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

Abstract

To propose an efficient sales method.SOLUTION: An information processing apparatus 1 includes: a result data acquisition unit 131 which acquires result data showing sales results of a plurality of salespersons; a behavioral history data acquisition unit 132 which acquires behavioral history data showing relations between times and positions of information terminals used by the salespersons, in association with the respective salespersons; a tendency specification unit 133 which specifies a tendency of relations between the result data and the behavioral history data; and an output unit 140 which outputs presentation information indicating the specified tendency.SELECTED DRAWING: Figure 10

Description

[Technical Field]

[0001] This invention relates to an information processing device, an information processing method, and an information processing system. [Background technology]

[0002] Conventionally, route sales support systems that streamline sales activities involving route visits are known (see, for example, Patent Document 1). [Prior art documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2023-43401 [Overview of the project] [Problems that the invention aims to solve]

[0004] In organizations that conduct route sales, improving the overall sales performance requires analyzing which clients high-performing sales representatives visit, when they visit them, what they propose, the results, and what actions can be taken to improve closing rates and sales. This analysis should then be shared company-wide. However, in the past, this information sharing was lacking, resulting in a significant number of inefficient sales representatives within the organization.

[0005] Therefore, the present invention has been made in view of these points, and aims to propose an efficient way of conducting business. [Means for solving the problem]

[0006] An information processing device according to a first aspect of the present invention includes: a performance data acquisition unit that acquires performance data showing the sales performance of a plurality of sales representatives; an activity history data acquisition unit that acquires activity history data showing the relationship between the location and time of the information terminal used by each of the plurality of sales representatives in association with the respective sales representatives; a trend identification unit that identifies the trend in the relationship between the performance data and the activity history data; and an output unit that outputs presentation information showing the identified trend.

[0007] The performance data may include information on products sold by the sales representative, the trend identification unit may identify the trend in the relationship between the sales representative's performance data and behavioral history data that corresponds to the performance data that satisfies predetermined conditions and is associated with the product genre included in the performance data, and the output unit may output the presentation information showing the trend by product genre.

[0008] The performance data may include attribute information of customers to whom the sales representative has sold products, the trend identification unit may identify the trend in the relationship between the sales representative's performance data and behavioral history data that corresponds to the performance data that satisfies predetermined conditions and is associated with the customer attributes included in the performance data, and the output unit may output the presentation information showing the trend for each customer attribute.

[0009] The trend identification unit may identify customers corresponding to a geofence by referring to geofence data in which each of a plurality of geofences, which are virtual location areas, is associated with a customer, and identify the customer's stay time at that 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 stay time.

[0010] The information processing device may further include: a customer identification unit that 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 amount or duration of sales activities for a customer indicated by the behavioral history data and the sales amount for that customer indicated by the performance data; and a score calculation unit that calculates a total score for each customer based on the result 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 since the customer last purchased a product, the frequency of the customer purchasing a product, and the amount of the product purchased by the customer—with a predetermined standard; and the output unit may output the presentation information including a list of customers identified by the customer identification unit sorted in descending order of the total score.

[0011] The information processing device may further include a customer identification unit that identifies customers whose sales amount is below a predetermined threshold, based on the performance data, and which have the same attributes as customers whose sales amount is above the predetermined threshold, by referring to customer management data that associates customers with customer attributes, and identifies the identified customers as priority customers for future sales activities, and the output unit may output the presentation information indicating the priority customers.

[0012] The trend identification unit may identify trends in the relationship between the performance data and behavioral history data of the sales representative corresponding to the performance data that meets predetermined conditions with respect to the preferred customer.

[0013] The behavioral history data may further include communication history performed on the information terminal, and the information processing device may further include: a sales behavior identification unit that identifies a sales behavior history indicating the number of times or duration that at least one of the sales means of visiting a customer by the sales representative, a means of making a web call with the customer by the sales representative, a means of making a telephone call to the customer, and a means of sending a message to the customer was performed based on the behavioral history data; and a sales means identification unit that identifies a sales means that is effective for the first customer among a plurality of sales means included in the sales behavior history of the first sales representative for the first customer corresponding to the performance data that satisfies predetermined conditions, which is performed more often than a plurality of sales means included in the sales behavior history of the second sales representative for the first customer corresponding to the performance data that does not satisfy predetermined conditions, and the output unit may output the presentation information that associates the sales means identified by the sales means identification unit with the first customer.

[0014] The behavioral history data may further include communication history performed on the information terminal, and the information processing device may further include a sales behavior identification unit that identifies a sales behavior history indicating the number of times or duration of sales actions performed by at least one of the following based on the behavioral history data: visiting a customer, making a web call with a customer, making a telephone call to a customer, and sending a message to a customer; and a sales representative identification unit that identifies a second sales representative whose performance data does not meet the predetermined conditions, and whose second activity amount is within a predetermined range of difference from a first activity amount which is the number of times or duration of sales actions performed by a first sales representative whose performance data meets predetermined conditions, and whose performance data does not meet the predetermined conditions, and the output unit may output the presentation information indicating the second sales representative.

[0015] The output unit may output the presentation information, associated with the second sales representative, indicating the sales method that the first sales representative performed most frequently or for the most time.

[0016] The information processing device may further include an estimation unit that estimates the estimated position and estimated time of the information terminal during an unrecorded period in the activity history data, based on the first position and first time of the information terminal immediately before the unrecorded period, and the second position and second time of the information terminal immediately after the unrecorded period.

[0017] The behavior 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 between the first time and the second time.

[0018] A second aspect of the present invention relates to an information processing method comprising: a performance data acquisition step, which is performed by a computer to acquire performance data showing the sales performance of a plurality of sales representatives; an activity history data acquisition step, which is associated with each of the plurality of sales representatives to acquire activity history data showing the relationship between the location and time of the information terminal used by the sales representative; a trend identification step, which identifies the trend in the relationship between the performance data and the activity history data; and an output step, which outputs presentation information showing the identified trend.

[0019] An information processing system according to a third aspect of the present invention includes an information processing apparatus and an information terminal communicable with the information processing apparatus. The information processing apparatus includes: a performance data acquisition unit that acquires performance data indicating the business performance of a plurality of salespersons; a behavior history data acquisition unit that acquires behavior history data indicating the relationship between the position and time of the information terminal used by each of the plurality of salespersons, associated with each salesperson; a tendency identification unit that identifies a tendency of the relationship between the performance data and the behavior history data; and a device communication unit that transmits presentation information indicating the identified tendency to the information terminal. 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.

Effect of the Invention

[0020] According to the present invention, there is an effect that an efficient sales method can be proposed.

Brief Description of the Drawings

[0021] [Figure 1] It is a diagram showing the configuration of the information processing system S. [Figure 2] It is a diagram showing the configuration of the information processing apparatus 1. [Figure 3] It is a diagram showing an example of performance data. [Figure 4] It is a diagram showing an example of behavior history data. [Figure 5] It is a diagram showing an example of product management data. [Figure 6] It is a diagram showing an example of customer management data. [Figure 7] It is a diagram showing an example of geofence data. [Figure 8] It is a diagram showing an example of customer purchase data. [Figure 9] It is a diagram showing the configuration of the information terminal 2. [Figure 10] It is a diagram showing the configuration of the control unit in the first embodiment. [Figure 11] It is a flowchart showing the flow of processing executed by the control unit in the first embodiment. [Figure 12] This figure shows the configuration of the control unit in the second embodiment. [Figure 13] This figure shows an example of a list of customers that should be prioritized for sales. [Figure 14] This figure shows an example of dividing customers into four groups based on their sales amount and potential. [Figure 15] This figure shows the configuration of the control unit in the third embodiment. [Figure 16] This figure shows an example of information presented to indicate effective sales methods for different customer groups. [Figure 17] This figure shows the configuration of the control unit in the fourth embodiment. [Figure 18] This figure shows an example of information presented to indicate a second sales representative whose behavior needs improvement. [Figure 19] This is a diagram showing the configuration of the control unit in the fifth embodiment. [Figure 20] This diagram illustrates the identification of the means of transportation used by sales representatives. [Figure 21] This diagram illustrates a method for estimating a desired position using line segments. [Figure 22] This diagram illustrates a method for estimating a desired position using circles. [Figure 23] This diagram illustrates a method for estimating a location by considering the terrain near the customer. [Modes for carrying out the invention]

[0022] [Overview of Information Processing System S] The outline of the information processing system S according to this embodiment will be explained using Figure 1. Figure 1 is a diagram showing the configuration of the information processing system S. The information processing system S comprises an information processing device 1 and an information terminal 2. The information processing system S may operate in cooperation with the performance management system 3. The information processing system S may also include other equipment such as servers and terminals. The information processing system S is a system that identifies trends in the relationship between the sales performance of sales representatives obtained from the performance management system 3 and the behavioral history of sales representatives obtained from the information terminal 2, and outputs presentation information showing the identified trends, thereby enabling sales representatives to understand how to conduct sales efficiently. In other words, the information processing system S proposes sales methods that are efficient and improve the closing rate and sales.

