Sales support information processing system, sales support information processing method, and program

Abstract

To enable appropriate control over the referencing of sales data based on the relationships between sales data and evaluation information. [Solution] The sales support information processing system includes a storage unit that stores multiple sales data relating to customers, business negotiations, materials, personnel in charge, and sales know-how, relational data showing the relationships between the sales data, and evaluation information including the source, freshness, reliability, approval status, and inconsistency status associated with each relational data; a request acquisition unit that acquires reference requests related to sales operations by sales personnel or AI support programs; a reference control unit that references relational data in response to reference requests and determines whether the sales data to be referenced can be referenced, the presentation priority, the basis display target, and the attention target based on the evaluation information; and an output unit that outputs sales data based on the decision result by the reference control unit.

Description

Technical Field

[0001] The present invention relates to a sales support information processing system, a sales support information processing method, and a program.

Background Art

[0002] There has been proposed a sales support system that analyzes the operation status by the customer based on the operation history of the customer device and determines the necessity of urgent follow-up (see Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Conventional sales support systems determine the necessity of follow-up based on the operation history of the customer device, manage the relationships among multiple sales data regarding customers, business negotiations, materials, persons in charge, and sales know-how, and control the accessibility, presentation priority, basis display target, and attention-calling target of the sales data based on evaluation information including the source, freshness, reliability, approval status, and conflict status associated with each related data. However, this aspect has not been considered.

[0005] The present invention has been made in view of such a background, and an object thereof is to provide a technology capable of appropriately controlling the reference of sales data based on the relationships among sales data and evaluation information.

Means for Solving the Problems

[0006] The main invention of the present invention for solving the above problems is a sales support information processing system comprising: a storage unit that stores a plurality of sales data relating to customers, business negotiations, materials, personnel in charge and sales know-how; relationship data indicating the relationships between the sales data; and evaluation information including source, freshness, reliability, approval status and inconsistency status associated with each of the relationship data; a request acquisition unit that acquires reference requests related to sales operations by sales personnel or AI (Artificial Intelligence) support programs; a reference control unit that references the relationship data in response to the reference request and determines whether the sales data to be referenced can be referenced, the presentation priority, the basis display target and the attention target based on the evaluation information; and an output unit that outputs the sales data based on the determination result by the reference control unit.

[0007] Further issues and solutions disclosed in this application will be made clear in the section on embodiments of the invention and in the drawings. [Effects of the Invention]

[0008] According to the present invention, it is possible to appropriately control the referencing of sales data based on the relationships between sales data and evaluation information. [Brief explanation of the drawing]

[0009] [Figure 1] This diagram shows an example of the overall configuration of a sales support information processing system. [Figure 2] This figure shows an example of the hardware configuration of management server 2. [Figure 3] This figure shows an example of the software configuration of user terminal 1. [Figure 4] This figure shows an example of the software configuration for management server 2. [Figure 5] This diagram illustrates the basic reference processing flow in a sales support information processing system. [Figure 6] This diagram illustrates the process flow for generating reuse candidate information in a sales support information processing system. [Figure 7]This diagram illustrates the system expansion processing flow in a sales support information processing system. [Modes for carrying out the invention]

[0010] <System Overview> The following describes a sales support information processing system according to one embodiment of the present invention. The sales support information processing system of this embodiment manages multiple sales data related to customers, business negotiations, materials, personnel, and sales know-how, relational data showing the relationships between the sales data, and evaluation information including source, freshness, reliability, approval status, and inconsistency status associated with each relational data. In response to a reference request from a sales representative or an AI (Artificial Intelligence) support program, the system determines whether the information can be referenced based on the evaluation information, determines the presentation priority, selects the items to be displayed as evidence, and identifies items to be cautioned about, and outputs appropriate sales data. Here, an AI support program refers to a software program that autonomously or semi-autonomously supports sales operations using machine learning models or natural language processing models, and includes those that operate as AI agents. An AI agent refers to an AI program that autonomously plans actions for a given goal and aims to achieve the goal by repeatedly referencing and manipulating external systems and data sources. The sales support information processing system of this embodiment can also apply reference control based on evaluation information to reference requests from such AI agents.

[0011] In this embodiment, "sales data" is a concept that includes information about customers (customer name, industry, size, contact person information, past transaction history, etc.), information about business negotiations (negotiation name, progress status, amount, schedule, etc.), information about documents (proposals, quotations, product catalogs, etc.), information about the person in charge (name, department, skills, assigned customers, etc.), and information about sales know-how (success stories, failure stories, sales procedures, customer problem countermeasures, etc.). "Relational data" is data that shows the relationships between these sales data, and refers to information that links two or more sales data, such as "the relationship between customer A and negotiation B," "the relationship between negotiation B and document C," and "the relationship between person in charge D and customer A." Relational data may be managed as corresponding to edges in a graph structure. "Evaluation information" refers to information indicating the reliability and validity of each related data, and includes "source" (information indicating the source of the related data), "freshness" (information indicating the newness of the related data or the time elapsed since the last update), "reliability" (a numerical value or indicator indicating the accuracy and reliability of the related data), "approval status" (information indicating whether the related data has undergone the prescribed approval procedure), and "inconsistency status" (information indicating whether the related data is inconsistent with other related data). "Accessibility" refers to the decision of whether or not to allow the sales data to be referenced in response to an access request. "Presentation priority" refers to the priority of output when multiple sales data are referenced. "Evidence to be displayed" refers to identifying the related data or evaluation information that should be displayed as evidence for the outputted sales data. "Attention required" refers to identifying the sales data or related data that users should be cautioned about.

[0012] Figure 1 shows an example of the overall configuration of a sales support information processing system. The sales support information processing system of this embodiment includes a management server 2. The management server 2 is connected to the user terminal 1 via a communication network. The communication network is, for example, the internet and is constructed using public telephone lines, mobile phone lines, wireless communication channels, Ethernet (registered trademark), etc.

