Information processing device, information processing method, and information processing system

Abstract

An information processing device 1 includes: a performance data acquisition unit 131 for acquiring performance data indicating sales performance of a plurality of sales persons; an action history data acquisition unit 132 for acquiring action history data indicating a relationship between a time and the position of an information terminal used by a sales person in association with each of the plurality of sales persons; a tendency specification unit 133 for specifying the tendency of a relationship between the performance data and the action history data; and an output unit 140 for outputting presentation information indicating the specified tendency.

Description

Information processing device, information processing method, and information processing system

[0001] The present invention relates to an information processing device, an information processing method, and an information processing system.

[0002] 2. Description of the Related Art Conventionally, a route sales support system that improves the efficiency of sales activities involving route patrols is known (see, for example, Patent Document 1).

[0003] JP 2023-43401 A

[0004] In order to improve the sales performance of the entire organization in an organization that conducts route sales, it is important for sales representatives with good sales results to analyze which customers they visit, when they visit, what they propose, register the results, and what can be done to improve the closing rate and sales, and share this information throughout the company.However, in the past, this information sharing was not possible, and there were quite a few sales representatives throughout the organization who conducted inefficient sales.

[0005] The present invention has been made in consideration of these points, and aims to propose an efficient sales method.

[0006] An information processing device according to a first aspect of the present invention comprises a performance data acquisition unit that acquires performance data indicating the sales performance of a plurality of sales representatives, a behavioral history data acquisition unit that acquires behavioral history data associated with each of the plurality of sales representatives and indicating the relationship between the location of an information terminal used by the sales representative and time, a tendency identification unit that identifies a trend in the relationship between the performance data and the behavioral history data, and an output unit that outputs presentation information indicating the identified trend.

[0007] The performance data may include information on products sold by the sales representative, and the trend identification unit may identify the trend in the relationship between the performance data of the sales representative corresponding to the performance data that satisfies a predetermined condition and the behavioral history data, which is associated with the product genre included in the performance data, and the output unit may output the presentation information indicating the trend by product genre.

[0008] The performance data may include attribute information of customers to whom the sales representative sold products, and the trend identification unit may identify the trend in the relationship between the performance data of the sales representative corresponding to the performance data that satisfies predetermined conditions and the behavioral history data, which is associated with the customer attributes included in the performance data, and the output unit may output the presentation information indicating the trend by customer attributes.

[0009] The trend identification unit may identify the customer corresponding to the geofence by referring to geofence data in which each customer is associated with a plurality of geofences, which are virtual location areas, and identify the length of time the customer spent at the location based on the time associated with the customer's location in the behavioral history data, thereby identifying the trend in the relationship between the performance data and the length of time spent.

[0010] The information processing device may further have a customer identification unit that identifies customers for whom sales activities are effective for each sales unit by extracting sales units and customers for which there is a positive correlation between the amount of sales activity or the length of time of activity toward the customer as indicated by the behavioral history data and the amount of sales toward the customer as indicated by the performance data, and a score calculation unit that calculates a total value of points to be awarded for each customer based on the results of comparing at least one element of the period of no visit or no contact with the customer, the period since the customer last purchased a product, the frequency with which the customer purchases products, and the amount of products purchased by the customer, which are included in customer purchase data that indicates the customer's product purchasing status, with a predetermined standard, and the output unit may output the presented information including a list in which the customers identified by the customer identification unit are arranged in descending order of the total value.

[0011] The information processing device may further have a customer identification unit that identifies, among customers whose sales amount identified based on the performance data is less than a predetermined threshold, customers who have the same attributes as customers whose sales amount is equal to or greater than the predetermined threshold by referring to customer management data that associates customers with their attributes, and identifies the identified customers as priority customers who should be given priority in future sales activities, and the output unit may output the presented information indicating the priority customers.

[0012] The trend identifying unit may identify a trend in the relationship between the performance data of the sales representative corresponding to the performance data that satisfies a predetermined condition regarding the priority customer and the behavior history data.

[0013] The behavioral history data may further include a communication history executed on the information terminal, and the information processing device may further have a sales behavior identification unit that identifies, based on the behavioral history data, a sales behavior history indicating the number of times or time that at least any of the sales methods, including a visit by the sales representative to the customer, a web call by the sales representative to the customer, a telephone call to the customer, and a message sending method to the customer, was used, and a sales method identification unit that identifies, as an effective sales method for the first customer, a sales method that is used more frequently than a sales method that is used more frequently than a sales method that is used more frequently than a sales method that is used more frequently than a sales method that is used more frequently than a sales method that is used more frequently than a sales method that is used more frequently than a sales method that is used more frequently than a sales method that is used more frequently than a sales method that is used more frequently than a sales method that is used more frequently

[0014] The behavioral history data may further include a communication history executed on the information terminal, and the information processing device may further have a sales behavior identification unit that identifies, based on the behavioral history data, a sales behavior history indicating the number of times or the amount of time that at least one of the sales means, namely, a visit by the sales representative to the customer, a means for making a web call with the customer, a means for making a telephone call to the customer, and a means for sending a message to the customer, performed; and a sales representative identification unit that identifies a second sales representative whose performance data does not satisfy the specified condition and has a second activity amount that differs within a specified range from a first activity amount, which is the number of times or the amount of time that a sales means is performed by a first sales representative whose performance data satisfies a specified condition, and

[0015] The output unit may output the presentation information indicating the sales method that has been used the most times or for the most time by the first sales representative, in association with the second sales representative.

[0016] The information processing device may further have an estimation unit that, if there is an unrecorded time period in the behavioral history data where the location was not recorded because the information terminal was unable to receive radio waves for using a satellite positioning system or a communication line, estimates an estimated location and estimated time of the information terminal during the unrecorded time period based on a first location and a first time of the information terminal immediately before the unrecorded time period and a second location and a second time of the information terminal immediately after the unrecorded time period.

[0017] The behavioral history data acquisition unit may further acquire acceleration data indicating the acceleration of the information terminal, and the estimation unit may estimate the position and time of the information terminal between the first time and the second time based on the first position and the second position and the acceleration data corresponding to the period from the first time to the second time.

[0018] An information processing method according to a second aspect of the present invention includes a performance data acquisition step executed by a computer to acquire performance data indicating the sales performance of multiple sales representatives; a behavioral history data acquisition step to acquire behavioral history data associated with each of the multiple sales representatives and indicating the relationship between the location of an information terminal used by the sales representative and time; a trend identification step to identify a trend in the relationship between the performance data and the behavioral history data; and an output step to output presentation information indicating the identified trend.

[0019] An information processing system according to a third aspect of the present invention comprises an information processing device and an information terminal capable of communicating with the information processing device, wherein the information processing device has a performance data acquisition unit that acquires performance data indicating the sales performance of a plurality of sales representatives, a behavioral history data acquisition unit that associates with each of the plurality of sales representatives and acquires behavioral history data indicating the relationship between the location and time of the information terminal used by the sales representative, a tendency identification unit that identifies a trend in the relationship between the performance data and the behavioral history data, and a device communication unit that transmits presentation information indicating the identified trend to the information terminal, and the information terminal has a device communication unit that receives the presentation information transmitted from the information processing device and a display unit that displays the presentation information.

[0020] The present invention has the effect of being able to propose efficient sales methods.

[0021] 1 is a diagram showing the configuration of an information processing system S. FIG. 1 is a diagram showing the configuration of an information processing device 1. FIG. 1 is a diagram showing an example of performance data. FIG. 2 is a diagram showing an example of behavior history data. FIG. 2 is a diagram showing an example of product management data. FIG. 3 is a diagram showing an example of customer management data. FIG. 4 is a diagram showing an example of geofence data. FIG. 5 is a diagram showing an example of customer purchase data. FIG. 6 is a diagram showing the configuration of an information terminal 2. FIG. 7 is a diagram showing the configuration of a control unit in a first embodiment. FIG. 8 is a flowchart showing the flow of processing executed by the control unit in the first embodiment. FIG. 9 is a diagram showing the configuration of a control unit in a second embodiment. FIG. 10 is a diagram showing an example of a list of customers for which sales should be prioritized. FIG. 11 is a diagram showing an example of dividing customers into four groups based on the sales amount and potential of each customer. FIG. 12 is a diagram showing the configuration of a control unit in a third embodiment. FIG. 13 is a diagram showing an example of presented information indicating effective sales methods for each customer. FIG. 14 is a diagram showing the configuration of a control unit in a fourth embodiment. FIG. 15 is a diagram showing an example of presented information showing a second sales representative whose behavior needs to be improved. FIG. 16 is a diagram showing the configuration of a control unit in a fifth embodiment. FIG. 17 is a diagram for explaining identification of a sales representative's means of transportation. FIG. 18 is a diagram for explaining a method of estimating a desired location using a line segment. FIG. 19 is a diagram for explaining a method of estimating a desired location using a circle. FIG. 20 is a diagram for explaining a method of estimating a desired location taking into account the topography near the customer.