[0023] Information processing device 1 is a computer such as a server that identifies trends in the relationship between a sales representative's sales performance and their behavioral history, and outputs 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 practices. To improve the overall sales performance of the organization, it is important to analyze what actions high-performing sales representatives take, and conversely, what actions low-performing sales representatives take, and to share the results of this analysis throughout the organization.

[0024] However, organizations that have traditionally lacked this information sharing have faced the problem of stagnant overall sales performance. Therefore, the information processing device 1 identifies trends in the relationship between the sales performance of multiple sales representatives obtained from the performance management system 3 and the behavioral history showing the relationship between the location and time of the information terminal 2 used by the sales representatives, obtained from multiple information terminals 2, and outputs presentation information showing the identified trends.

[0025] The information processing device 1 is connected to multiple information terminals 2 and a performance management system 3 via a communication network such as the Internet. The information terminals 2 are used by sales representatives and can communicate with the information processing device 1. The information terminals 2 are portable devices 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 trends in the relationship between sales performance and behavioral history, and outputs information indicating the identified trends. By referring to this 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 refine their sales activities. As a result, the overall sales performance of the organization improves.

[0027] [Configuration of Information Processing Device 1] Next, the configuration of the information processing device 1 will be described. Figure 2 is a diagram showing the configuration of the information processing device 1. As shown in Figure 2, the information processing device 1 comprises 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 information such as presentation information showing trends in the relationship between performance data and behavioral history data to the information terminal 2.

[0029] The memory unit 12 is a storage medium including ROM (Read Only Memory) and RAM (Random Access Memory). The memory unit 12 stores the program that the control unit 13 executes. For example, the memory unit 12 stores an information processing program that causes the control unit 13 to function as a performance data acquisition unit 131, an activity history data acquisition unit 132, a trend identification unit 133, a customer identification unit 134, a score calculation unit 135, a sales activity 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 memory unit 12 stores performance data, behavioral history data, product management data, customer management data, geofence data, geofence location data, customer purchase data, and acceleration data.

[0031] Figure 3 shows an example of performance data. This performance data represents the sales performance of multiple sales representatives. In the performance data shown in Figure 3, the sales representative, sales date, product ID, customer ID, and sales amount are associated.

[0032] The product ID is information used to identify the product that a sales representative sold to a customer. The customer ID is information used to identify the customer from whom a product was sold. Although not shown in the diagram, performance data may also include expenses incurred in sales activities and gross profit (sales minus expenses) associated with the sales representative.

[0033] Figure 4 shows an example of activity history data. Activity history data is data that shows the relationship between the location and time of each information terminal 2 used by multiple sales representatives. In the activity history data shown in Figure 4, the sales representative, their location, the time, and the history of communications conducted by the sales representative are associated. In the activity history data shown in Figure 4, it is recorded when and where each sales representative was. For example, in Figure 4, sales representative "Suzuki" was at location P1 at time T1.

[0034] The location of the sales representative is the location of the information terminal 2, determined by the information terminal 2 based on radio waves received from a satellite transmitting radio waves for determining longitude and latitude, and is defined, for example, by a combination of longitude and latitude. The sales representative's location information may be obtained using a mobile phone antenna or Wi-Fi®. The time is the time when the information terminal 2 determined the location. The location and time are transmitted from the information terminal 2 to the information processing device 1 at any time or at predetermined time intervals (for example, every minute).

[0035] The communication history is a record of communications performed on information terminal 2. Examples of communication history include web call history with customers, telephone call history to customers, and message sending history to customers.

[0036] Figure 5 shows an example of product management data. In the product management data shown in Figure 5, the product ID and the product category are associated. The product ID is an ID used to identify a product. The product category may be a broad category indicating the industry, a subcategory indicating the product classification within a specific industry, or a minor category indicating differences in specifications within a specific product classification.

[0037] Figure 6 shows an example of customer management data. In customer management data, customer IDs and customer attributes are associated. The customer ID is an ID used to identify a customer. Customer attributes include, for example, regional characteristics, company size, industry, and products / services.

[0038] Figure 7 shows an example of geofence data. Geofence data is data that associates a customer with each of several geofences, which are virtual location areas. A geofence is a geographical area enclosed by a virtual boundary. In the geofence data shown in Figure 7, the customer ID, geofence ID, latitude, and longitude are associated. The customer ID is information used to identify the geofence. The latitude is the latitude of the customer's company location. The longitude is the longitude of the customer's company location. A geofence can be represented, for example, by a circular area. In this case, the geofence is defined as a circle with a radius of a predetermined length (e.g., 100m) 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] Figure 8 shows an example of customer purchase data. Customer purchase data is data that shows a customer's product purchase status. In the customer purchase data shown in Figure 8, the period of no visit or no contact, the elapsed time, the purchase frequency, and the purchase amount are associated. The period of no visit or no contact is the period during which a sales representative has not visited the customer or has not made contact through any sales means other than a visit. The elapsed time is the period since the last time the customer purchased a product. The purchase frequency is the average frequency of the customer purchasing a product. The purchase amount is the amount of money spent on products purchased by the customer, for example, the cumulative amount of money spent on products purchased by the customer.

[0040] The acceleration data represents the acceleration of information terminal 2. The acceleration data is associated with the terminal ID used to identify information terminal 2, the time, and the acceleration.

[0041] The control unit 13 is, for example, a CPU (Central Processing Unit). By executing the information processing program stored in the memory unit 12, the control unit 13 functions as a performance data acquisition unit 131, an activity history data acquisition unit 132, a trend identification unit 133, a customer identification unit 134, a score calculation unit 135, a sales activity 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 performed by each unit will be described later.

[0042] [Configuration of Information Terminal 2] Next, the configuration of the information terminal 2 will be described. Figure 9 is a diagram showing the configuration of the information terminal 2. As shown in Figure 9, the information terminal 2 comprises 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 to the information processing device 1 activity history, etc., showing the relationship between the location and time of the information terminal 2 used by the sales representative. The terminal communication unit 21 also receives presentation information, etc., showing trends in the relationship between performance data and activity history data.

[0044] The display unit 22 is composed of, for example, a liquid crystal display or an organic EL (Electro-Luminescence) display. The display unit 22 displays various information in accordance with the control of the display processing unit 241. The display unit 22 may have a touch panel and may accept data input from the user. The display unit 22 displays presentation information such as trends in the relationship between performance data and behavioral history data.

[0045] The memory unit 23 is a storage medium including ROM and RAM. The memory unit 23 stores the program that the control unit 24 will execute. For example, the memory 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 functions as a display processing unit 241 and a recording unit 242 by executing a program stored in the storage unit 23.

[0047] The display processing unit 241 displays various types of information on the display unit 22. The display processing unit 241 receives information such as the trend in the relationship between performance data and behavioral history data from the information processing device 1 via the terminal communication unit 21, and then displays this 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 an activity history, and transmits the recorded activity history to the information processing device 1 via the terminal communication unit 21.

[0049] The following describes in detail the various functions provided by the information processing system S.

[0050] <First Embodiment: Identification of Trends in the Relationship between Sales Performance and Behavioral History> As a first embodiment, we will describe the basic operation of identifying trends in the relationship between performance data and behavioral history data.

[0051] [Process Overview] Referring to Figure 1, the overview of the processing performed by the information processing device 1 will be explained. Information terminal 2 records the relationship between the location and time of the information terminal 2 used by the sales representative as an activity history, and transmits the recorded activity 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 associates it with each of the multiple sales representatives and obtains activity history data showing the relationship between the location and time of the information terminal 2 used by the sales representative.

