Service recommendation device and service recommendation method

Abstract

To identify a service that is likely to meet the needs of a target company.SOLUTION: A service recommendation device holds financial information for a target company, a procurement conditions score for the target company, weights of issues and procurement conditions for each service, and a model for outputting an issues score from financial information for a company, calculates an issues score for the target company on the basis of the financial information for the target company and the model, calculates a first recommendation degree for a service on the basis of the issues score for the target company and the weights of the issues for each service, calculates a second recommendation degree for the service on the basis of the procurement conditions score for the target company and the weights of the procurement conditions for each service, and calculates an overall recommendation degree for the service on the basis of the first recommendation degree and the second recommendation degree.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a service recommendation device and a service recommendation method. [Background technology]

[0002] Japanese Patent Application Laid-Open Publication No. 2020-113213 (Patent Document 1) is a background technology in this technical field. This publication states that "the system includes a goal acquisition unit that acquires goal data of a user company, a model company data generation unit that calculates average financial data of similar companies that are in the same industry and have financial data similar to that of the user company and have achieved the goals of the user company, a determination unit that determines a solution to propose to the user company based on the user company's goals and the average financial data of the similar companies, and a solution proposal unit that proposes the solution determined by the determination unit" (see abstract). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2020-113213 Summary of the Invention [Problem to be solved by the invention]

[0004] The technology described in Patent Document 1 determines solutions to be proposed to a user company based on financial data of the user company and companies similar to the user company, but there is a risk that the needs of the user company cannot be adequately predicted based on financial data alone, and as a result, there is a risk that an appropriate solution cannot be proposed to the user company. Therefore, one aspect of the present invention identifies services that are likely to meet the needs of the target company. [Means for solving the problem]

[0005] In order to solve the above problem, one embodiment of the present invention employs the following configuration: A service recommendation device includes a processor and a memory, wherein the memory stores financial information of a target company, a score for each of the target company's procurement conditions, service information indicating, for each service, a weight for each of the issues and a weight for each of the procurement conditions, and a model that outputs a score for each of the company's issues when a company's financial information is input, and the processor calculates a score for each of the target company's issues based on the financial information of the target company and the model, calculates a first recommendation level for each of the services based on the score for each of the target company's issues and the weight for each of the issues for each service indicated in the service information, calculates a second recommendation level for each of the services based on the score for each of the target company's procurement conditions and the weight for each of the procurement conditions for each service indicated in the service information, and calculates an overall recommendation level for each of the services indicated in the service information for the target company based on the first recommendation level and the second recommendation level. [Effects of the Invention]

[0006] According to one aspect of the present invention, one aspect of the present invention can identify services that are likely to meet the needs of a target company.

[0007] Problems, configurations, and effects other than those described above will become apparent from the following description of the embodiments. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a block diagram showing an example of the configuration of a service recommendation device according to a first embodiment. [Figure 2] 1 is a block diagram showing an example of a hardware configuration of a service recommendation device 100 according to a first embodiment. [Figure 3A] FIG. 3 is a diagram illustrating an example of a data configuration of company attribute data included in basic company data according to the first embodiment. [Figure 3B] 10 is a diagram illustrating an example of a data configuration of enterprise product liability data included in enterprise basic data in the first embodiment. FIG. [Figure 3C] FIG. 10 is a diagram illustrating an example of the data configuration of company BS data included in the company basic data. [Figure 4] FIG. 2 is a diagram illustrating an example of the data configuration of corporate behavior data according to the first embodiment. [Figure 5] FIG. 3 is a diagram illustrating an example of a data configuration of procurement transaction data in the first embodiment. [Figure 6] FIG. 10 is a diagram illustrating an example of a data configuration of service adoption result data in the first embodiment. [Figure 7A] 10 is a diagram illustrating an example of the data configuration of service-company issue data included in the service data in the first embodiment. FIG. [Figure 7B] FIG. 10 is a diagram illustrating an example of a data configuration of service-procurement condition data included in service data according to the first embodiment. [Figure 8] 10 is a flowchart illustrating an example of an overall process of the service recommendation device according to the first embodiment. [Figure 9] 10 is a flowchart illustrating an example of a company behavior prediction model generation process according to the first embodiment. [Figure 10] 10 is a flowchart illustrating an example of a service recommendation probability calculation process according to the first embodiment. [Figure 11] FIG. 10 is an explanatory diagram illustrating an example of a calculation process of a service recommendation probability 1 in the first embodiment. [Figure 12] 10 is an explanatory diagram showing an example of a calculation process of a service recommendation probability 2 and a recommended service list generation process in the first embodiment. FIG. [Figure 13] FIG. 10 is an explanatory diagram illustrating an example of a recommended service list generation process according to the first embodiment. [Figure 14] FIG. 10 is a diagram illustrating an example of a screen configuration of a recommended service list presentation screen in the first embodiment. [Figure 15] 10 is a flowchart illustrating an example of a service adoption result registration process according to the first embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, an embodiment of the present invention will be described in detail with reference to the drawings. In this embodiment, the same components are generally designated by the same reference numerals, and repeated explanations will be omitted. It should be noted that this embodiment is merely an example for realizing the present invention, and does not limit the technical scope of the present invention. [Example]

[0010] 1 is a block diagram showing an example of the configuration of a service recommendation device 100. The service recommendation device 100 holds basic company data 101, company behavior data 102, procurement transaction data 103, service adoption result data 104, a company behavior prediction model 105, and service data 106.

