 10 extracted common elements that differed only in whether or not they participated in the measure by matching the first user group and the second user group (i.e., in step S106). Here, we will explain how to calculate the effectiveness of the target measure using a comparison between the first user group (the so-called intervention group) and the second user group (the so-called comparison group).

[0047] Figure 6 is a flowchart illustrating a method for calculating the effectiveness of the target measure. This flowchart shows the details of the process in step S107 described above. The information processing device 10 starts the following process when it identifies the second user group. In step S201, the information processing device 10 acquires data related to the sales of the target store from the log information of the first user group and the second user group. More specifically, the information processing device 10 refers to the platform database 1000 and extracts only the usage history of the target store for each user belonging to the first user group and the second user group. In this example, the information processing device 10 identifies the total amount of payments made by users at the target store as the sales of the target store. This process is performed separately for the first user group and the second user group, respectively.

[0048] In step S202, the information processing device 10 performs calculations according to the definition formula of Fuzzy DID. Here, the information processing device 10 calculates the effect of the target measure using a predetermined calculation method known as Fuzzy DiD. For example, the following formula (1) is used to calculate an index that quantifies the effect of the measure (hereinafter referred to as "effectiveness W").

[0049] In this example, the effectiveness W represents an indicator of the effect that the target measure had on the sales performance of individual, partial, or all stores within the group of stores belonging to the platform. Y "DID" is the difference in payment amounts between the first user group and the second user group identified at the target store. D" represents the difference in the intervention rate, and is a variable that, for example, represents the percentage or number of people who participated in the measure. In this example, Fuzzy DID adjusts for the intervention rate before and after the implementation of the target measure.

[0050] Here, in equation (1), "DID Y " is calculated based on a well-known method (or definition formula) known as DID. For example, "DID" is calculated by the following formula (2). Y The result is calculated.

[0051] In this example, "Y GT For variables expressed as ", G=1 represents the group of users who participated in the target measure (i.e., the first user group), and G=0 represents the group of users who did not participate (i.e., the second user group). Also, T=1 represents the time after the implementation of the measure, and T=0 represents the time before the implementation of the measure. For example, "Y G1T1 The variable labeled "Y" represents the total amount spent by the first group of users at the target store immediately after the implementation of the target measure (i.e., during the period of the measure's implementation). Note that any metric can be specified in advance for Y. For example, if the payment amount for credit cards is set as Y, the aggregated value for each variable based on each user's usage history will be applied.

[0052] Furthermore, in equation (2), "DID Y This allows for the selective determination and calculation of individual, partial, or all stores within the group of stores belonging to the platform. For example, if Y is an indicator for an individual store or company only (for example, the payment amount at affiliated store A), then the output will show the impact on the individual. Alternatively, if Y is an indicator for some or all companies on the platform (for example, the payment amount at all affiliated stores of the point service), then the output will show the impact on the overall system. The user of the information processing system 1 can specify in advance, for example in step S102, which of the stores within the group of stores to apply the indicator to (individual, partial, or all).

[0053] On the other hand, in equation (1), "DID D" is a variable that specifies the proportion of people who participated in the measure. In one example, the information processing apparatus 10 identifies the intervention rate according to the following formula (3).

[0054] Here, Ns represents the number of users who participated in the target measure among the users who made settlements at the target store. Nv represents the total number of users who used the target store identified based on the log information. Further, in addition to the method described above, when calculating "DID D ", the following mathematical formula (4) based on the same DID format as formula (2) may also be applied.

[0055] Note that regarding "DID D ", as another example, known information such as what proportion of store users participated in the target measure based on a prior market survey (or academic survey) may be introduced. As long as it is defined in advance, the specific method for identifying the intervention rate is not limited to the above and may be any method. As described above, according to these mathematical formulas, the information processing apparatus 10 can estimate the causal effect of how much the index related to the usage amount of the target store has been increased per person by the target measure.

[0056] Returning to FIG. 6. In step S203, the information processing apparatus 10 evaluates the influence on the stores belonging to the platform by the target measure using the effectiveness degree W. Here, the method for evaluating the measure effect will be described.

[0057] Figure 7 is an example of a policy effectiveness list 2000. Figure 7 is an example of a list summarizing the policy effects calculated based on formula (1) described above. In this example, the policy effectiveness list 2000 includes multiple records related to policy effects. Each record corresponds to information for each platform or for each target policy. Each record includes the platform ID, policy ID, target stores, target period, effectiveness level, and remarks. The platform ID is unique identification information defined for each platform. The policy ID is identification information managed with a common identifier with the platform database 1000. The target stores are information about the stores that are subject to evaluation of the impact of the target policy, and include, for example, the store ID and store name. In this example, the target stores may include information on which stores from the group of stores belonging to the platform are to be evaluated individually, partially, or as a whole. The target period is the period applied in formula (1) as the target period for calculating the effectiveness level. The effectiveness level is an index calculated in formula (1).

