Information processing system, information processing method, and information processing program

Abstract

To enable more accurate classification of customers in consideration of action trends of the customers regarding handling of customer information on the customers after death.SOLUTION: An information processing system is provided, comprising: a customer information acquisition unit configured to acquire customer information on a customer; an after-death handling setting unit configured to receive an input for setting how to handle customer information after death of the customer from a customer terminal, and generate after-death handling information indicative of how to handle the customer information; a classification unit configured to classify the customer into at least one of multiple types on the basis of the after-death handling information; and a classification information provision unit configured to provide classification information showing a classification result to the customer terminal.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Conventionally, there have been many efforts to classify customers into predetermined types (so-called personas, etc.) based on information about the customers, and to utilize the classification results in marketing businesses, etc.

[0003] For example, in the information processing system described in Patent Document 1, customers are classified into predetermined types based on customer information related to personal information of the customers and information on services used. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] JP 2021-105868 A Summary of the Invention [Problem to be solved by the invention]

[0005] In recent years, however, there has been an increasing need for customers to manage their own settings regarding how their customer information will be handled after their death (such as deleting data and accounts, transferring information to family members, etc.) while they are still alive.

[0006] However, the system described in Patent Document 1 merely classifies customers into predetermined types based on customer information.

[0007] Therefore, an object of the present invention is to provide an information processing system that can classify customers with higher accuracy, taking into account customers' behavioral tendencies regarding how to respond to customer information after their death. [Means for solving the problem]

[0008] An information processing system according to one aspect of the present invention includes a customer information acquisition unit that acquires customer information about a customer, a post-mortem response setting unit that receives input from a customer terminal to set how to handle the customer information after the customer's death and generates post-mortem response information indicating the handling of the customer information, a classification unit that classifies the customer into at least one of a plurality of types based on the post-mortem response information, and a classification information provision unit that provides classification information indicating the results of the classification to the customer terminal.

[0009] In one aspect of the information processing method of the present invention, a computer acquires customer information about a customer, accepts input from a customer terminal to set how to handle the customer information after the customer's death, generates post-death handling information indicating the handling of the customer information, classifies the customer into at least one of a plurality of types based on the post-death handling information, and provides classification information indicating the classification results to the customer terminal.

[0010] An information processing program according to one embodiment of the present invention causes a computer to implement a customer information acquisition unit that acquires customer information about a customer, a post-mortem response setting unit that receives input from a customer terminal to set how to handle the customer information after the customer's death and generates post-mortem response information indicating the handling of the customer information, a classification unit that classifies the customer into at least one of a plurality of types based on the post-mortem response information, and a classification information provision unit that provides classification information indicating the results of the classification to the customer terminal.

[0011] In this invention, a "unit" does not simply mean a physical means, but also includes cases where the functions of the "unit" are realized by software. Furthermore, the functions of one "unit" or device may be realized by two or more physical means or devices, and the functions of two or more "units" or devices may be realized by one physical means or device. [Effects of the Invention]

[0012] According to the present invention, it is possible to provide an information processing system that can classify customers with higher accuracy, taking into account customers' behavioral tendencies regarding how to respond to customer information after their death. [Brief explanation of the drawings]

[0013] [Figure 1] 1 is a diagram showing a configuration of an information processing system 100 according to an embodiment of the present invention. [Figure 2] 10 is a diagram showing an example of information stored in a customer information storage unit 122. FIG. [Figure 3] FIG. 10 is a diagram showing an example of information stored in a post-mortem correspondence information storage unit 124. [Figure 4] FIG. 10 is a diagram illustrating an example of a method by which the classification unit 131 classifies customers. [Figure 5] FIG. 10 is a diagram showing an example of display of types for classifying customers. [Figure 6] 10 is a diagram showing an example of information stored in a classification information storage unit 132. FIG. [Figure 7] 10 is a diagram showing an example of information stored in an ideal classification information storage unit 135. FIG. [Figure 8] 10 is a diagram showing an example of information stored in a behavior information storage unit 142. FIG. [Figure 9] 10 is a diagram showing an example of a display by a display processing unit 151. FIG. [Figure 10] 10 is a flowchart illustrating an example of a process for generating classified information in the information processing system 100. [Figure 11] 10 is a flowchart showing an example of processing related to ideal classification information in the information processing system 100. [Figure 12] FIG. 10 is a diagram illustrating a modified example of the configuration of the information processing system 100a. [Figure 13] FIG. 13 illustrates an example of the hardware configuration of a computer 1300. DETAILED DESCRIPTION OF THE INVENTION

[0014] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Preferred embodiments of the present invention will now be described with reference to the accompanying drawings, in which: Figure 1 is a diagram showing the configuration of an information processing system 100 according to one embodiment of the present invention;

[0015] The information processing system 100 is a system that is communicably connected to a customer terminal 110 via a network such as the Internet.

[0016] The information processing system 100 acquires customer information about the customer based on operational input by the customer to the customer terminal 110, and generates post-mortem correspondence information indicating the correspondence to the customer information after the customer's death. Thereafter, the information processing system 100 classifies the customer into at least one of a plurality of types based on the customer information and the post-mortem correspondence information, and provides classification information indicating the classification results to the customer terminal 110 and an external information processing system.

[0017] Furthermore, the information processing system 100 sets an ideal type for the customer based on an operation input by the customer to the customer terminal 110. Thereafter, the information processing system 100 generates behavioral information regarding behaviors for bringing the customer closer to the ideal type based on the classification information and ideal classification information indicating the set ideal type, and provides the behavioral information to the customer terminal 110.

[0018] Furthermore, the information processing system 100 displays the difference between the classification information and the ideal classification information on the element axes based on two or more element axes that distinguish a plurality of types on the customer terminal 110. The element axes will be described later.

[0019] The customer terminal 110 is a computer used by a customer who is a user of the information processing system 100, and may be a smartphone, tablet terminal, personal computer, or the like. The customer uses the customer terminal 110 to input information to be provided to the information processing system 100 through operational inputs to the customer terminal 110, to set up procedures for handling the customer's death. The customer may provide the information processing system 100 with customer information directly through the customer terminal 110, or may provide the customer information indirectly by instructing a reference to an external information processing system. Although FIG. 1 shows three examples of the customer terminal 110, namely, customer terminals 110a, 110b, and 110c, the number of customer terminals 110 is not limited thereto.