[0011] The company basic data 101 includes company attribute data indicating attributes such as the company's industry and personnel. The company basic data 101 also includes company PL (Profit and Loss) data and company BS (Balance Sheet) data, which indicate the company's financial data (purchasing capacity). The company basic data 101 makes it possible to grasp the company's medium-term purchasing power.

[0012] The corporate behavior data 102 includes information indicating the behavior of personnel belonging to a company. The corporate behavior data 102 includes, as information indicating behavior, for example, web pages viewed by the personnel, seminars attended, and questionnaire responses. The corporate behavior data 102 allows understanding of the company's long-term activity policy.

[0013] The procurement transaction data 103 includes information related to a company's procurement operations and transactions. The procurement transaction data 103 includes, for example, an overview of the estimated and procured products (such as type, quantity, and timing), and the terms of the estimate and procurement (such as cost-focused or environmentally conscious). The procurement transaction data 103 allows the company's most recent procurement terms to be understood.

[0014] The service adoption result data 104 includes information indicating whether or not the company has adopted the recommended service. As will be described in detail later, the service recommendation device 100 can calculate the probability (degree) that the company will accept the service based on the company's status by analyzing the relationship between the company basic data 101, the company behavior data 102, the procurement transaction data 103, and the service adoption result data 104. Note that in this embodiment, the service may include not only intangible services but also tangible services (for example, the sale or provision of tangible goods).

[0015] The corporate behavior prediction model 105 is a model for calculating predicted corporate behavior data based on corporate P&L data and corporate B&S data. The service data 106 shows a list of services that can be provided to a company. The service data 106 also includes, for example, service-company issue data indicating a weight (score) of the contribution of the service provided to solving a corporate issue, and service-procurement condition data indicating a weight (score) of a procurement condition that is emphasized when the service is provided.

[0016] The service recommendation device 100 also includes, for example, a company basic data registration unit 111, a company behavior data registration unit 112, a procurement transaction data registration unit 113, a service adoption / rejection registration unit 114, a company behavior prediction model generation unit 115, a service recommendation probability calculation unit 116, and a recommended service display unit 117, all of which are functional units.

[0017] The company basic data registration unit 111 registers the company basic data 101. The company behavior data registration unit 112 registers the company behavior data 102. The procurement transaction data registration unit 113 registers the procurement transaction data 103. The service adoption / rejection registration unit 114 registers the service adoption result data 104.

[0018] The company behavior prediction model generation unit 115 generates the company behavior prediction model 105. The service recommendation probability calculation unit 116 calculates predicted company behavior data for the target company based on the company behavior prediction model 105 and the company basic data 101 of the target company, and calculates a service recommendation probability (overall recommendation degree) for the target company based on the calculated predicted company behavior data and the service data 106. The recommended service display unit 117 displays recommended services for the target company.

[0019] 2 is a block diagram showing an example of the hardware configuration of the service recommendation device 100. The service recommendation device 100 is configured by a computer having, for example, a CPU (Central Processing Unit) 201, a memory 202, an auxiliary storage device 203, a communication device 204, an input device 205, and an output device 206.

[0020] The CPU 201 includes a processor and executes programs stored in the memory 202. The memory 202 includes a ROM (Read Only Memory), which is a nonvolatile storage element, and a RAM (Random Access Memory), which is a volatile storage element. The ROM stores unchanging programs (e.g., a BIOS (Basic Input / Output System)). The RAM is a high-speed, volatile storage element such as a DRAM (Dynamic Random Access Memory), and temporarily stores programs executed by the CPU 201 and data used when the programs are executed.

[0021] The auxiliary storage device 203 is a large-capacity, non-volatile storage device such as a magnetic storage device (HDD (Hard Disk Drive)) or a flash memory (SSD (Solid State Drive)), and stores programs to be executed by the CPU 201 and data to be used when the programs are executed. That is, the programs are read from the auxiliary storage device 203, loaded into the memory 202, and executed by the CPU 201.

[0022] The input device 205 is a device such as a keyboard or mouse that receives input from an operator. The output device 206 is a device such as a display device or printer that outputs the results of program execution in a format that can be viewed by the operator.

