Method, program, and information processing apparatus for performing processing related to analysis of purchase of goods

Abstract

Provided are a method, a program, and an information processing apparatus for performing processing related to analysis of purchase of goods so that a plurality of indexes related to analysis of purchase of goods are easily understandable. 【Solution means】The information processing apparatus 1 extracts a plurality of purchase information related to purchase transactions performed during a target period in the purchase information DB 21, calculates a plurality of indexes related to analysis of purchase of goods based on the extracted plurality of purchase information, generates a tree diagram representing the calculated indexes as nodes, and causes the display device to display the tree diagram. The information processing apparatus 1 calculates, as indexes, the sales amount of goods during the target period, the number of purchasers who purchased the goods during the target period, and the average purchase total amount which is the average of the total amount of goods purchased by one customer during the target period among the customers who performed purchase transactions during the target period. The tree diagram generated by the information processing apparatus 1 includes branches that branch from the sales amount node to the purchaser number node and the average purchase total amount node.

Description

Technical Field

[0001] The present invention relates to a method, a program, and an information processing apparatus for performing processing related to the analysis of the purchase of goods.

Background Art

[0002] In the field of sales, it is common to collect and analyze information (purchase information) related to the purchase transactions of goods by customers. By analyzing the purchase information, the purchase tendencies of customers can be revealed, and the needs of customers can be explored. As a result, it becomes easier to take measures to improve customer satisfaction (such as providing popular goods), and thus it is possible to improve profits. The following patent documents describe a method of analyzing sales data and generating a report based on the analysis results.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, it has become common to sell goods on e-commerce sites, and the number of cases where goods are sold both in stores and on e-commerce sites is also increasing. In addition, systems (such as cloud services) that can aggregate the purchase information collected on e-commerce sites and stores into a single database and perform a comprehensive analysis of purchases have become widespread. Therefore, the analysis results of purchase information are becoming available not only to some people such as the management and strategic departments but also to on-site personnel (such as sales representatives). However, various indicators included in the analysis results of purchase information are often listed as numbers in a table. Therefore, there is a problem that it is difficult for those who are not accustomed to the numerical representation of the analysis results to understand the content represented by the numerical values of each indicator.

[0005] The present invention has been made in view of such circumstances, and an object thereof is to provide a method, a program, and an information processing apparatus for performing processing related to the analysis of the purchase of a product so that a plurality of indicators obtained as analysis results are easier to understand.

Means for Solving the Problems

[0006] A first aspect of the present invention is a method in which an information processing apparatus performs processing related to the analysis of the purchase of a product. The information processing apparatus is accessible to a storage device, and the storage device stores a plurality of purchase information. One purchase information includes information related to the date on which one purchase transaction was made, customer identification information for identifying the one customer, and information related to the amount of the one purchase transaction, as information related to one purchase transaction in which one customer purchases a product. An information extraction step of extracting a plurality of purchase information related to purchase transactions made during the target period, an index calculation step of calculating a plurality of indicators related to the analysis of the purchase of the product based on the plurality of purchase information extracted in the information extraction step, and generating a tree diagram in which at least a part of the plurality of indicators calculated in the index calculation step is represented as one node each, and a display step of displaying the tree diagram on a display device. The index calculation step includes a sales amount calculation step of calculating the sales amount of the product during the target period as an indicator based on the plurality of purchase information extracted in the information extraction step, a purchase number calculation step of calculating the number of customers who purchased the product during the target period as an indicator based on the plurality of purchase information extracted in the information extraction step, and an average purchase total amount calculation step of calculating the average purchase total amount, which is the average of the total amount of products purchased by one customer during the target period, as an indicator based on the plurality of purchase information extracted in the information extraction step. The display step includes generating a tree diagram including a branch that branches from the sales amount node to the purchase number node and the average purchase total amount node. According to the method according to the first aspect, by using a tree diagram in which a plurality of nodes representing indicators are connected by branches, the subordinate relationship and the dependency relationship between the indicators are visually clearly shown, so that it becomes easier to understand the content represented by the plurality of indicators.

[0007] A second aspect of the present invention is a program including instructions for causing an information processing apparatus to perform processing related to analysis of purchase of goods, and the processing performed by the information processing apparatus according to the instructions includes each step of the method according to the first aspect above.

[0008] A third aspect of the present invention is an information processing apparatus for performing processing related to analysis of purchase of goods, having a processing unit and a storage unit storing instructions for causing the processing unit to perform processing, and the processing performed by the processing unit according to the instructions includes each step of the method according to the first aspect above.

[0009] A fourth aspect of the present invention is an information processing apparatus for performing processing related to analysis of purchase of goods, including means for performing each step of the method according to the first aspect above.

Advantages of the Invention

[0010] According to the present invention, it is possible to provide a method, a program, and an information processing apparatus for performing processing related to analysis of purchase of goods so that a plurality of indexes related to analysis of purchase of goods can be easily understood.

Brief Description of the Drawings

[0011]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Figure 10

Figure 11

Figure 12

DETAILED DESCRIPTION OF THE INVENTION

[0012] FIG. 1 is a diagram showing an example of the configuration of the system according to the present embodiment. The system shown in the example of FIG. 1 includes an information processing apparatus 1 capable of communicating via a communication network 9 such as the Internet, an e-commerce server 3, a store management apparatus 4, and a manager terminal apparatus 5. The information processing apparatus 1 is an example of the information processing apparatus of the present invention.

[0013] In the system shown in FIG. 1, the e-commerce server 3 and the store management apparatus 4 collect information (purchase information) related to the purchase transaction of products by customers. The information processing apparatus 1 performs an analysis related to the purchase of products based on the collected purchase information, and the manager terminal apparatus 5 displays the analysis result by the information processing apparatus 1. FIGS. 2A, 2B, and 3 show an example of a screen for displaying the analysis result on the display device of the manager terminal apparatus 5.

[0014] Note that the "product" described in this specification broadly means an object to be bought and sold, and is not limited to goods (products, etc.), and includes, for example, services, rights (securities, etc.), information, content, and the like.

[0015] [Information Processing Apparatus 1] Based on customer information collected by an e-commerce server 3 or the like, the information processing apparatus 1 performs analysis of product purchases by customers, and performs processing to display the analysis results on a display device such as an administrator terminal device 5. For example, the information processing apparatus 1 includes one or a plurality of computers. The information processing apparatus 1 shown in the example of FIG. 1 has a communication unit 11, a storage unit 12, and a processing unit 13.

[0016] The communication unit 11 communicates with other devices (such as the e-commerce server 3, the store management device 4, the administrator terminal device 5, etc.) via the communication network 9. The communication unit 11 includes, for example, a device (such as a network interface card) that communicates in accordance with a predetermined communication standard such as Ethernet (registered trademark) or wireless LAN.

[0017] The storage unit 12 stores one or more programs 121 including instructions executed by the processing unit 13, data temporarily stored in the process of processing by the processing unit 13, data used in the processing of the processing unit 13, data obtained as a result of the processing of the processing unit 13, and the like. The storage unit 12 may include, for example, a main storage device (such as a RAM or a ROM) and an auxiliary storage device (such as a flash memory, an SSD, a hard disk, a memory card, an optical disk, etc.). The storage unit 12 may be composed of one storage device or a plurality of storage devices. When the storage unit 12 is composed of a plurality of storage devices, each storage device is connected to the processing unit 13 via a computer bus or any other communication means.

[0018] The processing unit 13 overall controls the operation of the information processing apparatus 1 and performs predetermined information processing. The processing unit 13 includes, for example, one or more processors (such as a CPU (central processing unit), MPU (micro-processing unit), DSP (digital signal processor), GPU (graphics processing unit), NPU (neural network processing unit), etc.) that perform processing according to the instructions of one or more programs 121 stored in the storage unit 12. The processing unit 13 operates as one or more computers by the one or more processors executing the instructions of one or more programs 121 stored in the storage unit 12. The information processing apparatus 1 may have a plurality of computers, and at least a part of the processing according to the present embodiment may be performed in cooperation by the plurality of computers.