[0020] Next, the information processing system 100 will be described in detail. As shown in FIG. 1, the information processing system 100 includes a customer information acquisition unit 121, a customer information storage unit 122, a posthumous correspondence setting unit 123, a posthumous correspondence information storage unit 124, a classification unit 131, a classification information storage unit 132, a classification information provision unit 133, an ideal classification setting unit 134, an ideal classification information storage unit 135, a behavioral information generation unit 141, a behavioral information storage unit 142, a behavioral information provision unit 143, a display processing unit 151, and an external provision unit 152. The computer constituting the information processing system 100 includes a processor and a storage area. Each unit shown in FIG. 1 can be realized, for example, by using the storage area or by the processor executing a program stored in the storage area. Note that the information processing system 100 does not have to be configured as a single device, but may be configured as multiple devices.

[0021] The customer information acquisition unit 121 acquires customer information based on an operation input to the customer terminal 110 by the customer, and stores the information in the customer information storage unit 122 .

[0022] The customer information is information about the customer, and includes, for example, personal information of the customer, terminal information about the customer terminal 110, transaction information about transactions performed by the customer, and service information about services used by the customer.

[0023] Personal information may include, for example, a customer's name, date of birth, gender, address, telephone number, personal identification number, email address, account information in messaging software, information about bank accounts (e.g., account type, account number, name, etc.), information about credit cards (e.g., credit card number, name, expiration date, security code, etc.), authentication information for identity verification (e.g., information about biometric characteristics (e.g., fingerprints, irises, voiceprints, facial images, etc.)), etc. Personal information may also include, for example, a customer's family composition, health status, hobbies, interests, etc.

[0024] Terminal information is information about the terminal used by the customer. Terminal information may include, for example, location information (e.g., GPS data, etc.) stored by the terminal, web history information (e.g., browsing history, search history, cookie information, etc., as a history of access to websites), device information (e.g., MAC address, IP address, model number, information about installed applications and OS, memory capacity, etc.), etc.

[0025] Transaction information is information about transactions conducted by customers. Transaction information may include, for example, payment history information (e.g., product / service name, product / service ID, payment amount, payment method, product / service purchase history, etc., paid for electronically or via an e-commerce site), point information (e.g., point identification information, point allocation / usage history, current point balance, etc.).

[0026] Service information is information about services used by a customer. The service information may be, for example, information about whether a specific service is registered for use, or information about the usage history and frequency of the specific service. The service information may include, for example, information about leisure services, accommodation services, financial services, education services, medical services, rental services, information-related services, etc., and may also include, for example, account information and history information for news sites used by the customer, account information and history information for music distribution services used by the customer, etc.

[0027] The customer information acquisition unit 121 may acquire customer information directly from the customer terminal 110, or may acquire customer information from an external information processing system based on an instruction entered by the customer through an operation input to the customer terminal 110. The customer information acquisition unit 121 may also acquire multiple pieces of customer information relating to the same customer.

[0028] 2 is a diagram showing an example of information stored in the customer information storage unit 122. The information stored in the customer information storage unit 122 includes, for example, a customer ID and customer information. The customer ID is customer identification information that identifies a customer who uses the information processing system 100. The customer information is as described above.

[0029] The post-mortem correspondence setting unit 123 generates post-mortem correspondence information indicating how to handle the customer information after the customer's death, based on the customer's operation input to the customer terminal 110. In addition, the post-mortem correspondence setting unit 123 stores the generated post-mortem correspondence information regarding how to handle the customer information in the post-mortem correspondence information storage unit 124.

[0030] The post-death handling information is information generated by the post-death handling setting unit 123 through operational input by the customer to the customer terminal 110 for each piece of customer information, and is information indicating how to handle the customer information after the customer's death. Examples of how to handle the customer information after the customer's death include "deleting data," "deleting the account," and "transferring the account to a family member." Specifically, based on the customer's operational input, the post-death handling setting unit 123 sets the processing for transferring the customer information to the management of the surviving family members after the customer's death, and the processing for automatically deleting the customer information after the customer's death.

[0031] Depending on the customer's personality, the customer decides how to handle their customer information after their death (such as deleting their account or handing it over to a family member), and performs operation input on the customer terminal 110. Here, a customer may wish to have different post-death handling for each piece of customer information. For example, a customer may wish to have their bank account handed over to their family member, but to have their web service usage history deleted without anyone being able to view it. Therefore, the information processing system 100 uses post-death handling information, which indicates the post-death handling of the customer generated for each piece of customer information, as an element for classifying customers into at least one of a plurality of types.

[0032] In the information processing system 100, it is not necessary for post-mortem handling information to be generated for all customer information; it is sufficient that post-mortem handling information is generated for customer information for which the customer wishes to set up post-mortem handling.

[0033] 3 is a diagram showing an example of information stored in the post-mortem correspondence information storage unit 124. The information stored in the post-mortem correspondence information storage unit 124 includes, for example, a customer ID and post-mortem correspondence information. The customer ID and post-mortem correspondence information are as described above.

[0034] The classification unit 131 classifies the customer into at least one of a plurality of types based on the post-death correspondence information, and stores classification information indicating the classification results in the classification information storage unit 132.

[0035] Here, the term "type" refers to a distinctive characteristic possessed by a customer, such as the customer's preferences, thoughts, outlook on life, etc., which is information about the so-called persona. Specifically, the type refers to the characteristics possessed by customers, such as "organic lover," "health-conscious," "artist," "highly conscious businessman," etc.

[0036] The classification unit 131 can classify customers into at least one of a plurality of types that are set in advance in the information processing system 100. Here, for example, two or more elements that distinguish each of the plurality of types may be set for each of the plurality of types. Furthermore, a certain index may be set for each element of each of the plurality of types. In other words, each of the plurality of types can be identified based on the index of each element. Specifically, for example, if the type is "artist," the index "2" may be set for the element of "emotion" and the index "5" may be set for the element of "logic." Note that the index of each element may be a value or a range of values.

[0037] Furthermore, the multiple types may have the same index set for some of the same elements, or the multiple types may each have the same index set for multiple elements.

[0038] The process of classifying customers into at least one of a plurality of types will be described below.

[0039] First, weights of two or more elements that distinguish each of the multiple types are set in advance in the post-death correspondence information (for example, weights of elements such as "conservative" and "logic" that are assigned to the post-death correspondence information "account deletion"). Next, the classification unit 131 calculates an evaluation value for each of the two or more elements, for example, based on the weights of each of the two or more elements set in the post-death correspondence information. Thereafter, the classification unit 131 classifies customers into at least one of the multiple types based on the calculated evaluation value for each element and the index of each element set for each of the multiple types.