[0023] The communication device 204 is a network interface device that controls communication with other devices in accordance with a predetermined protocol. The communication device 204 may also include a serial interface such as a USB (Universal Serial Bus).

[0024] A part or all of the programs executed by the CPU 201 may be provided to the service recommendation device 100 from a removable medium (CD-ROM, flash memory, etc.) which is a non-transitory storage medium, or from an external computer equipped with a non-transitory storage device via a network, and may be stored in the non-volatile auxiliary storage device 203 which is a non-transitory storage medium. For this reason, the service recommendation device 100 may preferably have an interface for reading data from removable media.

[0025] The service recommendation device 100 is a computer system configured on one physical computer or on multiple logically or physically configured computers, and may operate in separate threads on the same computer, or may operate on a virtual computer built on multiple physical computer resources.

[0026] The CPU 201 includes, for example, a company basic data registration unit 111, a company behavior data registration unit 112, a procurement transaction data registration unit 113, a service adoption / rejection registration unit 114, a company behavior prediction model generation unit 115, a service recommendation probability calculation unit 116, and a recommended service display unit 117.

[0027] For example, the CPU 201 functions as the basic company data registration unit 111 by operating in accordance with the basic company data registration program loaded into the memory 202, and functions as the basic company data registration unit 112 by operating in accordance with the basic company data registration program loaded into the memory 202. The same relationship between programs and functional units applies to the other functional units included in the CPU 201.

[0028] Note that some or all of the functions of the functional units included in the CPU 201 may be realized by hardware such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field-Programmable Gate Array).

[0029] The auxiliary storage device 203 stores, for example, company basic data 101, company behavior data 102, procurement transaction data 103, service adoption result data 104, company behavior prediction model 105, and service data 106.

[0030] It should be noted that some or all of the information stored in the auxiliary storage device 203 may be stored in the memory 202, or may be stored in an external database connected to the service recommendation device 100.

[0031] In this embodiment, the information used by the service recommendation device 100 does not depend on the data structure and may be expressed in any data structure. In this embodiment, the information is expressed in a table format, but the information can be stored in any data structure appropriately selected from, for example, a list, a database, or a queue.

[0032] The service recommendation device 100 is a computer system configured on one physical computer or on multiple logically or physically configured computers, and may operate in separate threads on the same computer, or may operate on a virtual computer constructed on multiple physical computer resources.

[0033] 3A is a diagram showing an example of the data configuration of company attribute data 1011 included in company basic data 101. The company attribute data 1011 includes, for example, a company ID for identifying a company, as well as information on company attributes such as the company name, company location, number of employees, and type of business of the company.

[0034] 3B is a diagram showing an example of the data configuration of the company P&L data 1012 included in the company basic data 101. The company P&L data 1012 includes, for example, a company ID and the target year of the information of the record, as well as information on the company's profit and loss statement, such as sales, cost, selling and administrative expenses, operating profit, ordinary profit, and net profit for the target year.

[0035] 3C is a diagram showing an example of the data configuration of the company balance sheet data 1013 included in the company basic data 101. The company balance sheet data 1013 includes, for example, information on the balance sheet of the company for the relevant year, such as accounts receivable, inventory, current assets, fixed assets, current liabilities, fixed liabilities, and shareholders' equity, in addition to the company ID and the relevant year of the information in the record.

[0036] 4 is a diagram showing an example of the data configuration of the corporate behavior data 102. The corporate behavior data 102 holds, for example, a corporate ID, the type of behavior taken by the company, the date of the behavior, and a flag of a corporate issue corresponding to the type of behavior (if "1" is stored, the company has the issue). In this embodiment, the corporate issues include human resource development, sales power, cost reduction, IT (Information Technology) introduction, technical capabilities, quality improvement, logistics inventory, environmental response, production management, procurement, planning, finance, and suppliers.

[0037] For example, the top record of the corporate behavior data 102 in Figure 4 indicates that a company with company ID "A001" took the action of "attending a manufacturing DX seminar," and as a result, it was found to have issues related to "sales power," "cost reduction," "IT implementation," "quality improvement," and "production management."

[0038] FIG. 5 is a diagram showing an example of the data configuration of procurement transaction data 103. In addition to a company ID, procurement transaction data 103 includes, for example, the type of service procured by the company, the procurement date, the procurement phase, and a flag for the procurement conditions (if "1" is stored, the procurement conditions are specified). In this embodiment, the procurement phase includes the phases of quotation and purchase. Furthermore, the procurement phase may include phases such as contract and ordering, and may also include post-purchase phases such as repair and return.