[0013] User terminal 1 is a computer operated by a sales representative. User terminal 1 can be, for example, a smartphone, tablet computer, or personal computer. The sales representative sends a reference request to management server 2 via user terminal 1 and receives sales data output from management server 2 to use in their work. The AI ​​support program may run on management server 2, on user terminal 1, or on an external server separate from management server 2. If the AI ​​support program runs on an external server, that external server sends a reference request to management server 2 via a communication network.

[0014] The management server 2 may be a general-purpose computer such as a workstation or personal computer, or it may be logically implemented through cloud computing.

[0015] <Management Server 2> FIG. 2 is a diagram showing an example of the hardware configuration of the management server 2. Note that the illustrated configuration is an example, and it may have other configurations. The management server 2 includes a CPU (Central Processing Unit) 201, a memory 202, a storage device 203, a communication interface 204, an input device 205, and an output device 206. The storage device 203 stores various data and programs, such as a hard disk drive, a solid state drive, or a flash memory. The communication interface 204 is an interface for connecting to a communication network, such as an adapter for connecting to Ethernet (registered trademark), a modem for connecting to a public telephone network, a wireless communication device for performing wireless communication, a USB (Universal Serial Bus) connector or an RS232C connector for serial communication, etc. The input device 205 inputs data, such as a keyboard, a mouse, a touch panel, a button, a microphone, etc. The output device 206 outputs data, such as a display, a printer, a speaker, etc. Note that each functional unit of the management server 2 described later is realized by the CPU 201 reading a program stored in the storage device 203 into the memory 202 and executing it, and each storage unit of the management server 2 is realized as a part of the storage area provided by the memory 202 and the storage device 203.

[0016] FIG. 3 is a diagram showing an example of the software configuration of the user terminal 1. The user terminal 1 includes an input unit 111, a display unit 112, a transmission / reception unit 113, and a storage unit 131.

[0017] FIG. 4 is a diagram showing an example of the software configuration of the management server 2. The management server 2 includes a request acquisition unit 211, a reference control unit 212, an expansion unit 213, a system expansion unit 214, a condition update unit 215, an output unit 216, and a storage unit 231. <User Terminal 1>

[0018] Hereinafter, the functional units of the user terminal 1 will be described.

[0019] The storage unit 131 is the storage device of the user terminal 1. The storage unit 131 stores the authentication information of the salesperson, the history of past reference requests, and the display cache of the sales data received from the management server 2, etc.

[0020] The input unit 111 receives data input from the salesperson. The input unit 111 displays, for example, a user interface for receiving input of a reference request related to negotiation preparation, customer response, document creation, or consideration of the next action, and acquires the content of the reference request input by the salesperson. The input unit 111 may support a plurality of input methods such as text input, selection of options, voice input, etc.

[0021] The display unit 112 displays the display content based on the sales data received from the management server 2 and its evaluation information on the output device of the user terminal 1. The display unit 112 arranges and displays the sales data according to the presentation priority, attaches the related data or evaluation information determined as the basis display target as basis information, and performs a warning display for the sales data determined as the attention - calling target.

[0022] The transceiver unit 113 transmits the reference request received by the input unit 111 to the management server 2 and receives the sales data and display control information output from the management server 2. The transceiver unit 113 communicates with the management server 2 via a communication network. <Management Server 2>

[0023] Hereinafter, the functional units of the management server 2 will be described.

[0024] The memory unit 231 is the storage device of the management server 2. The memory unit 231 stores multiple sales data related to customers, deals, documents, personnel, and sales know-how. The memory unit 231 also stores relational data that shows the relationships between sales data. Relational data is managed as information that links two or more sales data, such as "the relationship between customer A and deal B," "the relationship between deal B and proposal document C," and "the relationship between personnel D and customer A." The memory unit 231 also stores evaluation information associated with each relational data. The evaluation information includes source, freshness, reliability, approval status, and inconsistency status. Source is information that indicates the source of the relational data, and is managed by categories such as "manual input by sales personnel," "automatic linkage from CRM (Customer Relationship Management) system," and "acquisition from deal analysis service." Freshness is information that indicates the newness of the relational data or the time elapsed since the last update, and is managed as, for example, the last update date and time or the number of days elapsed. The reliability level is a numerical value or indicator that shows the accuracy and reliability of the relevant data, and is managed as a numerical value from 0 to 100, for example. The approval status is information that shows whether or not the relevant data has gone through the prescribed approval procedure, and is managed in categories such as "approved," "not approved," and "awaiting approval," for example. The inconsistency status is information that shows whether or not the relevant data is inconsistent with other relevant data, and is managed in categories such as "no inconsistency," "inconsistency," and "under review," for example.

[0025] The memory unit 231 further stores information obtained from at least one of the existing services—automatic categorization, internal sharing, sales negotiation analysis, and sales role-playing—as an input source for sales data or related data. The automatic categorization service is a service that automatically classifies and tags documents and information related to sales activities. The internal sharing service is a service for sharing information among sales representatives, such as an internal SNS (Social Networking Service) or internal portal site. The sales negotiation analysis service is a service that analyzes the progress and closing rate of sales negotiations. The sales role-playing service is a service that simulates conversations with customers for the training of sales representatives. The memory unit 231 stores the information obtained from these services, linking it to the corresponding sales data or related data.

[0026] The memory unit 231 further stores the new information type or relationship type extracted by the system expansion unit 214, and the reference control conditions updated by the condition update unit 215. The memory unit 231 further stores the reuse candidate information generated by the expansion unit 213. At least a portion of the evaluation information of the original sales data or relationship data is associated with and stored in the reuse candidate information.