[0040] Furthermore, the classification unit 131 may classify customers into at least one of a plurality of types based on customer information in addition to the post-mortem correspondence information. Here, weights of each element for distinguishing between the plurality of types are set in advance in the customer information. In other words, the classification unit 131 may classify customers into at least one of a plurality of types based on, for example, the calculated evaluation value of each element in each piece of post-mortem correspondence information and the calculated evaluation value of each element in each piece of customer information.

[0041] Specifically, an example of a method in which the classification unit 131 identifies a type based on the evaluation value of each element will be described with reference to Fig. 4. Fig. 4 is a diagram showing an example of a method for classifying customers based on the evaluation value of each element. For convenience, the line connecting a combination of two elements having symmetrical properties, arranged symmetrically on the paper, will be referred to as an "element axis" below.

[0042] As an example, four types (types A to D) are set for two element axes connecting two symmetrical elements, such as "logical / emotional" and "progressive / conservative." The classification unit 131 classifies customers into at least one of the four types (types A to D) based on the information "transferring bank account A to spouse" included in the posthumous correspondence information. Here, an index indicating a range of values ​​is set for each element of each of types A to D. The classification unit 131 may classify customers into a specific type based on, for example, the relationship between the range of each of types A to D formed by the index indicating the range of values ​​and the result of calculating a vector indicating the weight of each element.

[0043] Specifically, in the diagram shown in FIG. 4, for example, the weight "3" of the "Emotion" element, which is an element indicated by the information "transferring bank account A to spouse" included in the posthumous correspondence information, is shown as vector 401 on the "Logic / Emotion" element axis, and the weight "2" of the "Innovation" element is shown as vector 402 on the "Conservative / Innovative" element axis. Furthermore, the vector indicated by the combination of vector 401 and vector 402 is shown as vector 403. That is, vector 403 indicates an evaluation value of "3" for the element "Emotion" and an evaluation value of "2" for the element "Innovation." The classification unit 131 determines that the end point 404 of vector 403 is in the area of ​​type A. As a result, the classification unit 131 can classify customers who have registered the information "transferring bank account A to spouse" included in their customer information into type A.

[0044] The classification method by the classification unit 131 shown in FIG. 4 is merely an example, and the classification method is not limited to this.

[0045] Next, with reference to Fig. 5, an example in which the display processing unit 151, which will be described later, displays the results of classification by the classification unit 131 will be described. Fig. 5 is a diagram showing an example of the types into which the classification unit 131 classifies customers. Fig. 5 includes an area 501 showing an outline of the type, an area 502 showing the name of the type, an area 503 (503a, 503b) showing two or more elements that distinguish each of the multiple types, and an area 504 showing an evaluation value for each of the two or more elements calculated based on posthumous correspondence information and customer information. Fig. 5 shows an example of display for a customer classified as a "highly conscious businessman" and a customer classified as an "artist."

[0046] FIG. 5 lists eight elements, each of which is a combination of two symmetrical elements: "logic / emotion," "progressive / conservative," "dog / cat," and "sound / picture," as elements for distinguishing each of the multiple types. Hereinafter, as with FIG. 4, the line connecting each combination of two symmetrical elements, arranged symmetrically on the paper, is referred to as the "element axis." Objects (e.g., shapes, symbols, etc.) are displayed on the element axis according to the evaluation value of each element. For example, in FIG. 5, black circle objects are displayed as shown in area 504. For example, since the evaluation value of "emotion" for a customer classified as a "highly conscious businessman" is lower than the evaluation value of "emotion" for a customer classified as an "artist," the black circle object on the element axis for the customer classified as a "highly conscious businessman" is positioned closer to "logic" than to "emotion."

[0047] 5 shows eight elements, namely, "logic / emotion," "innovative / conservative," "dog / cat," and "sound / picture," but the elements are not limited to these, and the number of elements is not limited to eight. The diagram shown in FIG. 5 can be displayed on the customer terminal 110 by the display processing unit 151, which will be described later.

[0048] The weights assigned to each element of the customer information and post-death correspondence information may be set only to specific elements, or the same weight may be set to multiple elements.

[0049] Furthermore, the classification unit 131 may classify customers into at least one of a plurality of types, for example, by using a classification model for classifying customers into at least one of a plurality of types. For example, the classification model may be a model that classifies customers by keyword matching or a model that classifies customers by a trained model.

[0050] Specifically, when keyword matching is used, the classification unit 131 may classify a customer into an "organic lover" category, which is associated with keywords such as "nature" or "natural," if the names of products that the customer has previously purchased, as shown in the payment history information among the transaction information included in the customer information, contain these keywords.

[0051] Furthermore, when using a trained model, the classification unit 131 may use, for example, at least one of the pieces of information included in the post-mortem correspondence information (e.g., whether or not an account for a specific service has been deleted or transferred) as an explanatory variable, and the evaluation value of each element as a target variable. Furthermore, in addition to the information included in the post-mortem correspondence information, the classification unit 131 may use information included in the customer information (e.g., attributes such as gender, age, family structure, occupation, preferences, health status, website access history, product purchase history, etc.) as explanatory variables. Here, the machine learning algorithm is not particularly limited, and examples that can be used include Random Forest, SVM (Support Vector Machine), clustering, and deep learning.

[0052] Furthermore, for example, when specific information is stored among the posthumous correspondence information in the posthumous correspondence information storage unit 124, the classification unit 131 may classify the information into a predetermined type, or may select a plurality of predetermined types as candidates for the classification result. Specifically, for example, when information "transferring bank account A to spouse" (customer information with a high weight for "emotion") among the posthumous correspondence information is stored in the posthumous correspondence information storage unit 124, the classification unit 131 may classify the information into "artist," which is a type with a high index for "emotion," regardless of other customer information stored in the posthumous correspondence information storage unit 124, or may select "artist" or a type related thereto as a candidate for the classification result. When "artist" or a type related thereto is selected as a candidate for the classification result, the classification unit 131 may classify the information into one type based on an evaluation value calculated using the weights of each element assigned to each piece of posthumous correspondence information, or may select one type based on a classification model.

[0053] Furthermore, the method of classifying customers into at least one of several types based on the weight of each element is suitable in today's society, where new services are constantly being created, and can be said to be a method that can flexibly respond to situations where the types of information that can be included in post-death correspondence information and the types of information that can be included in customer information are increasing.