[0039] In this embodiment, the procurement conditions include, for example, specifications, cost priority, delivery date priority, environmental friendliness, labor conditions, and legal compliance. For example, the top record of the procurement transaction data 103 in Fig. 5 indicates that a company with company ID "A001" requested a "quote" for a product corresponding to a "machined part" on "2020 / 09 / 30," and at that time specified "specifications," "delivery date priority," and "legal compliance" as procurement conditions.

[0040] 6 is a diagram showing an example of the data configuration of the service adoption result data 104. The service adoption result data 104 includes, for example, a company ID, a service ID (information for identifying the service) of the service presented to the company, and a flag indicating the adoption result of the presented service ("1" means adopted, "0" means not adopted).

[0041] 7A is a diagram showing an example of the data configuration of service-company issue data included in the service data 106. The service-company issue data 1061 includes, for example, a service ID for identifying a service, a service name, and a weight for each company issue.

[0042] In addition, the higher the weight of a company problem corresponding to a service in the service-company problem data 1061, the more likely it is that receiving the provision of that service will contribute to solving that company problem (a company problem with a weight of 0.0 will not contribute to solving the service even if it is provided). In addition, the total value of the weights of the company problems in each record of the service-company problem data 1061 is a predetermined value (1.0 in this embodiment).

[0043] 7B is a diagram showing an example of the data configuration of service-procurement condition data included in the service data 106. The service-procurement condition data 1062 includes, for example, a service ID, a service name, and a weight for each of the procurement conditions.

[0044] In the service-procurement condition data 1062, the higher the weight of a procurement condition corresponding to a service, the higher the degree to which that procurement condition is fulfilled in that service (a procurement condition with a weight of 0.0 is not fulfilled in that service). The total weight of the procurement conditions in each record of the service-procurement condition data 1062 is a predetermined value (1.0 in this embodiment).

[0045] 8 is a flowchart showing an example of the overall processing of the service recommendation device 100. The company basic data registration unit 111 registers company basic data 101, the company behavior data registration unit 112 registers company behavior data 102, the procurement transaction data registration unit 113 registers procurement transaction data 103, and the service adoption / rejection registration unit 114 registers service adoption result data 104 (S201). Note that, for example, the data registered in step S201 is transmitted from the corporate system of the user company, etc.

[0046] The corporate behavior prediction model generation unit 115 generates the corporate behavior prediction model 105 based on the corporate basic data 101 and the corporate behavior data 102 (S202). Details of step S202 will be described later with reference to FIG.

[0047] The service recommendation probability calculation unit 116 calculates predicted corporate behavior data of the target company X (the company whose service is to be recommended) based on the basic company data 101 of the target company X and the corporate behavior prediction model 105 generated in step S202, and calculates the recommendation probability of each service to the target company X based on the calculated predicted corporate behavior data, the procurement conditions of the target company X, and the service data 106 (S203). Details of step S203 will be described later using FIGS. 10 to 13.

[0048] The recommended service display unit 117 displays the recommended service list for the target company X, which is generated based on the calculated recommendation probability, on the output device 206 or a display device included in the corporate system of the target company X (S204). Details of the screen presenting the recommended service list will be described later with reference to FIG. 14.

[0049] The service adoption registration unit 114 checks whether the target company X has adopted the presented (recommended) service, registers it in the service adoption result data 104 (S205), and ends the process. Details of step S205 will be described later with reference to FIG. 15.

[0050] 9 is a flowchart showing an example of the corporate behavior prediction model generation process in step S202. The corporate behavior prediction model generation unit 115 learns the corporate behavior prediction model 105 from the corporate basic data 101 and the corporate behavior data 102 (S2021).

[0051] The corporate behavior prediction model 105 is a model (e.g., a neural network) that takes as input, for example, some or all of the items related to the income statement in the corporate P&L data 1012 and some or all of the items related to the balance sheet in the corporate BS data 1013, and outputs predicted corporate behavior data that indicates the probability (score) of occurrence of each corporate issue.

[0052] In step S2021, the corporate behavior prediction model generation unit 115 executes the following process for each target year for each company, for example: The corporate behavior prediction model generation unit 115 identifies records from the corporate behavior data 102 in which the company's behavior date belongs to the target year, and adds up the flags of the same company issue (null values ​​are treated as 0). This calculates the total value (score) of the flags of each company issue for each target year for each company.

[0053] The corporate behavior prediction model generation unit 115, for example, normalizes each of the scores of the corporate issues for each target year of each company so that they do not exceed 1.0. Specifically, for example, the corporate behavior prediction model generation unit 115 performs this normalization by dividing each of the scores of the corporate issues for each target year of each company by the maximum score of the corporate issues for that company for that target year. Furthermore, for example, the corporate behavior prediction model generation unit 115 performs this normalization by replacing the scores of the corporate issues for each target year of each company that are equal to or greater than a predetermined value with 1.0, and replacing the scores of the corporate issues that are less than the predetermined value with 0.0.

