Information processing device, information processing method, and program

Abstract

To support the business of an insurance agency.SOLUTION: A customer database 100 associates each of multiple agencies to sell at least one of insurance products among multiple types of insurance products to customers, with information about the customers of each agency and stores them. A sales information database 101 associates each of multiple agencies with information about the insurance products sold to the customers by each agency and stores them. A model storage unit 102 inputs information about the customers and the information about the insurance products sold by the agency having the customers and stores the learning model machine-learned to output one or multiple digital contents for the agency with the customer. A reception unit 120 accepts the designated agency which is the agency designated from the multiple agencies. A model application unit 121 inputs information about the designated agency customer and information about the insurance products of the designated agency into a learning model and outputs the digital contents for the designated agency.SELECTED DRAWING: Figure 2

Description

[Technical field]

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

[0002] There are many types of insurance products, and even if they are the same type, the contents may differ. For this reason, many customers who wish to purchase insurance products often meet face-to-face with insurance sales representatives who sell insurance products at insurance agencies, etc. Patent Document 1 discloses a technology for supporting the sales activities of insurance sales representatives who sell insurance products to customers. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2007-26147 A Summary of the Invention [Problem to be solved by the invention]

[0004] The above technology is intended to support insurance salespeople when they are actually meeting customers face-to-face. However, insurance salespeople often take time to get customers interested and begin selling face-to-face. As such, there is room for improvement in technology to support insurance agency sales.

[0005] The present invention has been made in consideration of these points, and has an object to provide a technique for supporting the sales of insurance agents. [Means for solving the problem]

[0006] A first aspect of the present invention is an information processing device. This device includes a customer database that stores information on each of a plurality of agencies that sell at least one insurance product among a plurality of types of insurance products to a customer, and information on the customers of each agency, in association with each other, a sales information database that stores information on each of the plurality of agencies and information on the insurance products sold by each agency to a customer, in association with each other, a model storage unit that stores a learning model that has been machine-learned to output one or more digital contents for an agency that has a customer when information on a customer and information on the insurance products sold by an agency that has a customer are input, a reception unit that receives a designated agency that is an agency designated from among the plurality of agencies, and a model application unit that inputs information on the customers of the designated agency acquired by referring to the customer database and information on the insurance products of the designated agency acquired by referring to the sales information database into the learning model, and causes the learning model to output digital contents for the designated agency.

[0007] The information processing device may further include a content display unit that displays the digital content output by the learning model on a website of the designated agency.

[0008] The information processing device may further include a product database that links and stores a plurality of agents that sell at least one insurance product among a plurality of types of insurance products to customers with the type of insurance product handled by each agent among the plurality of types of insurance products, and a selection unit that refers to the product database and selects digital content related to the insurance products handled by the designated agent from the digital content output by the learning model, and the content display unit may provide the selected digital content to a website of the designated agent.

[0009] The multiple types of insurance products may include automobile insurance that assumes the installation of a drive recorder, and the information about the customer may include location information and acceleration information obtained by the drive recorder.

[0010] The information about the customer may include personal attributes of the customer, the browsing history of websites visited by the customer, response records to insurance products, and the customer's past contract history.

[0011] The model memory unit may store a learning model that has been machine-learned for each customer so that, when information about a customer and information about the insurance products sold by an agency that has the customer are input, one or more digital contents for the agency that has the customer are output, and the reception unit may further receive customer identification information for identifying a customer of the designated agency, and the model application unit may input information about a customer identified by the customer identification information obtained by reference to the customer database and information about the insurance products of the designated agency obtained by reference to the sales information database into a learning model for the customer identified by the customer identification information, and output digital content for the customer.