[0054] 6 is a diagram showing an example of information stored in the classification information storage unit 132. The information stored in the classification information storage unit 132 includes, for example, a customer ID and classification information. Note that the classification information stored in the classification information storage unit 132 may be a name of a type such as "highly conscious businessman," an index of each element of the type corresponding to the classification information, or an evaluation value of each element calculated by the classification unit 131.

[0055] The classification information providing unit 133 provides the classification information stored in the classification information storage unit 132 to the customer terminal 110. The classification information providing unit 133 may provide the name of the type corresponding to the classification information or an explanation of the type content, or may provide an index for each element of the type corresponding to the classification information, or may provide an evaluation value for each element calculated by the classification unit 131.

[0056] In addition to providing the classification information, the classification information providing unit 133 may also provide statistical results regarding the classification information of other customers (for example, the number of customers with the same type, the proportion of customers with the same type to all customers, etc.).

[0057] The ideal classification setting unit 134 generates ideal classification information relating to the customer's ideal type based on the customer's operational input to the customer terminal 110, and stores the ideal classification information in association with the customer ID in the ideal classification information storage unit 135. The ideal classification information is information generated by the ideal classification setting unit 134 for each customer through the customer's operational input to the customer terminal 110, and is information relating to the customer's ideal type.

[0058] 7 is a diagram showing an example of information stored in the ideal classification information storage unit 135. The information stored in the ideal classification information storage unit 135 includes, for example, a customer ID and ideal classification information. Note that the ideal classification information stored in the ideal classification information storage unit 135 may be a category name such as "artist," or may be an index for each element of the category corresponding to the ideal classification information.

[0059] The ideal classification setting unit 134 can, for example, display on the customer terminal 110 a list of multiple types generated in advance in the information processing system 100, and generate ideal classification information for the customer based on a selection operation by the customer on the customer terminal 110. Furthermore, the ideal classification setting unit 134 may display on the customer terminal 110 questions for the customer to generate ideal classification information, and generate ideal classification information for the customer based on a response operation by the customer on the customer terminal 110.

[0060] Furthermore, the ideal classification setting unit 134 may, for example, display on the customer terminal 110 a list of widely recognized people such as celebrities and great figures that correspond to the types previously set in the information processing system 100. In this case, the ideal classification setting unit 134 can generate ideal classification information corresponding to the acquired people such as celebrities and great figures based on the operation input to the customer terminal 110 by the customer.

[0061] In addition, the ideal classification setting unit 134 may acquire evaluation values ​​for each element for distinguishing between multiple types based on operational input by the customer to the customer terminal 110, identify the ideal type based on the acquired evaluation values, and generate corresponding ideal classification information.

[0062] The classification information and the ideal classification information may be information about the same type.

[0063] The behavioral information generation unit 141 generates behavioral information to bring the customer closer to the customer's ideal type based on the classification information in the classification information storage unit 132 and the ideal classification information in the ideal classification information storage unit 135, and stores the generated behavioral information in the behavioral information storage unit 142.

[0064] Here, the behavioral information is information that the information processing system 100 proposes to the customer, and is information for encouraging the customer to take actions that the customer should take in order to approach the customer's ideal type. Specifically, the behavioral information may be, for example, solicitation information such as advertisements for products or web services, information about events or preferential treatment at events, news information such as news articles or useful information, information encouraging registration for services for which other customers have registered, or any other information that encourages the customer to take action.

[0065] The behavioral information generation unit 141 may generate behavioral information based on, for example, a database (hereinafter referred to as a "classification database") that associates behavioral information with weights of each element for distinguishing between multiple types. In this case, the behavioral information generation unit 141 generates behavioral information based on, for example, the classification database, that reduces the difference between the evaluation value of each element calculated by the classification unit 131 and the index of each element of the type corresponding to the ideal classification information. Here, in the classification database, a weight of "+2" may be associated with one of the elements, "emotion," for the behavioral information "using a cut flower subscription service," and a weight of "+1" may be associated with one of the elements, "conservative," for the behavioral information "setting 'delete account' as a post-death action for customer information." Note that the weights of each element associated with behavioral information set in the classification database may be "0" or a negative value.

[0066] For example, if the evaluation value of the element "emotion" calculated by the classification unit 131 is lower than the index of the element "emotion" of the type "artist" corresponding to the ideal classification information, the behavioral information generation unit 141 generates behavioral information for increasing the evaluation value of "emotion" (reducing the difference between the evaluation value of each element calculated by the classification unit 131 and the index of each element of the type corresponding to the ideal classification information). Specifically, the behavioral information generation unit 141 generates behavioral information such as "use a cut flower subscription service" to which a weight of "emotion" of "+2" is assigned, for example, so as to increase the evaluation value of the customer's "emotion" based on the classification database.

[0067] Furthermore, the behavioral information generation unit 141 may generate behavioral information to bring a customer closer to the customer's ideal type, based on the classification information and customer information of other customers. For convenience, the customer to whom the behavioral information is provided is referred to as the "recipient customer," and other customers who are not the recipient customer and whose classification information and customer information are referenced when generating the behavioral information are referred to as the "reference customer."

[0068] When the behavioral information generation unit 141 generates behavioral information based on the classification information of reference customers, the behavioral information generation unit 141 refers to, for example, classification information of reference customers that are ideal for the destination customer. The destination customer can set the reference customers that are ideal for the destination customer in advance in the information processing system 100. Then, the behavioral information generation unit 141 generates behavioral information that brings the destination customer closer to the type corresponding to the classification information of the reference customers. In this case, the behavioral information generation unit 141 can generate behavioral information using, for example, a classification database.

[0069] Furthermore, when the behavioral information generation unit 141 generates behavioral information based on the customer information of the reference customer, the behavioral information generation unit 141 can generate, for example, customer information of a reference customer who has, as a type corresponding to the ideal classification information of the destination customer, a type corresponding to the classification information (i.e., reference customer B who has, as a current type, the type that destination customer A considers ideal). In other words, the behavioral information is generated based on the behavior that the reference customer has already performed, and the destination customer is prompted to perform the behavior that the reference customer has already performed.

[0070] The behavior information generating unit 141 may generate only one piece of behavior information, or may generate multiple pieces of behavior information.

[0071] 8 is a diagram showing an example of information stored in the behavior information storage unit 142. The information stored in the behavior information storage unit 142 includes, for example, a customer ID and behavior information.