[0054] For each target year of each company, the corporate behavior prediction model generation unit 115 uses, as input learning data, predetermined items related to the income statement of the company PL data 1012 for that target year and predetermined items related to the income statement of the company BS data 1013 for that target year, and uses, as output learning data, the normalized corporate issue scores of that company for that target year, to learn the parameters of the neural network. In this way, the corporate behavior prediction model 105 is generated. The corporate behavior prediction model generation unit 115 saves the corporate behavior prediction model 105 in the auxiliary storage device 203 (S2022), and ends the corporate behavior prediction model generation process.

[0055] 10 is a flowchart showing an example of the service recommendation probability calculation process in step S203. The service recommendation probability calculation unit 116 inputs the predetermined items related to the profit and loss statement of the company PL data 1012 for the target year (for example, the latest target year) of the target company X and the predetermined items related to the profit and loss statement of the company BS data 1013 for the target year of the target company X into the company behavior prediction model 105, thereby outputting predicted company behavior data of the target company X (i.e., predicted values ​​of the scores of each of the company issues of the company) (S2031).

[0056] The service recommendation probability calculation unit 116 multiplies the table of predicted corporate behavior data of the target company X with the table of service-company issue data 1061, and calculates a service recommendation probability 1 for the target company X (an example of the first recommendation level, i.e., the probability that the target company X will use the service if it is recommended the service) (S2032).

[0057] Fig. 11 is an explanatory diagram showing an example of the calculation process of step S2032 for the service recommendation probability 1. Note that in the example of Fig. 12, records from the fourth line onwards of the service-company issue data 1061 are omitted.

[0058] The service recommendation probability calculation unit 116 calculates the service recommendation probability 1 corresponding to each service in the service-company issue data 1061 by multiplying and adding the weight value of each company issue of the service by the score of the corresponding company issue in the predicted company behavior data 1100 of the target company X.

[0059] For example, in the example of Fig. 11, for the corporate issues of the service with the service ID "S002", the weights of human resource development and sales ability are both 0.5, and all other weights are 0.0. Also, in the example of Fig. 11, for the corporate issues in the predicted corporate behavior data 1100 of the target company X, the score value of human resource development is 0.5, the score value of sales ability is 0.3, the score value of cost reduction is 0.3, and the other score values ​​are 0.0.

[0060] Therefore, in the example of FIG. 11, the service recommendation probability calculation unit 116 calculates 0.5×0.5+0.3×0.5+0.8×0.0+0.0×0.0+···+0.0×0.0=0.4 as the service recommendation probability 1 corresponding to the service with the service ID "S002".

[0061] Returning to the explanation of Fig. 10, the service recommendation probability calculation unit 116 calculates procurement condition weight data of the target company X from the procurement transaction data 103, and multiplies the procurement condition weight table of the target company X by the service-procurement condition data 1062 table to calculate a service recommendation probability 2 (which is an example of the second recommendation level, and indicates the probability of satisfying the procurement conditions of the target company X) based on the procurement conditions of the target company X (S2033).

[0062] 12 is an explanatory diagram showing an example of the calculation process of the service recommendation probability 2 and the recommended service list generation process in step S2033. Note that in the example of FIG. 12, records from the fourth line onwards of the service procurement condition data 1062 are omitted.

[0063] The service recommendation probability calculation unit 116 identifies records of the target company X whose procurement dates belong to the target year from the procurement transaction data 103. The service recommendation probability calculation unit 116 calculates a total value (score) of each flag of the procurement conditions for the target company X in the target year by adding up the flag values ​​of the same procurement conditions in the identified records.

[0064] The service recommendation probability calculation unit 116 calculates the procurement condition weight data 1200 of the target company X by normalizing each of the scores of the procurement conditions of the target company X in the target year. Specifically, for example, the service recommendation probability calculation unit 116 performs the normalization by dividing each of the scores of the procurement conditions of the target company X in the target year by the sum of all of the total values. As a result of the normalization, the sum of all of the scores of the procurement conditions in the procurement condition weight data 1200 of the target company X becomes 1.0.

[0065] The service recommendation probability calculation unit 116 calculates the service recommendation probability 2 corresponding to each service in the service-procurement condition data 1062 by multiplying and adding the weight value of the procurement condition of the service by the score of the corresponding procurement condition in the procurement condition weight data 1200 of the target company X.

[0066] 12, in the procurement conditions for a service with service ID "S001", the weight of specification is 0.6, the weight of cost priority is 0.3, the weight of delivery date priority is 0.1, and all other weights are 0.0. Also in the example of Fig. 12, in the procurement conditions in the procurement condition weight data 1200 of target company X, the score of specification is 0.1, the score of cost priority is 0.1, the score of delivery date priority is 0.3, the score of environmental friendliness is 0.5, and all other scores are 0.0.