[0058] Here, when evaluating the impact of the target measure, using the settlement amount mentioned above as an example, a larger calculated effectiveness W value indicates a greater effect. Furthermore, the presence or absence of an effect at the target store may be determined based on an indicator other than the effectiveness W, such as a predetermined threshold or ROI (Return on Investment). In this example, the information processing device 10 performs a revenue conversion process for the indicator obtained by Fuzzy DID and can estimate the cost-effectiveness by comparing it with the implementation costs of the target measure obtained in advance. For example, consider the case where the effectiveness W is an indicator related to the amount of credit card settlements. In this example, the cost-effectiveness can be calculated by dividing (A) the total revenue from the target measure, estimated from the number of participants in the measure (i.e., the first user group) and various fees, by (B) the costs incurred in implementing the measure. In other words, by defining ROI = (A) / (B), if this value is greater than "1", it can be determined that there was an effect because a profit was made, and if it is less than "1", it can be determined that there was no effect because the costs were greater. Note that the costs related to the measure are provided to the information processing device 10 from various databases. Furthermore, the method used to evaluate the impact of the target measures may be of any kind.

[0059] As described above, the information processing device 10 can evaluate the impact of measures implemented on the platform. In this example, the information processing device 10 can evaluate with greater accuracy the impact of the measures on individual, partial, or all stores belonging to the platform. Therefore, for example, even if the implemented measures affect stores other than those targeted by the measures, or regardless of which stores the measures apply to, the information processing device 10 can perform an appropriate evaluation of the impact of the implemented measures.

[0060] 3. Modifications The present invention is not limited to the embodiments described above, and various modifications are possible. Several modifications are described below. Two or more of the matters described below may be combined and applied.

[0061] (1) Information Processing System 1 The hardware configuration and network configuration in the information processing system 1 are not limited to those illustrated in the embodiment. The information processing system 1 may have any hardware configuration and network configuration as long as it can realize the required functions. For example, multiple physical devices may cooperate to function as the information processing system 1. For example, at least a part of the functions of the information processing device 10 may be implemented on the platform server 30. For example, the platform server 30 may have a function related to the information processing device 10 that identifies user groups based on log information.

[0062] (2) Information Processing Device 10 Some of the functions of the information processing device 10 may be implemented on other servers. These servers may be, for example, physical servers or virtual servers (including so-called clouds). Furthermore, the correspondence between functional elements and hardware is not limited to those illustrated in the embodiments. For example, at least some of the functions described in the embodiments as being implemented on the information processing device 10 may be implemented on other devices or systems, or conversely, at least some of the functions described as being implemented on other devices or systems may be implemented on the information processing device 10. In this example, at least some of the information processing device 10 may be implemented on a user terminal 20, a platform server 30, or a store terminal 40. For example, the user terminal 20 may directly output information to the information processing device 10 regarding participation in the target measure. The platform server 30 may have a function to aggregate and process data regarding the settlement amount (or usage log) of the first user group and the second user group in the target measure. The store terminal 40 may request the information processing device 10 to evaluate the impact of the target measure on its store.

[0063] (3) User terminal 20, platform server 30, and store terminal 40 The user terminal 20, platform server 30, and store terminal 40 are not limited to those exemplified in the embodiment. The user terminal 20, platform server 30, and store terminal 40 may perform the above-described processing using any display screen, input device, external device, or various UI. The platform server 30 may implement any functions necessary for recording and managing information about services performed on the platform. The platform server 30 may have a function to automatically acquire log information regarding the use of the platform from the user terminal 20 and store terminal 40 with the consent of the user or store manager.

[0064] (4) Method for estimating the effectiveness of measures on the platform The sequence chart shown in Figure 4 is merely an example of operation, and the operation of the information processing system 1 is not limited thereto. Some of the illustrated operations may be changed or omitted, the order may be changed, or new operations may be added. In step S105, the information processing device 10 may calculate the propensity score by any method. The log information used to calculate the propensity score is not limited to that described in the embodiment, and may be any information, for example, the user's membership rank on the platform. In step S106, the information processing device 10 may identify the second user group by any method. In addition, any method may be applied to the matching process between the first user group and the second user group. In step S107, the information processing device 10 may perform the effect estimation using any method. Modifications of the effect estimation process are shown in (5) below.

