Business support system, business support method, and business support program

Abstract

To enable more accurate prediction of business activity results.SOLUTION: A model creation unit 120 creates, for each completed project for which business activities have been completed, a prediction model by learning completion indication data indicating satisfaction information representing customer's satisfaction with one or more past projects related to the completed project's customer and results of the business activities for the completed project. A result prediction unit 130 inputs incomplete indication data indicating satisfaction information representing target customer's satisfaction with one or more past projects related to the target customer who is a customer of an incomplete project for which business activities have not been completed, and uses the prediction model to predict the results of the business activities for the incomplete project.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to techniques for predicting the results of business activities. [Background technology]

[0002] Patent Document 1 discloses a method for more effectively supporting users' sales activities. Patent Document 1 discloses the following method: Positive / negative expressions are extracted from free text in the user's sales activity data, and the extracted expressions are converted into indices. The progress of a sales negotiation is determined by expressing indices of multiple activities for one sales negotiation in chronological order. In addition, future progress (won / lost) is predicted based on the similarity with sales negotiations that have been completed up to the point of winning / losing the deal. Then, an activity plan is created based on the prediction. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2005-149489 Summary of the Invention [Problem to be solved by the invention]

[0004] The present disclosure aims to enable more accurate prediction of business results. [Means for solving the problem]

[0005] The sales support system of the present disclosure includes: a model creation unit that creates a prediction model by learning, for each completed project for which sales activities have been completed, satisfaction level information that indicates the customer's satisfaction with one or more past projects related to the customer of the completed project and completion indicator data that indicates the results of the sales activities for the completed project; a result prediction unit that receives as input incomplete index data indicating satisfaction information representing the satisfaction of a target customer, who is a customer of an incomplete project for which sales activities have not been completed, with respect to one or more past projects relating to the target customer, and predicts the results of the sales activities for the incomplete project using the prediction model; Equipped with. [Effects of the Invention]

[0006] According to the present disclosure, it becomes possible to more accurately predict the results of sales activities. [Brief explanation of the drawings]

[0007] [Figure 1] FIG. 1 is a configuration diagram of a sales support system 100 according to a first embodiment. [Figure 2] 1 is a functional configuration diagram of a sales support system 100 according to a first embodiment. [Figure 3] 1 is a diagram showing the functional relationships of a sales support system 100 according to the first embodiment. [Figure 4] 3 is a flowchart of data acquisition according to the first embodiment. [Figure 5] FIG. 2 shows an example of a registration screen 210 according to the first embodiment. [Figure 6] FIG. 10 is a diagram showing an example of sales activity data 191 according to the first embodiment. [Figure 7] FIG. 10 is a diagram showing an example of sales activity data 192 according to the first embodiment. [Figure 8] FIG. 10 shows an example of reaction data 193 according to the first embodiment. [Figure 9] FIG. 3 is a diagram showing an example of a questionnaire in the first embodiment. [Figure 10] FIG. 10 shows an example of questionnaire data 194 according to the first embodiment. [Figure 11] 3 is a flowchart of model creation according to the first embodiment. [Figure 12] 3 is a flowchart of model creation according to the first embodiment. [Figure 13] FIG. 10 is a diagram showing an example of number data 195 according to the first embodiment. [Figure 14] FIG. 10 is a diagram showing an example of satisfaction level data 196 according to the first embodiment. [Figure 15] FIG. 10 is a diagram showing an example of completion indicator data 197 according to the first embodiment. [Figure 16] 10 is a flowchart of result prediction in the first embodiment. [Figure 17] 10 is a flowchart of result prediction in the first embodiment. [Figure 18] FIG. 10 is a diagram showing an example of incomplete indicator data 198 according to the first embodiment. [Figure 19] A table showing examples of sales opportunities. [Figure 20] FIG. 10 is a diagram showing an example of completion indicator data 197 according to the first embodiment. [Figure 21] FIG. 10 is a diagram showing an example of completion indicator data 197 according to the first embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0008] In the embodiments and drawings, the same or corresponding elements are denoted by the same reference numerals. The description of elements denoted by the same reference numerals as those already described will be omitted or simplified as appropriate. Arrows in the drawings primarily indicate the flow of data or the flow of processing.

[0009] Embodiment 1 The sales support system 100 will be described with reference to FIGS.

[0010] ***Configuration Description*** The configuration of the sales support system 100 will be described with reference to FIG. The sales support system 100 is a computer that includes hardware such as a processor 101, a memory 102, an auxiliary storage device 103, and an input / output interface 104. These pieces of hardware are connected to one another via signal lines.

[0011] The processor 101 is an IC that performs arithmetic processing and controls other hardware. For example, the processor 101 is a CPU. IC is an abbreviation for Integrated Circuit. CPU is an abbreviation for Central Processing Unit.

