Information processing apparatus and information processing method

Abstract

To distribute data so as to improve the satisfaction of a user.SOLUTION: An information processing apparatus includes a type information acquisition unit, a statistical information acquisition unit, and a control unit. The type information acquisition unit acquires type information which is classified for a customer of a service provided by a business operator, the type information being classified according to varieties of the customer. The statistical information acquisition unit acquires, using a predetermined item for a seller included in a purchase history of the customer, purchase history of the customer according to the type information associated with the predetermined item, as statistical information obtained by statistically processing the purchase history. The control unit manages the statistical information so that a predetermined business operator can use the statistical information.SELECTED DRAWING: Figure 4

Description

[Technical field]

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

[0002] Conventionally, there have been many efforts to classify customers into a certain type (so-called persona, etc.) based on information about the customers, and to utilize the results of the classification in marketing business, 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] Patent Publication No. 2021-105868 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the above-mentioned conventional technology merely classifies customers into predetermined types based on customer information, and there is room for improvement in terms of realizing data distribution that increases user satisfaction.

[0006] Therefore, the present invention proposes an information processing device and an information processing method that can realize data distribution that increases user satisfaction. [Means for solving the problem]

[0007] In order to solve the above problems, one form of information processing device according to the present invention is characterized in having a type information acquisition unit that acquires type information classified for customers of a service provided by a business operator, the type information being classified according to the diversity possessed by the customers, a statistical information acquisition unit that acquires purchasing trends of the customer corresponding to the type information linked to specified items as statistical information obtained by statistically processing the purchase history using specified items of the seller included in the customer's purchase history, and a control unit that manages the statistical information so that a specified business operator can use the statistical information. Effect of the Invention

[0008] According to the present invention, data distribution that increases user satisfaction can be realized. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is an explanatory diagram for explaining an overview of the present invention. [Diagram 2] FIG. 2 is a diagram showing an example of various personas that a user has. [Diagram 3] FIG. 3 is a diagram illustrating an example of a system according to the embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of a configuration of a platform management device according to the embodiment. [Diagram 5] FIG. 5 is a diagram illustrating an example of a persona information storage unit according to the embodiment. [Figure 6] FIG. 6 is a diagram illustrating an example of a store persona unit purchasing trend data storage unit according to the embodiment. [Figure 7] FIG. 7 is a diagram illustrating an example of a human persona-based customer tendency data storage unit according to the embodiment. [Figure 8] FIG. 8 is a diagram illustrating a method for providing purchasing trend data according to the embodiment. [Figure 9] FIG. 9 is a diagram (1) for explaining a data integration method according to an embodiment. [Figure 10] FIG. 10 is a diagram (2) for explaining the data integration method according to the embodiment. [Figure 11] FIG. 11 is a flowchart showing the procedure of the integration process executed by the platform management device. [Figure 12] FIG. 12 is a block diagram illustrating an example of a hardware configuration of an information processing device according to an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. In this specification and the drawings, components having substantially the same functional configurations are denoted by the same reference numerals, and redundant description will be omitted.

[0011] One or more embodiments (including examples, modifications, and application examples) described below can be implemented independently. Meanwhile, at least a part of the embodiments described below may be implemented in appropriate combination with at least a part of another embodiment. These embodiments may include novel features different from each other. Thus, these embodiments may contribute to solving different purposes or problems and may provide different effects.

[0012] (Embodiment) 1. Overview First, an overview of the present invention will be described with reference to Fig. 1. Fig. 1 is an explanatory diagram for explaining the overview of the present invention.

[0013] Recently, with the advancement of IT technology, provision of information personalized to each user has become widespread. For example, Fig. 1(a) shows an example of a conventional marketing method. According to Fig. 1(a), in a conventional marketing method, a business such as a company collects historical information (e.g., behavioral history) and personal information of users, and takes action against users based on assumptions (e.g., user segment A is interested in XX / user segment B is interested in XX) obtained by analyzing the collected information.

[0014] However, in the conventional marketing method, for example, content that the user is not interested in may be displayed, and the user may feel that his / her actions are being tracked by the company. In other words, the conventional marketing method has a problem that it makes the user feel uncomfortable and does not adequately protect the privacy of the user.

[0015] One of the causes of these problems is that conventional methods are company-driven (companies take the lead). For example, in conventional methods, companies can classify users into specific types based on information such as the user's behavior and provide content related to those types, but there is room for improvement in understanding the individuality and diversity of users and taking actions that are tailored to the user. In this way, in a situation where individual diversity is becoming more and more important in society as a whole, company-driven personalization is becoming less acceptable, and it is also difficult for companies to properly understand the diversity of their users.

[0016] Furthermore, it is difficult for conventional methods to respond to the situation where users are becoming more conscious of privacy issues. For example, users may have doubts about whether their personal information is being stolen while using the Internet, and conventional methods have room for improvement in this regard as well.

[0017] As mentioned above, conventional methods do not necessarily realize the distribution of data that increases user satisfaction. For example, "well-being" is known as a concept that means people are in a good physical, mental, and social condition, i.e., in a happy state, but conventional methods do not necessarily realize the distribution of data that increases user well-being.

[0018] Therefore, in this invention, in order to utilize valuable personal data, we propose a new marketing method to replace conventional methods, which can increase customer satisfaction in both the utilization of personal data and well-being. For example, this invention proposes a new personalization method based on data distribution with sufficient consideration for privacy, and premised on a shift from corporate-led to user-led personalization.

[0019] In this way, the present invention aims to extract, for example, the diversity within a group or the diverse needs within an individual, and to propose information according to these needs, rather than judging a user by their attributes. In other words, the present invention differs from conventional methods in that it does not treat a user simply as an "individual," but instead focuses on the user's "persona," shifting the focus to user-driven personalization.

[0020] For example, the platform PF according to the present invention acts as an information bank that realizes new personalization based on personas and acts as an intermediary between businesses. In addition, well-being indicators are incorporated into this information bank. With the platform PF incorporating well-being indicators, the information bank will have a wealth of data on places, stores, individuals, etc., and as a result, member businesses will be able to carry out marketing that captures the situation in a comprehensive manner.

[0021] First, the information processing realized by the platform PF according to the present invention (information processing according to the embodiment) will be briefly described with reference to FIG.