[0022] [Outline of Information Processing System S] An outline of the information processing system S according to this embodiment will be described using FIG. 1. FIG. 1 is a diagram showing the configuration of the information processing system S. The information processing system S includes an information processing device 1 and an information terminal 2. The information processing system S may operate in cooperation with a performance management system 3. The information processing system S may also include other devices such as a server and a terminal. The information processing system S is a system that identifies trends in the relationship between the sales performance of a sales representative acquired from the performance management system 3 and the behavioral history of the sales representative acquired from the information terminal 2, and outputs presentation information indicating the identified trends, thereby enabling the sales representative to understand how to sell efficiently. In other words, the information processing system S proposes sales methods that are efficient and improve the closing rate and sales.

[0023] The information processing device 1 is a computer such as a server that identifies trends in the relationship between the sales performance of a sales representative and the sales representative's behavioral history, and outputs presentation information indicating the identified trends. In organizations that conduct route sales, sales methods vary from sales representative to sales representative, and some sales representatives engage in inefficient sales. In order to improve the sales performance of the entire organization, it is important to analyze the behavior of sales representatives with good sales performance, or conversely, the behavior of sales representatives with poor sales performance, and share the analysis results with the entire organization.

[0024] However, in the past, organizations that were unable to share information in this way faced the problem of stagnant sales performance across the organization. Therefore, the information processing device 1 identifies trends in the relationship between the sales performance of multiple sales representatives, acquired from the performance management system 3, and the behavioral history, acquired from multiple information terminals 2, indicating the relationship between the location and time of the information terminals 2 used by the sales representatives, and outputs presentation information indicating the identified trends.

[0025] The information processing device 1 is connected to a plurality of information terminals 2 and a performance management system 3 via a communication network such as the Internet. The information terminals 2 are information terminals used by sales representatives and are capable of communicating with the information processing device 1. The information terminals 2 are portable terminals such as smartphones and tablet personal computers. The performance management system 3 is a server that manages the sales performance of sales representatives.

[0026] In this way, the information processing device 1 identifies the trend in the relationship between sales performance and behavioral history, and outputs presentation information indicating the identified trend. By referring to this presentation information, sales representatives with poor sales performance can understand the shortcomings and areas for improvement in their sales activities, and sales representatives with good sales performance can further improve their sales activities. As a result, the sales performance of the entire organization improves.

[0027] [Configuration of information processing device 1] Next, a description will be given of the configuration of the information processing device 1. Fig. 2 is a diagram showing the configuration of the information processing device 1. As shown in Fig. 2, the information processing device 1 includes a device communication unit 11, a storage unit 12, and a control unit 13.

[0028] The device communication unit 11 is a communication interface for communicating with the information terminal 2 and the performance management system 3 via a communication network such as the Internet. The device communication unit 11 transmits to the information terminal 2 presentation information indicating the tendency of the relationship between performance data and behavior history data.

[0029] The storage unit 12 is a storage medium including a ROM (Read Only Memory), a RAM (Random Access Memory), etc. The storage unit 12 stores programs executed by the control unit 13. For example, the storage unit 12 stores information processing programs that cause the control unit 13 to function as a performance data acquisition unit 131, a behavior history data acquisition unit 132, a tendency identification unit 133, a customer identification unit 134, a score calculation unit 135, a sales behavior identification unit 136, a sales method identification unit 137, a sales representative identification unit 138, an estimation unit 139, and an output unit 140.

[0030] The storage unit 12 stores performance data, behavior history data, product management data, customer management data, geofence data, geofence position data, customer purchase data, and acceleration data.

[0031] 3 is a diagram showing an example of performance data. The performance data is data showing the sales performance of multiple sales representatives. In the performance data shown in FIG. 3, the sales representative, the sales date, the product ID, the customer ID, and the sales amount are associated with each other.

[0032] The product ID is information for identifying the product that the salesperson sold to the customer. The customer ID is information for identifying the customer to whom the salesperson sold the product. Although not shown, the performance data may also associate the salesperson with expenses incurred in sales activities and gross profits obtained by subtracting expenses from sales amounts.

[0033] FIG. 4 is a diagram showing an example of behavioral history data. The behavioral history data is data showing the relationship between the location and time of the information terminal 2 used by each of multiple sales representatives. In the behavioral history data shown in FIG. 4, the sales representative, the location of the sales representative, the time, and the history of communications conducted by the sales representative are associated with each other. The behavioral history data shown in FIG. 4 records when and where the sales representative was. For example, in FIG. 4, the sales representative "Suzuki" was at location P1 at time T1.

[0034] The position of the sales representative is the position of the information terminal 2 identified by the information terminal 2 based on radio waves received from a satellite that transmits radio waves for identifying longitude and latitude, and is defined, for example, as a combination of longitude and latitude. The position information of the sales representative may be acquired using a mobile phone antenna or Wi-Fi (registered trademark). The time is the time when the information terminal 2 identified the position. The position and time are transmitted from the information terminal 2 to the information processing device 1 at any timing or at predetermined time intervals (for example, one-minute intervals).

[0035] The communication history is a history of communications executed in the information terminal 2. The communication history is, for example, a history of web calls with a customer, a history of outgoing calls to a customer, a history of messages sent to a customer, and the like.

[0036] Fig. 5 is a diagram showing an example of product management data. In the product management data shown in Fig. 5, a product ID is associated with a product genre. The product ID is an ID for identifying a product. The product genre may be a broad genre indicating an industry, a medium genre indicating a product classification within a specific industry, or a narrow genre indicating differences in standards within a specific product classification.

[0037] 6 is a diagram showing an example of customer management data. In the customer management data, a customer ID is associated with customer attributes. The customer ID is an ID for identifying a customer. The customer attributes include, for example, regional characteristics, company size, industry, and product.

[0038] FIG. 7 is a diagram illustrating an example of geofence data. The geofence data is data in which each of multiple geofences, which are virtual location areas, is associated with a customer. A geofence refers to a geographical area surrounded by virtual boundaries. In the geofence data illustrated in FIG. 7, a customer ID, a geofence ID, latitude, and longitude are associated. The customer ID is information for identifying the geofence. The latitude is the latitude of the customer's company location. The longitude is the longitude of the customer's company location. The geofence is represented, for example, by a circular area. In this case, the geofence is defined as a circle with a predetermined radius (e.g., 100 m) centered at a point corresponding to the latitude and longitude of the customer's company location. The size of the geofence may differ for each customer. In this case, the geofence data may include the size of the geofence associated with the customer ID.

[0039] FIG. 8 is a diagram showing an example of customer purchase data. The customer purchase data is data showing the product purchase status of a customer. In the customer purchase data shown in FIG. 8, a no-visit period or no-contact period is associated with an elapsed period, a purchase frequency, and a purchase amount. The no-visit period or no-contact period is a period during which a sales representative does not visit the customer or has no contact with the customer through sales means other than a visit. The elapsed period is the period since the customer last purchased a product. The purchase frequency is the average frequency at which a customer purchases a product. The purchase amount is the amount of the product purchased by the customer, for example, the cumulative amount of the product purchased by the customer.

[0040] The acceleration data is data that indicates the acceleration of the information terminal 2. The acceleration data is data in which a terminal ID for identifying the information terminal 2, a time, and an acceleration are associated with each other.

[0041] The control unit 13 is, for example, a CPU (Central Processing Unit). By executing an information processing program stored in the storage unit 12, the control unit 13 functions as a performance data acquisition unit 131, a behavior history data acquisition unit 132, a tendency identification unit 133, a customer identification unit 134, a score calculation unit 135, a sales behavior identification unit 136, a sales method identification unit 137, a sales representative identification unit 138, an estimation unit 139, and an output unit 140. Details of the processing executed by each unit will be described later.

[0042] [Configuration of Information Terminal 2] Next, a description will be given of the configuration of the information terminal 2. Fig. 9 is a diagram showing the configuration of the information terminal 2. As shown in Fig. 9, the information terminal 2 includes a terminal communication unit 21, a display unit 22, a storage unit 23, and a control unit 24.

[0043] The terminal communication unit 21 is a communication interface for communicating with the information processing device 1 via a communication network such as the Internet. The terminal communication unit 21 transmits behavioral history and the like indicating the relationship between the location and time of the information terminal 2 used by the sales representative to the information processing device 1. The terminal communication unit 21 also receives presented information and the like indicating the tendency of the relationship between performance data and behavioral history data.

[0044] The display unit 22 is configured by, for example, a liquid crystal display or an organic EL (Electro-Luminescence) display. The display unit 22 displays various information according to the control of the display processing unit 241. The display unit 22 has a touch panel and may receive data input by the user. The display unit 22 displays presentation information indicating the tendency of the relationship between the performance data and the behavior history data.

[0045] The storage unit 23 is a storage medium including a ROM, a RAM, etc. The storage unit 23 stores a program executed by the control unit 24. For example, the storage unit 23 stores a program that causes the control unit 24 to function as a display processing unit 241 and a recording unit 242.