[0012] The memory 102 is a volatile or non-volatile storage device. The memory 102 is also called a primary storage device or a main memory. For example, the memory 102 is a RAM. Data stored in the memory 102 is saved in the secondary storage device 103 as needed. RAM is an abbreviation for Random Access Memory.

[0013] The auxiliary storage device 103 is a non-volatile storage device. For example, the auxiliary storage device 103 is a ROM, a HDD, a flash memory, or a combination thereof. Data stored in the auxiliary storage device 103 is loaded into the memory 102 as needed. ROM is an abbreviation for Read Only Memory. HDD is an abbreviation for Hard Disk Drive.

[0014] The input / output interface 104 is a port to which an input device, an output device, and a communication device are connected. For example, the input / output interface 104 is a USB terminal, the input device is a keyboard and a mouse, and the output device is a display. Input and output of the sales support system 100 is performed using the input / output interface 104. USB is an abbreviation for Universal Serial Bus.

[0015] The sales support system 100 includes elements such as a data acquisition unit 110, a model creation unit 120, and a result prediction unit 130. These elements are realized by software.

[0016] The auxiliary storage device 103 stores a sales support program for causing the computer to function as a data acquisition unit 110, a model creation unit 120, and a result prediction unit 130. The sales support program is loaded into the memory 102 and executed by the processor 101. The auxiliary storage device 103 also stores an OS. At least a part of the OS is loaded into the memory 102 and executed by the processor 101. The processor 101 executes the sales support program while executing the OS. OS is an abbreviation for Operating System.

[0017] The data (input data, output data, etc.) of the sales support program is stored in the storage unit 190. The auxiliary storage device 103 functions as the storage unit 190. However, a storage device such as the memory 102, a register in the processor 101, or a cache memory in the processor 101 may function as the storage unit 190 instead of or together with the auxiliary storage device 103.

[0018] The sales support program can be recorded (stored) in a computer-readable manner on a non-volatile recording medium such as an optical disk or a flash memory.

[0019] The functional configuration of the sales support system 100 will be described with reference to FIG. The data acquisition unit 110 includes a sales activity acquisition unit 111 , a response digitization unit 112 , a response aggregation unit 113 , and a questionnaire acquisition unit 114 . The model creation unit 120 includes a data separation unit 121 , a number counting unit 122 , a satisfaction level extraction unit 123 , an index integration unit 124 , and a learning unit 125 . The result prediction unit 130 includes a data separation unit 131 , a number counting unit 132 , a satisfaction level extraction unit 133 , an index integration unit 134 , and a prediction unit 135 .

[0020] FIG. 3 shows the relationship between the functions of the sales support system 100. The various data (191 to 198) and the prediction model 199 are stored in the storage unit 190.

[0021] ***Explanation of Operation*** The operation procedure of the sales support system 100 corresponds to a sales support method, and also corresponds to a processing procedure by a sales support program.

[0022] Data acquisition will be explained based on FIG. Data acquisition is a process included in the sales support method.

[0023] In step S101 , the sales activity acquisition unit 111 acquires sales activity information and registers the acquired sales activity information in the sales activity data 191 . In the sales activity data 191, sales activity information is registered for each sales activity that has been carried out. The sales activity information indicates details of the sales activities that have been carried out.

[0024] For example, sales activity information is acquired as follows. First, the sales activity acquisition unit 111 displays the registration screen 210 on the display. The registration screen 210 is a screen for inputting business activity information. The user inputs business activity information into the registration screen 210 . Then, the sales activity acquisition unit 111 acquires the sales activity information entered on the registration screen 210 .

[0025] FIG. 5 shows an example of the registration screen 210. The registration screen 210 has a user interface for inputting business activity information. "Date" is a field for inputting the date on which the business activity was carried out. "Results" is a field for entering the results of sales activities. If an order is placed, "Order Placed" is entered, and if the order is lost, "Order Lost" is entered. If there is neither an order placed nor a lost order, the field is left blank. "Product" is a field for inputting products (for example, services) being sold in sales activities. "Customer" is a field for inputting information (for example, a name or identifier) ​​that identifies a customer who is the counterpart of business activities. "Project" is a field for inputting information (for example, an identifier) ​​that identifies the project for which sales activities were carried out. "Response" is a field for inputting information indicating the customer's response to sales activities (for example, a value on a 10-point scale). "Sales item" is a field for inputting the items of the sales activities (sales items) that have been carried out. The sales items indicate the stage or type of the sales activities. "Free text" is a field for entering the progress of sales activities in text.

[0026] FIG. 6 shows an example of the sales activity data 191. "Customer ID" is the customer identifier. "Product ID" is the identifier of the product. "Project ID" is the identifier of the project.

[0027] Returning to FIG. 4, the description will continue from step S102. In step S102, the response quantification unit 112 expresses, for each piece of sales activity information indicated in the sales activity data 191, a customer's response to the sales activity as a numerical value based on the sales activity information.