[0022] In the information processing according to the embodiment, an information bank that makes use of the diversity of individuals is used to propose services based on the user's "desired self." For example, in the information processing according to the embodiment, a "persona diagnosis" is performed on the user by incorporating well-being indicators into the information bank, and the persona information resulting from the diagnosis is used to statistically process purchase histories provided by specific businesses. The persona information includes persona classification as type information classified according to the diversity possessed by users.

[0023] More specifically, in the information processing according to the embodiment, the purchase history is statistically processed using a combination of "store," which is one item included in the purchase history, and the "persona classification" obtained as a diagnosis result as the axis of the statistical processing. As a result, in the information processing according to the embodiment, it is possible to obtain purchasing trend data of the user according to the "persona classification" linked to the "store" (statistical information obtained by statistically processing the purchase history), and this purchasing trend data is provided to another specific business. The business that receives the purchasing trend data can, for example, realize user-driven personalization by analyzing the purchasing trend data. For example, the business can provide content that meets the potential needs of the user.

[0024] [2. About Persona] Next, personas will be described with reference to Fig. 2. Fig. 2 is a diagram showing examples of various personas that users have. According to Fig. 2, a user does not have a uniform "personality," and even a single user has various "personalities," such as the persona at work and the persona in front of family members.

[0025] Furthermore, as shown in FIG. 2, for example, in addition to the actual "self," a user also has a "self" that is in relation to an ideal self, such as the ideal self at work or the self they want to be in front of their family. These various aspects that a user possesses, that is, "self," are called "personas." Also, users divided into different personas are called "dividuals." For example, even for the same user, a user who is in persona classification A can be called dividual X, and a user who is in persona classification B can be called dividual Y. Note that the personas shown in FIG. 2 are merely examples, and personas may be various types that classify users, that is, people.

[0026] As explained in Fig. 2, a user naturally adopts different personalities depending on the environment and time axis, for example, when at work, at home, in a network environment, etc. This differentiation is called a "division," and personal data corresponding to the division is managed in an information bank. Therefore, the platform PF according to the present invention allows for a deeper and more layered expression of individuality and diversity, enabling businesses to provide services that realize natural humanness in the cyber world in the real world, which is believed to lead to improved well-being.

[0027] 3. System Configuration Fig. 3 is a diagram showing an example of a system according to an embodiment. Fig. 1 shows a system 1 as an example of a system according to an embodiment. Information processing according to the embodiment is realized in the system 1.

[0028] 3, the system 1 includes a customer device 10, a cooperator device 20, a user device 30, and a platform management device 100. The customer device 10, the cooperator device 20, the user device 30, and the platform management device 100 are connected to each other via a network N so as to be able to communicate with each other via wired or wireless communication. The number of the customer devices 10, the cooperator devices 20, and the user devices 30 in the system 1 is not limited.

[0029] First, the collaborator Cx and the user Ex in the embodiment will be described. In this embodiment, both the collaborator Cx and the user Ex correspond to businesses that are expanding their business in a specific service, but their positions in the system 1 are different. For example, the collaborator Cx is in a position to cooperate with the operating organization OP that provides and manages the platform PF, and provides the operating organization OP (platform management device 100) with purchasing trend data obtained by statistically processing the purchasing history corresponding to the company's customers CUx. Since this purchasing trend data is necessary for the information processing according to the embodiment, the business that provides the purchasing trend data is the collaborator Cx for the operating organization OP.

[0030] The user Ex is a business entity that acquires (e.g., purchases) purchasing trend data from the operating organization OP (platform management device 100) and intends to utilize the acquired purchasing trend data for its own services. For example, the user Ex may be a local company or a local government that promotes development of the target area in order to revitalize the target area. In addition, according to this example, the customer EUx of the user Ex may be, for example, a person who resides in the target area or a person who works in the target area.

[0031] In the following embodiment, the "x" of each of the collaborator Cx, user Ex, customer CUx, and customer EUx may be assigned an arbitrary numerical value to distinguish them from one another. For example, the collaborator C1's customer CU1 and the user E1's customer EU1 may be used as examples.

[0032] Furthermore, when there is no need to distinguish between a customer CUx of a collaborator Cx and a customer EUx of a user Ex, they will be simply referred to as a customer U. The customer U can be understood as a user according to the embodiment.

[0033] Next, each device will be described. The customer device 10 is an information processing terminal used by a customer U of any of the businesses (collaborators Cx or users Ex) affiliated with the services of the platform PF, and corresponds to an edge computer. For example, the customer device 10 is a smartphone, a wearable device, a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, a PDA (Personal Digital Assistant), etc. The customer U can receive a persona diagnosis using the customer device 10.

[0034] The collaborator device 20 is a server device that realizes the service CSAx provided by the collaborator Cx, and corresponds to a cloud computer. For example, the collaborator device 20 manages various information obtained when the service CSAx is used by the customer EUx, by linking it to an account ID (customer ID). For example, the collaborator device 20 generates purchase trend data by statistically processing the purchase history corresponding to the customer CUx. The service CSAx may also be expressed as a distinction, such as service CSA1, by assigning an arbitrary numerical value to "x".

[0035] The user device 30 is a server device that realizes the service ESAx provided by the user Ex, and corresponds to a cloud computer. For example, the user device 30 manages customer information of the customer EUx who uses the service ESAx by linking it to an account ID (customer ID). For example, the user device 30 manages customer information including attribute information and service specific information of the customer EUx. The service ESAx may also be expressed as a distinction, such as service ESA1, by substituting an arbitrary numerical value for "x".

[0036] The platform management device 100 is a server device that realizes information processing according to the embodiment, and corresponds to a cloud computer. The platform management device 100 corresponds to a platform PF having a function as an information bank that makes use of the diversity of individuals, and has a role of, for example, mediating data exchange between businesses. The platform management device 100 provides a service that performs information processing and information provision for businesses to realize user-driven personalization. In other words, the platform management device 100 provides a service that supports businesses so that they can carry out marketing that captures situations in a comprehensive manner.

[0037] 4. Configuration of the Platform Management Device The platform management device 100 according to the embodiment will be described with reference to Fig. 4. Fig. 4 is a diagram showing a configuration example of the platform management device 100 according to the embodiment. As shown in Fig. 4, the platform management device has a communication unit 110, a storage unit 120, and a control unit 130.