[0067] Therefore, in the example of FIG. 12, the service recommendation probability calculation unit 116 calculates 0.1×0.6+0.1×0.3+0.3×0.1+0.5×0.0+0.0×0.0+0.0×0.0=0.12 as the service recommendation probability 2 corresponding to the service with the service ID "S001".

[0068] Returning to the description of Fig. 10, the service recommendation probability calculation unit 116 generates a recommended service list by weighting and adding the service recommendation probability 1 and the service recommendation probability 2 (S2034), and ends the calculation process of the service recommendation probability 2 and the recommended service list generation process.

[0069] 13 is an explanatory diagram showing an example of the recommended service list generation process in step S2034. The service recommendation probability calculation unit 116 calculates the weighted sum of the service recommendation probability 1 and the service recommendation probability 2 for each service ID (in the example of FIG. 12, the weight a is 1.0) to calculate the recommendation probability of each service (an example of the overall recommendation degree).

[0070] The service recommendation probability calculation unit 116 generates a recommended service list including service IDs and service recommendation probabilities calculated by the weighted sum. Note that the service recommendation probability calculation unit 116 may include only services with a high recommendation probability calculated by the weighted sum (for example, services with a recommendation probability equal to or greater than a predetermined value, or a predetermined number of services in descending order of recommendation probability) in the recommended service list, or may include all services in the recommended service list.

[0071] 14 is a diagram showing an example of the screen configuration of a recommended service list presentation screen 1400. The recommended service list presentation screen 1400 includes, for example, a recommended service list display area 1401, a peer recruitment service list display area 1402, and a company problem service list display area 1403.

[0072] In the recommended service list display area 1401, the service names of the services included in the recommended service list for the target company X generated in step S2034 are displayed.

[0073] The trade recruitment service list display area 1402 displays the names of services that are frequently adopted by companies in the same industry as the target company X. Specifically, for example, the recommended service display unit 117 identifies companies in the same industry as the target company X from the company attribute data 1011, identifies services that have been adopted by the identified companies the total number of times equal to or greater than a predetermined value from the service adoption result data 104, and displays the identified services in the trade recruitment service list display area 1402.

[0074] The company problem service list display area 1403 displays the service names of services that are frequently adopted by other companies that have company problems (hereinafter also referred to as predicted problems) whose scores are equal to or greater than a predetermined value in the predicted company behavior data 1100 of the target company X. Specifically, for example, the recommended service display unit 117 identifies companies whose scores corresponding to the predicted problems are equal to or greater than a predetermined value from the company problems of each company normalized in step S2021. Furthermore, for example, the recommended service display unit 117 identifies services whose total number of adoptions by the identified companies is equal to or greater than a predetermined value from the service adoption result data 104, and displays the identified services in the company problem service list display area 1403.

[0075] Therefore, for example, the recommended service list presentation screen 1400 may include the same number of company issue service list display areas 1403 as the number of predicted issues. Furthermore, the recommended service list presentation screen 1400 may include a procurement condition service list display area in which the names of services frequently adopted by other companies that prioritize procurement conditions (hereinafter also referred to as important procurement conditions) whose weight values ​​in the procurement condition weight data 1200 of the target company X are equal to or greater than a predetermined value are displayed. Specifically, for example, the recommended service display unit 117 generates the procurement condition weight data 1200 for each company using a method similar to the method described in step S2033. Furthermore, for example, the recommended service display unit 117 identifies companies whose weight values ​​for the important procurement conditions are equal to or greater than a predetermined value. Furthermore, for example, the recommended service display unit 117 identifies services whose total number of adoptions by the identified companies is equal to or greater than a predetermined value from the service adoption result data 104, and displays the identified services in the procurement condition service list display area.

[0076] For example, when a service name is selected in each display area included in the recommended service list presentation screen 1400, it is possible to inquire about the content of the service, request an estimate, or apply for purchase.

[0077] It is preferable that the service names be displayed in descending order of recommendation probability in each display area included in the recommended service list presentation screen 1400. Furthermore, the recommendation probability of each service may be displayed together with the service name in each display area included in the recommended service list presentation screen 1400.

[0078] 15 is a flowchart showing an example of the service adoption result registration process in step S205. The service adoption / rejection registration unit 114 executes confirmation and collection of data entered via the recommended service list presentation screen 1400 for the services presented to the target company X by the recommended service list presentation screen 1400 (S2051). Specifically, for example, the service adoption / rejection registration unit 114 confirms whether there have been inquiries about the contents of the presented services, requests for estimates, purchase applications, etc., and collects this information.

[0079] When inquiries about the content of the presented services, requests for estimates, purchase applications, etc. are made by email or other electronic means other than input via the recommended service list presentation screen 1400, the service adoption / rejection registration unit 114 collects the information obtained by these emails, etc. (S2052).