[0028] For example, customer response to sales activities can be quantified as follows: First, the response digitization unit 112 extracts the text of the "free text" and the numerical value of the "response" from the sales activity data 191 (see FIG. 6). Next, the reaction digitization unit 112 performs text mining using the "free text" as input, and digitizes the "free text." For example, if the extracted text contains a positive expression, the extracted text is converted into a large numerical value. Then, the response quantification unit 112 calculates a numerical value representing the customer's response to the sales activity using the numerical value of the "free text" and the numerical value of the "response." For example, the average of the numerical value of the "free text" and the numerical value of the "response" is calculated. The numerical value of the "free text" and the numerical value of the "response" may each be weighted.

[0029] Sales activity data 191 in which customer responses to sales activities are quantified is referred to as sales activity data 192.

[0030] FIG. 7 shows an example of the sales activity data 192. Sales activity data 192 indicates, in numerical form, the customer's "response" to each sales activity carried out.

[0031] Returning to FIG. 4, the description continues from step S103. In step S103, the response counting unit 113 uses the sales activity data 192 to count the customer's response to the sales activity for each case.

[0032] For example, the response counting unit 113 counts customer responses to sales activities by business item. Examples of business items for which customer responses are counted are "proposal," "demonstration," and "quote presentation."

[0033] In step S103, reaction data 193 is created. In the reaction data 193, reaction information is registered for each case. The response information corresponds to sales activity information including the aggregated value of customer responses to sales activities.

[0034] 8 shows an example of reaction data 193. In reaction data 193, some information is omitted. The reaction data 193 indicates "reaction information" for each case. "Response information" shows the numerical values ​​calculated by aggregating customer "responses," divided into "proposal" responses, "demo" responses, and "quote presentation" responses. "Demo" refers to a demonstration.

[0035] For "Proposal," the number of proposals, the best value, and the worst value are shown. The number of proposals is the number of times a proposal was made. The best value is a number that represents the best response among the proposals of the number of proposals. The worst value is a number that represents the worst response among the proposals of the number of proposals. "Demonstration" indicates the number of demonstrations, the best value, and the worst value, just like "Proposal." "Estimate submission" indicates the number of submissions, the best value, and the lowest value, just like "proposal."

[0036] The response information for each case is arranged in the order in which the case occurred (in order of the date of the first sales activity).

[0037] Returning to FIG. 4, step S104 will be described. In step S104, the questionnaire acquisition unit 114 acquires the questionnaire information and registers the acquired questionnaire information in the questionnaire data 194. In the questionnaire data 194, questionnaire information is registered for each received order. The survey information indicates the customer's responses to questions about the sales activities carried out and the merchandise provided.

[0038] For example, the survey information is acquired as follows. First, a questionnaire is conducted for customers, and the responses to the questionnaire are input into the sales support system 100. The questionnaire may be conducted using a questionnaire form or a computer. The responses to the questionnaire are input, for example, into an input screen for the questionnaire. The input screen for the questionnaire is displayed on a display by the questionnaire acquisition unit 114, for example. Then, the questionnaire acquisition unit 114 acquires the input answers and generates questionnaire information indicating the acquired answers.

[0039] Figure 9 shows an example of the questionnaire content. A questionnaire contains one or more questions. The answers to the questions are given as numbers.

[0040] FIG. 10 shows an example of the questionnaire data 194. The questionnaire data 194 indicates the "customer ID", "product ID", "response date", and "response" for each product of the received order. "Response date" is the date on which the response to the questionnaire was received. "Answer" is a response to the questionnaire (see FIG. 9).

[0041] The model creation will be explained based on FIGS. 11 and 12. Model creation is a process included in the sales support method.

[0042] In step S111, the data separating unit 121 extracts one item of reaction information from the reaction data 193 that has not yet been extracted.

[0043] Specifically, the data separation unit 121 extracts the reaction information one by one in the order of registration (in the order of occurrence of the case).

[0044] In steps S112 and S113, "reaction information" refers to the reaction information extracted in step S110.

[0045] In step S112, the data separator 121 refers to the result indicated in the reaction information and determines whether the case is completed. If the result shows "Won" or "Lost", the deal is completed. If the case is completed, the process proceeds to step S113. If the case is not completed, the process proceeds to step S111.

[0046] In step S113, the number counting unit 122 counts the number of orders received or lost in one or more past cases related to the customer indicated in the response information, depending on the results indicated in the response information.

[0047] In step S113, the number data 195 is created. In the number of cases data 195, number of cases information is registered for each customer. The number information indicates at least one of the number of orders received in one or more past cases related to the same customer and the number of orders lost in one or more past cases related to the same customer.

[0048] FIG. 13 shows an example of the number data 195. The number data 195 indicates, for each customer, the number of orders received and the number of orders lost in one or more past cases.