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

[0053] Details of the trends in the relationship between sales performance and behavioral history will be described later, but for example, these trends include the relationship between sales performance rank and the number of customer visits, the length of visits, or the proportion of multiple sales methods used during a predetermined period (e.g., one month). The information processing device 1 may identify trends by product genre or by customer attributes.

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

[0055] [Processing executed by the control unit] The processes performed by the control unit in the first embodiment will be described below, specifically the processes performed by the performance data acquisition unit 131, the behavior history data acquisition unit 132, the trend identification unit 133, and the output unit 140. Figure 10 shows the configuration of the control unit in the first embodiment.

[0056] The performance data acquisition unit 131 acquires performance data showing the sales performance of multiple sales representatives. For example, the performance data acquisition unit 131 sends an inquiry to the performance management system 3 to acquire the sales performance of a sales representative. 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 it as performance data (Figure 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, at the beginning of the month).

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

[0058] The trend identification unit 133 identifies trends in the relationship between performance data and 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 multiple sales representatives indicated by the performance data. Based on sales performance, the trend identification unit 133 classifies, for example, the top 25% of sales representatives with the highest sales among multiple sales representatives belonging to an organization into rank A, the top 25% to 75% into rank B, and the bottom 25% into 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 identifies, for example, statistical values ​​(mean or median) of the proportion of sales activities or hours conducted by a specific sales method relative to the total number of sales activities, statistical values ​​of the number of sales activities, hours of sales, or frequency of sales activities conducted by a specific sales method during a predetermined period, or statistical average values ​​of the number of customer visits or the length of visits, for each sales performance rank.

[0060] The trend identification unit 133 identifies customers corresponding to a geofence by referring to geofence data (Figure 7), which associates customers with each of several geofences, which are virtual location areas, in order to determine the length of time a sales representative spent at a customer's location. Subsequently, the trend identification unit 133 determines the time spent at a customer's location by identifying the time spent at that location based on the time associated with the identified customer's location in the behavioral history data.

[0061] Specifically, the trend identification unit 133 identifies the customer's location from among the locations in the behavioral history data (Figure 4) that fall within the area indicated by the geofence data (Figure 7). Subsequently, the trend identification unit 133, for example, refers to the behavioral history data (Figure 4) to determine the customer's stay time at the identified customer location based on the time associated with that location.

[0062] The trend identification unit 133 may identify trends in the relationship between performance data and behavioral history data of sales representatives that meet predetermined conditions. The predetermined conditions are conditions that indicate good sales performance or conditions that indicate poor sales performance. Conditions that indicate good sales performance include, for example, sales amount being above a predetermined threshold, sales amount being within the top XX%, or sales amount being within the top XX people. Conditions that indicate poor sales performance include, for example, sales amount being below a predetermined threshold, sales amount being in the bottom XX%, or sales amount being below the bottom XX people. The predetermined conditions may be set by a manager responsible for managing sales representatives, or they 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 the company, 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 that shows the identified trend. By referring to this presentation information, sales representatives can understand the trend in the relationship between the sales performance and behavioral history of other sales representatives, and thus use this information to improve their own sales activities.

[0065] Furthermore, the trend identification unit 133 identifies trends in the relationship between performance data and behavioral history data of sales representatives with good sales performance under predetermined conditions, and the output unit 140 outputs presentation information indicating the identified trends. By referring to this presentation information, sales representatives can compare the differences between the actions of sales representatives with those of sales representatives with their own actions to understand the shortcomings and areas for improvement in their own sales activities, and to refine their own sales activities. Conversely, the trend identification unit 133 identifies trends in the relationship between performance data and behavioral history data of sales representatives with poor sales performance under predetermined conditions, and the output unit 140 outputs presentation information indicating the identified trends. By referring to this presentation information, sales representatives can take care not to engage in sales activities that are unlikely to improve their sales performance.

[0066] Incidentally, sales representatives may sometimes want to analyze the relationship between sales performance and behavioral history from the perspective of product genre or customer attributes. Therefore, the trend identification unit 133 may identify trends in the relationship between sales performance and behavioral history by product genre or customer attributes, as explained below.

[0067] If the performance data includes information on products sold by sales representatives, the trend identification unit 133 identifies the trend in the relationship between the performance data and behavioral history data of sales representatives that meet predetermined conditions and are associated with the product genres included in the performance data. For example, the trend identification unit 133 refers to product management data (Figure 5) in which product IDs and product genres are associated to identify the product genres associated with the product IDs included in the performance data. Then, the trend identification unit 133 identifies the trend in the relationship between the performance data and behavioral history data for each identified product genre. The output unit 140 outputs presentation information showing the trends identified for each product genre.

[0068] If the performance data includes attribute information of customers to whom sales representatives have sold products, the trend identification unit 133 identifies the trend in the relationship between the performance data and behavioral history data of sales representatives that satisfy predetermined conditions and are associated with the customer attributes included in the performance data. For example, the trend identification unit 133 refers to customer management data (Figure 6) in which customer IDs and customer attributes are associated, to identify customer attributes associated with customer IDs included in the performance data. Then, the trend identification unit 133 identifies the trend in the relationship between the performance data and behavioral history data for each identified customer attribute. The trend identification unit 133 may also identify the trend in the relationship between the performance data and behavioral history data for each specific attribute (for example, company size) among the identified customer attributes. The output unit 140 outputs presentation information showing the trends identified for each customer attribute.

[0069] In this way, the trend identification unit 133 identifies trends in the relationship between sales performance and behavioral history for each product genre or customer attribute, and the output unit 140 outputs presentation information showing the identified trends, so that sales representatives can grasp trends that are appropriate for the product genre of the products they are in charge of selling or the customer attributes of the customers they are in charge of selling. In other words, sales representatives who are in charge of selling multiple products or multiple customers can analyze what kind of sales actions are effective for each product genre or customer attribute. Therefore, it becomes easier for sales representatives to take sales actions that are appropriate for each product genre or customer attribute, and thus they can improve their 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 trends in the relationship between sales representative performance data and behavioral history data corresponding to performance data that meets predetermined conditions for each product genre or for each customer attribute. This allows sales representatives to analyze the sales behavior of other sales representatives who have achieved good sales results for a particular product genre or for customers with a particular customer attribute, thereby enabling them to efficiently improve their own sales performance.

[0071] [Processing flow executed by the control unit] The processing flow performed by the control unit in the first embodiment will be explained with reference to Figure 11. Figure 11 is a flowchart showing the processing flow performed by the control unit in the first embodiment.

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

[0073] The trend identification unit 133 identifies the trend in the relationship between the behavioral history data continuously acquired by the behavioral history data acquisition unit 132 and the performance data acquired by the performance data acquisition unit 131 at a predetermined time (for example, at the beginning of the month) (S3). The trend identification unit 133 may identify trends by product genre or by customer attributes. The trend identification unit 133 may also identify the trend in the relationship between performance data and the time spent at the customer's location. The output unit 140 outputs presentation information indicating the identified trend (S4).

[0074] <Second Embodiment: Identifying Customers Who Should Be Prioritized for Sales> As a second embodiment, we will describe a process for identifying customers who should be prioritized for sales.

[0075] [Process Overview] Information processing device 1 may indicate which customers should be prioritized for sales. Organizations that conduct route sales often have multiple customers. Some customers are highly productive, as sales efforts by sales representatives are likely to lead to purchases or contracts, while others are less productive, making it difficult to achieve purchases or contracts even with sales efforts. However, traditionally, sales representatives were unable to distinguish between highly productive and less productive customers, resulting in them expending the same amount of effort on less productive customers as on highly productive ones. As a result, the organization as a whole became inefficient in its sales efforts. Therefore, information processing device 1 may identify highly productive customers through multivariate analysis or cluster analysis and output information indicating which customers should be prioritized for sales.

[0076] In multivariate analysis, the information processing device 1 identifies customers for each sales method in which there is a positive correlation between the amount or duration of sales activities for that 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 high sales potential, the information processing device 1 outputs information indicating customers corresponding to a positive correlation, enabling sales representatives to understand which customers should be prioritized and which sales methods are appropriate for those customers.