[0012] A second aspect of the present invention is an information processing method. In this method, a processor executes the following steps: accepting a designated agent, which is a designated agent from among a plurality of agents who sell at least one insurance product among a plurality of types of insurance products to a customer; acquiring information on the customer of the designated agent by referring to a customer database that stores information on each of the plurality of agents and the customers of each agent in association with each other; acquiring information on the insurance product of the designated agent by referring to a sales information database that stores information on each of the plurality of agents and the insurance product sold to a customer by each agent in association with each other; acquiring the learning model by referring to a model storage unit that stores a learning model machine-trained to output one or more digital contents for the agent having the customer when information on a customer and information on the insurance product sold by the agent having the customer are input; and inputting the information on the customer of the designated agent acquired by referring to the customer database and the information on the insurance product of the designated agent acquired by referring to the sales information database into the learning model to output the digital content for the designated agent.

[0013] A third aspect of the present invention is a program. This program causes a computer to realize the following functions: accepting a designated agent, which is an agent designated from among a plurality of agents who sell at least one insurance product among a plurality of types of insurance products to a customer; acquiring information on the customer of the designated agent by referring to a customer database that stores information on each of the plurality of agents and the customers of each agent in association with each other; acquiring information on the insurance product of the designated agent by referring to a sales information database that stores information on each of the plurality of agents and the insurance product sold to a customer by each agent in association with each other; acquiring the learning model by referring to a model storage unit that stores a learning model machine-learned to output one or more digital contents for an agent having a customer when information on a customer and information on the insurance product sold by an agent having a customer are input; and inputting the information on the customer of the designated agent acquired by referring to the customer database and the information on the insurance product of the designated agent acquired by referring to the sales information database into the learning model to output digital content for the designated agent.

[0014] In order to provide this program or to update a part of the program, a computer-readable recording medium having this program recorded thereon may be provided, or this program may be transmitted over a communication line.

[0015] Any combination of the above components, and any transformation of the present invention into a method, device, system, computer program, data structure, recording medium, etc., are also effective as aspects of the present invention. Effect of the Invention

[0016] According to the present invention, a technique for supporting the sales of insurance agents can be provided. [Brief description of the drawings]

[0017] [Figure 1] FIG. 1 is a diagram for explaining an overview of an information processing device according to an embodiment. [Diagram 2] FIG. 2 is a diagram illustrating a functional configuration of an information processing device according to an embodiment. [Diagram 3] FIG. 2 is a diagram illustrating a data structure of a customer database according to an embodiment. [Figure 4] FIG. 2 is a diagram illustrating a data structure of a sales information database according to the embodiment. [Diagram 5] FIG. 2 is a diagram illustrating a data structure of a product database according to an embodiment. [Figure 6] 4 is a flowchart illustrating a flow of information processing executed by an information processing device according to an embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0018] <Outline of the embodiment> The information processing device 1 according to the embodiment is a device for customizing and providing digital content related to insurance products for each agency that handles insurance products. The digital content presented by the information processing device 1 according to the embodiment is, for example, the design of an agency's website, content to be posted on the website, digital data of materials to be distributed to customers, etc.

[0019] Here, an insurance company is an organization that develops insurance materials to compensate for damages that may occur due to accidents by calculating the amount of risk based on the policyholder's assets, the method of ownership, the method of use, and the method of management, the environment and social situation of the area where the policyholder lives, and the social trend. In contrast, an agency (insurance agency) is an organization that is commissioned by an insurance company to introduce insurance materials to customers on behalf of the insurance company and to provide after-sales support to customers who have contracted insurance products. If we were to compare it to the manufacture and sale of industrial products, an insurance company is a company that develops and manufactures products, and an agency is equivalent to a brick-and-mortar store or an EC (electronic commerce) site that sells products. In general, the subject of a contract made by a customer, such as automobile insurance or fire insurance, is composed of a combination of the above-mentioned compensations, such as a "personal liability rider" and an "attorney rider". Therefore, even if it is the same automobile insurance, there are automobile insurances that include an attorney rider and automobile insurances that do not include one. In this specification, the contract subject that is composed of a combination of these multiple compensations is described as an "insurance product". Therefore, even if it is the same type of automobile insurance, there are multiple different automobile insurances.