[0049] For example, the number data 195 is created as follows: The number counting unit 122 finds, from the number data 195, the number information of the same customer as the customer indicated in the response information. If the result indicated in the response information is “order”, the number counting unit 122 adds 1 to the number of orders in the number information found in the number data 195 . If the result indicated in the reaction information is “lost order”, the case counting unit 122 adds 1 to the number of lost orders in the case count information found in the case count data 195 . If the number information of the same customer as the customer indicated in the response information is not found, the number counting unit 122 adds the number information of the customer indicated in the response information to the number data 195. The number of orders and the number of lost orders indicated in the added number information are zero.

[0050] Returning to FIG. 11, the description continues from step S114. In step S114, the data separator 121 determines whether the reaction data 193 contains any unextracted reaction information. If there is unextracted reaction information, the process proceeds to step S111. If there is no unextracted reaction information, the process proceeds to step S121.

[0051] In step S121, the data separating unit 121 extracts one item of unextracted reaction information from the reaction data 193. The process of step S121 is the same as the process of step S111, except that the unextracted reaction information in step S121 means reaction information that has not been extracted from the reaction data 193 in step S121.

[0052] In steps S122 to S124, "reaction information" refers to the reaction data extracted in step S121.

[0053] In step S122, the data separator 121 refers to the result indicated in the reaction information and determines whether the case is completed. The process in step S122 is the same as the process in step S112. If the case is completed, the process proceeds to step S123. If the case is not completed, the process proceeds to step S121.

[0054] In step S123, the satisfaction level extraction unit 123 creates customer satisfaction level information for the case indicated in the response information, and registers the created satisfaction information in the satisfaction level data 196. In the satisfaction level data 196, customer satisfaction level information is registered for each customer. The satisfaction information represents the customer's satisfaction with one or more past cases involving the same customer.

[0055] The satisfaction level information includes at least one of the number of orders received in one or more past cases related to the same customer, the number of lost orders in one or more past cases related to the same customer, and the customer's service satisfaction. The customer's service satisfaction level means the customer's satisfaction with the service currently being provided to the customer.

[0056] The number of orders received and the number of orders lost are obtained from the number data 195.

[0057] The customer's service satisfaction level is calculated based on the survey data 194. For example, the customer's service satisfaction level is calculated based on the customer's most recent survey information shown in the response information. For example, the customer's service satisfaction level is calculated using a predetermined calculation method (e.g., average) using the numerical values ​​of the answers to each question. The customer's service satisfaction level may also be calculated using a predetermined calculation method (e.g., average) using multiple satisfaction levels based on multiple pieces of customer survey information shown in the response information.

[0058] FIG. 14 shows an example of the satisfaction level data 196. The satisfaction level data 196 indicates the "customer ID", "satisfaction level information", "update date", and "confirmation date" for each customer. "Satisfaction information" includes "number of orders received," "number of lost orders," and "service." "Service" means service satisfaction. The "update date" is the date on which the "satisfaction level information" was updated. The "confirmed date" is the date on which the order was confirmed or lost.

[0059] Returning to FIG. 12, the description continues from step S124. In step S124, the indicator integration unit 124 integrates the reaction information and the satisfaction level information to create completion indicator information, and registers the created completion indicator information in the completion indicator data 197. In the completion indicator data 197, completion indicator information is registered for each case. The completion indicator information includes reaction information and satisfaction level information.

[0060] 15 shows an example of the completion indicator data 197. In the completion indicator data 197, some information is omitted. The completion indicator data 197 indicates, for each case, a "case ID," a "product ID," "response information," "satisfaction level information," and a "result."

[0061] Returning to FIG. 12, the description continues from step S125. In step S125, the data separator 121 determines whether the reaction data 193 contains any unextracted reaction information. The process of step S125 is the same as the process of step S114, except that the unextracted reaction information in step S125 means reaction information that has not been extracted from the reaction data 193 in step S121. If there is reaction information that has not been extracted, the process proceeds to step S121. If there is no unextracted reaction information, the process proceeds to step S126.

[0062] In step S126, the learning unit 125 learns the completion indicator data 197 to create a prediction model 199. If the prediction model 199 has already been created, the learning unit 125 learns the completion indicator data 197 and updates the prediction model 199 . The predictive model 199 is a trained model for predicting the results of sales activities, and is created by machine learning.

[0063] In step S127, the index integrating unit 124 clears the completion index data 197. That is, the index integrating unit 124 deletes the completion index information registered in the completion index data 197, and empties the completion index data 197.

[0064] The result prediction will be explained based on FIGS. 16 and 17. The result prediction is a process included in the sales support method.

[0065] The processing from step S131 to step S134 is the same as the processing from step S111 to step S114. However, steps S131 to S134 are executed by the data separator 131 and the number counting unit 132.

[0066] The number data 195 is created through steps S131 to S134.

[0067] In step S141, the data separating unit 131 extracts one item of reaction information from the reaction data 193 that has not yet been extracted. The unextracted reaction information in step S141 means reaction information that has not been extracted from the reaction data 193 in step S141.