[0077] Furthermore, the information processing device 1 may calculate a total score for each customer based on a comparison of at least one of the following elements included in the customer purchase data, which shows the customer's product purchase status: the period of no visit or contact with the customer, the period elapsed since the last purchase, the purchase frequency, and the purchase amount, with a predetermined standard. The information processing device 1 then outputs presentation information that includes a list of the 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 for sales.

[0078] In cluster analysis, the information processing device 1 identifies customers with low sales figures who share the same attributes as customers with high sales figures, by referring to customer management data that associates customers with their attributes. The information processing device 1 then outputs information indicating that the identified customers should be prioritized for future sales activities.

[0079] In this way, the information processing device 1 identifies customers with high sales potential through multivariate analysis or cluster analysis, and outputs information indicating these identified customers as those to be prioritized for sales. By referring to this information, sales representatives can prioritize sales efforts to customers with high sales potential. As a result, the entire organization can conduct sales more efficiently, leading to an improvement in the overall sales performance of the organization.

[0080] [Processing performed by the control unit in multivariate analysis] Figure 12 shows the configuration of the control unit 13 in the second embodiment. The control unit 13 in the second embodiment differs from that in the first embodiment in that it further includes a customer identification unit 134 and a score calculation unit 135. The processes performed by the control unit in the multivariate analysis of the second embodiment will be described below, specifically the processes performed 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 spent on sales activities 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 identifies a customer's location, for example, by selecting a location from the behavioral history data (Figure 4) that falls within the area indicated by the geofence data (Figure 7). Subsequently, the trend identification unit 133, for example, refers to the behavioral history data (Figure 4) and, based on the time associated with the identified customer's location, identifies the length of time that a sales representative spent making direct sales visits to each customer during a predetermined period.

[0083] Furthermore, the customer identification unit 134, for example, refers to the behavioral history data (Figure 4) and aggregates the times when communication history exists, thereby identifying the length of time that a sales representative spent on sales activities via communication means such as web calls, telephone calls, or message sending for each customer during a predetermined period.

[0084] In this way, the customer identification unit 134 identifies the length of time spent on each sales activity using multiple sales methods, such as in-person sales visits, web-based sales calls, telephone sales calls, and message-based sales, for each customer.

[0085] Furthermore, the customer identification unit 134 identifies the sales amount for each customer by referring to performance data (Figure 3), thereby identifying 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. For example, for each predetermined period or for each sales representative, the customer identification unit 134 plots the length of time spent on sales activities for each of the multiple sales methods for each customer and the sales amount for each customer, and identifies whether there is a positive correlation between the two values ​​(for example, a correlation coefficient greater than 0.4), thereby extracting sales methods and customers with a positive correlation. For example, if the customer identification unit 134 finds a positive correlation between the number of direct visits or the length of activity time for customer A in each month of a certain year and the sales amount for customer A in each month of that year, it extracts customer A as a customer that should be prioritized for sales visits.

[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 direct visits are effective for customer A, while telephone sales are effective for customer B.

[0087] The scoring unit 135 calculates a total score for each customer based on the results of comparing at least one of the following elements included in the customer purchase data, which shows the customer's product purchase status: the period of time the customer has not been visited or contacted, the time elapsed since the last time the customer purchased a product, the frequency with which the customer purchases a product, and the amount of money spent on the products purchased by the customer, with predetermined criteria.

[0088] The score calculation unit 135, for example, refers to customer purchase data (Figure 8) and, for each customer, assigns a predetermined number of points (e.g., 1 point) for each of the following elements it meets, thereby calculating a total score for each customer. (element) • The period of time during which the customer has not been visited or contacted is longer than a specified period (e.g., 3 months). • The period elapsed since the last purchase date is longer than the specified period (e.g., 3 months). • The purchase frequency exceeds a predetermined threshold (e.g., the median frequency). • The purchase amount exceeds a predetermined threshold (e.g., the median amount).

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

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

[0091] Figure 13 shows an example of a list of customers that should be prioritized for sales. As shown, the output unit 140 outputs information that includes a list of customers sorted in order of sales priority. By referring to this information, sales representatives can prioritize sales activities with customers who have high sales priority and for whom sales activities are effective, but for whom they have not been actively engaged. As a result, sales representatives can efficiently improve their sales performance.

[0092] Furthermore, as shown in Figure 13, the output unit 140 may output presentation information including a list of effective sales methods for each customer identified by the customer identification unit 134. By referring to this presentation information, sales representatives can conduct sales activities using sales methods that are effective for that customer. As a result, sales representatives can efficiently improve their sales performance. In addition, sales representatives can eliminate wasted time and effort caused by conducting sales activities using sales methods that are not effective for that customer.

[0093] In the example described above, the scoring unit 135 assigned the same predetermined score for each element a customer met. However, it is also possible to assign weighted scores to important elements. For example, the scoring unit 135 may calculate a correlation coefficient showing the correlation between whether a customer met each of the aforementioned elements at the past first location and the sales amount during the period from the first location to the second location after the first location, which is identified by referring to the performance data (Figure 3). This correlation coefficient may be used as the contribution rate (weighting coefficient) of each element to the sales amount. This calculation may also be performed using machine learning.

[0094] In this way, the score calculation unit 135 calculates the contribution rate of each element to the sales amount, making it easier for customers corresponding to elements that contribute significantly to the sales amount to appear higher in the list shown in Figure 13. As a result, sales representatives can prioritize sales efforts towards customers who will yield greater sales results.

[0095] [Processing performed by the control unit during cluster analysis] In the cluster analysis of the second embodiment, the processes executed by the control unit are described below, specifically the processes executed by the customer identification unit 134 and the output unit 140.

[0096] The customer identification unit 134 identifies customers whose sales amount is below a predetermined threshold, based on performance data, and who have the same attributes as customers whose sales amount is above the predetermined threshold, by referring to customer management data that associates customers with their attributes. The customer identification unit 134 may also identify the identified customers as priority customers for future sales activities.

[0097] The customer identification unit 134, for example, refers to performance data (Figure 3) and calculates the sales amount for each customer for a predetermined period and customer combination. Subsequently, the customer identification unit 134 identifies customers whose calculated sales amount is below a predetermined threshold (for example, less than XX yen, less than the bottom XX%, less than the bottom XX%). On the other hand, the customer identification unit 134 identifies customers whose calculated sales amount is above a predetermined threshold (for example, XX yen or more, within the top XX%, within the top XX%).

[0098] The customer identification unit 134, for example, refers to customer management data (Figure 8) to identify customers whose identified sales amount is below a predetermined threshold and who have the same attributes as customers whose identified sales amount is above a predetermined threshold. Here, "having the same attributes" means that at least one of several attributes (regional characteristics, company size, industry, and products, etc.) is the same.

[0099] The customer identification unit 134 may use data that classifies customers into multiple groups based on a combination of sales volume and potential to identify customers for whom sales activities should be prioritized. Potential is an indicator of the high probability that future sales volume will increase, and it tends to be high when the customer's industry is growing, when the customer's sales growth rate is relatively high, or when there is little past sales activity record.

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

[0101] The output unit 140 then outputs presentation information indicating priority customers identified by the customer identification unit 134. For example, the output unit 140 outputs presentation information that includes a list of high-potential customers (priority customers) identified by the customer identification unit 134. By referring to this presentation information, sales representatives can prioritize sales activities with customers who are more likely to purchase products or close contracts. As a result, sales representatives can efficiently improve their own sales performance. They can also improve the overall sales of the organization.

[0102] Furthermore, the customer identification unit 134 may perform cluster analysis in conjunction with multivariate analysis. That is, in multivariate analysis, the customer identification unit 134 may identify customers who are in the high-potential group (priority customer group) identified by the customer identification unit 134 in cluster analysis, from among the customers included in the list obtained by arranging the customers identified by the customer identification unit 134 in descending order of the total value calculated by the score calculation unit 135, as customers with extremely high sales potential.

[0103] Incidentally, if there are sales representatives who have already achieved good sales results with the priority customers identified in the cluster analysis, the sales behavior of those sales representatives can be a reference for other sales representatives. Therefore, the trend identification unit 133 may identify trends in the relationship between the performance data and behavioral history data of sales representatives corresponding to performance data that meet predetermined conditions with respect to priority customers.