[0038] (Regarding communication unit 110) The communication unit 110 is realized by, for example, a network interface card (NIC) etc. For example, the communication unit 110 is connected to the network N by wire or wirelessly, and transmits and receives information between the customer device 10, the cooperator device 20, and the user device 300.

[0039] (Regarding the storage unit 120) The storage unit 120 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk, an optical disk, etc. The storage unit 120 has a persona information storage unit 121, a store persona unit purchasing trend data storage unit 122, and a human persona unit customer tendency data storage unit 123.

[0040] (Persona information storage unit 121) The persona information storage unit 121 stores persona classifications and the like as analysis results for persona diagnosis information (including customer U's answers to questions). Fig. 5 shows an example of the persona information storage unit 121 according to the embodiment. In the example of Fig. 5, the persona information storage unit 121 has items such as "business operator ID", "service ID", "customer ID", "persona configuration information", and "persona classification".

[0041] The "business operator ID" is identification information for identifying a business operator (collaborator Cx or user Ex) affiliated with the platform PF. The "service ID" is identification information for identifying a service provided by the business operator indicated by the "business operator ID." The "customer ID" is identification information for identifying a customer U (customer CUx or customer EUx) who belongs to the service indicated by the "service ID."

[0042] "Persona configuration information" includes, as information on persona diagnosis, answers, items, values, etc. of customer U (customer CUx or customer EUx). "Persona classification" indicates the persona classification estimated for customer U (customer CUx or customer EUx) by analyzing the information on persona diagnosis.

[0043] FIG. 5 shows an example in which a customer CU11 belonging to a service CSA1 of a collaborator Cx is classified into persona classification A by analyzing persona configuration information CF11 obtained from the customer CU11 through persona diagnosis of the customer CU11.

[0044] FIG. 5 also shows an example in which customer EU11 is classified into persona classification A by analyzing persona configuration information EF11 obtained from customer EU11 through persona diagnosis of customer EU11 belonging to service ESA1 of user Ex.

[0045] (Store persona unit purchasing trend data storage unit 122) As will be described later, the collaborator device 20 accumulates the purchasing history of the customer CUx in its own device, and this purchasing history includes, for example, a "store ID" as a seller's item. The "store ID" is identification information that identifies the store from which the customer CUx of the collaborator Cx made a purchase. The collaborator device 20 then performs statistical processing of the purchasing history using, as the axis of statistical processing, the "persona classification" (store persona) that is linked to each "store ID" included in the purchasing history, among the "persona classifications" classified for the customer CUx by the persona diagnosis. As a result, the collaborator device 20 can obtain purchasing trend data by store persona, i.e., purchasing trend data by store persona.

[0046] The store persona unit purchasing trend data is transmitted from the collaborator device 20 to the platform management device 100. As a result, the store persona unit purchasing trend data storage unit 122 stores the store persona unit purchasing trend data transmitted from the collaborator device 20.

[0047] Here, Fig. 6 shows an example of the store persona unit purchasing trend data storage unit 122 according to the embodiment. In the example of Fig. 6, the store persona unit purchasing trend data storage unit 122 has items such as "business ID", "store ID", "persona classification", and "purchasing trend data". Also, according to the example of Fig. 6, the "purchasing trend data" includes "proportion of number of users", "proportion of number of times used", and "proportion of total amount used".

[0048] The “business operator ID” is identification information that identifies the business operator that provided the “purchasing trend data” among the collaborators Cx affiliated with the platform PF.

[0049] The "store ID" is identification information for identifying a store from which a customer CUx of a collaborator Cx makes a purchase. The store may be a real store or a virtual store located in a specific electronic mall.

[0050] "Persona classification" is type information classified for customer CUx linked to "purchase trend data". In other words, "persona classification" is classification information classified for customer CUx of collaborator Cx indicated by "business ID". As shown in Figure 6, "persona classification" is used in combination with "store ID" as an axis for statistically processing purchase history, and is linked to "store ID". For this reason, "persona classification" on the collaborator Cx side can be called store persona.

[0051] "Number of users proportion" is an example of "purchasing trend data." The example in Figure 6 shows an example of a purchasing trend calculated such that, of the total number of customers CU1 who used the store indicated by store ID "SH1" (store SH1), "50%" are customers CU1 in persona classification A, "30%" are customers CU1 in persona classification B, and "20%" are customers CU1 in persona classification C.

[0052] "Use frequency ratio" is an example of "purchasing trend data." The example in Figure 6 shows an example of a purchasing trend calculated to indicate that of the total number of times customer CU1 uses store SH1, "40%" is accounted for by customer CU1 in persona classification A, "35%" is accounted for by customer CU1 in persona classification B, and "25%" is accounted for by customer CU1 in persona classification C.

[0053] "Total spending ratio" is an example of "purchasing trend data." The example in Figure 6 shows an example of a purchasing trend in which "60%" of the total spending by customer CU1 at store SH1 is accounted for by customer CU1 in persona classification A, "30%" of the number of times is accounted for by customer CU1 in persona classification B, and "10%" of the number of times is accounted for by customer CU1 in persona classification C.

[0054] (Persona-based customer tendency data storage unit 123) As will be described later, the user device 30 stores customer information of customer EUx in its own device as personal information about what kind of person customer EUx is. Therefore, the user device 30 statistically processes the customer information using a "persona classification" (human persona) linked to a "customer ID" that identifies customer EUx (person) as the axis of the statistical processing. As a result, the user device 30 can obtain customer tendency data in units of "persona classification" linked to a "customer ID", that is, customer tendency data in units of human personas. In this way, the user device 30 has a function of generating linking information in which customer tendencies are linked by persona classification units (using persona classification as the axis) by statistically processing the customer information of customer EUx belonging to a persona classification.

[0055] The human persona-based customer trend data is transmitted from the user device 30 to the platform management device 100. As a result, the human persona-based customer trend data storage unit 123 stores the human persona-based customer trend data transmitted from the user device 30.

[0056] 7 shows an example of the human persona-based customer trend data storage unit 123 according to the embodiment. In the example of FIG. 7, the human persona-based customer trend data storage unit 123 has items such as "user ID" and "customer trend data."