[0027] The request acquisition unit 211 acquires reference requests related to sales operations by sales representatives or AI-supported programs. The request acquisition unit 211 acquires reference requests from sales representatives by receiving reference requests transmitted from user terminal 1 via the communication interface 204. The request acquisition unit 211 also acquires reference requests transmitted from AI-supported programs running on management server 2, AI-supported programs running on user terminal 1, or AI-supported programs running on external servers. The AI-supported programs include those that operate as AI agents and can autonomously formulate action plans for given sales targets and strive to achieve those targets by repeatedly sending reference requests to management server 2.

[0028] A reference request can relate to at least one of the following: preparing for a business negotiation, handling customer inquiries, creating documents, or considering the next action. An example of a reference request related to business negotiation preparation would be, "I would like to refer to relevant past proposals and success stories for a business negotiation with customer A." An example of a reference request related to customer inquiries would be, "I would like to refer to documents that address the issues raised by customer B." An example of a reference request related to document creation would be, "I would like to refer to relevant documents and the skills of the person in charge in order to create a proposal for customer C." An example of a reference request related to considering the next action would be, "I would like to refer to relevant sales know-how in order to consider the next action for business negotiation D." The request acquisition unit 211 passes the acquired reference request to the reference control unit 212.

[0029] The reference control unit 212, in response to a reference request, refers to the relevant data stored in the storage unit 231 and, based on the evaluation information, determines whether the sales data to be referenced can be referenced, its presentation priority, the basis for which it should be displayed, and the target for which a warning should be issued.

[0030] The reference control unit 212 excludes sales data whose approval status is "unapproved" from the basis for display, or determines that it should be subject to a warning. For example, if the approval status of a certain related data is "unapproved," the reference control unit 212 prohibits the display of the corresponding sales data as a basis, or adds a warning stating that "unapproved data is included" when outputting the sales data. This prevents information that has not gone through the approval procedure from being misused as a basis.

[0031] The reference control unit 212 determines that sales data included in the evaluation information whose freshness does not meet predetermined conditions, sales data whose reliability is below a predetermined value, or sales data whose inconsistency status is inconsistent will be excluded from reference, subject to warning, or subject to administrator review. If the freshness does not meet predetermined conditions, for example, it means that a predetermined number of days (e.g., 180 days) or more have passed since the last update. If the reliability is below a predetermined value, for example, it means that the reliability score falls below a predetermined threshold (e.g., 60). If the inconsistency status is inconsistent, it means that a logical inconsistency has been detected between the relevant data and other relevant data. Sales data determined to be excluded from reference is excluded from output, sales data determined to be subject to warning is output with a warning display, and sales data determined to be subject to administrator review is controlled such as withholding output until administrator review is completed.

[0032] The reference control unit 212 determines the presentation priority based on at least the source, freshness, and reliability. For example, if the source is an automated link from an official CRM system, the source score is set high; if the freshness is high (short number of days since the last update), the freshness score is set high; and if the reliability score is high, the reliability score is used as is. The presentation priority can then be calculated by summing these scores with predetermined weights. The method for calculating the presentation priority is not limited to this; for example, a machine learning model may be used to predict the presentation priority.

[0033] The reference control unit 212 prioritizes the reference of at least some of the following, depending on the type of sales activity corresponding to the reference request: customers, business negotiations, past proposals, success stories, related materials, and the skills of the person in charge. For example, for a reference request related to business negotiation preparation, past proposals and success stories are prioritized as reference subjects, and for a reference request related to document creation, related materials and the skills of the person in charge are prioritized as reference subjects.

[0034] The reference control unit 212 can differentiate the scope of accessible sales data and the manner of displaying the basis for reference requests based on whether the request is made by a sales representative or by an AI-assisted program. For example, for reference requests made by an AI-assisted program, the system can restrict access to only approved sales data and ensure that the source and approval status are always included in the basis display. On the other hand, for reference requests made by sales representatives, the system can restrict access to unapproved sales data, provided that a warning is added.

[0035] The reference control unit 212 further determines, based on the evaluation information, whether the reuse candidate information generated by the expansion unit 213 can be referenced, its presentation priority, whether it should be used for evidence display, and whether it should be used for warnings. Since the evaluation information associated with the reuse candidate information inherits the evaluation information of the original sales data or related data, the presentation priority of the reuse candidate information is set low if the reliability of the original data is low.

[0036] The reference control unit 212 further determines whether to reference, the presentation priority, the target of evidence display, and the target of warnings for business data based on new information types or relationship types added by the system expansion unit 214, based on the source, freshness, reliability, approval status, and contradiction status.

[0037] The expansion unit 213 abstracts sales procedures, document combinations, personnel skills, or customer issue responses that meet predetermined evaluation conditions from multiple sales data, and generates reusable candidate information that is converted or reconfigured to suit other uses. The predetermined evaluation conditions include, for example, that the reliability is above a predetermined value, the approval status is approved, and the inconsistency status is consistent.

[0038] The development unit 213 generates abstract patterns of sales procedures, document combinations, personnel skills, or customer problem responses that meet predetermined evaluation conditions, with company-specific expressions removed. Company-specific expressions refer to information that can identify a specific company or individual, such as a specific customer name, personnel name, or unique product name. The development unit 213 generates abstract patterns by replacing these company-specific expressions with general expressions (e.g., placeholders such as "Customer X," "Person in Charge Y," and "Product Z"). The development unit 213 further concretizes the abstract patterns according to the customer attributes, deal attributes, or personnel attributes of other companies that are intended for other use. For example, an abstract pattern of a successful sales procedure for a customer in one industry can be concretized for another customer in the same industry to generate information that can be reused.

[0039] The reuse candidate information generated by the expansion unit 213 is associated with evaluation information based on at least part of the source, freshness, reliability, approval status, or inconsistency status of the original sales data or related data. For example, if the reliability of the original related data is 80 and the approval status is approved, the reuse candidate information will be associated with evaluation information of reliability 80 and approved. When reuse candidate information is generated by combining multiple related data, the evaluation information of the reuse candidate information can be determined by methods such as adopting the lowest value among the evaluation information of each related data.