[0104] The trend identification unit 133, for example, refers to performance data (Figure 3) to identify sales representatives whose sales amount for a predetermined period for priority customers is above a predetermined threshold (for example, above XX yen, within the top XX%, within the top XX rank). Then, for the identified sales representatives, the trend identification unit 133 identifies the trend in the relationship between performance data and behavioral history data in the same manner as 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 conduct 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, the overall sales performance for priority customers of the organization improves.

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

[0106] [Process Overview] Organizations that conduct route sales often have multiple customers. Some customers respond well to direct visits, while others respond better to telephone sales. In other words, the most effective sales method varies from customer to customer. However, traditionally, sales representatives were unable to determine which sales method was most effective for a particular customer, and sometimes used ineffective methods. As a result, this led to inefficient sales for the organization as a whole. Therefore, the information processing device 1 may suggest effective sales methods for each customer.

[0107] To present effective sales methods for each customer, the information processing device 1 first identifies sales activity history, which shows the number of times or time spent by each sales representative, based on activity history data that also includes communication history executed at the information terminal 2. Next, the information processing device 1 identifies the sales methods that are performed more frequently by the sales activity history of the high-performing first sales representative for a particular customer than by the sales activity history of the low-performing second sales representative for the same customer, as effective sales methods for that customer. In other words, the information processing device 1 identifies the sales methods that the high-performing first sales representative performs more frequently than the low-performing second sales representative, with respect to a specific customer. Finally, the information processing device 1 outputs presentation information that associates the identified sales methods with the customer in question.

[0108] In this way, the information processing device 1 outputs presentation information that associates an effective sales method with a particular customer. By referring to this presentation information, sales representatives can conduct sales activities with that customer using effective sales methods. As a result, the entire organization can conduct sales more efficiently, leading to an improvement in the overall sales performance of the organization.

[0109] [Processing executed by the control unit] Figure 15 shows 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 activity identification unit 136 and a sales means identification unit 137. The processes executed by the control unit 13 in the third embodiment, specifically the processes executed by the sales activity identification unit 136, the sales means identification unit 137, and the output unit 140, will be described below.

[0110] The sales activity identification unit 136 identifies sales activity history indicating the number of times or duration of sales activities performed by sales representatives, at least one of the following sales activities: visiting customers, making web calls with customers, making telephone calls to customers, and sending messages to customers, based on activity history data that further includes communication history performed on the information terminal 2.

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

[0112] The sales activity identification unit 136, for example, refers to activity history data (Figure 4) and aggregates the time associated with the location of a customer that falls within a range defined by the specified latitude, longitude, and size, thereby identifying the time that a sales representative made a direct sales visit to a customer, for each combination of sales representative, specified period, and customer.

[0113] The communication history includes information on the means of communication as well as information on which customer was contacted and for how long. Therefore, the sales activity identification unit 136, for example, refers to the activity history data (Figure 4) to identify the number of times and the duration of sales activities conducted by each sales representative using each means of communication, for each combination of sales representative, predetermined period, and customer.

[0114] The sales method identification unit 137 identifies a sales method that is effective for the first customer among multiple sales methods included in the sales activity history of the first sales representative for the first customer corresponding to performance data that meets predetermined conditions, which is performed more frequently than multiple sales methods included in the sales activity history of the second sales representative for the first customer corresponding to performance data that does not meet predetermined conditions.

[0115] The sales method identification unit 137, for example, refers to performance data (Figure 3) to identify a sales representative as the first sales representative whose sales amount to the first customer during a predetermined period meets predetermined conditions for the first customer (for example, sales amount is above a predetermined threshold, sales amount is in the top 0%, or sales amount is within the top 0 people, etc.). On the other hand, the sales method identification unit 137, for example, refers to performance data (Figure 3) to identify a sales representative as the second sales representative whose sales amount to the first customer during a predetermined period does not meet the said predetermined conditions.

[0116] Next, the sales method identification unit 137 refers, for example, to the sales activity history identified in the sales activity identification unit 136 to identify the sales methods that the first sales representative has performed more or for longer periods than the second sales representative with respect to the first customer. The identified sales methods are effective sales methods for the first customer.

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

[0118] <Fourth Embodiment: Presentation of Sales Representatives Who Need to Improve Their Behavior> As a fourth embodiment, we will describe a process for identifying sales representatives whose behavior needs improvement.

[0119] [Process Overview] Organizations that conduct route sales typically consist of multiple sales representatives. Among these sales representatives, some have good sales performance, while others do not. Among those with poor sales performance, some have low sales volume, while others have sufficient sales volume but poor sales methods. For the former, increasing sales volume is necessary to improve sales performance. On the other hand, for the latter, since their sales volume is already sufficient, increasing it further would lead to overwork, so improving sales methods is necessary to improve sales performance. In particular, improving the sales methods of sales representatives who have sufficient sales volume but poor sales methods can improve overall sales performance of the organization without lowering the quality of life (QOL) from the sales representative's perspective, and without increasing personnel costs from the employer's perspective. Therefore, the information processing device 1 may identify sales representatives whose behavior needs improvement.

[0120] To identify sales representatives who need to improve their behavior, the information processing device 1 first identifies sales behavior history, which shows the number of times or the time spent performing each sales method, based on behavior history data that also includes communication history performed at the information terminal 2. Next, the information processing device 1 identifies a second sales representative whose sales volume, as shown in the sales behavior history, is equivalent to that of a first sales representative with good sales performance, and 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 sales methods that were performed more frequently or for longer periods by the first sales representative with good sales performance. The presentation information may also include information showing the difference between the content of sales performed by the first sales representative and the content of sales performed by the second sales representative.

[0121] In this way, the information processing device 1 outputs information indicating sales representatives who have a relatively high sales volume but poor sales performance. By referring to this information, sales representatives with poor sales performance can understand that they need to improve their sales methods. Furthermore, if the information includes information on sales methods that high-performing sales representatives have used frequently or for extended periods, sales representatives with poor sales performance can understand which specific sales methods they should use more often to improve their own sales performance. As a result, the entire organization can conduct sales more efficiently, leading to an improvement in the overall sales performance of the organization.

[0122] [Processing executed by the control unit] Figure 17 shows 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 activity identification unit 136 and a sales representative identification unit 138. The processes executed by the control unit 13 in the fourth embodiment, specifically the processes executed by the sales activity identification unit 136, the sales representative identification unit 138, and the output unit 140, will be described below.

[0123] The sales activity identification unit 136 identifies sales activity history indicating the number of times or duration of sales activities performed by a sales representative, at least one of the following: customer visits, web calls with customers, telephone calls to customers, and message transmissions to customers, based on activity history data that further includes communication history performed on the information terminal 2. The sales activity identification unit 136 identifies the number of times or duration of sales activities performed by a sales representative for each combination of sales activity, sales representative, and predetermined period. The method of identification is the same as the method described in the third embodiment, so a detailed explanation is omitted here.

[0124] The sales representative identification unit 138 identifies a second sales representative whose performance data does not meet the predetermined conditions, and whose second activity level is within a predetermined range of difference from the first activity level, which is the number of times or the amount of time spent performing sales actions by the first sales representative whose performance data meets the predetermined conditions. The sales representative identification unit 138, for example, refers to the performance data (Figure 3) and identifies a sales representative as the first sales representative whose sales amount during a predetermined period meets predetermined conditions (for example, sales amount is above a predetermined threshold, sales amount is in the top 0%, and sales amount is within the top 0 people, etc.).

[0125] The Sales Representative Identification Unit 138, for example, refers to the sales activity history identified by the Sales Activity Identification Unit 136 to identify the average cumulative number of times or total time spent on sales activities by multiple first sales representatives over a predetermined period using all sales methods. Specifically, the Sales Representative Identification Unit 138 calculates the cumulative number of times or total time spent on sales activities by a particular first sales representative for a given month of a given year. Then, the Sales Representative Identification Unit 138 calculates the average of the cumulative number of times or total time spent by multiple first sales representatives. This average corresponds to the first activity level.