[0046] The control unit 24 is, for example, a CPU. The control unit 24 executes the programs stored in the storage unit 23 to function as a display processing unit 241 and a recording unit 242.

[0047] The display processing unit 241 displays various types of information on the display unit 22. After receiving presentation information indicating the tendency of the relationship between the performance data and the behavior history data from the information processing device 1 via the device communication unit 21, the display processing unit 241 displays the presentation information on the display unit 22.

[0048] The recording unit 242 records the relationship between the location and time of the information terminal 2 used by the sales representative as a behavior history, and transmits the recorded behavior history to the information processing device 1 via the terminal communication unit 21.

[0049] The various functions provided by the information processing system S will be described in detail below.

[0050] First Embodiment: Identifying the Tendency of the Relationship Between Sales Performance and Behavioral History As a first embodiment, a basic operation for identifying the tendency of the relationship between performance data and behavioral history data will be described.

[0051] [Processing Overview] An overview of the processing executed by the information processing device 1 will be described with reference to Fig. 1. The information terminal 2 records the relationship between the location and time of the information terminal 2 used by the sales representative as a behavioral history, and transmits the recorded behavioral history to the information processing device 1. By receiving the relationship between the location and time of the information terminal 2 from multiple information terminals 2, the information processing device 1 acquires behavioral history data indicating the relationship between the location and time of the information terminal 2 used by the sales representative, associated with each of the multiple sales representatives.

[0052] The information processing device 1 acquires performance data from the performance management system 3 in order to identify trends in the relationship between the performance data and the behavioral history data. Specifically, the information processing device 1 sends an inquiry to the performance management system 3 to acquire the sales performance of the sales representatives, and the performance management system 3 sends performance data indicating the sales performance of multiple sales representatives to the information processing device 1. The information processing device 1 identifies trends in the relationship between the sales performance and the behavioral history based on the received performance data and behavioral history data.

[0053] The details of the trend in the relationship between sales performance and behavioral history will be described later, but for example, it is the trend in the relationship between the rank of sales performance and the number of visits to customers or the length of visits in a predetermined period (for example, one month), or the ratio of multiple sales methods. The information processing device 1 may identify trends by product genre or by customer attribute.

[0054] The information processing device 1 transmits presentation information indicating the identified trends to, for example, an information terminal 2 used by the sales representative. This allows the sales representative to understand the trends in the relationship between the sales performance and behavioral history of other sales representatives and use this information in their own sales activities. The information processing device 1 may also transmit the presentation information to an information terminal used by the sales representative's manager. This makes it easier for the manager to instruct the sales representative.

[0055] [Processing Executed by Control Unit] As the processing executed by the control unit in the first embodiment, a description will be given of processing executed by the performance data acquisition unit 131, the behavior history data acquisition unit 132, the tendency identification unit 133, and the output unit 140. Fig. 10 is a diagram showing the configuration of the control unit in the first embodiment.

[0056] The performance data acquisition unit 131 acquires performance data indicating the sales performance of multiple sales representatives. For example, the performance data acquisition unit 131 transmits an inquiry to the performance management system 3 to acquire the sales performance of the sales representatives. The performance data acquisition unit 131 then receives the sales performance transmitted from the performance management system 3 via the device communication unit 11 and registers the sales performance data ( FIG. 3 ) in the storage unit 12, thereby acquiring the performance data. The performance data acquisition unit 131 acquires the performance data at a predetermined timing (for example, the beginning of the month).

[0057] The behavioral history data acquisition unit 132 acquires behavioral history data that indicates the relationship between the location and time of the information terminal 2 used by each of the multiple sales representatives, in association with each of the multiple sales representatives. The behavioral history data acquisition unit 132 receives, for example, the relationship between the location and time transmitted from the information terminal 2 of the multiple sales representatives via the device communication unit 11. The performance data acquisition unit 131 then acquires the behavioral history data by registering data that associates this relationship with each of the multiple sales representatives as behavioral history data ( FIG. 4 ) in the storage unit 12. The behavioral history data acquisition unit 132 continuously acquires the behavioral history data.

[0058] The trend identification unit 133 identifies a trend in the relationship between the performance data and the behavioral history data. For example, the trend identification unit 133 first classifies multiple sales representatives into multiple performance ranks based on the sales performance of the multiple sales representatives indicated by the performance data. The trend identification unit 133 classifies the sales performance, for example, by classifying the top 25% of the sales representatives belonging to the organization as rank A, the top 25% to 75% as rank B, and the bottom 25% as rank C. Next, the trend identification unit 133 identifies the characteristics of the behavior indicated by the behavioral history for each classified rank.

[0059] The trend identification unit 133, for example, identifies, for each rank of sales performance, a statistical value (average value or median value) of the proportion of sales counts or business hours using a specific sales method to the total number of sales, a statistical value of the number of sales counts, business hours or business frequency using a specific sales method during a specified period, or a statistical average value of the number of visits to customers or the length of visit times.

[0060] In order to identify the length of time that a salesperson stayed at a customer's location, the trend identification unit 133 identifies a customer corresponding to a geofence by referring to geofence data ( FIG. 7 ) in which each geofence, which is a virtual location area, is associated with a customer. Next, the trend identification unit 133 identifies the length of time that the salesperson stayed at the customer's location based on the time associated with the identified customer's location in the behavior history data, thereby identifying the length of time that the salesperson stayed at the customer's location.

[0061] Specifically, the trend identification unit 133 identifies, among the positions in the behavior history data ( FIG. 4 ), a position that falls within the area indicated by the geofence data ( FIG. 7 ) as the customer's position. Next, the trend identification unit 133, for example, refers to the behavior history data ( FIG. 4 ) and identifies the customer's stay time at the position based on the time associated with the identified customer's position.

[0062] The trend identification unit 133 may identify a trend in the relationship between the performance data and the behavioral history data of a sales representative corresponding to performance data that satisfies a predetermined condition. The predetermined condition is a condition indicating good sales performance or a condition indicating poor sales performance. Conditions indicating good sales performance include, for example, sales amount equal to or greater than a predetermined threshold, sales amount being in the top xx%, or sales amount being in the top xx people. Conditions indicating poor sales performance include, for example, sales amount being less than a predetermined threshold, sales amount being less than the bottom xx%, or sales amount being less than the bottom xx people. The predetermined condition may be set by a manager who manages the sales representatives, or may be set individually by each sales representative.

[0063] The output unit 140 outputs presentation information indicating the identified trend. For example, the output unit 140 outputs the presentation information indicating the identified trend by transmitting it to the information terminal 2 used by the sales representative via the device communication unit 11. Alternatively, if the sales representative is in-house, the output unit 140 may output the presentation information indicating the identified trend to a printer so that it can be printed.

[0064] In this way, the trend identification unit 133 identifies the trend in the relationship between performance data and behavioral history data, and the output unit 140 outputs presentation information indicating the identified trend, so that sales representatives who refer to this presentation information can understand the trend in the relationship between the sales performance and behavioral history of other sales representatives, and can use this information to help their own sales activities.

[0065] Furthermore, the trend identification unit 133 identifies a trend in the relationship between the performance data and behavior history data of sales representatives with good sales results as a predetermined condition, and the output unit 140 outputs presentation information indicating the identified trend, so that a sales representative who refers to this presentation information can compare the differences between the behavior of sales representatives with good sales results and his or her own behavior to identify shortcomings and areas for improvement in his or her own sales activities, or can brush up his or her own sales activities.In contrast, the trend identification unit 133 identifies a trend in the relationship between the performance data and behavior history data of sales representatives with poor sales results as a predetermined condition, and the output unit 140 outputs presentation information indicating the identified trend, so that a sales representative who refers to this presentation information can be careful not to engage in sales activities that are unlikely to improve their sales results.

[0066] In some cases, a salesperson may want to analyze the relationship between sales performance and behavioral history from the perspective of product category or customer attribute. Therefore, the trend identification unit 133 may identify the trend of the relationship between sales performance and behavioral history by product category or customer attribute, as described below.

[0067] When the performance data includes information on products sold by the sales representative, the trend identification unit 133 identifies a trend in the relationship between the performance data of the sales representative corresponding to the performance data that satisfies a predetermined condition and the behavioral history data, which is associated with the product genre included in the performance data. The trend identification unit 133 identifies the product genre associated with the product ID included in the performance data, for example, by referring to product management data ( FIG. 5 ) in which product IDs are associated with product genres. Then, the trend identification unit 133 identifies a trend in the relationship between the performance data and the behavioral history data for each identified product genre. The output unit 140 outputs presentation information indicating the identified trend for each product genre.