[0040] The system extension unit 214 extracts new information types or relationship types from heterogeneous sales data that are not included in existing relationship data, and adds them to the relationship data system corresponding to the target organization based on predetermined evaluation indicators. Heterogeneous sales data includes information obtained from at least one of the following: automatic categorization, internal sharing, sales opportunity analysis, and sales role-playing. For example, if "comparison information with competitors" obtained from the sales opportunity analysis service is not included in the existing relationship data system, the system extension unit 214 extracts a candidate for a new relationship type called "competitor comparison."

[0041] The system expansion unit 214 evaluates at least some of the frequency of occurrence, relevance to sales results, source, and reliability for each candidate for a new information type or relationship type, and adds only the candidates that meet the predetermined conditions to the relationship data system corresponding to the target organization. Frequency of occurrence refers to how often the candidate appears in the sales data; for example, if it appears a predetermined number of times or more within a predetermined period, it can be determined that the predetermined conditions are met. Relevance to sales results refers to the statistical correlation between the candidate and sales results such as the closing rate and customer satisfaction in the sales activities that include the candidate. The system expansion unit 214 can add only the candidates whose frequency of occurrence is above a predetermined value, whose relevance to sales results is above a predetermined value, whose source is reliable, and whose reliability is above a predetermined value to the relationship data system.

[0042] The addition of new information types or relationship types to the relational data system is performed subject to administrator approval or the fulfillment of predetermined conditions. If administrator approval is required, the system expansion unit 214 presents the candidate for addition to the administrator and performs the addition to the relational data system after receiving approval from the administrator. If the fulfillment of predetermined conditions is required, the system expansion unit 214 automatically evaluates predetermined conditions based on frequency of occurrence, relevance to sales results, source, and reliability, and automatically performs the addition to the relational data system if the conditions are met.

[0043] The condition update unit 215 reflects the new information type or relationship type added by the system expansion unit 214 in the conditions used for reference control or business decision-making in the AI ​​support program. Specifically, after the addition of a new information type or relationship type, the condition update unit 215 updates the selection conditions for reference targets in at least one of the customer response, document creation, or next action proposals performed by the AI ​​support program. For example, if a new relationship type called "competitor comparison" is added, the condition update unit 215 adds sales data related to "competitor comparison" to the selection conditions for reference targets in reference requests related to customer response.

[0044] The condition update unit 215 further readjusts the conditions used for reference control or business decisions based on the difference in sales results before and after the addition of a new information type or relationship type. For example, if the closing rate improves after adding a relationship type called "competitor comparison," the conditions can be readjusted to increase the reference priority of that relationship type. Conversely, if no improvement in sales results is seen after the addition, the unit can suggest to the administrator that the reference priority of that relationship type be lowered or that it be removed from the relationship data system.

[0045] The condition update unit 215 further re-evaluates the effectiveness of the newly added information type or relationship type based on the reference results or sales results after the condition update. The effectiveness re-evaluation may be performed periodically, for example, at predetermined intervals, or it may be triggered when a predetermined change occurs in the reference results or sales results.

[0046] The output unit 216 outputs sales data based on the decision results by the reference control unit 212 and the reuse candidate information generated by the expansion unit 213. The output unit 216 outputs the sales data in order of presentation priority, adds related data or evaluation information determined to be subject to justification display as justification information, and adds warning information to sales data determined to be subject to attention. The output unit 216 can transmit the output data to the display unit 112 of the user terminal 1 as the output destination. The output unit 216 can also provide output data to the AI ​​support program.

[0047] Figure 5 illustrates the basic reference processing flow in a sales support information processing system.

[0048] The request acquisition unit 211 acquires a reference request related to sales operations from a sales representative or an AI support program (S501). The reference control unit 212 determines the type of sales operation corresponding to the reference request and decides the type of sales data to be prioritized for reference (S502). The reference control unit 212 refers to the related data stored in the storage unit 231 and extracts the sales data and related data related to the reference request (S503). The reference control unit 212 acquires evaluation information associated with the extracted related data and decides whether each sales data can be referenced based on the approval status, freshness, reliability, and inconsistency status (S504). For the sales data determined to be referenced, the reference control unit 212 calculates the presentation priority based on the source, freshness, and reliability (S505). The reference control unit 212 determines the items to be subject to evidence display and the items to be subject to warning (S506). The output unit 216 arranges the sales data in order of presentation priority, adds evidence information and warning information, and outputs it (S507).

[0049] Figure 6 illustrates the generation process flow for reuse candidate information in a sales support information processing system.

[0050] The expansion unit 213 refers to multiple sales data stored in the storage unit 231 and extracts sales procedures, material combinations, personnel skills, or customer issue responses that satisfy predetermined evaluation conditions (S601). The expansion unit 213 removes company-specific expressions from the extracted sales data and generates abstract patterns (S602). The expansion unit 213 acquires customer attributes, deal attributes, or personnel attributes for other uses and generates reuse candidate information by concretizing the abstract patterns according to those attributes (S603). The expansion unit 213 stores at least a portion of the evaluation information of the original sales data or related data in the storage unit 231, associating it with the reuse candidate information (S604). The reference control unit 212 also determines whether the reuse candidate information can be referenced, its presentation priority, the target for evidence display, and the target for warning based on the evaluation information (S605). The output unit 216 outputs sales data including the reuse candidate information based on the determination results (S606).

[0051] Figure 7 illustrates the system expansion processing flow in a sales support information processing system.

[0052] The system extension unit 214 acquires heterogeneous sales data obtained from at least one of the following: automatic categorization, internal sharing, sales negotiation analysis, and sales role-playing (S701). The system extension unit 214 analyzes the acquired heterogeneous sales data and extracts candidates for new information types or relationship types not included in the existing relationship data system (S702). For each candidate, the system extension unit 214 evaluates the frequency of occurrence, relevance to sales results, source, and reliability, and determines whether or not it meets predetermined conditions (S703). For candidates that meet the predetermined conditions, the system extension unit 214 determines whether or not administrator approval is required, requests approval from the administrator if necessary, and adds the data to the relationship data system after approval is received (S704). The condition update unit 215 reflects the added new information types or relationship types in the conditions used for reference control or business decision-making in the AI ​​support program (S705). The condition update unit 215 re-evaluates the effectiveness of the added information type or relationship type based on the reference results or sales results after the condition update, and readjusts the conditions as necessary (S706).