[0019] The processing unit 13 may include one or more dedicated hardware (such as an ASIC (application specific integrated circuit), FPGA (field-programmable gate array), etc.) configured to realize a specific function. The processing unit 13 may perform all of the processing described in the present embodiment on a computer, or may perform some of the processing on a computer and some on dedicated hardware, or may perform all of the processing on dedicated hardware.

[0020] Program 121 may be recorded, for example, on a computer-readable recording medium (optical disk, memory card, USB memory, or other non-transitory tangible medium). The processing unit 13 may read at least a part of one or more programs 121 recorded on such a recording medium by a recording medium reading device (such as an optical disk device) or an interface device (such as a USB interface) not shown in the figure and write it to the storage unit 12. Alternatively, the processing unit 13 may download at least a part of one or more programs 121 from another device connected to the communication network 9 via the communication unit 11 and write it to the storage unit 12. One or more programs 121 include instructions for causing the processing unit 13 to perform at least a part of the processing according to the present embodiment described later.

[0021] [Storage device 2] The storage device 2 stores various information used in the processing of the information processing apparatus 1. The information processing apparatus 1 and the storage device 2 can communicate with each other via an arbitrary communication path (LAN, dedicated line network, Internet, etc.). For example, the storage device 2 may be included in a file server, a database server, a cloud server, etc. that accepts access from a plurality of devices, or may be a dedicated storage device that can be accessed only by the information processing apparatus 1. Alternatively, the storage device 2 may be a storage device connected to the processing unit 13 of the information processing apparatus 1 via a computer bus. In the example of FIG. 1, the storage device 2 stores a purchase information database 21, a customer information database 22, a performance index database 23, and a budget index database 24. In the following description, the database may be abbreviated as "DB" in some cases.

[0022] The purchase information DB 21 includes a plurality of purchase information regarding the purchase transactions of products by customers. One piece of purchase information includes information regarding one purchase transaction. One piece of purchase information includes, for example, at least a part of the following information. · Information for identifying the purchase transaction (transaction identification ID) · Date and time when the purchase transaction was made · Information for identifying the product targeted by the purchase transaction (product ID, product model number, product name, etc.) · Amount of the purchase transaction · Quantity of purchased goods · Information for identifying the customer who purchased the goods (customer ID)

[0023] Customer information DB22 includes a plurality of customer information regarding customers who use the e-commerce site or store. One customer information includes information about one customer. One customer information includes, for example, at least a part of the following information. · Information for identifying the customer (customer ID) · Registration date of the customer · Personal information of the customer (Example) Name, address, email address, phone number / ++, age, gender, etc. · Information regarding the delivery destination of the goods (Example) Name of the delivery destination, address of the delivery destination · Most recent date on which the customer purchased the goods

[0024] In the following description, a customer (customer identified by the customer ID) whose customer information is registered in the customer information DB22 is referred to as a "member". A customer other than a member (customer not identified by the customer ID) is referred to as a "non-member".

[0025] Also, among members, a member newly registered in the customer information DB22 during the target period that serves as the basis for calculating the indicators described later (member whose registration date of the customer in the customer information is within the target period) is referred to as a "new member" during that target period. A member who purchased goods before the start date of the target period and within a predetermined period (for example, within one year) up to the start date is referred to as a "continuing member" during that target period. A member who is not a new member or a continuing member during the target period and who purchased goods during the target period is referred to as a "revived member" during that target period.

[0026] Performance indicator DB23 includes a plurality of performance indicator information regarding the analysis of the purchase of goods based on the purchase information. One performance indicator information includes a plurality of indicators (also referred to as an indicator group) regarding the analysis of the purchase made during one target period. One performance indicator information includes, for example, at least a part of the following information. · Information indicating the target period (start date, end date, etc. of the target period) The length of the target period is constant (for example, one year). · A plurality of indicators (indicator group) calculated by the information processing apparatus 1 based on the purchase information regarding the purchase transactions during the target period

[0027] The indicator group includes at least a part of the indicators (A, A1 to A5, B, B1 to B5, C, C1 to C5, D, D1 to D4, E, E1 to E4, F, F1 to F4, G, H, J) described below, for example.

[0028] "Number of purchases A" The number of purchases A is the number of purchase transactions made during the target period. "Number of member purchases A1" The number of member purchases A1 is the number of purchase transactions made by members during the target period among the number of purchases A. "Number of purchases by new members A2" The number of purchases by new members A2 is the number of purchase transactions made by new members during the target period among the number of member purchases A1. "Number of purchases by continuing members A3" The number of purchases by continuing members A3 is the number of purchase transactions made by continuing members during the target period among the number of member purchases A1. "Number of purchases by revived members A4" The number of purchases by revived members A4 is the number of purchase transactions made by revived members during the target period among the number of member purchases A1. "Number of purchases by non - members A5" The number of purchases by non - members A5 is the number of purchase transactions made by non - members during the target period among the number of purchases A.

[0029] The number of purchases A and the number of member purchases A1 are respectively represented by the following formulas. A = A1 + A5 …(1 - 1) A1 = A2 + A3 + A4 …(1 - 2)

[0030] "Sales amount B" The sales amount B is the sales amount of the products during the target period. "Member sales amount B1" The member sales amount B1 is the total amount of the sales amount B for the products purchased by members during the target period. "New member sales amount B2" The new member sales amount B2 is the total amount of the sales amount B1 for the products purchased by new members during the target period. "Continuing member sales amount B3" The continuing member sales amount B3 is the total amount of the sales amount B1 for the products purchased by continuing members during the target period. "Reactivated member sales amount B4" The reactivated member sales amount B4 is the total amount of the sales amount B1 for the products purchased by reactivated members during the target period. "Non-member sales amount B5" The non-member sales amount B5 is the total amount of the sales amount B for the products purchased by non-members during the target period.

[0031] The sales amount B and the member sales amount B1 are respectively represented by the following formulas. B = B1 + B5 …(2-1) B1 = B2 + B3 + B4 …(2-2)

[0032] "Number of purchasers C" The number of purchasers C is the number of customers who purchased products during the target period. "Number of member purchasers C1" The number of member purchasers C1 is the number of members who purchased products during the target period among the number of purchasers C. "Number of new member purchasers C2" The number of new member purchasers C2 is the number of new members who purchased products during the target period among the number of member purchasers C1. "Number of continuing member purchasers C3" The number of continuing member purchasers C3 is the number of continuing members who purchased products during the target period among the number of member purchasers C1. "Number of reactivated member purchasers C4" The number of reactivated member purchasers C4 is the number of reactivated members who purchased products during the target period among the number of member purchasers C1. "Number of non-member purchasers C5" The non-member purchase number C5 is the number of non-members who purchased products during the target period among the purchase numbers C. Since non-members cannot be individually identified, the non-member purchase number C5 is equal to the number of purchase transactions made by non-members during the target period (i.e., the non-member purchase case number A5).

[0033] The purchase number C, the member purchase number C1, and the non-member purchase number C5 are respectively represented by the following formulas. C = C1 + C5 …(3-1) C1 = C2 + C3 + C4 …(3-2) C5 = A5 …(3-3)

[0034] "Average total purchase amount D" The average total purchase amount D is the average among customers who conducted purchase transactions during the target period, and is the average of the total amount of products purchased by one customer during the target period. "Member average total purchase amount D1" It is the average among members who conducted purchase transactions during the target period, and is the average of the total amount of products purchased by one member during the target period. "New member average total purchase amount D2" It is the average among new members who conducted purchase transactions during the target period, and is the average of the total amount of products purchased by one new member during the target period. "Continuing member average total purchase amount D3" It is the average among continuing members who conducted purchase transactions during the target period, and is the average of the total amount of products purchased by one continuing member during the target period. "Reactivated member average total purchase amount D4" It is the average among reactivated members who conducted purchase transactions during the target period, and is the average of the total amount of products purchased by one reactivated member during the target period. "Non-member average total purchase amount D5" It is the average among non-members who conducted purchase transactions during the target period, and is the average of the total amount of products purchased by one non-member during the target period.