[0068] When the performance data includes attribute information of customers to whom the salesperson sold products, the trend identification unit 133 identifies a trend in the relationship between the performance data of the salesperson corresponding to the performance data that satisfies a predetermined condition and the behavioral history data, which is associated with the customer attributes included in the performance data. The trend identification unit 133, for example, refers to customer management data ( FIG. 6 ) in which customer IDs are associated with customer attributes to identify the customer attributes associated with the customer IDs included in the performance data. Then, the trend identification unit 133 identifies a trend in the relationship between the performance data and the behavioral history data for each identified customer attribute. The trend identification unit 133 may also identify a trend in the relationship between the performance data and the behavioral history data for each specific attribute (e.g., company size) among the identified customer attributes. The output unit 140 outputs presentation information indicating the identified trends for each customer attribute.

[0069] In this way, the trend identification unit 133 identifies trends in the relationship between sales performance and behavioral history by product genre or customer attribute, and the output unit 140 outputs presentation information indicating the identified trends, allowing sales representatives to understand trends appropriate to the product genre of the products they are responsible for selling or the customer attributes of the customers they are responsible for selling. In other words, a sales representative who is responsible for selling multiple products or multiple customers can analyze what sales behaviors are effective by product genre or customer attribute. This makes it easier for sales representatives to take sales behaviors appropriate to the product genre or customer attribute, thereby improving their own sales performance. The trend identification unit 133 may also identify trends in the relationship between performance data and behavioral history data for each combination of product genre and customer attribute.

[0070] The trend identification unit 133 may identify a trend in the relationship between the performance data of a salesperson corresponding to performance data that satisfies a predetermined condition by product category or a predetermined condition by customer attribute and the behavioral history data. This allows a salesperson to analyze the sales behavior of other salespersons who have achieved good sales results for products in a certain product category or customers with certain customer attributes, thereby enabling the salesperson to efficiently improve their own sales performance.

[0071] [Flow of Processing Executed by Control Unit] The flow of processing executed by the control unit in the first embodiment will be described with reference to Fig. 11. Fig. 11 is a flowchart showing the flow of processing executed by the control unit in the first embodiment.

[0072] The performance data acquisition unit 131 acquires performance data indicating the sales performance of multiple sales representatives at a predetermined timing (for example, the beginning of the month) from the performance management system 3 (S1). The behavior history data acquisition unit 132 continuously receives the relationship between the location of the information terminal 2 and the time from the multiple information terminals 2, and thereby acquires behavior history data indicating the relationship between the location of the information terminal 2 used by the sales representative and the time, associated with each of the multiple sales representatives (S2).

[0073] The trend identification unit 133 identifies a trend in the relationship between the behavior history data continuously acquired by the behavior history data acquisition unit 132 and the performance data acquired at a predetermined timing (e.g., the beginning of the month) by the performance data acquisition unit 131 (S3). The trend identification unit 133 may identify a trend by product genre or by customer attribute. The trend identification unit 133 may also identify a trend in the relationship between the performance data and the length of time a customer stays at a location. The output unit 140 outputs presentation information indicating the identified trend (S4).

[0074] Second Embodiment: Presentation of Customers to Whom Sales Should Be Prioritized As a second embodiment, a process of presenting customers to whom sales should be prioritized will be described.

[0075] [Processing Overview] The information processing device 1 may present customers for whom sales should be prioritized. Organizations that conduct route sales often have multiple customers. Some customers are highly effective, leading to product purchases or contracts when a sales representative makes sales efforts, while other customers are less effective, leading to product purchases or contracts even when a sales representative makes sales efforts. However, in the past, sales representatives were unable to distinguish between customers with high and low sales effectiveness, and therefore often spent as much effort selling to customers with low sales effectiveness as they did selling to customers with high sales effectiveness. This resulted in inefficient sales efforts for the entire organization. Therefore, the information processing device 1 may identify customers with high sales effectiveness using multivariate analysis or cluster analysis and output presentation information indicating the identified customers as customers for whom sales should be prioritized.

[0076] In the multivariate analysis, the information processing device 1 identifies, for each sales method, customers for whom there is a positive correlation between the amount of sales activity or the length of time of the activity for the customer, as indicated by the behavioral history data, and the sales amount for that customer, as indicated by the performance data. Since customers corresponding to a positive correlation are considered to have a relatively large sales effect, by having the information processing device 1 output presentation information indicating customers corresponding to a positive correlation, the sales representative can identify customers for whom sales should be prioritized and the sales method suitable for that customer.

[0077] Furthermore, the information processing device 1 may calculate a total score for each customer based on the results of comparing at least one of the following elements included in customer purchase data indicating the customer's product purchase status: the period of time during which the customer has not been visited or contacted, the period since the last purchase date, the purchase frequency, and the purchase amount, with predetermined criteria.The information processing device 1 then outputs presentation information including a list of identified customers sorted in descending order of the calculated total score.This makes it easier for sales representatives to identify which customers should be prioritized when there are many customers who should be prioritized in sales efforts.

[0078] In the cluster analysis, the information processing device 1 identifies, among customers with low sales, customers who have the same attributes as customers with high sales by referring to customer management data in which customers are associated with their attributes. The information processing device 1 then outputs presentation information indicating the identified customers as priority customers who should be given priority in future sales activities.

[0079] In this way, the information processing device 1 identifies customers with high sales effectiveness through multivariate analysis or cluster analysis, and outputs presentation information indicating the identified customers as customers for whom sales should be prioritized. By referring to this presentation information, sales representatives can prioritize sales to customers with high sales effectiveness. As a result, sales can be conducted efficiently across the entire organization, improving the sales performance of the entire organization.

[0080] [Processing Executed by the Control Unit in Multivariate Analysis] Fig. 12 is a diagram showing the configuration of the control unit 13 in the second embodiment. The control unit 13 in the second embodiment differs from the first embodiment in that it further includes a customer identification unit 134 and a score calculation unit 135. The following describes the processing executed by the control unit in the multivariate analysis of the second embodiment, including the processing executed by the customer identification unit 134, the score calculation unit 135, and the output unit 140.

[0081] The customer identification unit 134 identifies customers for whom sales activities are effective for each sales method by extracting sales methods and customers for which there is a positive correlation between the length of time sales activities have been conducted for the customer as indicated by the behavioral history data and the sales amount for that customer as indicated by the performance data.

[0082] The customer identification unit 134, for example, identifies a position in the behavior history data ( FIG. 4 ) that falls within an area indicated by the geofence data ( FIG. 7 ) as the customer's location. Next, the trend identification unit 133, for example, refers to the behavior history data ( FIG. 4 ) and, based on the time associated with the identified customer's location, identifies, for each customer, the length of time that the sales representative made a direct sales visit during a predetermined period.

[0083] In addition, the customer identification unit 134, for example, refers to the behavioral history data (Figure 4) and tallys up the times when communication history exists, thereby identifying for each customer the length of time that the sales representative conducted sales via communication means such as web calls, telephone calls, or message transmissions during a specified period.

[0084] In this way, the customer identification unit 134 identifies, for each customer, the length of time that sales activities were conducted using each of a number of sales methods, such as door-to-door sales, sales via web calls, sales via telephone, and sales via message transmission.

[0085] Furthermore, the customer identification unit 134 identifies the relationship between the number of sales activities or the length of activity time for each of the multiple sales methods and the sales amount by referring to the performance data ( FIG. 3 ). For example, the customer identification unit 134 plots the length of time for each customer during which sales activities were performed using each of the multiple sales methods and the sales amount for each customer for multiple predetermined periods or for multiple sales representatives, and identifies whether there is a positive correlation between the two values ​​(e.g., a correlation coefficient greater than 0.4), thereby extracting sales methods and customers with a positive correlation. For example, the customer identification unit 134 compares the number of direct visits or the length of activity time for customer A in each month of a certain year with the sales amount for customer A in each month of the certain year, and if there is a positive correlation, extracts customer A as a customer for whom door-to-door sales should be prioritized.

[0086] In this way, the customer identification unit 134 identifies customers for whom sales activities are effective for each sales method. The customer identification unit 134 may also identify the type of sales method that is effective for each customer. For example, the customer identification unit 134 may identify that a direct visit is effective for customer A, while telephone sales are effective for customer B.

[0087] The score calculation unit 135 calculates the total score to be awarded for each customer based on the results of comparing at least one of the following elements, which are included in the customer purchase data indicating the customer's product purchase status: the period of no visit or no contact with the customer, the period elapsed since the customer last purchased a product, the frequency with which the customer purchases products, and the amount of the products purchased by the customer, with predetermined standards.

[0088] The score calculation unit 135, for example, refers to the customer purchase data ( FIG. 8 ) and assigns a predetermined score (e.g., 1 point) for each of the following elements for each customer, thereby calculating the total score for each customer: (Elements) - The period without visit or contact with the customer is a predetermined period (e.g., 3 months) or more. - The period since the last purchase date is a predetermined period (e.g., 3 months) or more. - The purchase frequency exceeds a predetermined threshold (e.g., median frequency). - The purchase amount exceeds a predetermined threshold (e.g., median amount).