[0020] Insurance companies also provide their agents with digital content to be posted on their websites and sales brochures. Insurance companies also share information on the insurance products handled by each of their agents nationwide, as well as information on each agent's customers and the products they have sold. In this respect, the relationship between an insurance company and its agents is different from the relationship between a company that manufactures industrial products and its dealers.

[0021] The information processing device 1 according to the embodiment is, for example, a server under the management of an insurance company, and aggregates and manages insurance materials, customer information, and information on sold products handled by each agency nationwide. The information processing device 1 according to the embodiment also maintains and manages a learning model that outputs digital content to be recommended to each agency by using a known machine learning method based on information aggregated from agencies nationwide. The information processing device 1 according to the embodiment uses this learning model to realize the provision of digital content related to insurance materials customized for each agency that handles the insurance materials. Note that it is sufficient for the information processing device 1 according to the embodiment to manage the learning model, and the execution of machine learning for model generation and re-learning may be performed by another device.

[0022] Based on the above, an overview of the embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram for explaining the overview of an information processing device 1 according to the embodiment.

[0023] An information processing device 1 according to an embodiment is connected to a plurality of agents in a mutually communicable manner via a communication network N using the Internet or a dedicated communication line. In the example shown in Fig. 1, a first agent A1 and a second agent A2 are shown, but generally, agents exist all over Japan, and their number is on the order of several thousand to several tens of thousands.

[0024] The information processing device 1 manages a plurality of databases generated by acquiring various information from each agency. Specifically, the information processing device 1 manages at least a customer database 100 that associates and stores information about each agency and its customers, a sales information database 101 that stores information about insurance products sold by each agency to its customers, and a model storage unit 102 that stores a learning model that outputs digital content to be presented to a specified agency.

[0025] Here, the learning model stored in the model storage unit 102 is trained to output digital content suitable for an agency when at least information about the agency's customers and information about insurance products sold by the agency to the customers are input.

[0026] In general, insurance products handled by an agency are different for each agency, and even insurance products of the same type have different contents. Insurance products suitable for an agency's customers may differ depending on the customer's date of birth and family structure, and may also differ depending on the climate and customs of the area where the customer lives (i.e., the area where the agency is located). The information processing device 1 according to the embodiment can provide digital content suitable for each agency by using a learning model generated by a machine learning method based on the agent's customer information and past sales performance, etc. This allows sales staff working at the agency to present information suitable for the agency's customers to the customers, increasing the possibility of stimulating the customers' interest.

[0027] Furthermore, by aggregating customer information and sales information from each agency located throughout the country, it can be used as a common platform, so that the behavioral data of Kokukaku can be analyzed by means of attracting customers, content, insurance type, etc., and accumulated as know-how. Therefore, the information processing device 1 can disseminate the effects and improvements of each agency's methods of attracting customers and website design, etc., as knowledge to other agencies.

[0028] <Functional configuration of information processing device 1 according to the embodiment> FIG. 2 is a diagram showing a schematic functional configuration of an information processing device 1 according to an embodiment. The information processing device 1 includes a storage unit 10, a communication unit 11, and a control unit 12. In FIG. 2, arrows indicate main data flows, and there may be data flows not shown in FIG. 2. In FIG. 2, each functional block indicates a functional unit configuration, not a hardware (device) unit configuration. Therefore, the functional blocks shown in FIG. 2 may be implemented in a single device, or may be implemented separately in multiple devices. Data may be exchanged between functional blocks via any means, such as a data bus, a network, or a portable storage medium.

[0029] The memory unit 10 is a large-capacity storage device such as a ROM (Read Only Memory) that stores the BIOS (Basic Input Output System) of the computer that realizes the information processing device 1, a RAM (Random Access Memory) that serves as the working area of ​​the information processing device 1, an OS (Operating System), application programs, a customer database 100 that is referenced when the application programs are executed, a sales information database 101, a model memory unit 102, a product database 103, and the like.