[0053] As described above, the sales support information processing system of this embodiment manages the relationships between multiple sales data related to customers, business negotiations, materials, personnel, and sales know-how as relational data, and appropriately controls whether sales data can be referenced, the priority of presentation, the target of evidence display, and the target of warnings based on evaluation information including source, freshness, reliability, approval status, and contradiction status associated with each relational data. Furthermore, by generating reuse candidate information by the expansion unit 213, past success stories and sales know-how can be reused in a form that is suitable for other uses. In addition, the system expansion unit 214 and the condition update unit 215 dynamically extract new information types or relationship types from different types of sales data to expand the relational data system, and the conditions used for reference control and business decision-making in the AI ​​support program can be continuously improved.

[0054] Although these embodiments have been described above, they are intended to facilitate understanding of the present invention and are not intended to limit its interpretation. The present invention can be modified and improved without departing from its spirit, and equivalents thereof are also included.

[0055] For example, the processing performed by each functional unit of the management server 2 described above may be executed by any of the functional units. Furthermore, different functional units may be added to perform some of the processing performed by each of the functional units described above. Also, the functional units of the management server 2 may be distributed across multiple computers.

[0056] Furthermore, the information stored in each memory unit of the management server 2 may be stored in any of the memory units. That is, the information stored in the multiple memory units mentioned above may be stored in a single memory unit, or a portion of the information stored in one memory unit may be stored in another memory unit.

[0057] <Example 1> In the embodiment described above, an example was shown in which the freshness of the evaluation information is managed as the last update date and time or the number of days elapsed, but the method of determining freshness is not limited to this. For example, the reference control unit 212 may change the criteria for determining freshness depending on the type of sales data. Specifically, different thresholds can be set for each type of sales data, such as determining that customer contact information is not fresh if more than 30 days have passed since the last update, and determining that success stories are not fresh if more than 365 days have passed since the last update. In addition, in determining freshness, not only simple elapsed time may be used, but also the number of times the sales data has been referenced and changes in the progress of related business negotiations may be taken into consideration. For example, even if a long period of time has passed since the last update, the freshness score can be adjusted to maintain a high level for sales data that has been frequently referenced in recent business negotiations.

[0058] <Modification 2> In the embodiment described above, an example was shown in which the reliability score is managed as a numerical value from 0 to 100, but the method of calculating the reliability score is not limited to this. For example, the reference control unit 212 may dynamically calculate the reliability score from multiple elements. Specifically, the reliability score can be calculated by summing the following elements with predetermined weights: the type of source of the relationship data (a high score for automatic linkage from a CRM system, a medium score for manual input by a sales representative, etc.), the evaluation by the sales representative who referred to the relationship data (a higher score for more feedback indicating usefulness), and the results of the sales activities carried out based on the relationship data (a higher score if a deal was closed). By dynamically updating the reliability score in this way, it becomes possible to manage the reliability score in a way that reflects the results of actual sales activities.

[0059] <Variation 3> In the embodiment described above, an example was shown in which inconsistencies are managed using the categories "no inconsistency," "inconsistency found," and "under review." However, the methods for detecting and managing inconsistencies are not limited to these. For example, the reference control unit 212 may further subdivide and manage the types of inconsistencies. Specifically, cases where the contact person information differs among multiple relationship data related to the same customer can be distinguished and managed as "contact person inconsistency," cases where the amount information differs among multiple relationship data related to the same business deal can be distinguished and managed as "amount inconsistency," and cases where the version information differs among multiple relationship data related to the same document can be distinguished and managed as "version inconsistency." The reference control unit 212 can change the content of the warning according to the type of inconsistency. For example, if a "contact person inconsistency" is detected, it can be controlled to output a specific warning message such as "There is an inconsistency in the contact person information. Please check the latest contact person information."

[0060] <Modification 4> The above-described embodiment shows an example where the scope of sales data that can be accessed differs between access requests from an AI-supported program and those from a sales representative. However, the method of controlling access permissions is not limited to this. For example, the request acquisition unit 211 may acquire information on the sales representative's job title, department, and assigned customers when the source of the access request is a sales representative, and the access control unit 212 may further narrow down the scope of accessible sales data based on this information. Specifically, access to sales data related to customers not handled by a particular sales representative can be restricted, while sales representatives at the management level or higher can access sales data for the entire department. Furthermore, the scope of access for the AI-supported program may also be controlled based on the permissions associated with the sales representative's account used by the AI-supported program.

[0061] <Modification 5> In the embodiment described above, an example was shown in which the expansion unit 213 generates an abstract pattern by replacing company-specific expressions with general-purpose placeholders, but the method of abstraction is not limited to this. For example, the expansion unit 213 may use a natural language processing model to analyze the text content of sales data, automatically detect and remove named entities such as customer names and contact persons' names, and extract the essential structure of sales procedures and customer problem handling. Furthermore, when concretizing the abstract pattern, the expansion unit 213 may refer to industry, company size, and region as customer attributes for other users, and preferentially select abstract patterns that have been successful for customers of the same industry and size as candidates for concretization. In addition, when outputting the concretized reuse candidate information, the expansion unit 213 can make it easier for sales representatives to judge the appropriateness of the reuse candidate information by adding an overview of the original successful case and applicable conditions.