[0057] The "user ID" is identification information for identifying a business that has requested the "purchase trend data" among users Ex affiliated with the platform PF. In this embodiment, a business can be either a user Ex or a collaborator Cx. For example, a business that has become a collaborator Cx by providing the "purchase trend data" in FIG. 6 also conducts a business of revitalizing a region using the "purchase trend data" of other businesses. In this case, this business is both a collaborator Cx and a user Ex with respect to the operating organization OP. In addition, the operating organization OP may establish a policy that, for example, a business that is a collaborator Cx cannot become a user Ex, and a business that is a user Ex cannot become a collaborator Cx.

[0058] "Persona classification" is type information classified for customer EUx linked to "customer tendency data". In other words, "persona classification" is classification information classified for customer EUx of user Ex indicated by "business ID". "Persona classification" here is used as an axis when statistically processing customer information, and is linked to the "customer ID" ("customer ID" in Figure 5) that identifies customer EUx (person) in the customer information. For this reason, the "persona classification" on the user Ex side can be called a human persona.

[0059] In the customer information, the attribute information of the customer EUx, the service specific information, and the persona classification are associated with the customer ID. The attribute information of the customer EUx includes personal information such as name, address, age, and occupation. The service specific information is unique information that is accumulated according to the use of the service ESAx.

[0060] "Customer trend data" is statistical information obtained by statistically processing customer information on a person persona basis. Figure 7 shows an example in which statistical information SDA11 is generated by statistically processing customer information on customer EU1, who belongs to persona classification A, among customers EUx of user Ex.

[0061] (Regarding the control unit 130) 4, the control unit 130 is realized by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like executing various programs (for example, an information processing program according to the embodiment) stored in a storage device inside the platform management device 100 using a RAM as a working area. The control unit 130 is also realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0062] As shown in Fig. 4, the control unit 130 has a response acquisition unit 131, an estimation unit 132, a persona information acquisition unit 133, a statistical information acquisition unit 134, a provision unit 135, a linking information acquisition unit 136, and an integration unit 137, and realizes or executes the functions and actions of information processing described below. Note that the internal configuration of the control unit 130 is not limited to the configuration shown in Fig. 4, and may be other configurations as long as they perform information processing described below. Also, the connection relationship between the processing units of the control unit 130 is not limited to the connection relationship shown in Fig. 4, and may be other connection relationships.

[0063] (Answer acquisition unit 131) The answer acquisition unit 131 acquires answer information input by the customer U to questions in the persona diagnosis. For example, the answer acquisition unit 131 acquires answer information input using the customer device 10, and registers the answer information in the persona information storage unit 121 as one piece of persona configuration information.

[0064] (Estimation part 132) The estimation unit 132 estimates the persona classification of each customer U of the business operator based on the answer information input by the customer U to the questions in the persona diagnosis. That is, the estimation unit 132 analyzes the answer information and classifies the customer U into a specific persona based on the analysis result. In addition, the estimation unit 132 registers information on the estimated persona classification in the persona information storage unit 121.

[0065] (Persona information acquisition unit 133) The persona information acquisition unit 133 acquires persona information including the persona classification estimated by the estimation unit 132. For example, the persona information acquisition unit 133 acquires persona information necessary for the current process from the persona information storage unit 121, and outputs it to a corresponding processing unit.

[0066] (Statistical information acquisition unit 134) The statistical information acquisition unit 134 uses a predetermined item (e.g., "store ID") on the seller's side included in the purchase history of the customer U to acquire purchasing trend data (e.g., "proportion of number of users," "proportion of number of times used," "proportion of total amount used," etc.) of the customer U according to the persona classification (store persona) linked to the predetermined item as statistical information obtained by statistically processing the purchase history. That is, the statistical information acquisition unit 134 acquires purchasing trend data per store persona.

[0067] In addition, the statistical information acquisition unit 134 registers the acquired purchasing trend data in the store persona unit purchasing trend data storage unit 122. As described above, the purchasing trend data is generated on the collaborator Cx (collaborator device 20) side and provided to the platform management device 100.

[0068] (Provider 135) The providing unit 135 corresponds to a control unit that manages the store persona unit purchasing trend data so that the user Ex can use the statistical information. For example, the providing unit 135 provides the store persona unit purchasing trend data to the user Ex who has requested the store persona unit purchasing trend data in a purchasable manner.

[0069] (Linked information acquisition unit 136) The linking information acquisition unit 136 acquires linking information that links customer information held by the user Ex with a persona classification classified for the customer included in the customer information. Specifically, the linking information acquisition unit 136 acquires linking information including customer tendency data in which customer information is statistically processed according to a persona classification (human persona) classified for the customer EUx of the user Ex. That is, the linking information acquisition unit 136 acquires human persona-based customer tendency data.

[0070] In addition, the association information acquisition unit 136 registers the acquired customer tendency data in the human persona unit customer tendency data storage unit 123. As explained above, the customer tendency data is generated on the user Ex (user device 30) side and provided to the platform management device 100.

[0071] (Integrated Section 137) The integration unit 137 corresponds to a control unit that manages store persona unit purchasing trend data so that the user Ex can use statistical information. For example, the integration unit 137 matches the identity of the human persona with the store persona, extracts combinations of human persona unit customer trend data and store persona unit purchasing trend data whose persona classifications match or are similar to each other, and integrates the customer trend data and purchasing trend data included in the extracted combinations. Through this integration process, integrated information X is generated in which the human persona unit customer trend data and the store persona unit purchasing trend data are integrated for the persona classifications that match or are similar to each other.

[0072] Here, for example, the user Ex can input an integration request including a desired cooperator Cx as an integration destination as an integration condition to the platform management device 100. Specifically, the user Ex can input an integration request including, as an integration condition, the cooperator Cx desired as an integration destination, indicating which cooperator Cx provides store persona unit purchasing trend data to be integrated with the human persona unit customer tendency data generated by the user device 30 of the user Ex.

[0073] Therefore, when an integration request by the user Ex is accepted, the linking information acquisition unit 136 acquires linking information corresponding to the integration request (linking information provided by the user Ex). The integration unit 137 integrates the human persona-based customer tendency data included in the acquired linking information with the store persona-based purchasing trend data provided by the cooperator Cx specified by the integration conditions, based on a matching process.

[0074] Furthermore, the providing unit 135 provides the integrated information X to the user Ex who has made the integration request so that the integrated information X can be purchased.