[0126] The Sales Representative Identification Unit 138 identifies, for example, sales representatives who have a second activity level, which is the cumulative number of sales activities or cumulative sales time that differs from the first activity level within a predetermined range (for example, the cumulative number of sales activities within ±X times, or the cumulative sales time within ±X hours). Furthermore, the Sales Representative Identification Unit 138 identifies, for example, sales representatives whose sales amount for a predetermined period does not meet the aforementioned predetermined conditions by referring to performance data (Figure 3). The Sales Representative Identification Unit 138 identifies, for example, a sales representative who has a second activity level and whose sales amount for a predetermined period does not meet the aforementioned predetermined conditions as a second sales representative.

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

[0128] Furthermore, as shown in Figure 18, the output unit 140 may output suggestion information indicating the sales method that the first sales representative, whose sales activity volume is equivalent to (within a predetermined range) that of the second sales representative, has performed the most frequently or for the most time. In Figure 18, "Sales Method to be Executed" is the sales method that the first sales representative has performed the most frequently or for the most time, and which the second sales representative should perform more frequently than currently. For example, the output unit 140 may output suggestion information indicating the sales method that has the highest average number of times or time spent by multiple first sales representatives performing each sales method over a predetermined period. By referring to this suggestion information, sales representatives whose sales volume is sufficient but whose sales performance is poor due to ineffective sales methods can understand which specific sales methods they should perform more frequently in order to improve their sales performance. As a result, sales representatives can efficiently improve their sales performance.

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

[0130] [Process Overview] The information processing device 1 may have a function to improve the accuracy of estimating the location of the information terminal 2. Sales representatives may visit locations where radio waves for using satellite positioning systems or communication lines cannot reach the information terminal 2 (such as underground, deep in the mountains, rural areas, and overseas). When customers are located in such places, it becomes difficult for the information processing device 1 to acquire behavioral history data showing the relationship between the location and time of the information terminal when the sales representative is visiting the customer, and therefore it also becomes difficult to identify trends in the relationship between performance data and behavioral history data.

[0131] Therefore, if there is a period of time when the information terminal 2 was unable to receive radio waves for using the satellite positioning system or communication lines and thus its position was not recorded, the information processing device 1 estimates the position and time of the information terminal 2 during that period of time of no recording based on the position and time obtained immediately before the period of no recording and the position and time obtained immediately after the period of no recording. In this process, the information processing device 1 uses acceleration data to estimate the time included in the period when the sales representative was stationary as the time of the information terminal 2 during the period of no recording.

[0132] Furthermore, the information processing device 1 uses acceleration data to estimate the means of transportation used by the sales representative before and after the time when the sales representative was stationary. Next, the information processing device 1 uses the estimated average speed of the means of transportation to estimate 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, if there is a period of time when 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 that period. This allows the information processing device 1 to identify trends in the relationship between performance data and behavioral history data of sales representatives who visit locations with poor radio wave reception, and to output information indicating these identified trends. By referring to this trend-indicating information, other sales representatives can conduct more efficient sales activities even with customers located in areas with poor radio wave reception. As a result, the overall sales performance of the organization improves.

[0134] [Processing executed by the control unit] Figure 19 shows 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. The processes performed by the estimation unit 139 and the output unit 140 as processes performed by the control unit 13 in the fifth embodiment will be described below.

[0135] If the estimation unit 139 finds that there are unrecorded time periods in the activity history data where the information terminal 2 was unable to receive radio waves for using satellite positioning systems or communication lines and therefore its location was not recorded, it 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] The estimation unit 139 identifies, for example, the time closest to the unrecorded time period among the time periods prior to the unrecorded time period in the behavioral history data as the first time period. The estimation unit 139 also identifies, for example, the location associated with the identified first time period in the behavioral history data as the first location.

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

[0138] The estimation unit 139 calculates the speed of movement 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. The estimation unit 139 then estimates the estimated time as, for example, the time obtained by adding the elapsed time from the first time to the first time. The estimation unit 139 also estimates the estimated position as, for example, the position obtained by adding the distance obtained by multiplying the calculated speed of movement by the elapsed time from the first time to the first position.

[0139] If the behavior history data acquisition unit 132 acquires further 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, the second position, and the corresponding acceleration data between the first time and the second time.

[0140] The estimation unit 139 identifies a means of transport by determining, for example, which means of transport's acceleration data is closest to the corresponding acceleration data between the first time step and the second time step. The estimation unit 139 then estimates the estimated time as the first time step plus the elapsed time from the first time step. The estimation unit 139 also estimates the estimated position as the first position plus the distance obtained by multiplying the travel speed of the identified means of transport by the elapsed time from the first time step.

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

[0142] The estimation unit 139 identifies the location and time information acquired immediately before the unrecorded time period as the first location (let's call it P0) and the first time (T0 in Figure 20). The estimation unit 139 also identifies the starting point of the time in the acceleration data that the information terminal 2 was stationary as the start time of stay (T0 in Figure 20), which is the time when the sales representative began staying at the customer's location. S ) is estimated to be the case.

[0143] The estimation unit 139 calculates the time from the first time (T0 in Figure 20) to the start time of stay (T0 in Figure 20). S Based on the corresponding acceleration data up to the start of the stay, the system identifies the means of transportation of the sales representative from the first time point to the start of the stay. Specifically, the estimation unit 139 identifies the means of transportation by determining which means of transportation's acceleration data is closest to the corresponding acceleration data from the first time point to the start of the stay. In this example, the estimation unit 139 identifies the means of transportation as a car ("Traveling by car" in Figure 20), and the average speed of the car is assumed to be Va.

[0144] The estimation unit 139 determines the end of the time in the acceleration data that indicates the information terminal 2 was stationary, and the end of the stay time (T in Figure 20) which is the time when the sales representative finished their stay at the customer's location. e The estimation unit 139 estimates that the time between the start time of stay and the end time of stay is the time when the sales representative was stationary ("stationary" in Figure 20).

[0145] The estimation unit 139 determines the end time of stay (T in Figure 20). e The location and time information obtained immediately after ) are identified as the second location (let's call it P1) and the second time (T1 in Figure 20).

[0146] The estimation unit 139 determines the end time of stay (T in Figure 20). eBased on the acceleration data corresponding to the period from the end time of stay to the second time (T1 in FIG. 20), the means of movement of the salesperson during the period from the end time of stay to the second time is identified. Specifically, the estimation unit 139 identifies the means of movement by determining which acceleration data of the means of movement the acceleration data during the period from the end time of stay to the second time is close to. In this example, the estimation unit 139 identifies the means of movement as walking ( "moving on foot" in FIG. 20), and assumes that the average speed of walking is Vw.

[0147] Subsequently, the estimation unit 139 identifies the distance (denoted as D0) from the first position (P0) to the stay position (denoted as P) to be estimated. For example, the estimation unit 139 identifies the distance D0 based on the average speed Va of the vehicle, which is the means of business used by the salesperson during the period from the first time (T0) corresponding to the first position to the start time of stay (T S ) Specifically, the estimation unit 139 identifies D0 based on the calculation formula "D0 = average speed Va of the vehicle × (start time of stay T S - first time T0)".

[0148] The estimation unit 139 identifies the distance (denoted as D1) from the position (P) to be estimated to the second position (P1). For example, the estimation unit 139 identifies the distance D1 based on the average speed Vw of walking, which is the means of business used by the salesperson during the period from the end time of stay (T e ) to the second time (T1) corresponding to the second position. Specifically, the estimation unit 139 identifies D1 based on the calculation formula "D1 = average speed Vw of walking × (second time T1 - end time of stay T e )".

[0149] The estimation unit 139 estimates the time to be estimated based, for example, on the first position (P0) and the second position (P1), the distance from the first position to the position to be estimated (D0), and the distance from the position to be estimated to the second position (D1). The estimation unit 139 calculates the time by multiplying the difference between the second time (T1) and the first time (T0) by the ratio that the distance from the first position to the position to be estimated (D0) occupies to the sum of the distance from the first position to the position to be estimated (D0) and the distance from the position to the second position (D1). In other words, the estimation unit 139 performs the calculation "(T1-T0)×(D0 / (D0+D1))". Then, the estimation unit 139 estimates the time to be estimated by adding this 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 from the first position to the position to be estimated (D0), and the distance from the desired position to the second position (D1), using, for example, one of the following three methods.

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

[0152] The second method involves estimating the desired location (P) using a circle with center P0 and a circle with center P1. Figure 22 is a diagram illustrating the method of estimating the desired location using circles. As shown in Figure 22, the estimation unit 139 estimates the point that is closer to the customer's location among the intersection points (P, P') of a circle with center P0 and radius D0 and a circle with center P1 and radius D1 as the desired location (P).