[0089] Then, the output unit 140 outputs presentation information including a list in which the customers identified by the customer identification unit 134 are sorted in descending order of the total value calculated by the score calculation unit 135. For example, the output unit 140 outputs presentation information including a list in which the customers identified by the customer identification unit 134 are sorted in descending order of the total value of the points (e.g., 0 points to 4 points) assigned by the score calculation unit 135.

[0090] The score calculation unit 135 may add a score (for example, 10 points) to a customer identified by the customer identification unit 134 as having a positive correlation between the length of sales activities and the amount of sales. In this case, even if the period of no visit or no contact is long, customers for whom sales effects are unlikely to be seen are unlikely to be included in the list, so it is possible to prevent sales representatives from visiting customers for whom sales effects are unlikely to be seen.

[0091] 13 is a diagram showing an example of a list of customers for whom sales should be prioritized. In this way, the output unit 140 outputs presented information including a list in which customers are sorted in descending order of sales priority. By referring to this presented information, the sales representative can prioritize sales to customers for whom the sales representative has not made much sales efforts, among customers for whom sales priority is high and sales activities are effective. As a result, the sales representative can efficiently improve his or her own sales performance.

[0092] The output unit 140 may output presented information including a list of effective sales methods for each customer identified by the customer identification unit 134, as shown in FIG. 13 . By referring to this presented information, the sales representative can conduct sales using sales methods that are effective for that customer. As a result, the sales representative can efficiently improve their own sales performance. Furthermore, the sales representative can eliminate wasted time and effort that would occur if they conducted sales using sales methods that are ineffective for that customer.

[0093] In the above example, the score calculation unit 135 assigns the same predetermined score for each element to which the customer corresponds, but weighted scores may be assigned to important elements. For example, the score calculation unit 135 may calculate, as the contribution rate (weighting coefficient) of each element to the sales amount, a correlation coefficient indicating the correlation between whether or not the customer corresponded to each of the above elements at the first location in the past and the sales amount in the period from the first location to the second location after the first location, which is specified by referring to the performance data ( FIG. 3 ). This calculation may be performed by machine learning.

[0094] In this way, the score calculation unit 135 calculates the contribution rate of each element to sales amount, so that customers corresponding to elements that contribute greatly to sales amount are more likely to be displayed at the top of the list shown in Figure 13, and sales representatives can prioritize sales to customers who will have a greater sales effect.

[0095] [Processing Executed by the Control Unit in Cluster Analysis] As processing executed by the control unit in cluster analysis according to the second embodiment, processing executed by the customer identification unit 134 and the output unit 140 will be described.

[0096] The customer identification unit 134 identifies, from among customers whose sales amount is less than a predetermined threshold and is identified based on the performance data, customers who have the same attributes as customers whose sales amount is equal to or greater than the predetermined threshold, by referring to customer management data in which customers are associated with their attributes. The customer identification unit 134 may identify the identified customers as priority customers for whom future sales activities should be prioritized.

[0097] The customer identification unit 134, for example, refers to the performance data ( FIG. 3 ) to calculate the sales amount for each customer combination for a predetermined period. The customer identification unit 134 then identifies customers whose calculated sales amount is less than a predetermined threshold (e.g., less than XX yen, less than the bottom XX%, or less than the bottom XX%). On the other hand, the customer identification unit 134 identifies customers whose calculated sales amount is equal to or greater than a predetermined threshold (e.g., XX yen or more, within the top XX%, or within the top XX).

[0098] The customer identification unit 134, for example, refers to the customer management data ( FIG. 8 ) to identify customers whose identified sales amount is less than a predetermined threshold and who have the same attributes as customers whose identified sales amount is equal to or greater than the predetermined threshold. Note that "having the same attributes" here means that at least one of multiple attributes (regional characteristics, company size, industry, product, etc.) is the same.

[0099] In order to identify customers for whom sales activities should be prioritized, the customer identification unit 134 may use data in which customers are classified into multiple groups based on a combination of the amount of sales to the customer and the amount of potential. Potential is an index that indicates the likelihood of an increase in future sales, and is high when the customer's industry is growing, the customer's sales growth rate is relatively high, or the customer has little track record of past sales activities.

[0100] FIG. 14 is a diagram showing an example in which customers are divided into four groups based on their sales amount and potential. The size of the ellipses in FIG. 14 corresponds to the number of customers belonging to each group. The meaning of each group in FIG. 14 is as follows: The prime customer group is a group of customers who have high sales figures and are expected to increase their sales figures in the future. The high-potential group (priority customer group) is a group of customers who have low sales figures but are expected to increase their sales figures in the future. The stagnant group is a group of customers who have high sales figures but are not expected to increase their sales figures in the future. The low-potential group is a group of customers who have low sales figures and are not expected to increase their sales figures in the future. The customer identification unit 134 identifies customers whose sales figures are below a threshold and whose potential is above a threshold as the high-potential group (priority customer group) for whom future sales activities should be prioritized.

[0101] The output unit 140 then outputs presented information indicating the priority customers identified by the customer identification unit 134. The output unit 140 outputs presented information including, for example, a list of customers in the high-potential group (priority customer group) identified by the customer identification unit 134. By referring to this presented information, the sales representative can prioritize sales to customers whose sales activities are likely to lead to the purchase of a product or the conclusion of a contract. As a result, the sales representative can efficiently improve their own sales performance. Furthermore, sales for the entire organization can also be improved.

[0102] The customer identification unit 134 may perform cluster analysis in addition to the multivariate analysis. That is, among the customers included in the list in which the customers identified by the customer identification unit 134 in the multivariate analysis are sorted in descending order of the total score calculated by the score calculation unit 135, the customer identification unit 134 may identify customers in the high-potential group (priority customer group) identified by the customer identification unit 134 in the cluster analysis as customers with extremely high sales effectiveness.

[0103] Incidentally, if there is a sales representative who has already achieved good sales results for a priority customer identified in the cluster analysis, the sales behavior of that sales representative can serve as a reference for other sales representatives. Therefore, the trend identification unit 133 may identify a trend in the relationship between the performance data and the behavior history data of the sales representative corresponding to the performance data that satisfies a predetermined condition for the priority customer.

[0104] The trend identification unit 133, for example, refers to the performance data ( FIG. 3 ) to identify sales representatives whose sales amount for a specified period for priority customers is equal to or greater than a specified threshold (e.g., XX yen or more, within the top XX%, or within the top XX ranks). Then, for the identified sales representatives, the trend identification unit 133 identifies a trend in the relationship between the performance data and the behavior history data using a method similar to that described in the first embodiment. The output unit 140 outputs presentation information indicating the identified trend. By referring to this presentation information, sales representatives can make sales to priority customers by referring to the sales behavior of sales representatives who have already achieved good sales results for priority customers. As a result, sales performance for priority customers across the entire organization is improved.

[0105] Third Embodiment: Presentation of Effective Sales Methods for Each Customer As a third embodiment, a process of presenting effective sales methods (customer contact channels) for each customer will be described.

[0106] [Processing Overview] Organizations that conduct route sales often have multiple customers. Some customers are effectively approached through in-person sales, while others are effectively approached through telephone sales. In other words, effective sales methods vary from customer to customer. However, in the past, sales representatives were unable to determine which sales method was most effective for a particular customer, and so they sometimes used sales methods that were ineffective for that customer. This resulted in inefficient sales for the organization as a whole. Therefore, the information processing device 1 may present effective sales methods for each customer.

[0107] To present effective sales methods for each customer, the information processing device 1 first identifies a sales behavior history indicating the number of times or duration that each sales method was used by a sales representative, based on behavior history data that further includes a communication history executed on the information terminal 2. The information processing device 1 then identifies, as effective sales methods for a particular customer, sales methods that are used more frequently by a first sales representative with good sales performance than by a second sales representative with poor sales performance, among multiple sales methods included in the sales behavior history for that particular customer. In other words, the information processing device 1 identifies, with respect to a particular customer, sales methods that are used more frequently by a first sales representative with good sales performance than a second sales representative with poor sales performance. The information processing device 1 then outputs presentation information that associates the identified sales methods with the particular customer.

[0108] In this way, the information processing device 1 outputs presentation information that shows an effective sales method for a certain customer in association with the customer. By referring to this presentation information, a sales representative can conduct sales using an effective sales method for the customer. As a result, the entire organization can conduct sales efficiently, and the sales performance of the entire organization can be improved.

[0109] [Processing Executed by the Control Unit] Fig. 15 is a diagram showing the configuration of the control unit 13 in the third embodiment. The control unit 13 in the third embodiment differs from the control unit 13 in the first embodiment in that it further includes a sales behavior identification unit 136 and a sales means identification unit 137. As the processing executed by the control unit 13 in the third embodiment, processing executed by the sales behavior identification unit 136, the sales means identification unit 137, and the output unit 140 will be described.

[0110] The sales behavior identification unit 136 identifies, based on the behavior history data that further includes the communication history executed on the information terminal 2, the sales behavior history indicating the number of times or the time when at least one of the sales methods, namely, a visit by a sales representative to a customer, a means of making a web call with a customer by the sales representative, a means of making a phone call to a customer, and a means of sending a message to a customer, was executed.