[0062] <Variation 6> In the embodiment described above, an example was shown in which the system expansion unit 214 evaluates the frequency of occurrence and the degree of relevance to sales results when extracting candidates for new information types or relationship types, but the method of evaluating candidates is not limited to this. For example, the system expansion unit 214 may collect evaluations from multiple sales representatives for candidates for new information types or relationship types. Specifically, the system expansion unit 214 can present the extracted candidates to sales representatives, collect evaluations of whether the candidates are useful in sales operations, and only add candidates that receive an evaluation of usefulness from a predetermined number of sales representatives to the relationship data system. In addition, when adding a new information type or relationship type, the system expansion unit 214 may verify consistency with the existing relationship data system and confirm whether the addition will cause inconsistencies between existing relationship data. If an inconsistency is detected during consistency verification, the system expansion unit 214 can be controlled to notify the administrator of the details of the inconsistency and have them decide whether or not to add the new type.

[0063] <Example 7> In the embodiment described above, an example was shown in which the condition update unit 215 readjusts the conditions based on the difference in sales results before and after the addition of a new information type or relationship type, but the method of readjustment is not limited to this. For example, the condition update unit 215 may optimize the reference control conditions using a machine learning model. Specifically, the condition update unit 215 can train a machine learning model using past reference requests, decision results by the reference control unit 212, and subsequent sales results as training data, and update the reference control conditions using the trained model. In addition, when readjusting conditions, the condition update unit 215 may notify the administrator of the content and basis of the readjustment so that the administrator can confirm and correct the content of the readjustment. This makes it possible to combine automatic condition updates by a machine learning model with human supervision by an administrator.

[0064] <Differentiation Example 8> In the embodiment described above, an example was shown in which the management server 2 comprises a request acquisition unit 211, a reference control unit 212, an expansion unit 213, a system expansion unit 214, a condition update unit 215, and an output unit 216. However, the system configuration is not limited to this. For example, the processing of the expansion unit 213 and the system expansion unit 214 may be performed periodically by a batch processing server separate from the management server 2. In this case, the batch processing server refers to the sales data stored in the storage unit 231 on a predetermined schedule (for example, every night) to generate reuse candidate information and extract new information type or relationship type candidates, and writes the results to the storage unit 231. When the reference control unit 212 of the management server 2 receives a reference request, it performs reference control by referring to the reuse candidate information and the updated relationship data system generated by the batch processing server. With such a configuration, it is possible to shorten the response time to reference requests while achieving periodic updates of reuse candidate information and system expansion.