[0035] The indicators (D, D1~D5) regarding the total purchase amount of products during the target period are respectively represented by the following formulas. D = B ÷ C …(4-1) D1 = B1 ÷ C1 …(4-2) D2 = B2 ÷ C2 …(4-3) D3 = B3 ÷ C3 …(4-4) D4 = B4 ÷ C4 …(4-5) D5 = B5 ÷ C5 …(4-6)

[0036] 「Average number of purchases E」 The average number of purchases E is the average among customers who made purchase transactions during the target period, and is the average number of times one customer made purchase transactions during the target period. 「Average number of purchases by members E1」 The average number of purchases by members E1 is the average among members who made purchase transactions during the target period, and is the average number of times one member made purchase transactions during the target period. 「Average number of purchases by new members E2」 The average number of purchases by new members E2 is the average among new members who made purchase transactions during the target period, and is the average number of times one new member made purchase transactions during the target period. 「Average number of purchases by continuing members E3」 The average number of purchases by continuing members E3 is the average among continuing members who made purchase transactions during the target period, and is the average number of times one continuing member made purchase transactions during the target period. 「Average number of purchases by reactivated members E4」 The average number of purchases by reactivated members E4 is the average among reactivated members who made purchase transactions during the target period, and is the average number of times one reactivated member made purchase transactions during the target period.

[0037] The indicators (E, E1~E4) regarding the number of purchases of products during the target period are each expressed by the following formulas. E = A ÷ C …(5-1) E1 = A1 ÷ C1 …(5-2) E2 = A2 ÷ C2 …(5-3) E3 = A3 ÷ C3 …(5-4) E4 = A4 ÷ C4 …(5-5)

[0038] Note that the number of purchase transactions E5 made by one non - member during the target period is 1 (= A5 / C5).

[0039] "Average purchase unit price F" The average purchase unit price F is the average among the customers who made purchase transactions during the target period, and is the average of the total amount of goods purchased by one customer in one purchase transaction during the target period. "Average purchase unit price F1 for members" The average purchase unit price F1 for members is the average among the members who made purchase transactions during the target period, and is the average of the total amount of goods purchased by one member in one purchase transaction during the target period. "Average purchase unit price F2 for new members" The average purchase unit price F2 for new members is the average among the new members who made purchase transactions during the target period, and is the average of the total amount of goods purchased by one new member in one purchase transaction during the target period. "Average purchase unit price F3 for continuing members" The average purchase unit price F3 for continuing members is the average among the continuing members who made purchase transactions during the target period, and is the average of the total amount of goods purchased by one continuing member in one purchase transaction during the target period. "Average purchase unit price F4 for revived members" The average purchase unit price F4 for revived members is the average among the revived members who made purchase transactions during the target period, and is the average of the total amount of goods purchased by one revived member in one purchase transaction during the target period. "Average purchase unit price F5 for non - members" The average purchase unit price F5 for non - members is the average among the non - members who made purchase transactions during the target period, and is the average of the total amount of goods purchased by one non - member in one purchase transaction during the target period.

[0040] The indicators (F, F1 - F4) for the total amount of goods purchased in one purchase transaction are represented by the following formulas respectively. F = D ÷ E = B ÷ A …(6 - 1) F1 = D1 ÷ E1 = B1 ÷ A1 …(6 - 2) F2 = D2 ÷ E2 = B2 ÷ A2 …(6 - 3) F3 = D3 ÷ E3 = B3 ÷ A3 …(6-4) F4 = D4 ÷ E4 = B4 ÷ A4 …(6-5)

[0041] Note that the non-member average purchase unit price F5 is the same as the total non-member average purchase amount D5 and is expressed by the following formula. F5 = D5 = B5 ÷ C5 …(6-6)

[0042] "Total number of purchased items G" The total number of purchased items G is the total number of items purchased during the target period. "Average number of purchased items H" The average number of purchased items H is the average number of items purchased by a customer in one purchase transaction. "Average purchase unit price J" The average purchase unit price J is the average unit price of items purchased by a customer in one purchase transaction.

[0043] The average purchase unit price J and the average number of purchased items H are respectively expressed by the following formulas. H = G ÷ A …(7-1) J = F ÷ H = B ÷ G …(7-2)

[0044] Return to the description of the storage device 2 shown in FIG. 1. The budget index DB24 includes a plurality of budget index information which is information regarding a pre-planned index (hereinafter may be referred to as "budget index"). One budget index information includes a plurality of budget indexes (hereinafter may be referred to as "budget index group") planned for one target period. One budget index information includes, for example, at least a part of the following information. · Information indicating the target period (start date, end date, etc. of the target period) · A plurality of budget indexes (budget index group) planned for the target period

[0045] The budget indicator group includes budget indicators related to at least a part of the above-mentioned indicators (A, A1 - A5, B, B1 - B5, C, C1 - C5, D, D1 - D4, E, E1 - E4, F, F1 - F4, G, H, J).

[0046] [E-commerce server 3] The e-commerce server 3 is a device that performs processing related to e-commerce. It performs processing such as providing product information to customer terminal devices (information communication devices such as personal computers and smartphones, not shown), processing for selling products in response to requests from customer terminal devices, and processing related to the shipment of products sold to customers. The e-commerce server 3 generates the above-mentioned purchase information for each purchase transaction and records it in a database or the like. The purchase information recorded in the database or the like of the e-commerce server 3 is transmitted to the information processing device 1 at an arbitrary timing.

[0047] [Store management device 4] The store management device 4 is a device that manages the sales (purchase transactions) of products in a store. It generates purchase information as information related to purchase transactions of products in the store and records it in a database or the like. The store management device 4 generates purchase information based on, for example, the information of purchase transactions input at a POS terminal. The purchase information recorded in the database or the like of the store management device 4 is transmitted to the information processing device 1 at an arbitrary timing.

[0048] [Administrator terminal device 5] The administrator terminal device 5 is a device operated by an administrator who utilizes the analysis results of product purchases, and is a device equipped with an information communication function such as a personal computer, a tablet, a smartphone, etc. The administrator terminal device 5 accesses the information processing device 1 according to the operation of the administrator, and displays the analysis results by the information processing device 1 on a display device. Further, the administrator terminal device 5 performs a process of inputting a budget index group according to the operation of the administrator and recording it in the information processing device 1 (budget index DB 24). The administrator terminal device 5 has, for example, a communication unit, a storage unit, and a processing unit similar to the communication unit 11, the storage unit 12, and the processing unit 13 of the information processing device 1 shown in FIG. 1, and also has an input device (touch panel, touch pad, keyboard, mouse, button, switch, microphone, camera, etc.) for inputting an administrator's instruction and other information, and a display device (liquid crystal display, organic EL display, etc.) for displaying video.

[0049] Next, an example of the screen of the administrator terminal device 5 that displays the analysis results of product purchases in the information processing device 1 will be described with reference to FIGS. 2A, 2B, and 3.

[0050] The screen S1 shown in FIG. 2A includes a tree diagram T1 that represents at least a part of a plurality of indicators (indicator group) calculated for one target period in the information processing device 1 as one node each. Hereinafter, any node included in the tree diagram may be referred to as "node N". Also, any branch connecting nodes N in the tree diagram may be referred to as "branch R".