[0153] The third method is to estimate the desired location (P) by considering the terrain (roads, railway lines, etc.) near the customer. Figure 23 is a diagram illustrating the method of estimating the desired location by considering the terrain near the customer. As shown in Figure 23, the estimation unit 139 estimates the desired location (P) as a point on the road where the distance from P0 is D0 and the distance from P1 is D1.

[0154] In this way, the estimation unit 139 estimates the location and time of the information terminal 2 during periods when the location of the information terminal 2 was not recorded. As a result, the trend identification unit 133 also identifies trends in the relationship between the performance data and behavioral history data of sales representatives who visit locations where radio waves are difficult to reach, and the output unit 140 can output presentation information that shows the identified trends. By referring to this presentation information, other sales representatives can conduct sales more efficiently even to customers located in areas where radio waves are difficult to reach. As a result, the overall sales performance of the organization improves.

[0155] Although the present invention has been described above using embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments, and various modifications and changes are possible within the scope of its gist. For example, all or part of the apparatus can be configured by functionally or physically distributing and 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 combinations are combined with the effects of the original embodiments. [Explanation of symbols]

[0156] 1. Information Processing Device 11. Device Communication Unit 12 Storage section 13 Control Unit 131 Performance Data Acquisition Unit 132 Behavioral History Data Acquisition Unit 133 Trend Identification Department 134 Customer Identification Department 135. Score Calculation Unit 136 Sales Activities Department 137 Business Methods Specification Section 138 Sales Representative Department 139 Estimation Department 140 Output section 2 Information terminals 21 Terminal Communication Section 22 Display section 23 Memory section 24 Control Unit 241 Display Processing Unit 242 Records Section 3. Grade Management System S Information Processing System

Claims

1. A behavioral history data acquisition unit acquires behavioral history data that shows the relationship between the location and time of the information terminal used by each of the multiple sales representatives, and acceleration data that shows the acceleration of the information terminal, In the aforementioned activity history data, if there is an unrecorded period in which the information terminal was unable to receive radio waves for using a satellite positioning system or communication line and therefore its location was not recorded, a first identification unit identifies the first position and the first time of the information terminal at the first time closest to the unrecorded period in the activity history data among the times prior to the unrecorded period, and the second position and the second time of the information terminal at the second time closest to the unrecorded period in the activity history data among the times after the unrecorded period. A second identification unit identifies the start time of the time in which the information terminal was stationary in the acceleration data as the start time of stay, which is the time when the sales representative began staying at the customer's location, and identifies the end time of the time in which the information terminal was stationary in the acceleration data as the end time of stay, which is the time when the sales representative ended their stay at the customer's location. The first distance is estimated by multiplying the time obtained by subtracting the first time from the start time of stay by the speed of movement of the sales representative corresponding to the acceleration in the acceleration data corresponding to the period from the first time to the start time of stay. The second distance is estimated by multiplying the time obtained by subtracting the end time of stay from the second time by the speed of movement of the sales representative corresponding to the acceleration in the acceleration data corresponding to the period from the end time of stay to the second time. An estimation unit estimates the position of the information terminal during the unrecorded time period by dividing the line segment connecting the first position and the second position by the ratio of the first distance and the second distance, An information processing device having

2. A behavioral history data acquisition unit acquires behavioral history data that shows the relationship between the location and time of the information terminal used by each of the multiple sales representatives, and acceleration data that shows the acceleration of the information terminal, In the aforementioned activity history data, if there is an unrecorded period in which the information terminal was unable to receive radio waves for using a satellite positioning system or communication line and therefore its location was not recorded, a first identification unit identifies the first position and the first time of the information terminal at the first time closest to the unrecorded period in the activity history data among the times prior to the unrecorded period, and the second position and the second time of the information terminal at the second time closest to the unrecorded period in the activity history data among the times after the unrecorded period. A second identification unit identifies the start time of the time in which the information terminal was stationary in the acceleration data as the start time of stay, which is the time when the sales representative began staying at the customer's location, and identifies the end time of the time in which the information terminal was stationary in the acceleration data as the end time of stay, which is the time when the sales representative ended their stay at the customer's location. The first distance is estimated by multiplying the time obtained by subtracting the first time from the start time of stay by the speed of movement of the sales representative corresponding to the acceleration in the acceleration data corresponding to the period from the first time to the start time of stay. The second distance is estimated by multiplying the time obtained by subtracting the end time of stay from the second time by the speed of movement of the sales representative corresponding to the acceleration in the acceleration data corresponding to the period from the end time of stay to the second time. An estimation unit estimates the position of the information terminal during the unrecorded time period by determining the intersection point of a circle centered at the first position and with radius equal to the first distance, and a circle centered at the second position and with radius equal to the second distance. An information processing device having

3. A behavioral history data acquisition unit acquires behavioral history data that shows the relationship between the location and time of the information terminal used by each of the multiple sales representatives, and acceleration data that shows the acceleration of the information terminal, In the aforementioned activity history data, if there is an unrecorded period in which the information terminal was unable to receive radio waves for using a satellite positioning system or communication line and therefore its location was not recorded, a first identification unit identifies the first position and the first time of the information terminal at the first time closest to the unrecorded period in the activity history data among the times prior to the unrecorded period, and the second position and the second time of the information terminal at the second time closest to the unrecorded period in the activity history data among the times after the unrecorded period. A second identification unit identifies the start time of the time in which the information terminal was stationary in the acceleration data as the start time of stay, which is the time when the sales representative began staying at the customer's location, and identifies the end time of the time in which the information terminal was stationary in the acceleration data as the end time of stay, which is the time when the sales representative ended their stay at the customer's location. The first distance is estimated by multiplying the time obtained by subtracting the first time from the start time of stay by the speed of movement of the sales representative corresponding to the acceleration in the acceleration data corresponding to the period from the first time to the start time of stay. The second distance is estimated by multiplying the time obtained by subtracting the end time of stay from the second time by the speed of movement of the sales representative corresponding to the acceleration in the acceleration data corresponding to the period from the end time of stay to the second time. An estimation unit estimates the location of the information terminal during the unrecorded time period as the point on the road where the distance from the first position is the first distance and the distance from the second position is the second distance, An information processing device having

4. A computer executes A step to acquire behavioral history data, which is associated with each of several sales representatives, and which acquires behavioral history data showing the relationship between the location and time of the information terminal used by the sales representative, and acceleration data showing the acceleration of the information terminal, If, in the aforementioned activity history data, there is an unrecorded period in which the location was not recorded because the information terminal was unable to receive radio waves for using a satellite positioning system or communication line, then a first identification step is to identify the first position and the first time of the information terminal at the first time closest to the unrecorded period in the activity history data among the times prior to the unrecorded period, and the second position and the second time of the information terminal at the second time closest to the unrecorded period in the activity history data among the times after the unrecorded period, A second identification step involves identifying the start time of the time in which the information terminal was stationary in the acceleration data as the start time of stay, which is the time when the sales representative began staying at the customer's location, and identifying the end time of the time in which the information terminal was stationary in the acceleration data as the end time of stay, which is the time when the sales representative ended their stay at the customer's location. The first distance is estimated by multiplying the time obtained by subtracting the first time from the start time of stay by the speed of movement of the sales representative corresponding to the acceleration in the acceleration data corresponding to the period from the first time to the start time of stay. The second distance is estimated by multiplying the time obtained by subtracting the end time of stay from the second time by the speed of movement of the sales representative corresponding to the acceleration in the acceleration data corresponding to the period from the end time of stay to the second time. An estimation step in which the line segment connecting the first position and the second position is divided proportionally by the ratio of the first distance and the second distance to determine a point, which is estimated to be the position of the information terminal during the unrecorded time period; An information processing method having