[0072] The behavioral information providing unit 143 provides the behavioral information stored in the behavioral information storage unit 142 to the customer terminal 110. The behavioral information providing unit 143 may provide all of the behavioral information stored in the behavioral information storage unit 142 to the customer terminal 110, or may extract and provide a portion of the behavioral information.

[0073] When extracting and providing a portion of the behavioral information, the behavioral information providing unit 143 may extract and provide a portion of the behavioral information based on setting information regarding whether extraction is necessary and the extraction conditions, which is set in advance for each piece of behavioral information. The setting information includes, for example, "no extraction required" provided to any customer, "for new users" provided to customers who have not yet used a predetermined service, and "for existing users" provided to customers who have used a predetermined service. The following describes the processing when a portion of the behavioral information is set to be extracted.

[0074] The behavioral information providing unit 143 extracts and provides a part of the behavioral information based on the service information included in the customer information. In this case, the behavioral information providing unit 143 may extract and provide a part of the behavioral information based on, for example, the usage status of a predetermined service (registration status, usage experience, usage frequency, etc.).

[0075] Specifically, when the behavioral information includes solicitation information for new users of a specific service (for example, a new registration campaign on a mail-order site), the behavioral information providing unit 143 may extract and provide a portion of the behavioral information that is different from the solicitation information for new users to customers whose customer information includes service information for the specific service (that is, customers who are already using the service). Also, when the behavioral information includes solicitation information for continuing users of a specific service (for example, a coupon for a paid service for members on a mail-order site), the behavioral information providing unit 143 may extract and provide a portion of the behavioral information that is different from the solicitation information for continuing users to customers whose customer information does not include service information for the service (that is, customers who are not considered to have used the service yet).

[0076] Furthermore, the behavioral information providing unit 143 may extract and provide a part of the behavioral information based on the post-death handling information. Specifically, the behavioral information providing unit 143 may extract and provide, as post-death handling information, information on services that can be enjoyed by the family from the behavioral information to a customer who has set many accounts to be handed over to family members (i.e., a customer who is considered to have deep affection for his or her family).

[0077] The display processing unit 151 displays, on the customer terminal 110, the evaluation values ​​of each element calculated by the classification unit 131 and the indicators of each element of the type corresponding to the ideal classification information, using two or more element axes that distinguish each of the multiple types.

[0078] An example of a display by the display processing unit 151 will now be described with reference to Fig. 9. Fig. 9 is a diagram showing an example of a display by the display processing unit 151. The screen shown in Fig. 9 includes an area 901 that displays an outline diagram of the type corresponding to the ideal classification information, an area 902 that displays the name of the type corresponding to the ideal classification information, an area 903 (903a, 903b) that displays two or more element axes that distinguish each of the multiple types, and an area 904 that displays the evaluation values ​​of each element calculated by the classification unit 131 and the indexes of each element of the ideal classification information on the same straight line as each of the element axes.

[0079] 9, four element axes corresponding to eight elements, namely, "logic / emotion," "innovative / conservative," "dog / cat," and "sound / picture," are displayed as element axes. The display processing unit 151 displays first objects (dotted white circles) on the element axes of the area 904, for example, according to the evaluation value of each element calculated by the classification unit 131. The display processing unit 151 also displays second objects (black circles) on the element axes of the area 904, for example, according to the index of each element of the type (here, "artist") corresponding to the ideal classification information. The difference between the dotted white circles and black circles on each element axis indicates the difference between the evaluation value of each element calculated by the classification unit 131 and the index of each element of the type corresponding to the ideal classification information.

[0080] Note that, although the screen shown in FIG. 9 shows four element axes corresponding to eight elements, namely, "logic / emotion," "innovative / conservative," "dog / cat," and "sound / picture," the elements and element axes are not limited to these, and the number of elements and element axes is not limited to these. Also, while positions on the element axes are indicated by dotted white and black circles, the shapes of the objects indicating positions on the element axes are not limited to these. Also, although the four element axes are arranged vertically, it is sufficient that the first object and the second object are displayed in a contrastable manner. For example, the element axes may be arranged horizontally, or may be displayed in the form of a radar chart using polygons.

[0081] The external providing unit 152 provides at least one of the classification information stored in the classification information storage unit 132 and the ideal classification information stored in the ideal classification information storage unit 135 to an external information processing system (not shown). The external information processing system (not shown) may utilize the acquired classification information and ideal classification information to provide specific information (e.g., a coupon that can be used on a mail-order site) corresponding to the classification information and ideal classification information to the customer terminal 110 via the information processing system 100, or may provide specific information corresponding to the classification information and ideal classification information directly to the customer terminal 110 without going through the information processing system 100.

[0082] In addition, the external providing unit 152 may provide at least one of the classification information and the ideal classification information to an external information processing system (not shown) based on a request made by the customer through operational input to the customer terminal 110, or may provide the information to an external information processing system (not shown) in response to a request from the external information processing system (not shown) after obtaining consent from the customer in advance regarding the external provision.

[0083] A processing flow for generating classified information in the information processing system 100 will be described with reference to Fig. 10. Fig. 10 is a flowchart showing an example of processing for generating classified information in the information processing system 100.

[0084] First, the information processing system 100 acquires, from the customer terminal 110, customer information that is generated based on operational input to the customer terminal 110 by the customer (S1001). Furthermore, the information processing system 100 generates and stores post-mortem correspondence information in association with the customer information based on operational input to the customer terminal 110 by the customer (S1002). Next, the information processing system 100 generates classification information based on the customer information stored in the customer information storage unit 122 and the post-mortem correspondence information stored in the post-mortem correspondence information storage unit 124 (S1003).

[0085] Next, a processing flow for providing behavioral information to a customer in the information processing system 100 will be described with reference to Fig. 11. Fig. 11 is a flowchart showing an example of processing for providing behavioral information to a customer in the information processing system 100.

[0086] First, the information processing system 100 generates ideal classification information based on an operation input (for example, an operation to select an ideal type) made by a customer to the customer terminal 110 (S1101).

[0087] Next, the information processing system 100 acquires classification information corresponding to the customer from the classification information storage unit 132, and acquires ideal classification information corresponding to the customer from the ideal classification information storage unit 135. The information processing system 100 causes the customer terminal 110 to display the calculated evaluation value of each element and the index of each element of the type corresponding to the customer's ideal classification information as objects (e.g., white circles, black circles) on the element axis (S1102).

[0088] Next, the information processing system 100 generates behavior information based on the classification information and the ideal classification information (S1103).