[0030] The communication unit 11 is a communication interface for the information processing device 1 to communicate with an external device, and is realized by a known communication module such as a LAN (Local Area Network) module, a Wi-Fi (registered trademark) module, etc. Hereinafter, in this specification, the description of the communication unit 11 may be omitted on the assumption that communication with an external device is performed via the communication unit 11.

[0031] The control unit 12 is a processor such as a CPU (Central Processing Unit) or GPU (Graphics Processing Unit) of the information processing device 1, and functions as a reception unit 120, a model application unit 121, a content display unit 122, and a selection unit 123 by executing programs stored in the memory unit 10.

[0032] 2 shows an example in which the information processing device 1 is configured as a single device. However, the information processing device 1 may be realized by a plurality of processors, memories, and other computing resources, such as a cloud computing system. In this case, each unit constituting the control unit 12 is realized by at least one of a plurality of different processors executing a program.

[0033] As described above, the information processing device 1 provides digital content suitable for each agency by utilizing a learning model generated by a machine learning technique based on the customer information of the agency and past sales performance, etc. For this reason, the customer database 100 stores information on each of a plurality of agencies that sell at least one insurance product among a plurality of types of insurance products to a customer in association with information on the customers of each agency.

[0034] FIG. 3 is a diagram showing a data structure of the customer database 100 according to the embodiment. In the example of the customer database 100 shown in FIG. 3, an agency identifier for identifying an agency, a customer identifier for identifying a customer of the agency, and information on each customer are linked. For example, an agency with an agency identifier AID00001 has a customer with a customer identifier UID00001. In addition, as information on the customer with the customer identifier UID00001, the customer's name, date of birth, sex, occupation, family structure, etc. are stored. Note that FIG. 3 shows that the customer with the customer identifier UID00001 is also a customer of an agency with an agency identifier AID00002. In this way, the information processing device 1 centrally manages information on each agency present throughout the country and the customers of the agency.

[0035] FIG. 4 is a diagram showing a schematic data structure of the sales information database 101 according to the embodiment. The sales information database 101 stores information on insurance products sold by each of a plurality of agents to customers in association with each other. In the example of the sales information database 101 shown in FIG. 4, a product identifier for identifying the insurance product handled by the agent, the type of the insurance product, a customer identifier indicating the customer who purchased the insurance product, and the like are associated with each agent identifier as information on the insurance product. For example, an agent with an agent identifier AID00001 handles an insurance product with a product identifier MID0001. The type of insurance product with a product identifier MID0001 is automobile insurance, and at least a customer with a customer identifier UID00001 and a customer with a customer identifier UID00002 have purchased the insurance product.

[0036] Returning to the explanation of FIG. 2, the model storage unit 102 stores a learning model. This learning model is a learning model that has been machine-trained so that, when information about a customer and information about insurance products sold by an agency having the customer are input, the learning model outputs one or more digital contents for the agency having the customer. The machine learning can be realized by using, for example, known deep learning.

[0037] The reception unit 120 receives a designated agent, which is an agent designated from among a plurality of agents. For example, when the reception unit 120 receives a request for provision of digital content from an administrator of an agent who wishes to provide digital content, the reception unit 120 accepts the agent as a designated agent. The model application unit 121 inputs information about the designated agent's customers obtained by referring to the customer database 100 and information about the designated agent's insurance materials obtained by referring to the sales information database 101 into the learning model, and outputs digital content for the designated agent. This allows the information processing device 1 to provide digital content suitable for each agent.

[0038] As described above, the digital content output by the learning model is, for example, the design of the agency's website, the content to be posted on the website, digital data of materials to be distributed to customers, etc. When the digital content output by the model application unit 121 to the learning model is the content to be posted on the website, the content display unit 122 displays the digital content output by the learning model on the website of the designated agency. Specifically, the content display unit 122 connects to the website of the designated agency via an API (Application Programming Interface), returns the digital content output by the learning model in response to a request from the designated agency website, and renders it on the designated agency website. Alternatively, the content display unit 122 is a web server that manages the website of the designated agency, and displays the digital content output by the learning model on the website of the designated agency via a web server built on a domain under the management of the information processing device 1. This allows the information processing device 1 to update the website of the designated agency without the assistance of staff of the designated agency.