[0051] The tree diagram T1 includes a node N_B representing the sales amount B, a node N_C representing the number of purchasers C, a node N_C1 representing the number of member purchasers C1, a node N_D representing the average total purchase amount D, a node N_F representing the average purchase unit price F, a node N_E representing the average number of purchases E, a node N_J representing the average purchase product unit price J, and a node N_H representing the average number of purchase products H. Also, the tree diagram T1 includes a branch R1 that branches from the node N_B of the sales amount B to the node N_C of the number of purchasers C and the node N_D of the average total purchase amount D, a branch R2 that branches from the node N_C of the number of purchasers C to the node N_C1 of the number of member purchasers C1, a branch R3 that branches from the node N_D of the average total purchase amount D to the node N_F of the average purchase unit price F and the node N_E of the average number of purchases E, and a branch R4 that branches from the node N_F of the average purchase unit price F to the node N_J of the average purchase commodity unit price J and the node N_H of the average number of purchase commodities H.

[0052] For each node N of the tree diagram T1, the name 81 of the indicator and the calculated value 82 of the indicator are described. Also, for each node N of the tree diagram T1, the performance comparison value 83 calculated for the indicator represented by that node N is described. The performance comparison value 83 is a numerical value for comparing the indicator calculated for one target period (for example, one year: the current year) with the indicator calculated for a target period in the past from that one target period (for example, one year of the previous year relative to the current year), and is calculated in step ST115 of FIG. 4 described later. In the example of FIG. 2A, the performance comparison value 83 is a ratio (percentage value) with respect to the past indicator value (indicator value of the previous year) to be compared, and is described in parentheses next to the past indicator value.

[0053] Below the node N_B of the tree diagram T1 on the screen S1, a graphic 70 similar to the node N is arranged. The graphic 70 is a graphic representing the number of purchase items A, and the name of the indicator (number of purchase items), the value of the number of purchase items A, and the performance comparison value calculated for the number of purchase items A are described.

[0054] Above the tree diagram T1 on the screen S1, input elements 71 to 75 for inputting instructions from the user (administrator) are arranged.

[0055] The input elements 71 to 73 are elements for selecting the type of numerical value to be described together with the index value 82 at each node N. In the example of the figure, the input elements 71 to 73 are radio buttons, and by the user operating these elements, one type is selected from among three types. On the screen S1, the input element 72 for describing the actual comparison value at each node N is selected.

[0056] The input element 74 is an element for the user to input the set value of the target of the sales amount B. In the example of the figure, the input element 74 is a list box, and the set value (percentage value) of the target of the sales amount B during the target period is selected from a predetermined list. According to the set value input to the input element 74, a target value (sales target value) regarding the sales amount B is set. On the screen S1, "120%" is set as the set value of the target of the sales amount B.

[0057] The input element 75 is an element for the user to set the target period that serves as the basis for calculating the index. In the example of the figure, the input element 75 is a list box, and the target period is selected from a predetermined list. On the screen S1, "January 2024 to December 2024" is set as the target period.

[0058] The screen S2 shown in FIG. 2B shows the screen when the input element 71 for describing the budget comparison value at each node N is selected. The budget comparison value is a numerical value for comparing the index calculated by the information processing apparatus 1 and the budget index, and is calculated in step ST125 of FIG. 4 described later.

[0059] The screen S2 shown in FIG. 2B includes the same tree diagram T1, graphic 70, and input elements 71 to 75 as the screen S1 shown in FIG. 2A. However, for each node N of the tree diagram T1 and the graphic 70 on the screen S2, the budget comparison value 84 is described together with the index value 82. In the example of FIG. 2B, the budget comparison value 84 is the difference (percentage value) with respect to the value of the budget index to be compared, and is described in parentheses next to the value of the budget index.

[0060] The screen S3 shown in FIG. 3 shows a screen when the input element 73 for causing the node N to describe a numerical value corresponding to the estimated value of the index (ST140, FIG. 4) described later is selected. The estimated value of the index indicates the value of the index estimated when the numerical value regarding the sales amount B reaches the sales target value.

[0061] The screen S3 shown in FIG. 3 includes the same tree diagram T1, graphic 70, and input elements 71 to 75 as the screens S1 and S2 shown in FIGS. 2A and 2B. However, in each node N of the tree diagram T1 and the graphic 70 on the screen S3, a numerical value 85 corresponding to the estimated value of the index is described together with the value 82 of the index. In the example of FIG. 3, the numerical value 85 corresponding to the estimated value of the index is the ratio (percentage value) of the estimated value of the index to the value 82 of the index calculated as the actual result. The estimated value of the index is an estimation of the value of the index when the target of the sales amount B set by the input element 74 is achieved. The numerical value described in parentheses next to the numerical value 85 is the estimated value of the index obtained by multiplying the value 82 of the index by the ratio of the numerical value 85.

[0062] Next, the processing regarding the analysis of the purchase of goods by the information processing apparatus 1 will be described with reference to the flowchart of FIG. 4. Step ST100 is an example of the information extraction step of the present invention. Step ST105 is an example of the index calculation step of the present invention. Step ST115 is an example of the actual result comparison value calculation step of the present invention. Step ST125 is an example of the budget comparison value calculation step of the present invention. Step ST135 is an example of the model generation step of the present invention. Step ST140 is an example of the estimation step of the present invention. Step ST145 is an example of the correction step of the present invention. Step ST150 is an example of the display step of the present invention.

[0063] Based on the target period set in the input element 75 of the screens (S1 to S3) of the administrator terminal device 5, the information processing apparatus 1 extracts a plurality of purchase information regarding purchase transactions conducted during the set target period from the purchase information DB 21 (ST100).

[0064] Based on the plurality of purchase information for the target period extracted in step ST100, the information processing apparatus 1 calculates a plurality of indicators (indicator group) regarding the analysis of product purchases during this target period (ST105).

[0065] FIG. 5 is a flowchart for explaining an example of the process (ST105) of calculating each standard of the indicator group. Step ST202 is an example of the sales amount calculation step of the present invention. Step ST258 is an example of the purchased person number calculation step of the present invention. Step ST260 is an example of the average purchase total amount calculation step of the present invention. Steps ST224, ST236, and ST248 are examples of the average purchase frequency calculation step of the present invention. Steps ST226, ST238, and ST250 are examples of the average purchase unit price calculation step of the present invention. Step ST268 is an example of the average product number calculation step of the present invention. Step ST270 is an example of the average product unit price calculation step of the present invention. Step ST218 is an example of the new member sales amount calculation step of the present invention. Step ST220 is an example of the new member purchased person number calculation step of the present invention. Step ST230 is an example of the continuing member sales amount calculation step of the present invention. Step ST232 is an example of the continuing member purchased person number calculation step of the present invention. Step ST242 is an example of the revived member sales amount calculation step of the present invention. Step ST244 is an example of the revived member purchased person number calculation step of the present invention. Step ST208 is an example of the step for calculating the number of member purchases in the present invention. Step ST252 is an example of the step for calculating the number of non-member purchases in the present invention.

[0066] The information processing apparatus 1 calculates the number of purchase transactions A during the target period based on the plurality of purchase information extracted in step ST100 (ST200). Also, the information processing apparatus 1 calculates the sales amount B during the target period by adding up the amounts of the purchase transactions in the extracted plurality of purchase information (ST202).

[0067] The information processing apparatus 1 extracts purchase information regarding the purchase transactions of members (purchase information including customer IDs) from the plurality of purchase information extracted in step ST100, and calculates the number of member purchase transactions A1, which is the number of purchase transactions made by members during the target period, based on the extracted purchase information (ST204). Also, the information processing apparatus 1 calculates the total member sales amount B1, which is the total amount of the goods purchased by members during the target period, by adding up the amounts of the purchase transactions of the purchase information extracted for members (ST206). Further, the information processing apparatus 1 calculates the number of member purchasers C1, which is the number of members who purchased goods during the target period, based on the customer IDs of the purchase information extracted for members (ST208).