[0065] (5) Method for Calculating the Effectiveness of the Target Measure The flowchart shown in Figure 6 is merely an example of the operation, and the operation of the information processing system 1 is not limited to this. Some of the illustrated operations may be changed or omitted, the order may be changed, or new operations may be added. In step S201, the information processing device 10 may use any log information as an indicator for the effectiveness W to perform the calculation. In step S202, the specific method for calculating the effectiveness of the measure is not limited to the methods described so far, and any method or formula based on Fuzzy DID may be used for calculation. In this example, formula (1) may be modified in any way depending on the probability value, etc. For estimating the effectiveness, a correction may be made using a probability value that indicates how much error may occur in the calculation result, or a value estimated using a predetermined machine learning model may be used. In addition, the aggregation period for indicators related to settlement amounts, etc., may be determined in any way, such as the period during which the measure was implemented or the period before and after it. In this example, the effectiveness W may be the difference over a different period (or multiple periods) other than the period during which the measure was implemented. In step S203, the information processing device 10 may determine whether or not there is an effect using a predetermined threshold value for the estimated degree of effect.

[0066] (6) Database (Data) The database (or the data itself) of the information processing system 1 shown in Figures 5 and 7 is not limited to those illustrated in the embodiments. Any data may be registered in the database in this example. Any data may be recorded in the platform database 1000. For example, tags, keywords, labels, or various metadata may be attached to the log information. Any data may be recorded in the database, for example, text, images, graphs, or lists. The policy effect list 2000 may be output via the network 9 to various terminals held by the administrator or system users of the information processing system 1.

[0067] (7) Platform, measures, and log information The platform, measures, and log information are not limited to those exemplified in the embodiments. The information processing system 1 may be adapted to at least some of its functions and operations depending on the type or form of the platform. The measures may be any activities that can be implemented depending on the form of the platform. The data acquired as log information may be any type and may include various logs recorded depending on the service form of the platform.

[0068] (8) Other programs executed by the processor 101 may be provided by download via a network such as the Internet, or they may be provided recorded on a computer-readable non-temporary recording medium such as a DVD-ROM. Each processor may be, for example, a CPU, an MPU (Micro Processing Unit), or a GPU (Graphics Processing Unit).

[0069] The block diagrams used in the description of the above embodiments show functional units. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method of realizing each functional block is not particularly limited. That is, each functional block may be realized using one device that is physically or logically coupled, or it may be realized using two or more physically or logically separated devices that are directly or indirectly connected (for example, using wired or wireless connections). A functional block may also be realized by combining software with the one or more of the above devices.

[0070] Functions include, but are not limited to, judgment, decision, determination, calculation, calculation, processing, derivation, investigation, exploration, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, assumption, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating (mapping), and assigning. For example, a functional block (configuration part) that enables transmission is called a transmission unit or transmitter. In all cases, as mentioned above, the method of implementation is not particularly limited.

[0071] For example, the information processing device 10 in one embodiment of the present disclosure may function as a computer that performs the processing of the present disclosure.

[0072] Each aspect or embodiment described in this disclosure may be applied to at least one of the following: LTE (Long Term Evolution), LTE-A (LTE-Advanced), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), FRA (Future Radio Access), NR (new Radio), W-CDMA®, GSM®, CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi®), IEEE 802.16 (WiMAX®), IEEE 802.20, UWB (Ultra-WideBand), Bluetooth®, and other appropriate systems, as well as next-generation systems extended based thereon. Furthermore, multiple systems may be applied in combination (for example, a combination of at least one of LTE and LTE-A with 5G).

[0073] The processing procedures, sequences, flowcharts, etc., of each aspect or embodiment described in this disclosure may be reordered, provided they do not contradict each other. For example, the methods described in this disclosure present various step elements in an exemplary order and are not limited to the specific order presented.

[0074] Input and output information may be stored in a specific location (e.g., memory) or managed using a management table. Input and output information may be overwritten, updated, or appended to. Output information may be deleted. Input information may be sent to other devices.

[0075] The determination may be made by a value represented by one bit (0 or 1), by a boolean value (true or false), or by a numerical comparison (for example, by comparing with a predetermined value).

[0076] Although the present disclosure has been described in detail above, it will be clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the intent and scope of the present disclosure as defined by the claims. Therefore, the descriptions in the present disclosure are illustrative and not intended to be restrictive in any way.