[0068] In steps S142 to S144, "reaction information" refers to the reaction data extracted in step S141.

[0069] In step S142, the data separation unit 131 refers to the result indicated in the reaction information and determines whether the case is completed. The process of step S142 is the same as the process of step S122. If the case is completed, the process proceeds to step S141. If the case is not completed, the process proceeds to step S143.

[0070] In step S143 , the satisfaction level extraction unit 133 creates customer satisfaction level information for the item indicated in the response information, and registers the created satisfaction information in the satisfaction level data 196 . The process in step S143 is the same as the process in step S123.

[0071] In step S144 , the index integration unit 134 integrates the reaction information and the satisfaction level information to create incomplete index information, and registers the created incomplete index information in the incomplete index data 198 . In the incomplete indicator data 198, incomplete indicator information is registered for each case. The incompletion indicator information includes reaction information and satisfaction level information.

[0072] 18 shows an example of the incomplete indicator data 198. In the incomplete indicator data 198, some information is omitted. The incomplete index data 198 indicates, for each case, a "case ID," a "product ID," "response information," "satisfaction level information," and a "result." However, since the reaction information included in the incompletion indicator information is reaction information for an incomplete case, the "Result" column is blank. In other words, the incomplete indicator data 198 does not indicate the results of sales activities for each case (whether the order is received or lost).

[0073] Returning to FIG. 17, the description continues from step S145. In step S145, the data separator 131 determines whether or not there is any unextracted reaction information in the reaction data 193. The unextracted reaction information in step S145 means reaction information that has not been extracted from the reaction data 193 in step S141. If there is reaction information that has not been extracted, the process proceeds to step S141. If there is no unextracted reaction information, the process proceeds to step S146.

[0074] In step S146, the prediction unit 135 receives the incomplete index data 198 as an input and uses the prediction model 199 to predict the outcome of sales activities (won or lost) for each incomplete case. For example, the probability of winning or losing an order is predicted.

[0075] Then, the prediction unit 135 outputs the prediction result. For example, the prediction unit 135 sets the prediction result as incomplete indicator data 198 and displays the incomplete indicator data 198 on a display.

[0076] In step S147, the index integrating unit 134 clears the incomplete index data 198. That is, the index integrating unit 134 deletes the incomplete index information registered in the incomplete index data 198, and empties the incomplete index data 198.

[0077] ***Effects of the First Embodiment*** If the customer currently in negotiation has had other negotiations in the past, the customer's satisfaction with that previous negotiation will affect the current negotiation. Also, if the customer currently in negotiation is already using another service, the customer's satisfaction with the service they are using will affect the current negotiation. For this reason, if predictions are made based only on sales activity data for the current negotiation, the accuracy of win / loss predictions will be low. The first embodiment uses whether or not an order was received in a past business negotiation and the degree of satisfaction with another service currently being used to improve the accuracy of predicting whether or not a current business negotiation will be received or not.

[0078] Conventional techniques, which predict the future of business negotiations (whether an order will be placed or not) based on sales activities during the negotiations, do not provide sufficient prediction accuracy. The first embodiment improves the accuracy of future predictions by using past business negotiations and the degree of satisfaction with currently provided services as prediction materials. Figure 19 shows some examples. In Examples 1 and 2, forecasts based on sales activity responses appear to be appropriate. Therefore, forecasts using conventional technology may be sufficient. However, in Examples 3 to 5, conducting sales activities based on forecasts based on sales activity responses would likely result in incorrect decisions. In Examples 3 and 4, decisions based solely on sales activity responses would lead to planned activities that anticipate orders. However, considering customer satisfaction, keeping a close eye on the status of sales negotiations is essential to securing orders. Furthermore, even higher-quality sales activities than usual are required. In Example 5, decisions based solely on sales activity responses would likely lead to fading out of sales activities for this negotiation. However, the customer is likely waiting for a good proposal, a good demonstration, or a price offer that fits the budget. As such, forecasts based solely on sales activity responses are not very accurate.

[0079] FIG. 20 shows an example of the completion indicator data 197. The completion indicator data 197 indicates, as responses to sales activities, responses to proposals, responses to demonstrations, and responses to estimate presentations, each on a scale of 10. The completion indicator data 197 also indicates, as satisfaction levels, the number of orders won in past sales negotiations and the number of sales lost in past sales negotiations, and indicates the satisfaction level of the service currently being provided on a scale of 10. Other items may be added to the responses to sales activities and satisfaction levels. The numerical values ​​may be expressed on a scale other than 10. Projects A001, A002, and A003 have had a good response to sales activities. However, their satisfaction ratings are all zero, and they have no past sales negotiation records. Project A003 has had a perfect response to sales activities and has received an order. However, projects A001 and A002 have had fairly good evaluations of their sales activities, but have lost the orders. For this reason, predictions should be made based on not only the past history of sales activities, but also the past history of satisfaction ratings. The response to sales activities for projects A004 and A005 is good. However, the number of past lost sales for projects A004 and A005 is the same as the number of past orders won, so the situation is tense. As a result, project A005, which has a near-perfect evaluation of its sales activities, is the one that receives the order. The satisfaction rating during the provision of projects A006 and A007 is not good. However, project A006, which has a perfect evaluation of its sales activities, has been ordered.