[0080] When inquiries about the content of the presented service, requests for estimates, purchase applications, etc. are made through human means such as by a sales representative, the service adoption / rejection registration unit 114 collects the information obtained through such means (for example, by accepting input from the sales representative, etc.) (S2053).

[0081] The service adoption / rejection registration unit 114 registers the collected data obtained in steps S2051 to S2053 in the service adoption / rejection result data 104 (S2054), and ends the service adoption / rejection result registration process. In the example of Fig. 6, the service adoption / rejection result data 104 only records the adoption / rejection result of the presented service (for example, whether or not there was a purchase application), but it may also record whether or not there was an inquiry about the content of the presented service or whether or not there was a request for an estimate.

[0082] By registering the service adoption result of the target company X through the service adoption result registration process in step S205, when the recommended service list presentation screen 1400 is displayed for another target company, the service adoption result of the target company X will also be taken into consideration when selecting services to be displayed in the peer recruitment service list display area 1402.

[0083] As described above, the service recommendation device 100 of this embodiment generates a corporate behavior prediction model 105 based on the corporate P&L data 1012, the corporate BS data 1013, and the corporate behavior data 102, thereby making it possible to predict with high accuracy the corporate behavior (corporate issues) of a target company based on the financial situation and purchasing capacity of the target company.

[0084] Furthermore, the service recommendation device 100 of this embodiment calculates the service recommendation probability 1 from the predicted corporate behavior data 1100 of the target company and the service-corporate issue data 1061, thereby making it possible to discover services that are likely to solve the corporate issues that are predicted to be faced by the target company and correspond to the financial characteristics, purchasing capacity, and activity policy of the target company.

[0085] In addition, the service recommendation device 100 of this embodiment can discover services that are likely to satisfy the target company's procurement conditions (items that are important during procurement and transactions) by calculating the service recommendation probability 2 based on the target company's procurement condition weight data 1200 and the service-procurement condition data 1062.

[0086] Furthermore, the service recommendation device 100 of this embodiment can present to the target company services that are highly likely to solve the above-mentioned corporate issues of the target company and that are highly likely to satisfy the procurement conditions of the target company by calculating a service recommendation probability that combines the service recommendation probability 1 and the service recommendation probability 2. In other words, the service recommendation device 100 can predict the requirements and needs of the target company with higher accuracy, and can present useful services (services that are highly likely to satisfy the requirements and needs) for the target company.

[0087] The present invention is not limited to the above-described embodiments, but includes various modifications. For example, the above-described embodiments have been described in detail to clearly explain the present invention, and the present invention is not necessarily limited to those including all of the described configurations. Furthermore, it is possible to replace part of the configuration of one embodiment with the configuration of another embodiment, or to add the configuration of another embodiment to the configuration of one embodiment. Furthermore, it is possible to add, delete, or replace part of the configuration of each embodiment with other configurations.

[0088] Furthermore, the above-described configurations, functions, processing units, processing means, etc. may be partially or entirely implemented in hardware, for example, by designing them as integrated circuits. The above-described configurations, functions, etc. may also be implemented in software, with a processor interpreting and executing a program that implements each function. Information such as the programs, tables, and files that implement each function can be stored in a memory, a recording device such as a hard disk or SSD (Solid State Drive), or a recording medium such as an IC card, SD card, or DVD.

[0089] In addition, the control lines and information lines shown are those that are considered necessary for the explanation, and do not necessarily show all the control lines and information lines in the product. In reality, it can be assumed that almost all components are interconnected. [Explanation of symbols]

[0090] 100 Service recommendation device, 101 Company basic data, 102 Company behavior data, 103 Procurement transaction data, 104 Service adoption result data, 105 Company behavior prediction model, 106 Service data 106, 111 Company basic data registration unit, 112 Company behavior data registration unit, 113 Procurement transaction data registration unit, 114 Service adoption registration unit, 115 Company behavior prediction model generation unit, 116 Service recommendation probability calculation unit, 117 Recommended service display unit, 201 CPU, 202 Memory, 203 Auxiliary storage device, 204 Communication device, 205 Input device, 206 Output device, 1100 Predicted company behavior data, 1200 Procurement condition weight data, 1400 Recommended service list presentation screen

Claims

1. A service recommendation device, a processor and a memory, The memory includes: Financial information of the target company; The score for each of the target company's procurement conditions, Service information indicating the weight of each issue and the weight of each procurement condition for each service; A model that outputs a score for each of the company's issues when a company's financial information is input, and The processor: Calculating a score for each of the target company's issues based on the target company's financial information and the model; Calculating a first recommendation level for each of the services based on the score for each of the issues of the target company and the weight for each of the issues for each service indicated by the service information; calculating a second recommendation level for each of the services based on the score of each of the procurement conditions of the target company and the weight of each of the procurement conditions for each service indicated by the service information; a service recommendation device that calculates an overall recommendation level for each service indicated by the service information for the target company based on the first recommendation level and the second recommendation level.