[0068] The information processing apparatus 1 calculates the average total purchase amount D1 per member according to the value obtained by dividing the member sales amount B1 by the number of member purchasers C1 based on formula (4-2) (ST210). Also, the information processing apparatus 1 calculates the average number of purchases E1 per member according to the value obtained by dividing the number of member purchase transactions A1 by the number of member purchasers C1 based on formula (5-2) (ST212). Further, the information processing apparatus 1 calculates the average purchase unit price F1 per member according to the value obtained by dividing the member sales amount B1 by the number of member purchase transactions A1 based on formula (6-2) (ST214).

[0069] The information processing apparatus 1 extracts purchase information regarding the purchase transactions of new members from among the purchase information of the members extracted in step ST204. That is, the information processing apparatus 1 refers to the customer information (customer information DB 22) indicated by the customer ID of the purchase information of the members extracted in step ST204, and extracts the purchase information of new members whose registration date is within the target period. Based on the extracted purchase information of the new members, the information processing apparatus 1 calculates the number of purchase transactions A2 made by new members during the target period, which is the number of new member purchases (ST216). Further, the information processing apparatus 1 calculates the total amount of sales B2 of the products purchased by new members during the target period by summing up the amounts of the purchase transactions of the purchase information extracted for the new members (ST218). Furthermore, the information processing apparatus 1 calculates the number of new members C2 who purchased products during the target period based on the customer IDs of the purchase information extracted for the new members (ST220).

[0070] The information processing apparatus 1 calculates the average total purchase amount D2 per new member according to the value obtained by dividing the new member sales amount B2 by the number of new member purchases C2 based on formula (4-3) (ST222). Also, the information processing apparatus 1 calculates the average number of purchases E2 per new member according to the value obtained by dividing the number of new member purchases A2 by the number of new member purchases C2 based on formula (5-3) (ST224). Furthermore, the information processing apparatus 1 calculates the average purchase unit price F2 per new member according to the value obtained by dividing the new member sales amount B2 by the number of new member purchases A2 based on formula (6-3) (ST226).

[0071] The information processing device 1 extracts purchase information regarding the purchase transactions of continuous members from among the purchase information of the members extracted in step ST204. That is, the information processing device 1 refers to the customer information (customer information DB22) indicated by the customer ID of the purchase information of the members extracted in step ST204, and extracts the purchase information of continuous members whose purchase date of the most recent product is before the start date of the target period and the purchase date is included within a predetermined period (for example, within one year) up to the start date. The information processing device 1 calculates the number of purchase transactions A3 made by continuous members during the target period, which is the number of purchase transactions of continuous members, based on the extracted purchase information of continuous members (ST228). Further, the information processing device 1 calculates the total sales amount B3 of the products purchased by continuous members during the target period by summing up the amounts of the purchase transactions of the purchase information extracted for continuous members (ST230). Furthermore, the information processing device 1 calculates the number of continuous members C3 who purchased products during the target period based on the customer ID of the purchase information extracted for continuous members (ST232).

[0072] The information processing device 1 calculates the average total purchase amount D3 of continuous members corresponding to the value obtained by dividing the total sales amount B3 of continuous members by the number of continuous members C3 based on formula (4-4) (ST234). The information processing device 1 also calculates the average number of purchases E3 of continuous members corresponding to the value obtained by dividing the number of purchase transactions A3 of continuous members by the number of continuous members C3 based on formula (5-4) (ST236). Furthermore, the information processing device 1 calculates the average purchase unit price F3 of continuous members corresponding to the value obtained by dividing the total sales amount B3 of continuous members by the number of purchase transactions A3 of continuous members based on formula (6-4) (ST238).

[0073] The information processing device 1 calculates the number of revived member purchases A4 corresponding to the value obtained by subtracting the number of new member purchases A2 and the number of continuing member purchases A3 from the number of member purchases A1 based on Expression (1-2) (ST240). Further, the information processing device 1 calculates the revived member sales amount B4 corresponding to the value obtained by subtracting the new member sales amount B2 and the continuing member sales amount B3 from the member sales amount B1 based on Expression (2-2) (ST242). Still further, the information processing device 1 calculates the number of revived member purchases C4 corresponding to the value obtained by subtracting the number of new member purchases C2 and the number of continuing member purchases C3 from the number of member purchases C1 based on Expression (3-2) (ST244).

[0074] The information processing device 1 calculates the average total purchase amount D4 of revived members corresponding to the value obtained by dividing the revived member sales amount B4 by the number of revived member purchases C4 based on Expression (4-5) (ST246). Further, the information processing device 1 calculates the average number of purchases E4 of revived members corresponding to the value obtained by dividing the number of revived member purchases A4 by the number of revived member purchases C4 based on Expression (5-5) (ST248). Still further, the information processing device 1 calculates the average purchase unit price F4 of revived members corresponding to the value obtained by dividing the revived member sales amount B4 by the number of revived member purchases A4 based on Expression (6-5) (ST250).

[0075] The information processing device 1 extracts purchase information regarding non-member purchase transactions (purchase information not including customer IDs) from the plurality of purchase information extracted in step ST100, and calculates the number of non-member purchases C5 (= the number of non-member purchase items A5), which is the number of non-members who purchased goods during the target period, based on the extracted purchase information (ST252). Further, the information processing device 1 calculates the non-member sales amount B5, which is the total amount of goods purchased by non-members during the target period, by summing up the amounts of the purchase transactions of the purchase information extracted for non-members (ST254). Still further, the information processing device 1 calculates the non-member average purchase unit price F5 corresponding to the value obtained by dividing the non-member sales amount B5 by the number of non-member purchases C5 based on Expression (6-6) (ST256).

[0076] The information processing apparatus 1 calculates the number of purchases C according to the sum of the number of member purchases C1 and the number of non-member purchases C5 based on Equation (3-1) (ST258). Further, the information processing apparatus 1 calculates the average total purchase amount D according to the value obtained by dividing the sales amount B by the number of purchases C based on Equation (4-1) (ST260). Furthermore, the information processing apparatus 1 calculates the average number of purchases E according to the value obtained by dividing the number of purchase items A by the number of purchases C based on Equation (5-1). Also, the information processing apparatus 1 calculates the average purchase unit price F according to the value obtained by dividing the sales amount B by the number of purchase items A based on Equation (6-1) (ST264).

[0077] The information processing apparatus 1 calculates the total number of purchased items G, which is the total number of items purchased during the target period, based on the quantity of the items included in the purchase information extracted in step ST204 (ST266). Further, the information processing apparatus 1 calculates the average number of purchased items H according to the value obtained by dividing the total number of purchased items G by the number of purchase items A based on Equation (7-1) (ST268). Furthermore, the information processing apparatus 1 calculates the average unit price of purchased items J according to the value obtained by dividing the sales amount B by the total number of purchased items G based on Equation (7-2) (ST270).

[0078] Return to FIG. 4. As shown in the screen S1 of FIG. 2A, when the input element 72 for describing the performance comparison value 83 (compared with last year) for each node N of the tree diagram T1 is selected in the administrator terminal device 5 (Yes in ST100), the information processing apparatus 1 calculates the performance comparison value for at least a part of the plurality of indicators (indicator group) calculated in step ST105 (ST115).

[0079] The performance comparison value is a numerical value for comparing one indicator in one indicator group calculated for one target period with one past indicator in a past one indicator group calculated for a target period having the same length as the one target period and being in the past relative to the one target period. The performance comparison value represents the difference between one indicator in one indicator group and the past one indicator corresponding to the one indicator in the past one indicator group. For example, the performance comparison value represents the difference between one indicator and the past one indicator in terms of ratio, difference, etc.