[0111] The sales behavior identification unit 136, for example, refers to the geofence data ( FIG. 7 ) to identify a geofence ID associated with a customer ID. Next, the sales behavior identification unit 136 identifies the number of times the sales representative entered an area indicated by the geofence data ( FIG. 7 ) among the positions in the behavior history data ( FIG. 4 ) during a predetermined period as the number of direct visits to customers by the sales representative during the predetermined period. In this way, the sales behavior identification unit 136 identifies the number of times sales were made through direct visits to customers for each combination of sales representative, predetermined period, and customer.

[0112] The sales behavior identification unit 136, for example, refers to the behavior history data (Figure 4) and aggregates the times associated with the locations of customers that fall within the range defined by the identified latitude, longitude, and size, thereby identifying the times when sales representatives made sales visits to customers through direct visits for each combination of sales representative, specified period, and customer.

[0113] In addition to information about the communication method, the communication history also includes information about which customer the communication was with and for how long. Therefore, the sales behavior identification unit 136, for example, refers to the behavior history data (Figure 4) to identify the number of times and the length of time that a sales representative has made sales using each communication method for each combination of sales representative, specified period, and customer.

[0114] The sales method identification unit 137 identifies, from among a plurality of sales methods included in the sales behavior history of the first sales representative with respect to the first customer corresponding to performance data that satisfies a predetermined condition, a sales method that is used more frequently than a plurality of sales methods included in the sales behavior history of the second sales representative with respect to the first customer corresponding to performance data that does not satisfy the predetermined condition, as an effective sales method for the first customer.

[0115] The sales method identification unit 137, for example, refers to the performance data (FIG. 3) and identifies as the first sales representative a sales representative whose sales amount to the first customer in a predetermined period satisfies a predetermined condition related to the first customer (for example, sales amount equal to or greater than a predetermined threshold, sales amount in the top XX%, or sales amount within the top XX customers, etc.). On the other hand, the sales method identification unit 137, for example, refers to the performance data (FIG. 3) and identifies as the second sales representative a sales representative whose sales amount to the first customer in a predetermined period does not satisfy the predetermined condition.

[0116] Next, the sales method identification unit 137 identifies, for the first customer, a sales method that the first sales representative has used more or for a longer period of time than the second sales representative, for example, by referring to the sales behavior history identified by the sales behavior identification unit 136. The identified sales method is a sales method that is effective for the first customer.

[0117] The output unit 140 then outputs presented information that associates the sales methods identified by the salesperson identification unit 138 with the first customer, as shown in FIG. 16. FIG. 16 is a diagram showing an example of presented information that indicates effective sales methods for each customer. While FIG. 16 shows a character string consisting of letters and numbers as the customer ID, the customer ID may also be the customer's name. By referring to this presented information, the salesperson can conduct sales with the customer using effective sales methods. As a result, the salesperson can efficiently improve their own sales performance.

[0118] Fourth Embodiment: Presentation of Salespersons Whose Behavior Needs to be Improved As a fourth embodiment, a process of presenting salespersons whose behavior needs to be improved will be described.

[0119] [Processing Overview] An organization that conducts route sales typically consists of multiple sales representatives. Some sales representatives have good sales performance, while others have poor sales performance. Among those with poor sales performance, some have low sales volume, while others have sufficient sales volume but poor sales methods. For the former, sales volume should be increased to improve sales performance. On the other hand, for the latter, since sales volume is already sufficient, further increase in sales volume would lead to overwork, so sales methods should be improved to improve sales performance. In particular, improving the sales methods of sales representatives who have sufficient sales volume but poor sales methods can improve sales performance throughout the organization without reducing quality of life (QOL) from the perspective of the sales representatives and without increasing labor costs from the perspective of the employer. Therefore, the information processing device 1 may present sales representatives who need to improve their behavior.

[0120] In order to present sales representatives whose behavior needs improvement, the information processing device 1 first identifies a sales behavior history indicating the number of times or the amount of time each sales representative has used each sales method, based on behavior history data that further includes a communication history executed on the information terminal 2. Next, the information processing device 1 identifies a second sales representative whose sales volume indicated by the sales behavior history is equivalent to that of a first sales representative with good sales results, but whose sales performance is poor. The information processing device 1 then outputs presentation information indicating the identified second sales representative. The presentation information may also include information on the sales methods that the first sales representative with good sales results used most frequently or for the most time. The presentation information may also include information indicating the difference between the sales content performed by the first sales representative and the sales content performed by the second sales representative.

[0121] In this way, the information processing device 1 outputs presentation information indicating sales representatives who have relatively high sales volume but poor sales performance. By referring to this presentation information, sales representatives with poor sales performance can understand that they should improve their sales methods. Furthermore, if the presentation information includes information on sales methods that are used frequently or for long periods of time by sales representatives with good sales performance, sales representatives with poor sales performance can understand which specific sales methods they should use more in order to improve their sales performance. As a result, the organization as a whole can conduct more efficient sales, thereby improving the sales performance of the entire organization.

[0122] [Processing Executed by the Control Unit] Fig. 17 is a diagram showing the configuration of the control unit 13 in the fourth embodiment. The control unit 13 in the fourth embodiment differs from the control unit 13 in the first embodiment in that it further includes a sales behavior identification unit 136 and a salesperson identification unit 138. As the processing executed by the control unit 13 in the fourth embodiment, processing executed by the sales behavior identification unit 136, the salesperson identification unit 138, and the output unit 140 will be described.

[0123] The sales behavior identification unit 136 identifies a sales behavior history indicating the number of times or the length of time that at least one of the sales methods, i.e., a visit by a sales representative to a customer, a web call by a sales representative to a customer, a telephone call to a customer, and a message sending method to a customer, was performed based on the behavior history data that further includes a communication history performed on the information terminal 2. The sales behavior identification unit 136 identifies the number of times or the length of time that a sales representative performed a sales method for each combination of sales method, sales representative, and predetermined period. The method of identification is the same as the method described in the third embodiment, and therefore will not be described here.

[0124] The salesperson identification unit 138 identifies a second salesperson whose performance data does not satisfy the predetermined condition and whose second activity amount differs within a predetermined range from a first activity amount, which is the number of times or the amount of time that a sales method is used by a first salesperson whose performance data satisfies the predetermined condition. For example, the salesperson identification unit 138 refers to the performance data ( FIG. 3 ) and identifies as the first salesperson a salesperson whose sales amount in a predetermined period satisfies a predetermined condition (e.g., sales amount equal to or greater than a predetermined threshold, top xx% in sales amount, or within the top xx salespersons in sales amount).

[0125] The salesperson identification unit 138, for example, refers to the sales behavior history identified by the sales behavior identification unit 136 and identifies the average value of the cumulative number of times or cumulative duration of sales activities by all sales methods for a predetermined period for multiple first salespersons. Specifically, the salesperson identification unit 138 calculates the cumulative number of times or cumulative duration of sales activities by a first salesperson for a certain month of a certain year. Then, the salesperson identification unit 138 calculates the average value of the cumulative number of times or cumulative duration of sales activities for multiple first salespersons. This average value corresponds to the first activity amount.

[0126] The salesperson identification unit 138 identifies a salesperson who has a second activity amount, which is, for example, a cumulative number of sales activities or a cumulative total time spent on sales that differs from the first activity amount within a predetermined range (for example, the cumulative number of sales activities is within ±0 times, or the cumulative total time spent on sales is within ±0 hours). Furthermore, the salesperson identification unit 138 identifies a salesperson whose sales amount in a predetermined period does not satisfy the above-mentioned predetermined condition, for example, by referring to the performance data ( FIG. 3 ). The salesperson identification unit 138 identifies, for example, a salesperson who has the second activity amount and whose sales amount in a predetermined period does not satisfy the above-mentioned predetermined condition as the second salesperson.

[0127] The output unit 140 then outputs presentation information indicating the second sales representative, as shown in FIG. 18 . FIG. 18 is a diagram showing an example of presentation information indicating the second sales representative who needs to improve their behavior. By referring to this presentation information, a sales representative who has a sufficient sales volume but whose sales performance is poor due to poor sales methods can understand that they should improve their sales methods (activity quality). As a result, the sales representative can efficiently improve their sales performance.

[0128] Furthermore, as shown in FIG. 18 , the output unit 140 may output presentation information associated with a second sales representative, indicating the sales method that has been used the most frequently or for the most amount of time by a first sales representative whose sales activity volume is equivalent to that of the second sales representative (within a predetermined range). The "sales method to be used" in FIG. 18 refers to the sales method that has been used the most frequently or for the most amount of time by the first sales representative, and is the sales method that the second sales representative should use more frequently than currently. For example, the output unit 140 may output presentation information indicating the sales method that has the highest average number of times or average amount of time that multiple first sales representatives have used each sales method over a predetermined period. By referring to this presentation information, a sales representative who has sufficient sales volume but poor sales performance due to poor sales methods can determine which specific sales method they should use more frequently to improve their sales performance. As a result, the sales representative can efficiently improve their sales performance.