[0065] <Disclosure Items> Furthermore, this disclosure also includes the following configurations. [Item 1] A storage unit that stores multiple sales data related to customers, business negotiations, documents, personnel, and sales know-how, relational data showing the relationships between the sales data, and evaluation information including source, freshness, reliability, approval status, and inconsistency status associated with each of the relational data, A request acquisition unit that acquires reference requests related to sales operations by sales representatives or AI (Artificial Intelligence) support programs, A reference control unit that, in response to the reference request, refers to the relevant data and, based on the evaluation information, determines whether the sales data to be referenced can be referenced, its presentation priority, the basis for which it should be displayed, and the items to be cautioned about, An output unit that outputs the sales data based on the decision result by the reference control unit, A sales support information processing system equipped with the following features. [Item 2] The sales support information processing system described in item 1, wherein the aforementioned reference request is a request relating to at least one of the following: preparing for a business negotiation, handling customer interactions, creating documents, and considering the next action. [Item 3] The sales support information processing system described in item 1, wherein the reference control unit excludes sales data whose approval status is unapproved from the basis display target or determines it to be a target for warning. [Item 4] The sales support information processing system described in item 1, wherein the reference control unit determines sales data included in the evaluation information whose freshness does not meet predetermined conditions, sales data whose reliability is below a predetermined value, or sales data whose inconsistent state is inconsistent, to be excluded from reference, to be subject to warning, or to be subject to administrator confirmation. [Item 5] The sales support information processing system according to item 1, wherein the reference control unit determines the presentation priority based on at least the source, the freshness, and the reliability. [Item 6] The sales support information processing system described in item 1, wherein the reference control unit determines, in accordance with the type of sales activity corresponding to the reference request, to prioritize the reference of at least a portion of the customer, the business negotiation, past proposals, success stories, related materials, and the skills of the person in charge. [Item 7] The steps include storing in a memory unit multiple sales data related to customers, business negotiations, documents, personnel, and sales know-how, relational data showing the relationships between the sales data, and evaluation information including source, freshness, reliability, approval status, and inconsistency status associated with each of the relational data, Steps include obtaining reference requests related to sales operations by sales representatives or AI-assisted programs, The steps include: referring to the relevant data in response to the reference request, and determining, based on the evaluation information, whether the sales data to be referenced is accessible, its presentation priority, the basis for which it should be displayed, and the items for which a warning should be issued; A step of outputting the sales data based on the aforementioned decision result, A sales support information processing method performed by a computer. [Item 8] The steps include storing in a memory unit multiple sales data related to customers, business negotiations, documents, personnel, and sales know-how, relational data showing the relationships between the sales data, and evaluation information including source, freshness, reliability, approval status, and inconsistency status associated with each of the relational data, Steps include obtaining reference requests related to sales operations by sales representatives or AI-assisted programs, The steps include: referring to the relevant data in response to the reference request, and determining, based on the evaluation information, whether the sales data to be referenced is accessible, its presentation priority, the basis for which it should be displayed, and the items for which a warning should be issued; A step of outputting the sales data based on the aforementioned decision result, A program that causes a computer to execute something. [Item 9] A storage unit that stores multiple sales data related to customers, business negotiations, documents, personnel, and sales know-how, relational data showing the relationships between the sales data, and evaluation information including source, freshness, reliability, approval status, and inconsistency status associated with each of the relational data, A request acquisition unit that acquires reference requests related to sales operations by sales representatives or AI-assisted programs, A reference control unit that, in response to the reference request, refers to the relevant data and, based on the evaluation information, determines whether the sales data to be referenced can be referenced, its presentation priority, the basis for which it should be displayed, and the items to be cautioned about, An expansion unit abstracts sales procedures, document combinations, personnel skills, or customer issue responses that meet predetermined evaluation criteria from multiple sales data, and generates reuse candidate information that is converted or reconfigured to suit other uses. An output unit that outputs the sales data based on the decision result by the reference control unit and the reuse candidate information, A sales support information processing system equipped with the following features. [Item 10] The sales support information processing system described in item 9, wherein the development unit generates sales procedures, material combinations, personnel skills, or customer issue responses that satisfy the predetermined evaluation conditions as abstract patterns with company-specific expressions removed, and then concretizes them according to the customer attributes, deal attributes, or personnel attributes of other companies that are intended for other use. [Item 11] The sales support information processing system according to item 9, wherein the storage unit stores information obtained from at least one of the existing services of automatic categorization, internal sharing, sales negotiation analysis, and sales role-playing as an input source for the sales data or the related data. [Item 12] The reference control unit determines whether the reuse candidate information can be referenced, its presentation priority, the basis for which it should be displayed, and the target for which it should be cautioned, based on the evaluation information, as described in item 9 of the sales support information processing system. [Item 13] The sales support information processing system described in item 9, wherein the reference control unit causes the range of sales data that can be referenced or the manner of displaying the basis to differ between a reference request by the sales representative and a reference request by the AI ​​support program. [Item 14] The sales support information processing system described in item 9 associates the aforementioned reuse candidate information with evaluation information based on at least part of the source, freshness, reliability, approval status, or inconsistency status of the original sales data or related data. [Item 15] The steps include storing in a memory unit multiple sales data related to customers, business negotiations, documents, personnel, and sales know-how, relational data showing the relationships between the sales data, and evaluation information including source, freshness, reliability, approval status, and inconsistency status associated with each of the relational data, Steps include obtaining reference requests related to sales operations by sales representatives or AI-assisted programs, The steps include: referring to the relevant data in response to the reference request, and determining, based on the evaluation information, whether the sales data to be referenced is accessible, its presentation priority, the basis for which it should be displayed, and the items for which a warning should be issued; The steps include: generating reusable candidate information by abstracting sales procedures, document combinations, personnel skills, or customer problem responses that meet predetermined evaluation criteria from multiple sales data, and transforming or reconfiguring them to suit other uses; A step of outputting the sales data based on the decision result and the reuse candidate information, A sales support information processing method performed by a computer. [Item 16] The sales support information processing method described in item 15, A sales support information processing method further includes the step of associating the aforementioned reuse candidate information with evaluation information based on at least a part of the source, freshness, reliability, approval status, or inconsistency status of the original sales data or related data. [Item 17] The steps include storing in a memory unit multiple sales data related to customers, business negotiations, documents, personnel, and sales know-how, relational data showing the relationships between the sales data, and evaluation information including source, freshness, reliability, approval status, and inconsistency status associated with each of the relational data, Steps include obtaining reference requests related to sales operations by sales representatives or AI-assisted programs, The steps include: referring to the relevant data in response to the reference request, and determining, based on the evaluation information, whether the sales data to be referenced is accessible, its presentation priority, the basis for which it should be displayed, and the items for which a warning should be issued; The steps include: generating reusable candidate information by abstracting sales procedures, document combinations, personnel skills, or customer problem responses that meet predetermined evaluation criteria from multiple sales data, and transforming or reconfiguring them to suit other uses; A step of outputting the sales data based on the decision result and the reuse candidate information, A program that causes a computer to execute something. [Item 18] The program described in item 17, A program that causes a computer to further perform the step of associating the aforementioned reuse candidate information with evaluation information based on at least part of the source, freshness, reliability, approval status, or inconsistency status of the original sales data or related data. [Item 19] A storage unit that stores multiple sales data related to customers, business negotiations, documents, personnel, and sales know-how, relational data showing the relationships between the sales data, and evaluation information including source, freshness, reliability, approval status, and inconsistency status associated with each of the relational data, A request acquisition unit that acquires reference requests related to sales operations by sales representatives or AI-assisted programs, A reference control unit that, in response to the reference request, refers to the relevant data and, based on the evaluation information, determines whether the sales data to be referenced can be referenced, its presentation priority, the basis for which it should be displayed, and the items to be cautioned about, A system extension unit extracts new information types or relationship types not included in existing relationship data from different types of sales data, and adds them to the relationship data system corresponding to the target organization based on predetermined evaluation indicators. A condition update unit that reflects the newly added information type or relationship type in the conditions used for reference control or business decision-making in the AI ​​support program, An output unit that outputs the sales data based on the decision result by the reference control unit, A sales support information processing system equipped with the following features. [Item 20] The system extension unit is a sales support information processing system as described in item 19, which uses information obtained from at least one of the following as heterogeneous sales data: automatic categorization, internal sharing, sales negotiation analysis, and sales role-playing. [Item 21] The sales support information processing system described in item 19, wherein the system expansion unit evaluates at least a portion of the frequency of occurrence, relevance to sales results, source, and reliability for each candidate of the new information type or relationship type, and adds only the candidates that meet predetermined conditions to the relationship data system corresponding to the target organization. [Item 22] The sales support information processing system described in item 19, wherein the condition update unit updates the selection criteria for reference targets in at least one of customer support, document creation, or next action proposals by the AI-supported program after the addition of the new information type or relationship type. [Item 23] The sales support information processing system described in item 19, wherein the reference control unit also determines whether the sales data based on the new information type or relationship type can be referenced, its presentation priority, the target for evidence display, and the target for warning, based on the source, freshness, reliability, approval status, and contradiction status. [Item 24] The sales support information processing system described in item 19, wherein the condition update unit readjusts the conditions used for reference control or business decision-making based on the difference in sales results before and after the addition of the new information type or relationship type. [Item 25] The addition of the aforementioned new information type or relationship type to the aforementioned relationship data system is performed subject to administrator approval or fulfillment of prescribed conditions, as described in item 19 of the sales support information processing system. [Item 26] The sales support information processing system described in item 19, wherein the condition update unit re-evaluates the effectiveness of the newly added information type or relationship type based on the reference results or sales results after the condition update. [Item 27] The steps include storing in a memory unit multiple sales data related to customers, business negotiations, documents, personnel, and sales know-how, relational data showing the relationships between the sales data, and evaluation information including source, freshness, reliability, approval status, and inconsistency status associated with each of the relational data, Steps include obtaining reference requests related to sales operations by sales representatives or AI-assisted programs, The steps include: referring to the relevant data in response to the reference request, and determining, based on the evaluation information, whether the sales data to be referenced is accessible, its presentation priority, the basis for which it should be displayed, and the items for which a warning should be issued; The steps include: extracting new information types or relationship types not included in existing relationship data from different types of sales data, and adding them to the relationship data system corresponding to the target organization based on predetermined evaluation indicators; The steps include: reflecting the newly added information type or relationship type in the conditions used for reference control or business decision-making in the AI ​​support program; A step of outputting the sales data based on the aforementioned decision result, A sales support information processing method performed by a computer. [Item 28] The sales support information processing method described in item 27, A sales support information processing method wherein the addition of the aforementioned new information type or relationship type to the aforementioned relationship data system is performed subject to administrator approval or the fulfillment of prescribed conditions. [Item 29] The sales support information processing method described in item 27, A sales support information processing method further comprising the step of re-evaluating the effectiveness of the newly added information type or relationship type based on the reference results or sales results after the conditions have been updated. [Item 30] The steps include storing in a memory unit multiple sales data related to customers, business negotiations, documents, personnel, and sales know-how, relational data showing the relationships between the sales data, and evaluation information including source, freshness, reliability, approval status, and inconsistency status associated with each of the relational data, Steps include obtaining reference requests related to sales operations by sales representatives or AI-assisted programs, The steps include: referring to the relevant data in response to the reference request, and determining, based on the evaluation information, whether the sales data to be referenced is accessible, its presentation priority, the basis for which it should be displayed, and the items for which a warning should be issued; The steps include: extracting new information types or relationship types not included in existing relationship data from different types of sales data, and adding them to the relationship data system corresponding to the target organization based on predetermined evaluation indicators; The steps include: reflecting the newly added information type or relationship type in the conditions used for reference control or business decision-making in the AI ​​support program; A step of outputting the sales data based on the aforementioned decision result, A program that causes a computer to execute something. [Item 31] The program described in item 30, The addition of the aforementioned new information type or relationship type to the aforementioned relationship data system is performed by a program that requires administrator approval or the fulfillment of predetermined conditions. [Item 32] The program described in item 30, A program that causes a computer to perform a further step of re-evaluating the effectiveness of the newly added information type or relationship type based on the reference results or sales results after the conditions have been updated. [Explanation of Symbols]