[0075] [5. Provision of purchasing trend data] As one pattern of processing for mediating data exchange between the collaborator Cx and the user Ex, the platform management device 100 may sell and provide the store persona unit purchasing trend data generated by the collaborator Cx to the user Ex. This point will be described with reference to Fig. 8. Fig. 8 is a diagram for explaining a method of providing purchasing trend data according to an embodiment.

[0076] 8 shows a scene in which user E1 designates collaborator C1 as a data purchase destination, and store persona unit purchasing trend data generated by collaborator C1 is sold to user Ex. In this scene, collaborator C1 (C1 company) requests a persona diagnosis for customer CU1 who uses its company's service CSA1, and provides the store persona unit purchasing trend data generated by its company to the platform management device 100.

[0077] 8 shows an example in which a persona diagnosis is performed for each customer CU1 of a collaborator C1 via a customer device 10 (step S11). In this case, the platform management device 100 acquires information on the persona diagnosis from the customer device 10 (step S12).

[0078] Diversity includes the individual's innate personality (such as race and age), characteristics that change depending on the environment (such as hobbies and beliefs), and role in an organization (such as place of work and employment type), and in the persona diagnosis, questions related to these may be provided. For example, the persona diagnosis may be conducted in a Q&A format regarding emotional or theoretical, conservative or innovative, dog person or cat person, music lover or art lover, etc., and the platform management device 100 acquires persona diagnosis information including the answers of customer CU1. For this reason, the information on the diversity of customer CU1 acquired by the persona diagnosis can be said to be information used to estimate the persona classified as customer CU1.

[0079] Furthermore, the platform management device 100 may allow the collaborator C1 to carry out the persona diagnosis at any timing and in any medium by providing the collaborator device 20 with a persona diagnosis format (Q&A format). Therefore, for example, the collaborator C1 can freely implement measures such as increasing the number of respondents by carrying out the persona diagnosis as a point campaign.

[0080] When the platform management device 100 acquires the information of the persona diagnosis, it performs an analysis process to estimate a persona classification, which is a type of persona, for each customer CU1 based on the information of the persona diagnosis, and returns the analysis result to the cooperator C1 (step S13). Specifically, the platform management device 100 returns the persona classification classified for each customer CU1 as the analysis result to the cooperator C1 in a state in which the persona classification is associated with, for example, a customer ID.

[0081] According to the example of Fig. 8, the cooperator device 20-1 of the cooperator C1 holds a purchase history for each customer CU1, and manages the persona classification returned from the platform management device 100 and the purchase history by linking them based on, for example, a customer ID that identifies the customer CU1 (step S14). The purchase history includes a store ID that identifies the store where the purchase was made. Specifically, a store ID that identifies the store where the purchase was made is associated with each customer CU1.

[0082] Therefore, the collaborator device 20-1 extracts a purchase history based on a combination of "persona classifications" that are linked to each "store ID" included in the purchase history from the persona classifications classified for customer CU1 by the persona diagnosis, and links the extracted purchase history to the combination of "persona classifications" (step S15).

[0083] Then, the collaborator device 20-1 statistically processes the purchase history based on the combination of "persona classifications" (i.e., store persona) (step S16). According to the example of Fig. 8, the collaborator device 20-1 statistically processes the purchase history resulting from the customer CU1's use of store SH1 based on the store persona (a combination of persona classification A, persona classification B, and persona classification C). As a result, the collaborator device 20-1 can generate purchase trend data by store persona, as shown in Fig. 8.

[0084] The store persona unit purchasing trend data is shared between the collaborator device 20-1 and the platform management device 100 (step S17). Specifically, the collaborator device 20-1 transmits the store persona unit purchasing trend data to the platform management device 100. The store persona unit purchasing trend data is managed by the store persona unit purchasing trend data storage unit 122 included in the platform management device 100.

[0085] Here, it is assumed that the platform management device 100 receives a request from user E1 (company E1) to purchase the store persona unit purchasing trend data generated by collaborator C1. In this case, the store persona unit purchasing trend data provided by collaborator device 20-1 is linked between the platform management device 100 and user device 30-1 of user E1 (step S18). Specifically, the platform management device 100 provides the store persona unit purchasing trend data to user device 30-1 so that it can be purchased.

[0086] A user E1 who has purchased store persona-based purchasing trend data from collaborator C1 can, for example, analyze this store persona-based purchasing trend data and use it in an area development project. For example, user E1 can make various predictions, such as what persona classification of customers EU1 (for example, people who live or work in a target area managed by collaborator C1) is likely to use what kind of store in the target area, or what persona classification of people will contribute more to sales. In addition, user E1 can, for example, appropriately consider which stores to introduce into the target area based on the predictions.

[0087] In addition, the user E1 may not be able to properly analyze the store persona unit purchasing trend data by simply purchasing the store persona unit purchasing trend data. For this reason, although not shown in Fig. 8, a persona diagnosis may also be performed for each of the customers EU1 of the collaborator C1. In addition, data integration described below may be performed so that the user E1 can more efficiently analyze the store persona unit purchasing trend data.

[0088] [6. Data Integration] As another pattern of the process of mediating data exchange between the collaborator Cx and the user Ex, the platform management device 100 may integrate the store persona unit purchase trend data generated by the collaborator Cx and the human persona unit customer tendency data generated by the user Ex. For example, when the platform management device 100 receives an integration request from the user Ex requesting integration of the customer information of the user Ex and the store persona unit purchase trend data provided by a specific collaborator Cx, the platform management device 100 performs an integration process to integrate these data. This point will be described with reference to Figs. 9 and 10. Figs. 9 and 10 will continue to use an example of data exchange between the collaborator C1 and the user E1.

[0089] First, the statistical processing of the customer information held by the user E1 will be described with reference to Fig. 9. Fig. 9 is a diagram (1) for explaining the data integration method according to the embodiment.

[0090] 9 shows an example in which a persona diagnosis is performed for each customer EU1 of a user E1 via the customer device 10 (step S21). In this case, the platform management device 100 acquires information on the persona diagnosis from the customer device 10 (step S22).

[0091] When the platform management device 100 acquires the information of the persona diagnosis, the platform management device 100 performs an analysis process to estimate a persona classification, which is a type of persona, for each customer EU1 based on the information of the persona diagnosis, and returns the analysis result to the user E1 (step S23). Specifically, the platform management device 100 returns the persona classification classified for each customer EU1 as the analysis result to the user E1 in a state in which the persona classification is associated with, for example, a customer ID.