[0080] It is believed that multiple opportunities are given in each sales activity. The more opportunities there are, the higher the chances of ultimately receiving a high evaluation and winning an order, even if the initial proposal or demonstration is poorly evaluated. In other words, the number of opportunities given affects satisfaction. FIG. 21 shows an example of completion indicator data 197. Cases A001, A002, and A003 are cases where the initial proposals were poorly rated, leading to the abandonment of sales activities. On the other hand, cases A004, A005, and A006 are cases where the initial proposals were poorly rated, but customer satisfaction was high, leading to the customer continuing to work with them until a good proposal was made. High customer satisfaction is related to the quality of the relationship of trust with the customer. It can be inferred that if the relationship is good, even if a proposal is given a low rating, the customer will have high expectations, leading to the next proposal and effective feedback. It can also be inferred that an atmosphere of anticipation for the next proposal has been created. For this reason, it is believed that an order forecast that combines a low evaluation of sales activities at the time of proposal and satisfaction (including the number of orders received and the number of orders lost) is effective. The product ID is a unique number for each product (service, product, or other business object). Depending on the product, a high number of proposals may be a negative factor, so it is advisable to include the product ID and number of proposals in the prediction parameters.

[0081] In the first embodiment, whether or not an order was received in a past business negotiation and the degree of satisfaction with another service currently being used are used, which makes it possible to improve the accuracy of predicting whether or not a current business negotiation will be received or lost.

[0082] ***Supplement to the first embodiment*** The first embodiment is an example of a preferred embodiment and is not intended to limit the technical scope of the present disclosure. The first embodiment may be implemented in part or in combination with other embodiments. The procedures described using flowcharts and the like may be modified as appropriate.

[0083] The sales support system 100 may be realized by a plurality of devices (computers). Each element of the sales support system 100 may be realized by software, hardware, firmware, or a combination thereof. The "part" of each element of the sales support system 100 may be read as "processing," "step," "circuit," or "circuitry."

[0084] Various aspects of the present disclosure are described below as appendices. (Appendix 1) a model creation unit that creates a prediction model by learning, for each completed project for which sales activities have been completed, satisfaction level information that indicates the customer's satisfaction with one or more past projects related to the customer of the completed project and completion indicator data that indicates the results of the sales activities for the completed project; a result prediction unit that receives as input incomplete index data indicating satisfaction information representing the satisfaction of a target customer, who is a customer of an incomplete project for which sales activities have not been completed, with respect to one or more past projects relating to the target customer, and predicts the results of the sales activities for the incomplete project using the prediction model; A sales support system equipped with:

[0085] (Appendix 2) the satisfaction level information indicated in the completion indicator data includes at least one of the number of orders received in the one or more past projects related to the customer and the number of orders lost in the one or more past projects related to the customer; The satisfaction level information indicated in the incomplete index data includes at least one of the number of orders received in the one or more past projects related to the target customer and the number of orders lost in the one or more past projects related to the target customer. A sales support system as described in Appendix 1.

[0086] (Appendix 3) the satisfaction level information indicated in the completion indicator data includes the customer's satisfaction level with the service currently being provided to the customer; The satisfaction level information indicated in the incomplete indicator data includes the target customer's satisfaction level with the service currently being provided to the target customer. A sales support system as described in Appendix 1 or Appendix 2.

[0087] (Appendix 4) The completion indicator data further indicates, for each completed project, response information that numerically represents the customer's response to the sales activity for the completed project; The incomplete case index data further indicates reaction information that numerically expresses the reaction of the target customer to the sales activity for the incomplete case. A sales support system according to any one of appendices 1 to 3.

[0088] (Appendix 5) The response information indicated in the completion indicator data includes a proposal response, which is information that numerically represents the customer's response to a proposal made in the sales activity of the completed case, The response information indicated in the incomplete indicator data includes a proposal response, which is information that numerically represents the target customer's response to a proposal made in the sales activity for the incomplete case. A sales support system as described in Appendix 4.

[0089] (Appendix 6) The proposal response indicated in the completion indicator data includes the number of proposals made in the sales activities of the completed case, and at least one of a best value representing the best response among the proposals in the number of proposals and a minimum value representing the worst response among the proposals in the number of proposals, The sales support system described in Appendix 5, wherein the proposal response indicated in the incomplete indicator data includes the number of proposals made in the sales activities for the incomplete case, and at least one of a best value representing the best response among the proposals for the number of proposals and a minimum value representing the worst response among the proposals for the number of proposals.