[0039] Here, the multiple agents handle different insurance products. For this reason, it is preferable that the digital content that the information processing device 1 shows to each agent is digital content related to the insurance product handled by the agent. For this reason, the product database 103 stores, in association with each of the multiple agents that sell at least one insurance product to a customer, and the type of insurance product that each agent handles among the multiple types of insurance products.

[0040] Fig. 5 is a diagram showing a schematic data structure of the product database 103 according to the embodiment. In the example of the product database 103 shown in Fig. 5, an agency identifier is associated with the product identifier of the insurance product handled by the agency indicated by the agency identifier, and the type of insurance product. For example, an agency with an agency identifier AID00001 handles at least three insurance products identified by product identifiers MID00001, MID00002, and MID00003. The types of insurance products identified by product identifiers MID00001, MID00002, and MID00003 are automobile insurance, earthquake insurance, and life insurance, respectively.

[0041] The selection unit 123 selects digital content related to insurance products handled by the designated agency from among the digital content output by the learning model, with reference to the product database 103. The content display unit 122 provides the digital content selected by the selection unit 123 to the website of the designated agency. This enables the information processing device 1 to provide digital content suitable for the agency.

[0042] The above describes examples of information about a customer that include the customer's name, date of birth, sex, occupation, family structure, etc. However, by increasing the types of training data used to train the learning model, it is possible to increase the types of information about the customer that is input to the learning model.

[0043] For example, automobile insurance, which is one of the multiple types of insurance products, includes automobile insurance with a drive recorder, which is premised on the installation of a drive recorder. The drive recorder can acquire the position coordinates of the automobile in which the drive recorder is installed based on signals transmitted by positioning satellites, and can calculate the speed and acceleration of the automobile from the time change of the position coordinates. For customers who have purchased such automobile insurance, the above-mentioned information about the customer includes the position information and acceleration information acquired by the drive recorder. In this case, the learning model is trained to also use the position information and acceleration information acquired by the drive recorder as inputs.

[0044] Location information from the drive recorder provides information on the area where the customer uses the car, and this information reflects how the car is used in the area (use in mountainous areas, whether there is snow, whether the car is in an urban area, etc.). Acceleration information also reflects the customer's driving tendencies (whether or not the customer accelerates and decelerates suddenly, etc.). By including information obtained from the drive recorder in the learning model, the way the car is used in the area where the agency is located can be reflected in the digital content for car insurance.

[0045] The information on the customer may include the browsing history of the website visited by the customer, the response record of the insurance product, and the customer's past contract history in addition to the customer's personal attributes such as name, date of birth, and occupation. As described above, the information processing device 1 is connected to each agency and a web server managing the website of each agency in a communicable manner via the communication network N. Therefore, the information processing device 1 can acquire the history of activities such as the browsing history of the website and the posting of evaluations by the customers of each agency. In addition to the agency, if there is information provided by other insurance companies with which the insurance company managing the information processing device 1 is affiliated or a company to which the insurance company managing the information processing device 1 is entrusted with customer acquisition, the browsing history of the insurance company's website may be acquired. Furthermore, if the insurance company managing the information processing device 1 or its agency accepts purchases of insurance products or consultations over the phone, data obtained by converting the conversation between the operator and the customer into a character string using voice analysis technology may also be included as information on the customer.

[0046] A customer's website browsing history can be useful information for estimating what insurance products the customer considered before purchasing the insurance product that he or she ultimately purchased. In addition, if a customer who has purchased an insurance product writes about the insurance product's evaluation on a website, the content of that post can be useful information for estimating the reaction to the insurance product. By including customers' website browsing history, reactions to insurance products, and past contract history in the learning model, the accuracy of the digital content presented to each agent can be improved.