[0092] According to the example of Fig. 9, the user device 30-1 of the user E1 holds customer information (e.g., personal information) for each customer EU1, and manages the persona classification returned from the platform management device 100 and the customer information by linking them based on, for example, a customer ID that identifies the customer EU1 (step S24). The content of the customer information is not limited. For example, the customer information varies depending on the service ESA1 of the user E1, and examples include the payment history of the customer EU1 and the location history of the customer EU1.

[0093] Therefore, the user device 30-1 extracts the purchase history in units of the "persona classification" into which the customer EU1 is classified by the persona diagnosis, and links the extracted purchase history to the "persona classification" (step S25).

[0094] Then, the user device 30-1 statistically processes the customer information based on the "persona classification" (i.e., human persona) (step S26). According to the example of Fig. 9, the user device 30-1 statistically processes the customer information of the customer EU1 belonging to each persona classification (persona classification A, persona classification C) classified for the customer EU1 by the persona diagnosis. As a result, the user device 30-1 can generate human persona-based customer tendency data as shown in Fig. 9.

[0095] The person persona-based customer trend data is shared between the user device 30-1 and the platform management device 100 (step S27). Specifically, the user device 30-1 transmits the person persona-based customer trend data to the platform management device 100. The person persona-based customer trend data is managed in the person persona-based customer trend data storage unit 123 included in the platform management device 100.

[0096] Next, the integration process of integrating the store persona unit purchase trend data provided by the collaborator C1 and the human persona unit customer tendency data provided by the user E1 will be described with reference to Fig. 10. Fig. 10 is a diagram (2) for explaining the data integration method according to the embodiment.

[0097] First, the integration unit 137 of the platform management device 100 executes a matching process using persona classification (step S31). For example, the integration unit 137 performs identity matching between the "persona classification" linked to the store persona unit purchasing trend data (persona classification A, persona classification B, and persona classification C in the example of FIG. 10) and the "persona classification" linked to the human persona unit customer tendency data (persona classification A and persona classification C in the example of FIG. 10).

[0098] Based on the matching result, the integration unit 137 extracts combinations of "purchase trend data" and "customer tendency data" whose "persona classifications" match or are similar to each other (step S32).Then, the integration unit 137 generates integrated information X by integrating the extracted combinations (step S33).

[0099] Steps S31 to S33 will be described in more detail with reference to the example in FIG. 10. In the example in FIG. 10, the persona classifications linked to the "purchasing trend data" are persona classifications A, B, and C. On the other hand, the persona classifications linked to the "customer tendency data" are persona classifications A and C. The integration unit 137 compares the persona classifications A to C linked to the "purchasing trend data" with the persona classifications A and C linked to the "customer tendency data" to determine whether there are any matching or similar persona classifications between the two. In the example in FIG. 10, the integration unit 137 can determine that persona classifications A and C exist as persona classifications that match between the two data.

[0100] In this example, the integration unit 137 uses persona classification A as a data extraction key to extract data from each of the "purchasing trend data" and "customer tendency data" as shown in Fig. 10, and integrates the extracted data to generate integrated information X1. Also, the integration unit 137 uses persona classification C as a data extraction key to extract data from each of the "purchasing trend data" and "customer tendency data" as shown in Fig. 10, and integrates the extracted data to generate integrated information X2.

[0101] Furthermore, the integrated information X (X1, X2) is linked between the platform management device 100 and the user device 30-1 of the user E1 (step S34). Specifically, the platform management device 100 provides the integrated information X to the user device 30-1 so that it can be purchased.

[0102] A user E1 who has purchased the integrated information X can, for example, analyze the integrated information X and use it in an area development project. For example, the user E1 can make various predictions, such as what kind of stores in the target area a person of persona classification A among customers EU1 (for example, people who live in or work in the target area managed by collaborator C1) is likely to use, or whether a person of persona classification A will contribute more to sales. The same applies to persona classification C. In addition, the user E1 can, for example, appropriately consider which stores to introduce into the target area based on the predictions.

[0103] 7. Processing Procedure Next, the operation procedure of the platform management device 100 will be described with reference to Fig. 11. Fig. 11 is a flowchart showing the integration process executed by the platform management device 100.

[0104] First, the association information acquisition unit 136 judges whether or not an integration request has been received from the user Ex (step S1101). If an integration request has not been received from the user Ex (step S1101; No), the association information acquisition unit 136 waits until it is determined that an integration request has been received from the user Ex.

[0105] On the other hand, when the linking information acquisition unit 136 receives an integration request from the user Ex (step S1101; Yes), the linking information acquisition unit 136 acquires the linking information provided by the user Ex who made the integration request as the linking information to be used for integration (step S1102). The linking information includes the customer tendency data per persona generated by the user Ex.

[0106] In addition, the statistical information acquisition unit 134 acquires the store persona unit purchasing trend data provided by a business operator other than the user Ex who made the integration request, that is, the collaborator Cx (step S1103). For example, the statistical information acquisition unit 134 may acquire the store persona unit purchasing trend data provided by the collaborator Cx that is conditioned by the integration conditions included in the integration request.

[0107] Next, the integration unit 137 extracts persona classifications associated with the person persona-based customer tendency data and the store persona-based purchasing trend data (step S1104).

[0108] The integration unit 137 determines whether or not there is an identical persona classification between the persona classification extracted from the person persona unit customer trend data and the persona classification extracted from the store persona unit purchasing trend data (step S1105). For example, the integration unit 137 performs an identity determination between the persona classification extracted from the person persona unit customer trend data and the persona classification extracted from the store persona unit purchasing trend data.

[0109] When the integrating unit 137 determines that the same persona classification exists between the persona classification extracted from the human persona unit customer trend data and the persona classification extracted from the store persona unit purchasing trend data (step S1105; Yes), the integrating unit 137 extracts data corresponding to the same persona classification from each of the human persona unit customer trend data and the store persona unit purchasing trend data (step S1106).For example, if the persona classification A matches between the human persona unit customer trend data and the store persona unit purchasing trend data, the integrating unit 137 extracts data linked to persona classification A from the human persona unit customer trend data, and extracts data linked to persona classification A from the store persona unit purchasing trend data.