[0090] (Appendix 7) the response information indicated in the completion indicator data includes a demonstration response, which is information that numerically represents the customer's response to a demonstration conducted in the sales activity for the completed case; The response information indicated in the incomplete indicator data includes a demonstration response, which is information that numerically represents the target customer's response to a demonstration conducted in the sales activity for the incomplete case. A sales support system according to any one of appendices 4 to 6.

[0091] (Appendix 8) The demonstration response indicated in the completion indicator data includes the number of times the demonstration was performed in the sales activity of the completed case, and at least one of a best value representing the best response among the demonstrations of the number of demonstrations and a minimum value representing the worst response among the demonstrations of the number of demonstrations, The demonstration response indicated in the incomplete indicator data includes the number of times the demonstration was performed in the sales activity for the incomplete case, and at least one of a best value representing the best response among the demonstrations for the number of demonstrations and a minimum value representing the worst response among the demonstrations for the number of demonstrations. A sales support system as described in Appendix 7.

[0092] (Appendix 9) The response information indicated in the completion indicator data includes a quotation response, which is information that numerically represents the customer's response to the quotation presented in the sales activity for the completed project, The response information indicated in the incomplete indicator data includes a quotation response, which is information that numerically represents the response of the target customer to the quotation presented in the sales activity for the incomplete case. A sales support system according to any one of appendices 4 to 8.

[0093] (Appendix 10) The quotation response indicated in the completion indicator data includes the number of times the quotation was presented in the sales activities of the completed project, and at least one of a best value representing the best response among the quotation presentations for the number of presentations and a minimum value representing the minimum response among the quotation presentations for the number of presentations, The quotation response indicated in the incomplete index data includes the number of times the quotation was presented in the sales activity of the incomplete matter, and at least one of a best value representing the best response among the quotation presentations of the number of presentations and a minimum value representing the minimum response among the quotation presentations of the number of presentations. A sales support system as described in Appendix 9.

[0094] (Appendix 11) The completion indicator data further indicates, for each completed case, a product identifier that identifies a product that is the target of the sales activity for the completed case; The incomplete indicator data further indicates a product identifier that identifies a product that is the target of the sales activity of the incomplete case. A sales support system according to any one of appendices 1 to 10.

[0095] (Appendix 12) For each completed project for which sales activities have been completed, a prediction model is created by learning satisfaction information that indicates the customer's level of satisfaction with one or more past projects related to the customer of the completed project and completion indicator data that indicates the results of the sales activities for the completed project; Using incompletion index data indicating satisfaction information that represents the level of satisfaction of a target customer, who is a customer of an incomplete project for which sales activities have not been completed, with respect to one or more past projects relating to the target customer as an input, the results of the sales activities for the incomplete project are predicted using the prediction model. Sales support methods.

[0096] (Appendix 13) a model creation process for creating a prediction model by learning satisfaction level information representing the customer's satisfaction with one or more past projects related to the customer of the completed project for each completed project for which sales activities have been completed, and completion indicator data indicating the results of the sales activities for the completed project; a result prediction process that uses as input incompletion index data indicating satisfaction information representing the satisfaction of a target customer, who is a customer of an incomplete project for which sales activities have not been completed, with respect to one or more past projects relating to the target customer, and predicts the results of the sales activities for the incomplete project using the prediction model; A sales support program that allows a computer to execute the above. [Explanation of symbols]

[0097] 100 Sales support system, 101 Processor, 102 Memory, 103 Auxiliary storage device, 104 Input / output interface, 110 Data acquisition unit, 111 Sales activity acquisition unit, 112 Response quantification unit, 113 Response aggregation unit, 114 Questionnaire acquisition unit, 120 Model creation unit, 121 Data separation unit, 122 Number count unit, 123 Satisfaction level extraction unit, 124 Index integration unit, 125 Learning unit, 130 Result prediction unit, 131 Data separation unit, 132 Number count unit, 133 Satisfaction level extraction unit, 134 Index integration unit, 135 Prediction unit, 190 Memory unit, 191 Sales activity data, 192 Sales activity data, 193 Response data, 194 Questionnaire data, 195 Number data, 196 Satisfaction level data, 197 Completion index data, 198 Incomplete index data, 199 Predictive model, 210 registration screen.

Claims

1. a model creation unit that creates a prediction model by learning satisfaction level information that indicates the degree of satisfaction of a customer of a completed project with respect to one or more past projects related to the customer of the completed project for which sales activities have been completed, and completion indicator data that indicates the results of the sales activities for the completed project; a result prediction unit that receives as input incomplete index data indicating satisfaction information representing the satisfaction of a target customer, who is a customer of an incomplete project for which sales activities have not been completed, with respect to one or more past projects relating to the target customer, and predicts the results of the sales activities for the incomplete project using the prediction model; A sales support system equipped with:

2. the satisfaction level information indicated in the completion indicator data includes at least one of the number of orders received in the one or more past projects related to the customer and the number of orders lost in the one or more past projects related to the customer, The satisfaction level information indicated in the incomplete index data includes at least one of the number of orders received in the one or more past projects related to the target customer and the number of lost orders in the one or more past projects related to the target customer. The sales support system according to claim 1 .