[0066] 1 User terminal 2 Management Server

Claims

1. A storage unit that stores multiple sales data related to customers, business negotiations, documents, personnel, and sales know-how, relational data showing the relationships between the sales data, and evaluation information including source, freshness, reliability, approval status, and inconsistency status associated with each of the relational data, A request acquisition unit that acquires reference requests related to sales operations by sales representatives or AI-assisted programs, A reference control unit that, in response to the reference request, refers to the relevant data and, based on the evaluation information, determines whether the sales data to be referenced can be referenced, its presentation priority, the basis for which it should be displayed, and the items to be cautioned about, An output unit that outputs the sales data based on the decision result by the reference control unit, A sales support information processing system equipped with the following features.

2. The sales support information processing system according to claim 1, wherein the reference request is a request relating to at least one of the following: preparing for a business negotiation, handling customer interactions, creating documents, and considering the next action.

3. The sales support information processing system according to claim 1, wherein the reference control unit excludes sales data in which the approval status included in the evaluation information is unapproved from the basis display target or determines it to be a target for warning.

4. The sales support information processing system according to claim 1, wherein the reference control unit determines sales data in the evaluation information whose freshness does not meet predetermined conditions, sales data whose reliability is below a predetermined value, or sales data whose inconsistent state is inconsistent, to be excluded from reference, to be subject to warning, or to be subject to administrator confirmation.

5. The sales support information processing system according to claim 1, wherein the reference control unit determines the presentation priority based on at least the source, the freshness, and the reliability.

6. The sales support information processing system according to claim 1, wherein the reference control unit determines, in accordance with the type of sales activity corresponding to the reference request, to prioritize the reference of at least a portion of the customer, the business negotiation, past proposals, success stories, related materials, and the skills of the person in charge.

7. The steps include storing in a memory unit multiple sales data related to customers, business negotiations, documents, personnel, and sales know-how, relational data showing the relationships between the sales data, and evaluation information including source, freshness, reliability, approval status, and inconsistency status associated with each of the relational data, Steps include obtaining reference requests related to sales operations by sales representatives or AI-assisted programs, The steps include: referring to the relevant data in response to the reference request, and determining, based on the evaluation information, whether the sales data to be referenced is accessible, its presentation priority, the basis for which it should be displayed, and the items for which a warning should be issued; A step of outputting the sales data based on the aforementioned decision result, A sales support information processing method performed by a computer.

8. The steps include storing in a memory unit multiple sales data related to customers, business negotiations, documents, personnel, and sales know-how, relational data showing the relationships between the sales data, and evaluation information including source, freshness, reliability, approval status, and inconsistency status associated with each of the relational data, Steps include obtaining reference requests related to sales operations by sales representatives or AI-assisted programs, The steps include: referring to the relevant data in response to the reference request, and determining, based on the evaluation information, whether the sales data to be referenced is accessible, its presentation priority, the basis for which it should be displayed, and the items for which a warning should be issued; A step of outputting the sales data based on the aforementioned decision result, A program that causes a computer to execute something.

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