[0077] Software should be broadly interpreted to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, procedures, functions, etc., whether they are called software, firmware, middleware, microcode, hardware description languages, or by any other name. Furthermore, software, instructions, information, etc., may be transmitted and received via a transmission medium. For example, if software is transmitted from a website, server, or other remote source using at least one of wired technologies (such as coaxial cable, fiber optic cable, twisted pair, or digital subscriber line (DSL)) and wireless technologies (such as infrared or microwave), at least one of these wired and wireless technologies is included in the definition of a transmission medium.

[0078] The information, signals, etc., described herein may be represented using any of the following different technologies. For example, data, instructions, commands, information, signals, bits, symbols, chips, etc., which may be referred to throughout the above description, may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof. Terms used herein and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meaning.

[0079] Furthermore, the information, parameters, etc., described in this disclosure may be expressed using absolute values, relative values ​​from a predetermined value, or corresponding other information.

[0080] In this disclosure, the phrase "based on" does not mean "based solely on" unless otherwise specified. In other words, the phrase "based on" means both "based solely on" and "based at least on."

[0081] Any reference to elements using the designations “First,” “Second,” etc., as used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Accordingly, references to the First and Second elements do not imply that only two elements may be employed, or that the First element must precede the Second element in any way.

[0082] In the above-described configuration of each device, the term "part" may be replaced with "means," "circuit," "device," etc.

[0083] Where the terms “include,” “including,” and variations thereof are used in this disclosure, these terms are intended to be inclusive, as is the term “comprising.” Furthermore, the term “or” as used in this disclosure is not intended to mean exclusive OR.

[0084] In this disclosure, if articles are added by translation, such as a, an, and the in English, this disclosure may include the fact that the noun following these articles is plural.

[0085] In this disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "combine" may be interpreted similarly to "different."

[0086] 1... Information processing system, 10... Information processing device, 20... User terminal, 30... Platform server, 40... Store terminal, 9... Network, 11... Acquisition unit, 12... Output unit, 13... Identification unit, 14... Calculation unit, 15... Estimation unit, 191... Storage unit, 192... Control unit, 101... Processor, 102... Memory, 103... Storage, 104... Communication device, 1000... Platform database, 2000... Policy effectiveness list

Claims

1. An information processing device having an acquisition unit that acquires log information indicating whether a user participated in a measure at a group of stores belonging to a target platform, and an output unit that outputs an index indicating the effect of the measure on the sales performance of the group of stores, which is evaluated based on the log information.

2. The information processing apparatus according to claim 1, comprising an identification unit that identifies a group of users who did not participate in the measures as a second user group, with respect to a first user group which is a group of users who participated in the measures, and which also identifies a group of users who did not participate in the measures and who meet predetermined conditions, and the output unit outputs an evaluation of the first user group and the second user group as the index.

3. The information processing apparatus according to claim 2, wherein the conditions are conditions relating to a propensity score corresponding to the usage trends of individual users belonging to the first user group when using the platform, and the identification unit identifies the second user group depending on whether the propensity score of users who did not participate in the measures is similar to the propensity score of individual users belonging to the first user group.

4. The information processing apparatus according to claim 3, wherein the log information includes usage logs relating to user use on the platform, and the apparatus has a calculation unit that calculates the propensity score based on the usage logs acquired according to the form of the platform.

5. The information processing apparatus according to claim 1, comprising an estimation unit that estimates the effect of the measure based on the log information, wherein the output unit outputs the result obtained from the estimation unit as the index.

6. The information processing apparatus according to claim 5, wherein the estimation unit performs estimation using Fuzzy DiD (Differences-in-Differences).

7. The information processing apparatus according to claim 6, wherein the indicator includes a cost-benefit ratio calculated based on the costs incurred in implementing the measures, and the output unit outputs the cost-benefit ratio obtained from the estimation unit as the indicator.

8. The information processing device according to claim 1, comprising an estimation unit that estimates the effect of the measure based on the log information, wherein the output unit outputs the results obtained from the estimation unit for individual stores among the group of stores as the indicator.

9. The information processing apparatus according to claim 1, comprising an estimation unit that estimates the effect of the measure based on the log information, wherein the output unit outputs the results obtained from the estimation unit as the index for all stores in the group of stores.

10. An information processing method comprising the steps of: a computer acquiring log information indicating whether a user participated in a measure in a group of stores belonging to a target platform; and outputting an index indicating the effect of the measure on the sales performance of the group of stores, the index being evaluated based on the log information.

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