[0080] When calculating a single performance comparison value, the information processing apparatus 1 reads out a single past indicator corresponding to the single indicator calculated in step ST105 (for example, an indicator in the indicator group of the previous year) from the performance indicator DB23. The information processing apparatus 1 calculates, as the performance comparison value, the ratio or the amount of change (difference) of the single indicator with respect to the single past indicator. When the performance comparison value is calculated in step ST115, the information processing apparatus 1 proceeds to step ST150 described later.

[0081] As shown in the screen S2 of FIG. 2B, when an input element 71 for describing the budget comparison value 84 (budget-actual difference) is selected in the administrator terminal device 5 for each node N of the tree diagram T1 (Yes in ST120), the information processing apparatus 1 calculates the budget comparison value for at least a part of the plurality of indicators (indicator group) calculated in step ST105 (ST125).

[0082] The budget comparison value is a numerical value for comparing a single indicator in a single indicator group calculated for a single target period with a single budget indicator in a single budget indicator group planned for the single target period. The budget comparison value represents the difference between a single indicator in a single indicator group and a single budget indicator corresponding to the single indicator in a single budget indicator group. For example, the budget comparison value represents the difference between a single indicator and a single budget indicator in terms of ratio, difference, etc.

[0083] When calculating the budget comparison value of a single indicator calculated for a single target period, the information processing apparatus 1 reads out a single budget indicator corresponding to the single indicator from the budget indicator DB24 in a single budget indicator group planned for the single target period. The information processing apparatus 1 calculates, as the budget comparison value, the ratio or the amount of change (difference) of the single indicator with respect to the single budget indicator. When the budget comparison value is calculated in step ST125, the information processing apparatus 1 proceeds to step ST150 described later.

[0084] As shown in the screen S3 of FIG. 3, when the input element 71 for describing the numerical value 85 corresponding to the estimated value of the index is selected in the administrator terminal device 5 (Yes in ST130), the information processing apparatus 1 generates an index estimation model used for estimating the index (ST135).

[0085] The index estimation model is a machine learning model that estimates the numerical value regarding one index other than the sales amount B when the numerical value regarding the sales amount B reaches the sales target value, based on the correlation relationship between one index other than the sales amount B and the sales amount B. Any known algorithm (such as linear regression) can be used for the machine learning algorithm of the index estimation model.

[0086] The information processing apparatus 1 generates one or more index estimation models regarding one or more indexes other than the sales amount B based on a plurality of index groups (index groups with different start dates of the target period) stored in the performance index DB23 of the storage device 2. A specific example of the process of generating the index estimation model will be described later with reference to FIGS. 6 and 7.

[0087] The information processing apparatus 1 estimates the index when the sales amount B reaches the target by using the generated index estimation model (ST140). That is, the information processing apparatus 1 estimates one or more indexes when the numerical value regarding the sales amount B reaches the sales target value, based on the results obtained by applying the sales target value set according to the set value input to the input element 74 (FIGS. 2A, 2B, and 3) to each of the generated one or more index estimation models. A specific example of the process of estimating the index will be described later with reference to FIG. 8.

[0088] The information processing apparatus 1 corrects some of the indexes estimated in step ST140 so that a predetermined relationship (such as the relationship of Equation (3-2)) holds. A specific example of the process of correcting the index will be described later with reference to FIG. 9. When performing the index estimation in step ST140 and the index correction in step ST145, the information processing apparatus 1 proceeds to step ST150.

[0089] When transitioning to step ST150, the information processing apparatus 1 generates a tree diagram T1 (FIGS. 2A, 2B, 3) representing at least a part of a plurality of indicators (indicator group) calculated in step ST105 as one node N each, and causes it to be displayed on the display device of the administrator terminal device 5. When the actual result comparison value is calculated in step ST115, the information processing apparatus 1 generates a tree diagram T1 (FIG. 2A) including the actual result comparison value 83 as the numerical value representing the node. When the budget comparison value is calculated in step ST125, the information processing apparatus 1 generates a tree diagram T1 (FIG. 2B) including the budget comparison value 84 as the numerical value representing the node N. When the indicator is estimated in step ST140, the information processing apparatus 1 generates a tree diagram T1 (FIG. 3) including a numerical value corresponding to the sales target value as the numerical value representing the node N_B of the sales amount B, and including a numerical value 85 corresponding to the estimated indicator as the numerical value representing the node N of the indicators other than the sales amount B.

[0090] Note that when the numerical value 85 included in the node N relates to the corrected indicator in step ST145, the information processing apparatus 1 generates a tree diagram such that the numerical value 85 corresponding to the corrected indicator is included in the node N.

[0091] When the tree diagram T1 (FIGS. 2A, 2B, 3) is being displayed on the administrator terminal device 5 and the target period is newly set by an input operation to the input element 75 (Yes in ST155), the information processing apparatus 1 returns to step ST100 described above and repeats the processing after step ST100. As a result, new indicators for the new target period are calculated, and the tree diagram T1 is updated so that each node N represents the new indicator. When an input operation is performed on the input elements 71 to 74 while the tree diagram T1 (Figs. 2A, 2B, and 3) is being displayed on the administrator terminal device 5 (Yes in ST160), the information processing apparatus 1 returns to the above-described step ST110 and repeats the processing after step ST110. As a result, numerical values (actual performance comparison values, budget comparison values, etc.) described together with the indicators are calculated for each node N, and the tree diagram T1 is updated so that the calculated numerical values are included in each node N. When an instruction to end the display of the tree diagram is input on the administrator terminal device 5 (Yes in ST165), the information processing apparatus 1 ends the processing shown in the flowchart of FIG. 4.

[0092] As described above, according to the present embodiment, by using a tree diagram in which a plurality of nodes N representing indicators are connected by branches R, the subordinate relationship and the dependency relationship between the indicators are visually and clearly shown. Therefore, even a person who is not accustomed to the numerical expression of the purchase analysis result can easily understand the contents represented by the plurality of indicators (customer purchase tendency, customer needs, etc.).

[0093] According to the present embodiment, since the actual performance comparison value is included in the node N of the tree diagram, it is possible to easily compare the indicator calculated as the past performance with the current indicator, and it is possible to easily show the user the purchase situation compared with the past performance.

[0094] According to the present embodiment, since the budget comparison value is included in the node N of the tree diagram, it is possible to easily compare the pre-planned indicator with the indicator calculated as the actual performance, and it is possible to easily show the user the purchase situation compared with the prior plan.

[0095] According to the present embodiment, since the numerical value corresponding to the estimated value of the indicator when the sales amount B reaches the target is included in the node N of the tree diagram, it is possible to easily show the user the standard of the indicator other than the sales amount B when the sales amount B reaches the target.

[0096] According to the present embodiment, since the target period serving as a reference for the index can be set according to the instruction of the user input to the administrator terminal device 5, the analysis results for different target periods can be easily displayed.

[0097] Next, an example of the process of generating the index estimation model (ST135: FIG. 4) will be described with reference to the flowchart of FIG. 7. In the process shown in the flowchart of FIG. 7, an index estimation model is generated that estimates the difference in other indexes when the difference in the sales amount B reaches the sales target value based on the correlation between the difference in the sales amount B and the difference in indexes other than the sales amount B. First, terms related to the content of the process will be described.

[0098] "Difference index" The difference index is the difference between two corresponding indexes in two index groups calculated for two target periods. Here, the lengths of the two target periods serving as the reference for the difference index are equal, and the start dates of the two target periods are offset by a unit period. In one example, the length of the target period is one year, and the length of the unit period is one month.

[0099] The index estimation model for one index estimates the difference index of the one index when the difference index of the sales amount B reaches the sales target value based on the correlation relationship between the difference index of the sales amount B and the difference index of the one index. In one example, the index estimation model is a linear regression model that approximates the relationship between the difference index of the sales amount B and the difference index of the one index by a linear equation.