[0110] Then, the integration unit 137 integrates the person persona unit customer tendency data extracted in step S1106 with the store persona unit purchasing trend data extracted in step S1106 to generate integrated information (step S1107).

[0111] Then, the providing unit 135 provides the integration information to the user Ex who made the integration request (step S1108).

[0112] On the other hand, if the integration unit 137 determines that there is no identical persona classification between the persona classification extracted from the human persona-unit customer trend data and the persona classification extracted from the store persona-unit purchasing trend data (step S1105; No), it determines whether there is a similar persona classification (approximate persona classification) between the two data (step S1109).

[0113] For example, the integration unit 137 performs a similarity determination between a persona classification extracted from human persona-based customer trend data and a persona classification extracted from store persona-based purchasing trend data. For example, the integration unit 137 may calculate a degree of agreement based on the distance between the persona classifications, and determine that the two persona classifications are similar if the calculated degree of agreement is equal to or greater than a threshold. The integration unit 137 may also calculate a degree of agreement based on the nature of the persona classification, and determine that the two persona classifications are similar if the calculated degree of agreement is equal to or greater than a threshold.

[0114] If the integration unit 137 determines that no similar persona classification exists (step S1109; No), it determines that integration is impossible and ends the process.

[0115] On the other hand, if an approximate persona classification exists (step S1109; Yes), the integration unit 137 extracts (acquires) data corresponding to the approximate persona classification from each of the human persona-unit customer tendency data and the store persona-unit purchasing trend data (step S1110). For example, if persona classification C is approximate between the human persona-unit customer tendency data and the store persona-unit purchasing trend data, the integration unit 137 extracts data linked to persona classification C from the human persona-unit customer tendency data, and extracts data linked to persona classification C from the store persona-unit purchasing trend data.

[0116] The integration unit 137 integrates the human persona-based customer tendency data extracted in step S1110 with the store persona-based purchasing trend data extracted in step S1110 to generate integrated information (step S1107). The providing unit 135 also provides the integrated information to the user Ex who made the integration request (step S1108).

[0117] 8. Other Embodiments From here on, other embodiments of the platform management device 100 will be described. The platform management device 100 may be implemented in various different aspects other than the above-described embodiment. Therefore, other embodiments of the platform management device 100 will be described below.

[0118] [8-1. Variation 1] In the above embodiment, an example was shown in which the collaborator device 20 performs statistical processing on the purchase history using, as the axis of statistical processing, a "persona classification" (store persona) linked to each "store ID" included in the purchase history, among the "persona classifications" classified for the customer CUx by the persona diagnosis. Also, an example was shown in which the platform management device 100 acquires from the collaborator device 20 the purchase trend data per store persona obtained by the statistical processing, and transmits the data to the user device 30.

[0119] However, the collaborator device 20 may perform statistical processing of the purchase history using, as the axis of statistical processing, a "persona classification" (product persona) linked to each "product ID" included in the purchase history, among the "persona classifications" classified for the customer CUx by the persona diagnosis. In addition, in such an example, the platform management device 100 may obtain the product persona unit purchasing trend data obtained by the statistical processing from the collaborator device 20 and transmit it to the user device 30.

[0120] As yet another example, the collaborator device 20 may perform statistical processing of the purchase history using the "persona classification" (store / product persona) linked to each "store ID" and "product ID" included in the purchase history as the axis of statistical processing, among the "persona classifications" classified for customer CUx by persona diagnosis.

[0121] [8-2. Variation 2] Furthermore, the platform management device 100 may generate a single new purchasing trend data by integrating the purchasing trend data provided by a specific collaborator Cx among the purchasing trend data provided by each collaborator Cx. For example, assume that a user Ex wishes to purchase a single set of store persona-based purchasing trend data provided by collaborators C1 and C2. In this case, the platform management device 100 may integrate the store persona-based purchasing trend data provided by collaborator C1 and the store persona-based purchasing trend data provided by collaborator C2 to generate a single store persona-based purchasing trend data, and provide this to collaborator C1 for purchase.

[0122] [8-3. Variation 3] In the above embodiment, an example was shown in which the platform management device 100 performs integration processing with the store persona unit purchasing trend data of the cooperator Cx desired by the user Ex according to the integration conditions set by the user Ex. However, the platform management device 100 does not necessarily need to set the integration conditions, and may dynamically determine the cooperator Cx to be integrated, and integrate the store persona unit purchasing trend data of the determined cooperator Cx with the human persona unit customer tendency data of the user Ex.

[0123] For example, the platform management device 100 may dynamically determine a collaborator Cx that is highly related to the user Ex, and integrate the store persona unit purchase trend data of the determined collaborator Cx with the human persona unit customer tendency data of the user Ex. As an example, the platform management device 100 may determine a collaborator Cx that is similar to the user Ex in, for example, business content, service type, or business scale, as a collaborator Cx that is highly related to the user Ex.

[0124] [9. Hardware Configuration] Next, a hardware configuration example of an information processing device (platform management device 100) according to an embodiment will be described. FIG. 12 is a block diagram showing a hardware configuration example of the information processing device according to an embodiment. Referring to FIG. 12, the information processing device has, for example, a processor 801, a ROM 802, a RAM 803, a host bus 804, a bridge 805, an external bus 806, an interface 807, an input device 808, an output device 809, a storage 810, a drive 811, a connection port 812, and a communication device 813. Note that the hardware configuration shown here is an example, and some of the components may be omitted. Also, components other than those shown here may be further included.

[0125] (Processor 801) The processor 801 functions, for example, as an arithmetic processing device or control device, and controls all or part of the operation of each component based on various programs recorded in the ROM 802, the RAM 803, the storage 810, or the removable recording medium 901.

[0126] (ROM802, RAM803) The ROM 802 is a means for storing the programs to be read into the processor 801, data to be used for calculations, etc. The RAM 803 temporarily or permanently stores, for example, the programs to be read into the processor 801, various parameters that change appropriately when the programs are executed, etc.

[0127] (host bus 804, bridge 805, external bus 806, interface 807) The processor 801, ROM 802, and RAM 803 are connected to each other via, for example, a host bus 804 capable of high-speed data transmission. On the other hand, the host bus 804 is connected to an external bus 806 having a relatively low data transmission speed via, for example, a bridge 805. In addition, the external bus 806 is connected to various components via an interface 807.