[0129] Fifth Embodiment: Improvement of Estimation Accuracy of Location of Information Terminal As a fifth embodiment, a process for improving the estimation accuracy of the location of the information terminal 2 will be described.

[0130] [Processing Overview] The information processing device 1 may have a function to improve the accuracy of estimating the position of the information terminal 2. A salesperson may go on sales trips to places (underground, deep in the mountains, rural areas, overseas, etc.) where radio waves for using a satellite positioning system or a communication line cannot reach the information terminal 2. If a customer is located in such a place, it becomes difficult for the information processing device 1 to acquire behavior history data indicating the relationship between the position and time of the information terminal when the salesperson is visiting the customer, and it also becomes difficult to identify trends in the relationship between performance data and behavior history data.

[0131] Therefore, if there is an unrecorded time period in which the position was not recorded because the information terminal 2 was unable to receive radio waves for using a satellite positioning system or a communication line, the information processing device 1 estimates the position and time of the information terminal 2 during the unrecorded time period based on the position and time acquired immediately before the unrecorded time period and the position and time acquired immediately after the unrecorded time period.In this case, the information processing device 1 uses acceleration data to estimate the time of the information terminal 2 during the unrecorded time period to be a time included in the time period in which the sales representative was stationary.

[0132] Furthermore, by using the acceleration data, the information processing device 1 estimates the means of transportation used by the sales representative before and after the time period when the sales representative was stationary. Next, by using the average speed of the estimated means of transportation, the information processing device 1 estimates the distance D0 from the position P0 of the information terminal 2 immediately before the unrecorded time period to the position to be estimated, and the distance D1 from the position to be estimated to the position P1 of the information terminal 2 immediately after the unrecorded time period. Then, the information processing device 1 estimates the position of the information terminal 2 during the unrecorded time period based on P0 and P1, and D0 and D1.

[0133] In this way, when there is an unrecorded time period in which the location of the information terminal 2 is not recorded, the information processing device 1 estimates the location and time of the information terminal 2 during the unrecorded time period. This allows the information processing device 1 to identify trends in the relationship between the performance data and behavior history data of salespeople who visit locations with poor radio wave reception, and output presentation information indicating the identified trends. By referring to the presentation information indicating these trends, other salespeople can efficiently sell to customers even in locations with poor radio wave reception. This results in improved sales performance for the entire organization.

[0134] [Processing Executed by Control Unit] Fig. 19 is a diagram showing the configuration of the control unit 13 in the fifth embodiment. The control unit 13 in the fifth embodiment differs from the control unit 13 in the first embodiment in that it further includes an estimation unit 139. As the processing executed by the control unit 13 in the fifth embodiment, processing executed by the estimation unit 139 and the output unit 140 will be described.

[0135] If there is an unrecorded time period in the behavioral history data where the location was not recorded because the information terminal 2 was unable to receive radio waves for using a satellite positioning system or a communication line, the estimation unit 139 estimates the estimated location and estimated time of the information terminal 2 during the unrecorded time period based on the first location and first time of the information terminal 2 immediately before the unrecorded time period and the second location and second time of the information terminal 2 immediately after the unrecorded time period.

[0136] For example, the estimation unit 139 identifies, in the behavior history data, a time closest to the unrecorded time period among times before the unrecorded time period, as the first time period. For example, the estimation unit 139 identifies, in the behavior history data, a location associated with the identified first time period, as the first location.

[0137] For example, the estimation unit 139 identifies, in the behavior history data, a time closest to the unrecorded time period among times after the unrecorded time period, as the second time period. For example, the estimation unit 139 identifies, in the behavior history data, a location associated with the identified second time period, as the second location.

[0138] The estimation unit 139 calculates the moving speed by, for example, dividing the distance between the first position and the second position by the time difference between the first time and the second time. Then, the estimation unit 139 estimates the estimated time by, for example, adding the elapsed time since the first time to the first time. Furthermore, the estimation unit 139 estimates the estimated position by, for example, adding the distance obtained by multiplying the calculated moving speed by the elapsed time since the first time to the first position.

[0139] If the behavioral history data acquisition unit 132 further acquires acceleration data indicating the acceleration of the information terminal 2, the estimation unit 139 estimates the position and time of the information terminal 2 between the first time and the second time based on the first position and the second position and the acceleration data corresponding to the period from the first time to the second time.

[0140] The estimation unit 139, for example, identifies the mode of transportation by determining which mode of transportation acceleration data corresponds to the acceleration data corresponding to the period from the first time to the second time. Then, the estimation unit 139 estimates, for example, the first time plus the elapsed time since the first time as the estimated time. Furthermore, the estimation unit 139 estimates, for example, the first position plus the distance calculated by multiplying the travel speed of the identified mode of transportation by the elapsed time since the first time as the estimated position.

[0141] However, sales representatives do not always travel at a constant speed, and may change their means of transportation along the way (for example, changing from car to walking). Therefore, the estimation unit 139 may identify the sales representative's means of transportation between the first time and the second time, and then estimate the estimated time and estimated location. Figure 20 is a diagram for explaining a method for identifying the sales representative's means of transportation.

[0142] The estimation unit 139 identifies the position information and time information that were acquired immediately before the unrecorded time period as the first position (denoted as P0) and the first time (T0 in FIG. 20). The estimation unit 139 also determines the start point of the time that indicates that the information terminal 2 was stationary in the acceleration data as the stay start time (T0 in FIG. 20), which is the time when the salesperson started to stay at the customer's location. Ｓ ) is estimated.

[0143] The estimation unit 139 estimates the time from the first time (T0 in FIG. 20) to the stay start time (T Ｓ ) and identifies the means of transportation of the sales representative from the first time to the stay start time. Specifically, the estimation unit 139 identifies the means of transportation by determining to which means of transportation the acceleration data corresponding to the period from the first time to the stay start time is similar. In this example, the estimation unit 139 identifies the means of transportation as a car ("travel by car" in FIG. 20), and the average speed of the car is Va.

[0144] The estimation unit 139 calculates the end point of the time when the information terminal 2 was stationary in the acceleration data as the stay end time (T in FIG. 20 ) which is the time when the salesperson ended their stay at the customer's location. ｅThe estimation unit 139 estimates that the time between the stay start time and the stay end time is the time when the sales representative was stationary ("stationary" in FIG. 20).

[0145] The estimation unit 139 estimates the stay end time (T in FIG. 20 ). ｅ 20), the position information and time information that can be acquired immediately after the second position (P1) and the second time (T1 in FIG. 20) are identified as a second position (P1) and a second time (T1 in FIG. 20).

[0146] The estimation unit 139 estimates the stay end time (T in FIG. 20 ). ｅ ) to a second time (T1 in FIG. 20 ), the estimation unit 139 identifies the sales representative's mode of transportation from the stay end time to the second time. Specifically, the estimation unit 139 identifies the mode of transportation by determining to which mode of transportation the acceleration data from the stay end time to the second time is similar. In this example, the estimation unit 139 identifies the mode of transportation as walking ("traveling by foot" in FIG. 20 ), and the average walking speed is Vw.

[0147] Next, the estimation unit 139 specifies the distance (D0) from the first position (P0) to the stay position (P) to be estimated. For example, the estimation unit 139 specifies the distance (D0) from the first time (T0) corresponding to the first position to the stay start time (T Ｓ The estimation unit 139 determines the distance D0 based on the average speed Va of the car, which is the sales means used by the salesperson from the stay start time T Ｓ D0 is determined based on the calculation formula: D0=(D1 / D2)−(first time T0)

[0148] The estimation unit 139 specifies the distance (D1) from the position (P) to be estimated to the second position (P1). ｅ The estimation unit 139 determines the distance D1 based on the average walking speed Vw, which is the sales means used by the salesperson from the first time T1 to the second time T2 corresponding to the second location. Specifically, the estimation unit 139 calculates the distance D1 by calculating "D1 = average walking speed Vw × (second time T1 - stay end time T ｅ D1 is determined based on the calculation formula:

[0149] The estimation unit 139 estimates the desired time based on, for example, a first position (P0) and a second position (P1), a distance (D0) from the first position to the desired position to be estimated, and a distance (D1) from the desired position to the second position. The estimation unit 139 calculates the time by multiplying the difference between the second time (T1) and the first time (T0) by the proportion of the distance (D0) from the first position to the desired position to be estimated to the sum of the distance (D0) from the first position to the desired position to be estimated and the distance (D1) from the desired position to the second position. That is, the estimation unit 139 performs the calculation "(T1-T0) x (D0 / (D0+D1))." The estimation unit 139 then calculates the estimated time by adding the multiplied time to the first time (T0).