[0089] Next, the information processing system 100 determines whether or not each piece of behavior information included in the generated behavior information is set to be extracted and provided (S1104).

[0090] If some of the behavioral information included in the generated behavioral information is set to be provided to any customer (S1104: NO), the information processing system 100 transmits the part of the behavioral information to the customer terminal 110 of the customer (S1108).

[0091] If it is set to extract and provide some of the behavioral information contained in the generated behavioral information (S1104: YES), the information processing system 100 refers to the customer information storage unit 122 and determines whether the customer to whom the behavioral information is to be provided has experience using a service or the like corresponding to the portion of behavioral information (S1105).

[0092] If it is determined that the user has experience using a service or the like corresponding to the part of behavioral information (S1105: YES), the information processing system 100 determines whether the part of behavioral information is behavioral information for an existing user (S1106).

[0093] If it is determined that the part of the behavior information is behavior information for existing users (S1106: YES), the information processing system 100 proceeds to S1108.

[0094] On the other hand, if it is determined that the part of the behavioral information is not behavioral information for an existing user (S1106: NO), the information processing system 100 does not transmit the part of the behavioral information to the customer terminal 110 of the customer. The information processing system 100 ends the process.

[0095] If the user has no experience of using the service or the like corresponding to the part of the behavioral information (S1105: NO), the information processing system 100 determines whether the part of the behavioral information is behavioral information for a new user (S1107).

[0096] If the part of the behavioral information is behavioral information for a new user (S1107: YES), the information processing system 100 proceeds to S1108.

[0097] If the part of the behavioral information is not behavioral information for a new user (S1107: NO), the information processing system 100 does not transmit the part of the behavioral information to the customer terminal 110. The information processing system 100 ends the process.

[0098] The display of the classification information and ideal classification information on the element axis (S1102) may be before the provision of the behavioral information (S1108), after the provision of the behavioral information, or simultaneously with the provision of the behavioral information. When the information processing system 100 provides the behavioral information, some of the behavioral information may be extracted and provided based on the postmortem correspondence information stored in the postmortem correspondence information storage unit 124 and the trained model stored in the trained model storage unit 146.

[0099] <<Modifications>> A modified example of the information processing system 100a will be described with reference to Fig. 12. Fig. 12 is a diagram showing a modified example of the configuration of the information processing system 100a. Note that, hereinafter, only the configuration that differs from the information processing system 100 will be described, and unless otherwise specified, it will be assumed that the configuration is the same as the information processing system 100.

[0100] The information processing system 100a improves the behavioral information provided to the customer by learning the customer's reaction to the behavioral information provided to the customer. As shown in Fig. 12, the information processing system 100a further includes a reaction information acquisition unit 144, a learning unit 145, a trained model storage unit 146, and a reaction prediction unit 147.

[0101] The reaction information acquisition unit 144 acquires customer reaction information to the behavior information provided to the customer terminal 110.

[0102] The reaction information is, for example, information regarding a customer's reaction to the behavioral information. Specifically, the reaction information may be, for example, a reaction that a customer accessed a URL when the provided behavioral information is a "webpage URL," or a reaction that a customer applied for a service (e.g., a yoga discount coupon) when the provided behavioral information is a "service." The reaction information may also be information regarding a customer's failure to take action in response to the behavioral information. The reaction information may also be information indicating a two-stage reaction, such as "performed / did not perform the behavior corresponding to the behavioral information," as described above, or information indicating a multi-stage reaction, such as "accessed the site and performed the action / accessed the site but did not perform the action / did not even access the site." The reaction information may also be quantitative information, such as a score set corresponding to each stage, such as "accessed the site and performed the action."

[0103] The learning unit 145 generates a trained model by training a model using behavioral information provided to multiple customers and reaction information of the multiple customers. That is, when behavioral information of a specific customer to whom behavioral information is to be provided is input, the trained model predicts and outputs reaction information of the customer.

[0104] Furthermore, the learning unit 145 may train a model using, for example, at least one of classification information for each of a plurality of customers, ideal classification information for each of a plurality of customers, customer information for each of a plurality of customers, and posthumous care information for each of a plurality of customers, and reaction information for the customers as training data. That is, the learning unit 145 may train a model using information about the customers as explanatory variables and the reaction information for the customers as objective variables. Here, the learning algorithm is not particularly limited, and may be, for example, a random forest, an SVM (support vector machine), or deep learning. Details of the trained model will be described later.

[0105] The learning unit 145 may generate a trained model in response to an instruction from a system administrator, for example. Alternatively, the learning may be performed automatically at a predetermined timing such as during system maintenance, or immediately after the reaction information acquisition unit 144 acquires reaction information.

[0106] The trained model storage unit 146 stores the trained model trained by the training unit 145.

[0107] The reaction prediction unit 147 predicts customer reaction information to the behavioral information generated by the behavioral information generation unit 141, using the trained model generated by the learning unit 145. The information processing system 100a may cause the behavioral information providing unit 143 to extract and provide part of the behavioral information based on the customer reaction information generated by the reaction prediction unit 147.

[0108] Specifically, when behavioral information is generated by the behavioral information generation unit 141, the reaction prediction unit 147 predicts reaction information for the behavioral information based on the trained model stored in the trained model storage unit 146. The predicted reaction information may be, for example, a probability or score indicating the possibility of the behavior indicated in the behavioral information. That is, for example, when the predicted reaction information is a probability or score equal to or greater than a certain value (i.e., when, for example, a positive reaction is likely to be obtained), the behavioral information providing unit 143 may provide the behavioral information. Also, when the predicted reaction information is a probability or score less than a certain value (i.e., when, for example, a negative reaction is unlikely to be obtained), the behavioral information providing unit 143 may provide the behavioral information.

[0109] The details of the trained model and the processing of the behavioral information providing unit 143 in response to the predicted reaction information will be described below. For example, the trained model generated by the learning unit 145 can predict reaction information as follows for behavioral information A, "using a cut flower subscription service," which has a high weight for "emotion." Note that here, a higher predicted reaction information indicates a higher possibility of obtaining a positive reaction.

[0110] The trained model generated by the learning unit 145 can output predicted reaction information for the behavioral information A as a low probability or score if, when the behavioral information A was provided to multiple customers in the past, a lot of reaction information was obtained indicating that the customers did not take the action shown in the behavioral information A (here, they did not use the cut flower subscription service). As a result, the behavioral information providing unit 143 can avoid providing the behavioral information A to the customer. On the other hand, the trained model generated by the learning unit 145 can output predicted reaction information for the behavioral information A as a high probability or score if, when the behavioral information A was provided to multiple customers in the past, a lot of reaction information was obtained indicating that the multiple customers took the action shown in the behavioral information A. As a result, the behavioral information providing unit 143 can extract and provide the behavioral information A to the customer.