5. A computer executes A step to acquire behavioral history data, which is associated with each of several sales representatives, and which acquires behavioral history data showing the relationship between the location and time of the information terminal used by the sales representative, and acceleration data showing the acceleration of the information terminal, If, in the aforementioned activity history data, there is an unrecorded period in which the location was not recorded because the information terminal was unable to receive radio waves for using a satellite positioning system or communication line, then a first identification step is to identify the first position and the first time of the information terminal at the first time closest to the unrecorded period in the activity history data among the times prior to the unrecorded period, and the second position and the second time of the information terminal at the second time closest to the unrecorded period in the activity history data among the times after the unrecorded period, A second identification step involves identifying the start time of the time in which the information terminal was stationary in the acceleration data as the start time of stay, which is the time when the sales representative began staying at the customer's location, and identifying the end time of the time in which the information terminal was stationary in the acceleration data as the end time of stay, which is the time when the sales representative ended their stay at the customer's location. The first distance is estimated by multiplying the time obtained by subtracting the first time from the start time of stay by the speed of movement of the sales representative corresponding to the acceleration in the acceleration data corresponding to the period from the first time to the start time of stay. The second distance is estimated by multiplying the time obtained by subtracting the end time of stay from the second time by the speed of movement of the sales representative corresponding to the acceleration in the acceleration data corresponding to the period from the end time of stay to the second time. An estimation step in which the intersection point of a circle centered at the first position and with radius equal to the first distance and a circle centered at the second position and with radius equal to the second distance is estimated as the position of the information terminal during the unrecorded time period, An information processing method having

6. A computer executes A step to acquire behavioral history data, which is associated with each of several sales representatives, and which acquires behavioral history data showing the relationship between the location and time of the information terminal used by the sales representative, and acceleration data showing the acceleration of the information terminal, If, in the aforementioned activity history data, there is an unrecorded period in which the location was not recorded because the information terminal was unable to receive radio waves for using a satellite positioning system or communication line, then a first identification step is to identify the first position and the first time of the information terminal at the first time closest to the unrecorded period in the activity history data among the times prior to the unrecorded period, and the second position and the second time of the information terminal at the second time closest to the unrecorded period in the activity history data among the times after the unrecorded period, A second identification step involves identifying the start time of the time in which the information terminal was stationary in the acceleration data as the start time of stay, which is the time when the sales representative began staying at the customer's location, and identifying the end time of the time in which the information terminal was stationary in the acceleration data as the end time of stay, which is the time when the sales representative ended their stay at the customer's location. The first distance is estimated by multiplying the time obtained by subtracting the first time from the start time of stay by the speed of movement of the sales representative corresponding to the acceleration in the acceleration data corresponding to the period from the first time to the start time of stay. The second distance is estimated by multiplying the time obtained by subtracting the end time of stay from the second time by the speed of movement of the sales representative corresponding to the acceleration in the acceleration data corresponding to the period from the end time of stay to the second time. An estimation step of estimating the location of the information terminal during the unrecorded time period as the point on the road where the distance from the first position is the first distance and the distance from the second position is the second distance, An information processing method having

7. The system comprises an information processing device and an information terminal capable of communicating with the information processing device, The aforementioned information processing device is A behavioral history data acquisition unit acquires behavioral history data that shows the relationship between the location and time of the information terminal used by each of the multiple sales representatives, and acceleration data that shows the acceleration of the information terminal, In the aforementioned activity history data, if there is an unrecorded period in which the information terminal was unable to receive radio waves for using a satellite positioning system or communication line and therefore its location was not recorded, a first identification unit identifies the first position and the first time of the information terminal at the first time closest to the unrecorded period in the activity history data among the times prior to the unrecorded period, and the second position and the second time of the information terminal at the second time closest to the unrecorded period in the activity history data among the times after the unrecorded period. A second identification unit identifies the start time of the time in which the information terminal was stationary in the acceleration data as the start time of stay, which is the time when the sales representative began staying at the customer's location, and identifies the end time of the time in which the information terminal was stationary in the acceleration data as the end time of stay, which is the time when the sales representative ended their stay at the customer's location. The first distance is estimated by multiplying the time obtained by subtracting the first time from the start time of stay by the speed of movement of the sales representative corresponding to the acceleration in the acceleration data corresponding to the period from the first time to the start time of stay. The second distance is estimated by multiplying the time obtained by subtracting the end time of stay from the second time by the speed of movement of the sales representative corresponding to the acceleration in the acceleration data corresponding to the period from the end time of stay to the second time. An estimation unit estimates the position of the information terminal during the unrecorded time period by dividing the line segment connecting the first position and the second position by the ratio of the first distance and the second distance, A device communication unit that transmits the estimated location of the information terminal during the unrecorded time period to the information terminal, It has, The aforementioned information terminal is A terminal communication unit that receives the location of the information terminal during the unrecorded time period, A display unit that displays the location of the information terminal during the unrecorded time period received, Having, Information processing system.

8. The system comprises an information processing device and an information terminal capable of communicating with the information processing device, The aforementioned information processing device is A behavioral history data acquisition unit acquires behavioral history data that shows the relationship between the location and time of the information terminal used by each of the multiple sales representatives, and acceleration data that shows the acceleration of the information terminal, In the aforementioned activity history data, if there is an unrecorded period in which the information terminal was unable to receive radio waves for using a satellite positioning system or communication line and therefore its location was not recorded, a first identification unit identifies the first position and the first time of the information terminal at the first time closest to the unrecorded period in the activity history data among the times prior to the unrecorded period, and the second position and the second time of the information terminal at the second time closest to the unrecorded period in the activity history data among the times after the unrecorded period. A second identification unit identifies the start time of the time in which the information terminal was stationary in the acceleration data as the start time of stay, which is the time when the sales representative began staying at the customer's location, and identifies the end time of the time in which the information terminal was stationary in the acceleration data as the end time of stay, which is the time when the sales representative ended their stay at the customer's location. The first distance is estimated by multiplying the time obtained by subtracting the first time from the start time of stay by the speed of movement of the sales representative corresponding to the acceleration in the acceleration data corresponding to the period from the first time to the start time of stay. The second distance is estimated by multiplying the time obtained by subtracting the end time of stay from the second time by the speed of movement of the sales representative corresponding to the acceleration in the acceleration data corresponding to the period from the end time of stay to the second time. An estimation unit estimates the position of the information terminal during the unrecorded time period by determining the intersection point of a circle centered at the first position and with radius equal to the first distance, and a circle centered at the second position and with radius equal to the second distance. A device communication unit that transmits the estimated location of the information terminal during the unrecorded time period to the information terminal, It has, The aforementioned information terminal is A terminal communication unit that receives the location of the information terminal during the unrecorded time period, A display unit that displays the location of the information terminal during the unrecorded time period received, Having, Information processing system.

9. The system comprises an information processing device and an information terminal capable of communicating with the information processing device, The aforementioned information processing device is A behavioral history data acquisition unit acquires behavioral history data that shows the relationship between the location and time of the information terminal used by each of the multiple sales representatives, and acceleration data that shows the acceleration of the information terminal, In the aforementioned activity history data, if there is an unrecorded period in which the information terminal was unable to receive radio waves for using a satellite positioning system or communication line and therefore its location was not recorded, a first identification unit identifies the first position and the first time of the information terminal at the first time closest to the unrecorded period in the activity history data among the times prior to the unrecorded period, and the second position and the second time of the information terminal at the second time closest to the unrecorded period in the activity history data among the times after the unrecorded period. A second identification unit identifies the start time of the time in which the information terminal was stationary in the acceleration data as the start time of stay, which is the time when the sales representative began staying at the customer's location, and identifies the end time of the time in which the information terminal was stationary in the acceleration data as the end time of stay, which is the time when the sales representative ended their stay at the customer's location. The first distance is estimated by multiplying the time obtained by subtracting the first time from the start time of stay by the speed of movement of the sales representative corresponding to the acceleration in the acceleration data corresponding to the period from the first time to the start time of stay. The second distance is estimated by multiplying the time obtained by subtracting the end time of stay from the second time by the speed of movement of the sales representative corresponding to the acceleration in the acceleration data corresponding to the period from the end time of stay to the second time. An estimation unit estimates the location of the information terminal during the unrecorded time period as the point on the road where the distance from the first position is the first distance and the distance from the second position is the second distance, A device communication unit that transmits the estimated location of the information terminal during the unrecorded time period to the information terminal, It has, The aforementioned information terminal is A terminal communication unit that receives the location of the information terminal during the unrecorded time period, A display unit that displays the location of the information terminal during the unrecorded time period received, Having, Information processing system.

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