3. the satisfaction level information indicated in the completion indicator data includes the customer's satisfaction level with the service currently being provided to the customer; The satisfaction level information indicated in the incomplete indicator data includes the target customer's satisfaction level with the service currently being provided to the target customer. The sales support system according to claim 1 .

4. The completion indicator data further indicates, for each completed project, response information that numerically represents the customer's response to the sales activity for the completed project; The incomplete case index data further indicates reaction information that numerically expresses the reaction of the target customer to the sales activity for the incomplete case. The sales support system according to claim 1 .

5. The response information indicated in the completion indicator data includes a proposal response, which is information that numerically represents the customer's response to a proposal made in the sales activity of the completed case, The response information indicated in the incomplete indicator data includes a proposal response, which is information that numerically represents the target customer's response to a proposal made in the sales activity for the incomplete case. The sales support system according to claim 4.

6. The proposal response indicated in the completion indicator data includes the number of proposals made in the sales activities of the completed case, and at least one of a best value representing the best response among the proposals in the number of proposals and a minimum value representing the worst response among the proposals in the number of proposals, The proposal response indicated in the incomplete index data includes the number of times the proposal was made in the sales activity of the incomplete case, and at least one of a best value representing the best response among the proposals in the number of proposals and a minimum value representing the worst response among the proposals in the number of proposals. The sales support system according to claim 5.

7. the response information indicated in the completion indicator data includes a demonstration response, which is information that numerically represents the customer's response to a demonstration conducted in the sales activity for the completed case; The response information indicated in the incomplete indicator data includes a demonstration response, which is information that numerically represents the target customer's response to a demonstration conducted in the sales activity for the incomplete case. The sales support system according to claim 4.

8. The demonstration response indicated in the completion indicator data includes the number of times the demonstration was performed in the sales activity of the completed case, and at least one of a best value representing the best response among the demonstrations of the number of demonstrations and a minimum value representing the worst response among the demonstrations of the number of demonstrations, The demonstration response indicated in the incomplete indicator data includes the number of times the demonstration was performed in the sales activity for the incomplete case, and at least one of a best value representing the best response among the demonstrations for the number of demonstrations and a minimum value representing the worst response among the demonstrations for the number of demonstrations. The sales support system according to claim 7.

9. The response information indicated in the completion indicator data includes a quotation response, which is information that numerically represents the customer's response to the quotation presented in the sales activity for the completed project, The response information indicated in the incomplete indicator data includes a quotation response, which is information that numerically represents the response of the target customer to the quotation presented in the sales activity for the incomplete case. The sales support system according to claim 4.

10. The quotation response indicated in the completion indicator data includes the number of times the quotation was presented in the sales activities of the completed project, and at least one of a best value representing the best response among the quotation presentations for the number of presentations and a minimum value representing the minimum response among the quotation presentations for the number of presentations, The quotation response indicated in the incomplete index data includes the number of times the quotation was presented in the sales activity of the incomplete matter, and at least one of a best value representing the best response among the quotation presentations of the number of presentations and a minimum value representing the minimum response among the quotation presentations of the number of presentations. The sales support system according to claim 9.

11. The completion indicator data further indicates, for each completed case, a product identifier that identifies a product that is the target of the sales activity for the completed case; The incomplete indicator data further indicates a product identifier that identifies a product that is the target of the sales activity of the incomplete case. The sales support system according to any one of claims 1 to 10.

12. For each completed project for which sales activities have been completed, a prediction model is created by learning satisfaction level information that indicates the customer's satisfaction with one or more past projects related to the customer of the completed project and completion indicator data that indicates the results of the sales activities for the completed project; Using incompletion index data indicating satisfaction information representing the level of satisfaction of a target customer, who is a customer of an incomplete project for which sales activities have not been completed, with respect to one or more past projects relating to the target customer as an input, the prediction model is used to predict the results of the sales activities for the incomplete project. Sales support methods.

13. a model creation process for creating a prediction model by learning satisfaction level information representing the degree of satisfaction of a customer of a completed project with respect to one or more past projects related to the customer of the completed project and completion indicator data showing the results of the sales activities of the completed project; and a result prediction process that uses as input incompletion index data indicating satisfaction information representing the satisfaction of a target customer, who is a customer of an incomplete project for which sales activities have not been completed, with respect to one or more past projects relating to the target customer, and predicts the results of the sales activities for the incomplete project using the prediction model; A sales support program that allows a computer to execute the above.

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