[0128] (Input device 808) Examples of the input device 808 include a mouse, a keyboard, a touch panel, a button, a switch, and a lever. Furthermore, a remote controller capable of transmitting control signals using infrared rays or other radio waves may be used as the input device 808. The input device 808 also includes an audio input device such as a microphone.

[0129] (Output Device 809) The output device 809 is a device capable of visually or audibly notifying the user of acquired information, such as a display device such as a CRT (Cathode Ray Tube), LCD, or organic EL, an audio output device such as a speaker or a headphone, a printer, a mobile phone, or a facsimile. The output device 809 according to this embodiment also includes various vibration devices capable of outputting tactile stimulation. The output device 809 may also include an AI speaker or a wearable device.

[0130] (Storage 810) The storage 810 is a device for storing various types of data. For example, a magnetic storage device such as a hard disk drive (HDD), a semiconductor storage device, an optical storage device, or a magneto-optical storage device may be used as the storage 810.

[0131] (Drive 811) The drive 811 is a device that reads information recorded on a removable recording medium 901 such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, and writes information to the removable recording medium 901.

[0132] (Connection port 812) The connection port 812 is a port for connecting an external device 902, such as a Universal Serial Bus (USB) port, an IEEE1394 port, a Small Computer System Interface (SCSI), an RS-232C port, or an optical audio terminal.

[0133] (Communication device 813) The communication device 813 is a communication device for connecting to a network, such as a communication card for wired or wireless LAN, Bluetooth (registered trademark), or WUSB (Wireless USB), a router for optical communications, a router for ADSL (Asymmetric Digital Subscriber Line), or a modem for various types of communications.

[0134] (Removable recording media 901) The removable recording medium 901 is, for example, a DVD medium, a Blu-ray (registered trademark) medium, an HD DVD medium, various semiconductor storage media, etc. Of course, the removable recording medium 901 may be, for example, an IC card equipped with a non-contact type IC chip, or an electronic device.

[0135] (External connection device 902) The external connection device 902 is, for example, a printer, a portable music player, a digital camera, a digital video camera, or an IC recorder.

[0136] When the information processing device according to the embodiment is the platform management device 100, the memory unit 120 is realized by the ROM 802, the RAM 803, and the storage 810. Also, the control unit 130 realized by the processor 801 reads out and executes each control program (for example, the information processing program according to the embodiment) that realizes the answer acquisition unit 131, the estimation unit 132, the persona information acquisition unit 133, the statistical information acquisition unit 134, the provision unit 135, the association information acquisition unit 136, and the integration unit 137 from the ROM 802, the RAM 803, etc.

[0137] [10. Other] Among the above-mentioned processes, all or part of the processes described as being performed automatically may be performed manually. Also, all or part of the processes described as being performed manually may be performed automatically by a known method. In addition, the information including the processing procedures, specific names, various data and parameters shown in the above documents and drawings may be changed arbitrarily unless otherwise specified. For example, the various information shown in each drawing is not limited to the illustrated information.

[0138] In addition, each component of each device shown in the figure is a functional concept, and does not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure. Furthermore, each component may be configured by distributing and integrating functionally or physically in any unit, in whole or in part, depending on various loads and usage conditions. Furthermore, each process described above may be executed in appropriate combination within a range that does not contradict.

[0139] The above describes the embodiments of the present application in detail with reference to several drawings. However, these are merely examples, and the present invention can be embodied in other forms that incorporate various modifications and improvements based on the knowledge of those skilled in the art, including the aspects described in the Disclosure of the Invention section. [Explanation of symbols]

[0140] 1 System 10 Customer equipment 20 Partner Equipment 30 User Device 100 Platform management device 120 Storage section 121 Persona information storage unit 122 Store persona unit purchasing trend data storage section 123 persona-based customer tendency data storage section 130 Control section 131 Answer acquisition part 132 Estimation Department 133 Persona Information Acquisition Department 134 Statistical information acquisition section 135 Provision Department 136 Linked information acquisition unit 137 Integration Department

Claims

1. a type information acquiring unit that acquires type information classified for customers of a service provided by a business operator, the type information being classified according to the diversity possessed by the customers; a statistical information acquisition unit that acquires, as statistical information obtained by statistically processing the purchase history using predetermined items on the seller side included in the purchase history of the customer, the purchasing trends of the customer according to the type information linked to the predetermined items; a control unit for managing the statistical information so that a predetermined business entity can use the statistical information; 13. An information processing device comprising:

2. The predetermined item is at least one of the store where the customer made the purchase and the product purchased at the store.

2. The information processing apparatus according to claim 1,

3. The control unit provides the statistical information to a requesting business operator.

2. The information processing apparatus according to claim 1,

4. a linking information acquiring unit that acquires linking information in which customer information held by the business entity is linked to the type information classified for the customers included in the customer information; Furthermore, The control unit integrates the customer information and the statistical information based on the type information classified for the customers included in the customer information.

2. The information processing apparatus according to claim 1,

5. The linking information acquisition unit acquires, as the linking information, linking information including customer tendencies obtained by statistically processing the customer information according to the type information classified for the customer.

5. The information processing apparatus according to claim 4.

6. The control unit extracts combinations of the customer tendencies and the purchasing trends in which the type information matches or is similar to each other by matching the type information classified for the customers associated with the customer tendencies and the purchasing trends for identity, and generates integrated information in which the customer tendencies and purchasing trends included in the extracted combinations are integrated.

6. The information processing apparatus according to claim 5,

7. the linking information acquisition unit acquires the linking information included in an integration request when an integration request including the linking information as an integration condition is accepted; The control unit generates the integrated information by integrating the customer tendency obtained from the linking information included in the integration request and the purchasing trends corresponding to a business among the businesses designated by the integration conditions.

7. The information processing apparatus according to claim 6,

8. The control unit provides the integration information to the business entity that has made the integration request.

8. The information processing apparatus according to claim 7,

9. An information processing method executed by an information processing device, a type information acquiring step of acquiring type information classified for customers of a service provided by a business operator, the type information being classified according to the diversity possessed by the customers; a statistical information acquisition step of acquiring, as statistical information obtained by statistically processing the purchase history using predetermined items on the seller side included in the purchase history of the customer, the purchase trends of the customer corresponding to the type information linked to the predetermined items; a control step of managing the statistical information so that a predetermined business entity can use the statistical information; 13. An information processing method comprising:

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