[0100] The performance index DB23 includes (N + 1) index groups calculated for (N + 1) target periods with equal lengths and start dates offset by a unit period (N represents an integer greater than or equal to 1). The information processing apparatus 1 calculates a difference index based on the (N + 1) index groups included in the performance index DB23.

[0101] "The i-th target period" The i-th target period (i represents an integer from 1 to N + 1) is the target period with the i-th latest start date among the (N + 1) target periods.

[0102] The start date of the k-th target period (where k represents an integer from 1 to N) and the start date of the (k + 1)-th target period are shifted by a unit period.

[0103] "i-th indicator" The i-th indicator is an indicator included in the group of indicators calculated for the i-th target period.

[0104] "k-th difference indicator" The k-th difference indicator is a difference indicator that shows the difference obtained by subtracting one (k + 1)-th indicator corresponding to the one k-th indicator from the one k-th indicator.

[0105] Figure 6 is a diagram for explaining the difference indicator. In Figure 6, "M" indicates the length of the unit period. "P(1)", "P(2)",..., "P(N + 1)" indicate the 1st indicator, the 2nd indicator,..., and the (N + 1)-th indicator, respectively. "DP(1)", "DP(2)",..., "DP(N)" indicate the 1st difference indicator, the 2nd difference indicator,..., and the N-th difference indicator, respectively.

[0106] The information processing device 1 calculates the 1st difference indicator to the N-th difference indicator for each of a plurality of indicators including the sales amount B (ST300). That is, the information processing device 1 calculates the 1st difference indicator to the N-th difference indicator of the sales amount B based on the sales amount B included in each of the (N + 1) groups of indicators in the performance indicator DB23. Further, the information processing device 1 calculates the 1st difference indicator to the N-th difference indicator of one indicator (an indicator other than the sales amount B) based on one indicator included in each of the (N + 1) groups of indicators in the performance indicator DB23.

[0107] The information processing apparatus 1 selects one index (an index other than the sales amount B) for which a difference index has been calculated (ST305), and generates an index estimation model for the one index (ST310). That is, the information processing apparatus 1 generates an index estimation model for the one index based on the first to Nth difference indexes calculated for the sales amount B and the first to Nth difference indexes calculated for the one index. For example, the information processing apparatus 1 calculates a linear regression model that approximates the relationship between the difference index for the sales amount B and the difference index for the one index by a linear expression as the index estimation model.

[0108] If there is another index that has not yet been selected among the indexes for which the information processing apparatus 1 should generate an index estimation model (Yes in ST315), the information processing apparatus 1 selects the other one index (ST320) and generates an index estimation model (ST310). The information processing apparatus 1 repeats the process of generating an index estimation model for a predetermined plurality of indexes (ST310 to ST320).

[0109] According to the above-described embodiment, based on the correlation between the difference index of the sales amount B and the difference index of one index, when the difference index of the sales amount B reaches the sales target value, the difference index of the one index is estimated. Therefore, even when the sales amount B and other indexes tend to be linked with time, it is possible to reduce the influence of the tendency and estimate a highly accurate difference index.

[0110] Next, an example of the process of estimating an index (ST140: FIG. 4) will be described with reference to the flowchart of FIG. 8. The flowchart of FIG. 8 shows the process of estimating an index for one index. The information processing apparatus 1 performs the process shown in the flowchart of FIG. 8 for each of the plurality of indexes.

[0111] When a target change amount of sales amount B per target period (+20% in the example of the figure) is set according to a set value of the target of sales amount B input to the input element 74 (FIGS. 2A, 2B, 3), the information processing apparatus 1 converts the target change amount of sales amount B per target period indicated by this set value into a target change amount of sales amount B per unit period (ST400). For example, when the length of the target period is 1 year and the length of the unit period is 1 month, the information processing apparatus 1 calculates the target change amount of sales amount B per unit period (1 month) by dividing the target change amount of sales amount B per target period (1 year) by 12.

[0112] The information processing apparatus 1 applies the target change amount of sales amount B per unit period calculated in step ST400 as a sales target value to an index estimation model of one index. The information processing apparatus 1 estimates the differential index of the one index when the differential index of sales amount B reaches the sales target value based on the result of applying the sales target value to the index estimation model of the one index (ST405).

[0113] The information processing apparatus 1 converts the target change amount of one index per unit period indicated by the differential index estimated in step ST405 into a target change amount of the one index per target period (ST410). For example, when the length of the target period is 1 year and the length of the unit period is 1 month, the information processing apparatus 1 calculates the target change amount of the index per target period (1 year) by multiplying the index target value, which is the target change amount of the index per unit period (1 month), by 12.

[0114] The information processing apparatus 1 calculates an estimated value of the one index based on the target change amount of the one index per target period converted in step ST410 and a first index related to the one index (the most recent index used for generating the index estimation model) (ST415). For example, the information processing apparatus 1 calculates the estimated value of the index as the sum of the target change amount of the index per target period and the first index.

[0115] In the process of generating a tree diagram (ST150: Figure 4), the information processing apparatus 1 generates a tree diagram including a numerical value corresponding to the target change amount of the sales amount B per target period as the numerical value representing the node N_B of the sales amount B. Also, in this process, the information processing apparatus 1 generates a tree diagram including a numerical value corresponding to the estimated value of the index calculated in step ST415 as the numerical value representing the node N of the index other than the sales amount B.

[0116] According to the above-described embodiment, in the case of using an index estimation model for estimating the difference index of another index from the difference index of the sales amount B, the target change amount of the sales amount B per target period is converted into the target change amount of the sales amount B per unit period (sales target value), and the converted target change amount is applied to the index estimation model. Also, the target change amount of the index per unit period (difference index) estimated using the index estimation model is converted into the target change amount of the index per target period, and the estimated value of the index is calculated using the converted target change amount. Therefore, the user can easily grasp the estimated value of the index per target period corresponding to the target change amount of the sales amount B per target period without performing the above-described conversion.

[0117] Next, the process of correcting the estimated index (ST145: Figure 4) will be described.

[0118] Some of the indexes in the index group calculated in step ST105 have a predetermined relationship as shown in the tree diagram. For example, a relationship expressed by formula (3-2) holds between the number of member purchases C1 and the number of new member purchases C2, the number of continuing member purchases C3, and the number of revived member purchases C4. However, since some of the indexes estimated in step ST140 are values obtained using an index estimation model independent for each index, the above-described predetermined relationship does not hold. Therefore, the information processing apparatus 1 corrects some of the indexes estimated by the index estimation model so that the predetermined relationship holds.

[0119] Figure 9 is a flowchart for explaining an example of the process of correcting the estimated index (ST145: Figure 4). In the description of the flowchart of FIG. 9, the estimated value of the index "X" obtained in step ST140 is denoted as "V(X)". For example, "V(C1)" represents the estimated value of the number of member purchases C1. Also, the target value of the index "X" used in the calculation of the correction is denoted as "W(X)". For example, "W(B)" represents the target value of the sales amount B.