[0150] The estimation unit 139 estimates the desired position (P) based on, for example, a first position (P0) and a second position (P1), the distance (D0) from the first position to the desired position to be estimated, and the distance (D1) from the desired position to the second position, using, for example, one of the following three methods:

[0151] The first method is a method of estimating a desired position (P) using a line segment P0P1. Fig. 21 is a diagram for explaining a method of estimating a desired position using a line segment. As shown in Fig. 21, for example, the estimation unit 139 estimates a point obtained by dividing the line segment P0P1 proportionally at a ratio of D0:D1 as the desired position (P).

[0152] The second method is a method of estimating a desired location (P) using a circle with a center P0 and a circle with a center P1. Fig. 22 is a diagram for explaining a method of estimating a desired location using circles. As shown in Fig. 22, the estimation unit 139 estimates, as the desired location (P), the point closest to the customer's location among the intersections (P, P') of a circle with a center P0 and a radius D0 and a circle with a center P1 and a radius D1.

[0153] The third method is a method of estimating a desired position (P) by taking into consideration the topography (roads, railroads, etc.) near the customer. FIG. 23 is a diagram for explaining a method of estimating a desired position by taking into consideration the topography near the customer. As shown in FIG. 23 , for example, the estimation unit 139 estimates a point on the road that is a distance D0 from P0 and a distance D1 from P1 as the desired position (P).

[0154] In this way, if there is an unrecorded time period in which the location of the information terminal 2 is not recorded, the estimation unit 139 estimates the location and time of the information terminal 2 during the unrecorded time period. As a result, the trend identification unit 133 can also identify the trend in the relationship between the performance data and the behavior history data of sales representatives who visit locations where radio waves are difficult to reach, and the output unit 140 can output presentation information indicating the identified trend. By referring to this presentation information, other sales representatives can make efficient sales even to customers located in locations where radio waves are difficult to reach. As a result, the sales performance of the entire organization improves.

[0155] The present invention has been described above using embodiments, but the technical scope of the present invention is not limited to the scope described in the above embodiments, and various modifications and changes are possible within the scope of the gist of the present invention. For example, all or part of the device can be configured by functionally or physically distributing or integrating in any unit. Furthermore, new embodiments resulting from any combination of multiple embodiments are also included in the embodiments of the present invention. The effects of the new embodiments resulting from the combination also have the effects of the original embodiments.

[0156] REFERENCE SIGNS LIST 1 Information processing device 11 Device communication unit 12 Memory unit 13 Control unit 131 Performance data acquisition unit 132 Behavioral history data acquisition unit 133 Trend identification unit 134 Customer identification unit 135 Score calculation unit 136 Sales behavior identification unit 137 Sales method identification unit 138 Salesperson identification unit 139 Estimation unit 140 Output unit 2 Information terminal 21 Terminal communication unit 22 Display unit 23 Memory unit 24 Control unit 241 Display processing unit 242 Recording unit 3 Performance management system S Information processing system

Claims

1. A performance data acquisition unit that acquires performance data indicating the business performance of a plurality of sales representatives, an action history data acquisition unit that acquires, in association with each of the plurality of sales representatives, action history data indicating the relationship between the position and time of the information terminal used by the sales representative, a trend identification unit that identifies the trend of the relationship between the performance data and the action history data, and an output unit that outputs presentation information indicating the identified trend. An information processing apparatus having these components.

2. The performance data includes information on the products sold by the sales representative. The trend identification unit identifies the trend of the relationship between the performance data of the sales representative corresponding to the performance data that satisfies a predetermined condition, which is associated with the genre of the product included in the performance data, and the action history data. The output unit outputs the presentation information indicating the trend for each genre of product. The information processing apparatus according to claim 1.

3. The performance data includes attribute information of the customers to whom the sales representative sold the products. The trend identification unit identifies the trend of the relationship between the performance data of the sales representative corresponding to the performance data that satisfies a predetermined condition, which is associated with the attribute of the customer included in the performance data, and the action history data. The output unit outputs the presentation information indicating the trend for each attribute of the customer. The information processing apparatus according to claim 1.

4. The trend identification unit refers to geofence data in which each of a plurality of geofences, which are virtual location areas, is associated with a customer, identifies the customer corresponding to the geofence, and identifies the stay time at the position of the customer based on the time associated with the position of the customer in the action history data, thereby identifying the trend of the relationship between the performance data and the stay time. The information processing apparatus according to claim 1.

5. The information processing apparatus further includes a customer identification unit that identifies customers for whom business activities are effective for each business means by extracting business means and customers in which the relationship between the amount of business activities or the length of activity time for the customers indicated by the action history data and the sales amount for the customers indicated by the performance data is a positive correlation, and a score calculation unit that calculates, for each customer, a total value of scores assigned based on a result of comparing at least one of an unvisited period or an uncontacted period for the customer included in the customer purchase data indicating the customer's product purchase status, a period elapsed from the date when the customer last purchased a product, the frequency of product purchases by the customer, and the amount of the product purchased by the customer with a predetermined standard. The output unit outputs the presentation information including a list in which the customers identified by the customer identification unit are arranged in descending order of the total value. The information processing apparatus according to claim 1.

6. The information processing apparatus further includes a customer identification unit that, among customers whose sales amount specified based on the performance data is less than a predetermined threshold, identifies customers having the same attributes as customers whose sales amount is equal to or greater than the predetermined threshold by referring to customer management data in which customers and their attributes are associated, and identifies the identified customers as priority customers for whom future business activities should be preferentially conducted. The output unit outputs the presentation information indicating the priority customers. The information processing apparatus according to claim 1.

7. The tendency identification unit identifies a tendency of the relationship between the performance data of the salesperson corresponding to the performance data satisfying a predetermined condition for the priority customers and the action history data. The information processing apparatus according to claim 6.

8. The action history data further includes a communication history executed on the information terminal, and the information processing apparatus includes: a business action specifying unit that specifies a business action history indicating the number of times or the time of execution of at least any one of business means such as customer visits by the salesperson, web conferencing means with the customer by the salesperson, telephone calling means to the customer, and message sending means to the customer based on the action history data; a business means specifying unit that specifies, as effective business means for the first customer, business means that are executed more times than a plurality of business means included in the business action history for the first customer of the first salesperson corresponding to the performance data satisfying a predetermined condition among the plurality of business means included in the business action history for the first customer of the second salesperson corresponding to the performance data not satisfying the predetermined condition; and an output unit that outputs the presentation information indicating the business means specified by the business means specifying unit in association with the first customer. The information processing apparatus according to claim 1.

9. The action history data further includes a communication history executed on the information terminal, and the information processing apparatus includes: a business action specifying unit that specifies a business action history indicating the number of times or the time of execution of at least any one of business means such as customer visits by the salesperson, web conferencing means with the customer by the salesperson, telephone calling means to the customer, and message sending means to the customer based on the action history data; a salesperson specifying unit that specifies a second activity amount that is a difference within a predetermined range from a first activity amount that is the number of times or the amount of time of execution of business means by a first salesperson whose performance data satisfies a predetermined condition, and a second salesperson whose performance data does not satisfy the predetermined condition; and an output unit that outputs the presentation information indicating the second salesperson. The information processing apparatus according to claim 1.

10. The output unit outputs the presentation information indicating the business means with the most number of times or the most time of execution by the first salesperson in association with the second salesperson. The information processing apparatus according to claim 9.

11. The information processing apparatus according to claim 1, further comprising an estimation unit that, when there is an unrecorded time period in the action history data during which the information terminal could not receive radio waves for using a satellite positioning system or a communication line and thus the position was not recorded, estimates the estimated position and estimated time of the information terminal during the unrecorded time period based on the first position and first time of the information terminal immediately before the unrecorded time period and the second position and second time of the information terminal immediately after the unrecorded time period.

12. The action history data acquisition unit further acquires acceleration data indicating the acceleration of the information terminal, and the estimation unit estimates the position and time of the information terminal from the first time to the second time based on the first position and the second position and the acceleration data corresponding to the period from the first time to the second time. The information processing apparatus according to claim 11.

13. An information processing method, comprising: a performance data acquisition step of acquiring performance data indicating the business performance of a plurality of sales representatives, which is executed by a computer; an action history data acquisition step of acquiring action history data indicating the relationship between the position and time of an information terminal used by each of the plurality of sales representatives, in association with each of the plurality of sales representatives; a trend identification step of identifying the trend of the relationship between the performance data and the action history data; and an output step of outputting presentation information indicating the identified trend.

14. An information processing system, comprising: an information processing apparatus and an information terminal capable of communicating with the information processing apparatus, wherein the information processing apparatus includes: a performance data acquisition unit that acquires performance data indicating the business performance of a plurality of sales representatives; an action history data acquisition unit that acquires action history data indicating the relationship between the position and time of the information terminal used by each of the plurality of sales representatives, in association with each of the plurality of sales representatives; a trend identification unit that identifies the trend of the relationship between the performance data and the action history data; and an apparatus communication unit that transmits presentation information indicating the identified trend to the information terminal, and the information terminal includes: a terminal communication unit that receives the presentation information transmitted from the information processing apparatus; and a display unit that displays the presentation information.

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