[0047] The above describes the case where the digital content output by the learning model is suitable for each agency. Alternatively, the learning model may output digital content suitable for each customer. Specifically, the model storage unit 102 stores a learning model that is machine-learned for each customer so as to output one or more digital contents for the agency having the customer when information about the customer and information about the insurance product sold by the agency having the customer are input. More specifically, the model storage unit 102 stores a learning model dedicated to the customer identified by the customer identifier, in association with the customer identifier.

[0048] In this case, the reception unit 120 further receives customer identification information for identifying a customer of the designated agency in addition to the designated agency. The model application unit 121 inputs information on the customer identified by the customer identification information obtained by referring to the customer database 100 and information on the insurance products of the designated agency obtained by referring to the sales information database 101 into a learning model for the customer identified by the customer identification information, and outputs digital content for the customer.

[0049] For example, if a customer is a customer of multiple agencies, information about the customer at each agency is input to the customer-specific learning model. Therefore, the customer-specific learning model uses not only information about the customer at the designated agency, but also information about customers at agencies other than the designated agency as input, and outputs digital content for the customer. This allows the information processing device 1 to present digital content suitable for each customer of each agency to each agency.

[0050] <Processing flow of the information processing method executed by the information processing device 1> 6 is a flowchart for explaining the flow of information processing executed by the information processing device 1 according to the embodiment. The processing in this flowchart starts, for example, when the information processing device 1 is started.

[0051] The reception unit 120 receives a designated agent, which is an agent designated from among a plurality of agents who sell at least one of a plurality of types of insurance products to a customer (S2). The model application unit 121 refers to the customer database 100 that stores information on each of the plurality of agents and the customers of each agent in association with each other, and acquires information on the customers of the designated agent (S4).

[0052] The model application unit 121 refers to the sales information database 101, which stores information relating to each of a plurality of agencies and the insurance products that each agency sold to customers, and obtains information relating to the insurance products of the designated agency (S6).

[0053] The model application unit 121 acquires a learning model by referring to the model memory unit 102, which stores a learning model trained by machine learning to output one or more digital contents for an agency that has a customer when information about the customer and information about insurance products sold by the agency that has the customer are input (S8).

[0054] The model application unit 121 inputs information about the designated agent's customers obtained by referring to the customer database 100 and information about the designated agent's insurance products obtained by referring to the sales information database 101 into the learning model (S10). As a result, the model application unit 121 causes the learning model to output digital content for the designated agent (S12).

[0055] When the model application unit 121 causes the learning model to output digital content, the processing in this flowchart ends.

[0056] <Advantages of the Information Processing Device 1 According to the Embodiment> As described above, the information processing device 1 according to the embodiment can provide a technique for supporting the sales of insurance agents.

[0057] Although the present invention has been described above using the embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments, and various modifications and changes are possible within the scope of the gist of the present invention. For example, all or part of the device can be configured by distributing or integrating functionally or physically in any unit. In addition, new embodiments resulting from any combination of multiple embodiments are also included in the embodiments of the present invention. The effect of the new embodiment resulting from the combination also has the effect of the original embodiment.

[0058] <Modification> In the above, the machine learning model has been mainly described in the case where information about a customer and information about insurance products sold by an agency having a customer are input. This means that information about a customer and information about insurance products sold by an agency having a customer are used as training data for the machine learning model. However, the information used for learning the machine learning model is not limited to information about a customer and information about insurance products sold by an agency having a customer. Other information may be used instead of or in addition to these.