[0111] Furthermore, when behavioral information A has been provided to multiple customers whose type corresponding to the classification information is "artist" (a type with a high index of "emotion") in the past, if a large amount of reaction information is obtained that indicates that the multiple customers have taken the action shown in the behavioral information A (here, they used a cut flower subscription service), the trained model generated by the learning unit 145 can output predicted reaction information to the behavioral information A of customers whose type corresponding to the classification information is also "artist" as a high probability or score. As a result, the behavioral information providing unit 143 can provide behavioral information A to customers whose type corresponding to the classification information is "artist."

[0112] Furthermore, when behavioral information A has been provided to multiple customers whose type corresponding to the ideal classification information is "artist" in the past, and reaction information is obtained that the multiple customers have taken the action shown in the behavioral information A (here, they used a cut flower subscription service), the trained model generated by the learning unit 145 can output predicted reaction information to the behavioral information A of customers whose type corresponding to the ideal classification information is also "artist" as a high probability or score. As a result, the behavioral information providing unit 143 can provide behavioral information A to customers whose type corresponding to the ideal classification information is "artist."

[0113] Furthermore, when behavioral information A has been provided to multiple customers whose customer information includes the information "purchasing an annual pass to see a musical" (customer information with a high weight for "emotion") in the past, if a lot of response information is obtained that the multiple customers have taken the action shown in behavioral information A (here, using a cut flower subscription service), the trained model generated by the learning unit 145 can output predicted response information to behavioral information A of customers whose customer information also includes the information "purchasing an annual pass to see a musical" as a high probability or score. As a result, the behavioral information providing unit 143 provides behavioral information A to customers whose customer information includes the information "purchasing an annual pass to see a musical."

[0114] Furthermore, when the trained model generated by the learning unit 145 has provided behavioral information A to multiple customers whose post-death action information includes "handing over account to family" (post-death action information with a high weight for "emotion") in the past, and has obtained a large amount of response information that the multiple customers took the action shown in the behavioral information A (here, using a cut flower subscription service), the trained model can output predicted response information to the behavioral information A of customers whose post-death action information also includes "handing over account to family" as a high probability or score. As a result, the behavioral information providing unit 143 provides behavioral information A to customers whose post-death action information includes "handing over account to family."

[0115] <<Hardware configuration>> An example of a hardware configuration in which the information processing system 100 is realized by a computer 1300 will be described with reference to FIG.

[0116] 13 is a diagram illustrating an example of the hardware configuration of a computer 1300. As illustrated in FIG. 13, the computer 1300 includes, for example, a processor 1301, a memory 1302, a storage device 1303, an input I / F unit 1304, a data I / F unit 1305, a communication I / F unit 1306, and a display device 1307.

[0117] Computer 1300 may be, for example, a server computer, a personal computer (e.g., desktop, laptop, tablet, etc.), a media computing platform (e.g., cable, satellite set-top box, digital video recorder, etc.), a handheld computing device (e.g., PDA, email client, etc.), or any other type of computing or communications platform.

[0118] The processor 1301 is a control unit that controls various processes in the computer 1300 by executing programs stored in the memory 1302 .

[0119] The memory 1302 is a storage medium such as a RAM (Random Access Memory), etc. The memory 1302 temporarily stores the program code of the program executed by the processor 1301 and data required when the program is executed.

[0120] The storage device 1303 is a non-volatile storage medium such as a hard disk drive (HDD), flash memory, etc. The storage device 1303 stores an operating system and various programs for realizing the above-mentioned components.

[0121] The input I / F unit 1304 is a device for receiving input from a user. The input I / F unit 1304 is, for example, a keyboard, a mouse, a touch panel, various sensors, a wearable device, etc. The input I / F unit 1304 may be connected to the computer 1300 via an interface such as a USB (Universal Serial Bus).

[0122] The data I / F unit 1305 is a device for inputting data from outside the computer 1300. The data I / F unit 1305 is, for example, a drive device for reading data stored in various storage media. The data I / F unit 1305 may be provided outside the computer 1300. When the data I / F unit 1305 is provided outside the computer 1300, the data I / F unit 1305 is connected to the computer 1300 via an interface such as a USB.

[0123] The communication I / F unit 1306 is a device for performing data communication via a network such as the Internet, either wired or wirelessly, with a device external to the computer 1300. The communication I / F unit 1306 may be provided outside the computer 1300. When the communication I / F unit 1306 is provided outside the computer 1300, the communication I / F unit 1306 is connected to the computer 1300 via an interface such as a USB.

[0124] The display device 1307 is a device for displaying various types of information. The display device 1307 is, for example, a liquid crystal display, an organic EL (Electro-Luminescence) display, a display of a wearable device, or the like. The display device 1307 may be provided outside the computer 1300. When the display device 1307 is provided outside the computer 1300, the display device 1307 is connected to the computer 1300 via, for example, a display cable. Furthermore, when a touch panel is adopted as the input I / F unit 1304, the display device 1307 may be configured as an integral part of the input I / F unit 1304.

[0125] An embodiment of the present invention has been described above. The information processing system 100 generates post-mortem correspondence information, classifies customers into at least one of a plurality of categories based on the customer information and the post-mortem correspondence information, and can provide classification information indicating the classification results to the customer terminal 110. This allows customers to obtain their own classification results based on the customer information and the post-mortem correspondence information.

[0126] Furthermore, the information processing system 100 generates ideal classification information regarding the customer's ideal type, and based on the classification information and ideal classification information, provides behavioral information for bringing the customer closer to the customer's ideal type to the customer terminal 110. This allows the customer to receive suggestions for behaviors that will bring the customer closer to the ideal type, and the customer can take actions to bring the customer closer to the ideal type of their own volition.

[0127] Furthermore, the information processing system 100 can extract and provide only a portion of the behavioral information to the customer terminal 110 based on the service information or post-death support information included in the customer's customer information. This allows the information processing system 100 to provide the customer with behavioral information that is likely to lead to actual action by the customer, based on the customer's service usage status and behavioral tendencies.