2. 2. The service recommendation device according to claim 1, The memory includes: said financial information for each of a plurality of companies; retaining corporate behavior information indicating actions by each of the plurality of companies and issues corresponding to the actions; The processor: For each of the plurality of companies, calculate a score for each of the company's issues based on the actions of the company and the issues corresponding to the actions indicated by the corporate behavior information; A service recommendation device that generates the model by learning based on financial information of each of the multiple companies and the calculated scores for each of the issues for each of the multiple companies.

3. 2. The service recommendation device according to claim 1, The processor: selecting a recommended service from the services based on the overall recommendation level; a service recommendation device that generates data for outputting a recommended service presentation screen showing the recommended services;

4. 4. The service recommendation device according to claim 3, The memory stores service adoption information indicating whether a service recommended to a company has been adopted by the company; The recommended service is selectable on the recommended service presentation screen, The processor: acquiring information indicating the recommended service selected via the recommended service presentation screen; The service recommendation device stores information indicating that the recommended service selected via the recommended service presentation screen has been adopted in the service adoption / rejection information.

5. 5. The service recommendation device according to claim 4, The memory includes: said financial information for each of a plurality of companies; retaining corporate behavior information indicating actions by each of the plurality of companies and issues corresponding to the actions; The processor: For each of the plurality of companies, calculate a score for each of the company's issues based on the actions of the company and the issues corresponding to the actions indicated by the corporate behavior information; Identifying issues for which the target company has a score equal to or greater than a predetermined value, Identifying companies from the plurality of companies that have scores for the identified issues that are equal to or greater than a predetermined value; Identifying services that have been adopted by the identified companies in a number equal to or greater than a predetermined value from the service adoption information; a service recommendation device that includes information indicating the identified service in data for outputting the recommended service presentation screen;

6. A service recommendation method by a service recommendation device, the service recommendation device includes a processor and a memory; The memory includes: Financial information of the target company; The score for each of the target company's procurement conditions, Service information indicating the weight of each issue and the weight of each procurement condition for each service; A model that outputs a score for each of the company's issues when a company's financial information is input, and The service recommendation method includes: The processor calculates a score for each of the target company's issues based on the target company's financial information and the model; The processor calculates a first recommendation level for each of the services based on a score for each of the issues of the target company and a weight for each of the issues for each service indicated by the service information; the processor calculates a second recommendation level for each of the services based on the score of each procurement condition of the target company and the weight of each procurement condition for each service indicated by the service information; The service recommendation method, wherein the processor calculates an overall recommendation level for each service indicated by the service information for the target company based on the first recommendation level and the second recommendation level.

7. 7. A service recommendation method according to claim 6, comprising: The memory includes: said financial information for each of a plurality of companies; retaining corporate behavior information indicating actions by each of the plurality of companies and issues corresponding to the actions; The service recommendation method includes: The processor calculates, for each of the plurality of companies, a score for each of the company's issues based on the actions of the company and the issues corresponding to the actions indicated by the company action information; A service recommendation method in which the processor generates the model by learning based on financial information for each of the multiple companies and the calculated scores for each of the challenges for each of the multiple companies.

8. 7. A service recommendation method according to claim 6, comprising: the processor selects a recommended service from the services based on the overall recommendation level; The service recommendation method further comprises generating data for outputting a recommended service presentation screen showing the recommended services, the processor generating data for outputting a recommended service presentation screen showing the recommended services.

9. 9. A service recommendation method according to claim 8, comprising: The memory stores service adoption information indicating whether a service recommended to a company has been adopted by the company; The recommended service is selectable on the recommended service presentation screen, The service recommendation method includes: the processor acquires information indicating a recommended service selected via the recommended service presentation screen; The service recommendation method, wherein the processor stores information indicating that the recommended service selected via the recommended service presentation screen has been adopted in the service adoption information.

10. 10. A service recommendation method according to claim 9, comprising: The memory includes: said financial information for each of a plurality of companies; retaining corporate behavior information indicating actions by each of the plurality of companies and issues corresponding to the actions; The service recommendation method includes: The processor calculates, for each of the plurality of companies, a score for each of the company's issues based on the actions of the company and the issues corresponding to the actions indicated by the company action information; The processor identifies issues for which the target company has a score equal to or greater than a predetermined value; Identifying companies from the plurality of companies that have scores for the identified issues that are equal to or greater than a predetermined value; the processor identifies, from the service adoption information, services that have been adopted by the identified companies in a number of times equal to or greater than a predetermined value; The service recommendation method, wherein the processor includes information indicating the identified service in data for outputting the recommended service presentation screen.

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