[0059] Other examples of information include existing contract information between the insurance company and the customer (e.g., insurance information such as type, date of enrollment, and number of years of continuity, vehicle information, property information, and insurance claim history), event information (seasonal information such as typhoons, snowfall, and travel), life events such as marriage, childbirth, home purchase, and car purchase), and agent communications (communication history such as emails and social networking services, and information provided during phone calls and interviews). [Explanation of symbols]

[0060] 1. Information processing device 10 Storage section 100 Customer Database 101 Sales Information Database 102 Model memory section 103 Product Database 11 Communications Department 12 Control section 120 Reception 121 Model Application Section 122 Content display section 123 Selection section N Communication Network

Claims

1. a customer database that stores information on each of a plurality of agencies that sell at least one of a plurality of types of insurance products to a customer in association with information on the customer of each agency; a sales information database that stores information relating to each of the plurality of agents and the insurance products sold by each agent to customers in association with each other; a model storage unit that stores a learning model that has been machine-learned to output one or more digital contents for an agency having a customer when information about the customer and information about the insurance product sold by the agency having the customer are input; a reception unit that receives a designated agent that is a designated agent from among the plurality of agents; a model application unit that inputs information about the designated agent's customers, obtained by referring to the customer database, and information about the designated agent's insurance products, obtained by referring to the sales information database, into the learning model, and outputs digital content for the designated agent; Information processing device.

2. Further comprising a content display unit that displays the digital content output by the learning model on the website of the designated agency. The information processing device according to claim 1 .

3. A product database that stores a plurality of agents that sell at least one insurance product among a plurality of types of insurance products to customers and a type of insurance product handled by each agent among the plurality of types of insurance products in association with each other; A selection unit that refers to the product database and selects digital content related to insurance products handled by the designated agency from the digital content output by the learning model, the content display unit provides the selected digital content on the website of the designated agent; The information processing device according to claim 2 .

4. The plurality of types of insurance products include automobile insurance that assumes the installation of a drive recorder, The information about the customer includes position information and acceleration information acquired by the drive recorder. The information processing device according to claim 1 .

5. The information about the customer includes personal attributes of the customer, browsing history of websites visited by the customer, response records of insurance products, and past contract history of the customer. The information processing device according to claim 1 .

6. the model storage unit stores a learning model that has been machine-learned for each customer so as to output one or more digital contents for the agent having the customer when information about the customer and information about the insurance product sold by the agent having the customer are input; The reception unit further receives customer identification information for identifying a customer of the designated agency, the model application unit inputs information about the customer identified by the customer identification information obtained by referring to the customer database and information about the insurance products of the designated agency obtained by referring to the sales information database into a learning model for the customer identified by the customer identification information, and outputs digital content for the customer. The information processing device according to claim 1 .

7. The processor: A step of accepting a designated agent that is a designated agent from among a plurality of agents that sell at least one of a plurality of types of insurance products to a customer; acquiring information about the designated agent's customers by referring to a customer database that stores information about each of the plurality of agents and the agents' customers in association with each other; acquiring information about the insurance products of the designated agency by referring to a sales information database that stores information about each of the plurality of agencies and the insurance products sold to customers by each agency in association with each other; A step of acquiring a learning model by referring to a model storage unit that stores a learning model that has been machine-trained to output one or more digital contents for an agency having a customer when information about the customer and information about the insurance product sold by the agency having the customer are input; and inputting information about the designated agent's customers, obtained by referring to the customer database, and information about the designated agent's insurance products, obtained by referring to the sales information database, into the learning model, and outputting digital content for the designated agent. Information processing methods.

8. On the computer, A function of accepting a designated agent that is a designated agent from among a plurality of agents that sell at least one of a plurality of types of insurance products to a customer; A function of acquiring information about the designated agent's customers by referring to a customer database that stores information about each of the multiple agents and their customers in association with each other; A function of acquiring information about the insurance products of the designated agency by referring to a sales information database that stores information about each of the plurality of agencies and the insurance products sold to customers by each agency in association with each other; A function of acquiring a learning model by referring to a model storage unit that stores a learning model that has been machine-trained to output one or more digital contents for an agency that has a customer when information about the customer and information about the insurance product sold by the agency that has the customer are input; and and inputting information about the designated agent's customers, obtained by referring to the customer database, and information about the designated agent's insurance products, obtained by referring to the sales information database, into the learning model, thereby outputting digital content for the designated agent. program.

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