[0128] Furthermore, the information processing system 100 can extract and provide some behavioral information to the customer terminal 110 based on a trained model generated from the customer information, post-death correspondence information, classification information, or ideal classification information of each of the multiple customers, and the reaction information to the behavioral information of each of the multiple customers. This allows the information processing system 100 to predict the likelihood that a customer will actually take action corresponding to newly provided behavioral information based on the reaction results of multiple customers to behavioral information provided in the past, and then provide the customer with behavioral information that is likely to lead to the customer actually taking action.

[0129] Furthermore, the information processing system 100 can display, on the customer terminal 110, an object showing the evaluation value of each element calculated based on the post-mortem correspondence information and an object showing the index of each element of the type corresponding to the ideal classification information, on an element axis, based on two or more elements that distinguish each of the multiple types. This allows the customer to intuitively recognize the gap between the current type and the ideal type.

[0130] Furthermore, the information processing system 100 can provide at least one of the classification information and the ideal classification information to an external information processing system, thereby enabling the external information processing system to perform processing in accordance with the customer's classification information and ideal classification information.

[0131] It should be noted that the present embodiment is provided to facilitate understanding of the present invention and is not intended to limit the present invention. The present invention may be modified or improved without departing from the spirit thereof, and equivalents thereof are also included in the present invention. [Explanation of symbols]

[0132] 100 Information processing system, 110 Customer terminal, 121 Customer information acquisition unit, 122 Customer information storage unit, 123 Postmortem correspondence setting unit, 124 Postmortem correspondence information storage unit, 131 Classification unit, 132 Classification information storage unit, 133 Classification information provision unit, 134 Ideal classification setting unit, 135 Ideal classification information storage unit, 141 Behavioral information generation unit, 142 Behavioral information storage unit, 143 Behavioral information provision unit, 144 Reaction information acquisition unit, 145 Learning unit, 146 Learned model storage unit, 147 Reaction prediction unit, 151 Display processing unit, 152 External provision unit

Claims

1. a customer information acquisition unit that acquires customer information relating to the customer; a post-mortem correspondence setting unit that receives input from a customer terminal for setting a correspondence to the customer information after the death of the customer and generates post-mortem correspondence information indicating the correspondence to the customer information; a classification unit that calculates an evaluation value based on the elements assigned to the post-mortem correspondence information and the elements assigned to the customer information, and classifies the customer into at least one of a plurality of types based on the calculated evaluation values; a classification information providing unit that provides classification information indicating the result of the classification to the customer terminal; An information processing system comprising:

2. an ideal classification setting unit that receives an input for setting an ideal type of the customer from the customer terminal and generates ideal classification information indicating the ideal type of the customer; a behavioral information generation unit that generates behavioral information related to behaviors that bring the customer closer to the ideal type based on the classification information and the ideal classification information; a behavior information providing unit that provides the behavior information to the customer terminal; The information processing system according to claim 1 , further comprising:

3. The information processing system according to claim 2, wherein the behavioral information providing unit extracts and provides at least one piece of information contained in the behavioral information based on information regarding services used by the customer contained in the customer information.

4. The information processing system according to claim 2 or 3, wherein the behavioral information providing unit extracts and provides at least one piece of information included in the behavioral information based on the post-mortem correspondence information.

5. The information processing system according to any one of claims 2 to 4, wherein the behavioral information providing unit extracts and provides at least one piece of information contained in the behavioral information of the specified customer generated by the behavioral information generating unit based on reaction information regarding a predicted reaction of the specified customer, which is generated by inputting the behavioral information of a specified customer into a trained model that is trained using the behavioral information provided to each of the multiple customers and reaction information regarding each of the multiple customers' reactions to the behavioral information provided to each of the multiple customers as training data.

6. 6. The information processing system of claim 5, wherein the behavioral information providing unit extracts and provides at least one piece of information contained in the behavioral information of the specified customer generated by the behavioral information generating unit based on reaction information regarding a predicted reaction of the specified customer, which is generated by inputting at least one of the classification information, the ideal classification information, the customer information, and the post-death response information for the specified customer, and the behavioral information into a trained model that is trained using as training data at least one of: classification information indicating the result of classifying each of a plurality of customers into at least one of a plurality of types; ideal classification information which is an ideal type for each of the plurality of customers; customer information for each of the plurality of customers; and post-death response information regarding a response to the customer information after the death of each of the plurality of customers set by each of the plurality of customers; the behavioral information provided for each of the plurality of customers; and reaction information regarding each of the plurality of customers' reactions to the behavioral information provided for each of the plurality of customers.

7. Two or more elements that distinguish each of the multiple types are set, The classification unit classifies the customer into at least one of the plurality of types based on the evaluation values ​​of the two or more elements calculated by the weights of the two or more elements set in the post-death correspondence information, a display processing unit that displays a first object indicating the evaluation value of each of the two or more elements; The information processing system according to any one of claims 1 to 6.

8. Two or more elements that distinguish each of the multiple types are set, The ideal type has an index for each of the two or more elements, The classification unit classifies the customer into at least one of the plurality of types based on the evaluation values ​​of the two or more elements calculated by the weights of the two or more elements set in the post-death correspondence information, a display processing unit that displays a first object indicating the evaluation value of each of the two or more elements and a second object indicating the index set for the ideal type in a manner that allows comparison therebetween; The information processing system according to claim 2 .

9. 9. The information processing system according to claim 8, wherein the display processing unit displays the first object and the second object at positions on the same straight line according to the evaluation value indicated by the first object and the indicator indicated by the second object.

10. The information processing system according to claim 2 , further comprising an external providing unit that provides at least one of the classification information and the ideal classification information to an external information processing system.

11. The computer Obtain customer information about the customer; receiving, from a customer terminal, an input for setting a response to the customer information after the death of the customer, and generating post-death response information indicating the response to the customer information; Calculating an evaluation value based on the elements assigned to the post-death correspondence information and the elements assigned to the customer information, and classifying the customer into at least one of a plurality of types based on the calculated evaluation values; providing classification information indicating the results of the classification to the customer terminal; Information processing methods.

12. On the computer, a customer information acquisition unit that acquires customer information relating to the customer; a post-mortem correspondence setting unit that receives input from a customer terminal for setting a correspondence to the customer information after the death of the customer and generates post-mortem correspondence information indicating the correspondence to the customer information; a classification unit that calculates an evaluation value based on the elements assigned to the post-mortem correspondence information and the elements assigned to the customer information, and classifies the customer into at least one of a plurality of types based on the calculated evaluation values; a classification information providing unit that provides classification information indicating the result of the classification to the customer terminal; An information processing program to achieve this.

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