[0120] ST500: Based on the estimated value V(C1) of the number of member purchases C1, the information processing apparatus 1 corrects the estimated value V(C2) of the number of new member purchases C2, the estimated value V(C3) of the number of continuing member purchases C3, and the estimated value V(C4) of the number of revived member purchases C4 so as to satisfy the following conditions. <Condition> · The following equation based on Equation (3-2) holds. V(C1)=V(C2)+V(C3)+V(C4) · The ratio of the following estimated values before and after correction is equal. V(C2):V(C3):V(C4)

[0121] ST505: Based on the target value W(B) of the sales amount B, the information processing apparatus 1 corrects the estimated value V(B2) of the new member sales amount B2, the estimated value V(B3) of the continuing member sales amount B3, the estimated value V(B4) of the revived member sales amount B4, and the estimated value V(B5) of the non-member sales amount B5 so as to satisfy the following conditions. <Condition> · The following equation based on Equation (2-1) and Equation (2-2) holds. W(B)=V(B2)+V(B3)+V(B4)+V(B5) · The ratio of the following estimated values before and after correction is equal. V(B2):V(B3):V(B4):V(B5)

[0122] ST510: Based on the estimated value V(C2) of the number of new member purchases C2 and the estimated value V(B2) of the new member sales amount B2 corrected in steps ST500 and ST505, the target value W(D2) of the average total purchase amount D2 of new members is set by the following equation that satisfies Equation (4-3). W(D2) = V(B2) ÷ V(C2)

[0123] ST515: Correct the estimated value V(E2) of the average number of purchases per new member and the estimated value V(F2) of the average purchase unit price per new member so as to satisfy the following conditions. <Condition> · The following equation based on Equation (6-3) holds. W(D2) = V(E2) × V(F2) · The ratio of the following estimated values before and after correction is equal. V(E2):V(F2)

[0124] ST520: Based on the estimated value V(C3) of the number of purchases by continuing members and the estimated value V(B3) of the sales amount of continuing members corrected in Steps ST500 and ST505, set the target value W(D3) of the total average purchase amount D3 of continuing members by the following equation that satisfies Equation (4-4). W(D3) = V(B3) ÷ V(C3)

[0125] ST525: Correct the estimated value V(E3) of the average number of purchases per continuing member and the estimated value V(F3) of the average purchase unit price per continuing member so as to satisfy the following conditions. <Condition> · The following equation based on Equation (6-4) holds. W(D3) = V(E3) × V(F3) · The ratio of the following estimated values before and after correction is equal. V(E3):V(F3)

[0126] ST530: Based on the estimated value V(C4) of the number of purchases by reactivated members and the estimated value V(B4) of the sales amount of reactivated members corrected in Steps ST500 and ST505, set the target value W(D4) of the total average purchase amount D4 of reactivated members by the following equation that satisfies Equation (4-5). W(D4) = V(B4) ÷ V(C4)

[0127] ST535: Correct the estimated value V(E4) of the average number of purchases per revived member and the estimated value V(F4) of the average purchase unit price per revived member so as to satisfy the following conditions. <Condition> · The following equation based on Equation (6-5) holds. W(D4) = V(E4) × V(F4) · The ratio of the following estimated values before and after correction is equal. V(E4) : V(F4)

[0128] According to the above-described embodiment, while maintaining the relative ratio in the estimated values of a plurality of indicators obtained using independent indicator estimation models, the estimated values of the plurality of indicators can be corrected so that a predetermined relationship holds in the plurality of estimated values.

[0129] FIGS. 10 to 12 each show an example of a screen of the administrator terminal device 5 including the estimated value of the indicator corrected by the process shown in the flowchart of FIG. 9.

[0130] The screen S4 shown in FIG. 10 includes the same graphic 70 and input elements 71 to 75 as the screen S3 shown in FIG. 3, and also includes a tree diagram T2. The tree diagram T2 includes, in addition to the same configuration (node N, branch R) as the tree diagram T1 of the screen S3 shown in FIG. 3, a node N_C5 representing the number of non-member purchases C5, a node N_C2 representing the number of new member purchases C2, a node N_C3 representing the number of continuing member purchases C3, and a node N_C5 representing the number of revived member purchases C4. The tree diagram T2 also includes a branch R2 that branches from the node N_C representing the number of purchases C to the node N_C1 representing the number of member purchases C1 and the node N_C5 representing the number of non-member purchases C5, and a branch R5 that branches from the node N_C1 representing the number of member purchases C1 to the node N_C2 representing the number of new member purchases C2, the node N_C3 representing the number of continuing member purchases C3, and the node N_C5 representing the number of revived member purchases C4.

[0131] The screen S5 shown in FIG. 11 includes the same graphics 70 and input elements 71 to 75 as the screen S3 shown in FIG. 3, and also includes a tree diagram T3. The tree diagram T3 includes a node N_B representing the sales amount B, a node N_B1 representing the member sales amount B1, a node N_B5 representing the non-member sales amount B5, a node N_C1 representing the number of member purchases C1, a node N_D1 representing the average total purchase amount D1 per member, a node N_C5 representing the number of non-member purchases C5, a node N_F5 representing the average purchase unit price F5 of non-members, a node N_C2 representing the number of new member purchases C2, a node N_C3 representing the number of continuing member purchases C3, a node N_C5 representing the number of revived member purchases C4, a node N_F1 representing the average purchase unit price F1 of members, and a node N_E1 representing the average number of purchases E1 per member. The tree diagram T3 also includes a branch R6 that branches from the node N_B representing the sales amount B to the node N_B1 representing the member sales amount B1 and the node N_B5 representing the non-member sales amount B5, a branch R7 that branches from the node N_B1 representing the member sales amount B1 to the node N_C1 representing the number of member purchases C1 and the node N_D1 representing the average total purchase amount D1 per member, a branch R8 that branches from the node N_B5 representing the non-member sales amount B5 to the node N_C5 representing the number of non-member purchases C5 and the node N_F5 representing the average purchase unit price F5 of non-members, a branch R5 that branches from the node N_C1 representing the number of member purchases C1 to the node N_C2 representing the number of new member purchases C2, the node N_C3 representing the number of continuing member purchases C3, and the node N_C5 representing the number of revived member purchases C4, and a branch R9 that branches from the node N_D1 representing the average total purchase amount D1 per member to the node N_F1 representing the average purchase unit price F1 of members and the node N_E1 representing the average number of purchases E1 per member.

[0132] The screen S6 shown in FIG. 12 includes the same graphics 70 and input elements 71 to 75 as the screen S3 shown in FIG. 3, and also includes a tree diagram T4. The tree diagram T4 includes a node N_B representing the sales amount B, a node N_B2 representing the sales amount B2 of new members, a node N_B3 representing the sales amount B3 of continuing members, a node N_B4 representing the sales amount B4 of revived members, a node N_B5 representing the sales amount B5 of non-members, a node N_C2 representing the number of purchases C2 of new members, a node N_D2 representing the average total purchase amount D2 of new members, a node N_C3 representing the number of purchases C3 of continuing members, a node N_D3 representing the average total purchase amount D3 of continuing members, a node N_C4 representing the number of purchases C4 of revived members, a node N_D4 representing the average total purchase amount D4 of revived members, a node N_C5 representing the number of purchases C5 of non-members, a node N_F5 representing the average purchase unit price F5 of non-members, a node N_F2 representing the average purchase unit price F2 of new members, a node N_E2 representing the average number of purchases E2 of new members, a node N_F3 representing the average purchase unit price F3 of continuing members, a node N_E3 representing the average number of purchases E3 of continuing members, a node N_F4 representing the average purchase unit price F4 of revived members, and a node N_E4 representing the average number of purchases E4 of revived members. In addition, in the tree diagram T4, a branch R10 branches from a node N_B representing the sales amount B to nodes N_B2 representing the new member sales amount B2, N_B3 representing the continuing member sales amount B3, N_B4 representing the revived member sales amount B4, and N_B5 representing the non-member sales amount B5; a branch R11 branches from the node N_B2 representing the new member sales amount B2 to nodes N_C2 representing the new member purchase number C2 and N_D2 representing the new member average purchase total amount D2; a branch R12 branches from the node N_B3 representing the continuing member sales amount B3 to nodes N_C3 representing the continuing member purchase number C3 and N_D3 representing the continuing member average purchase total amount D3; a branch R13 branches from the node N_B4 representing the revived member sales amount B4 to nodes N_C4 representing the revived member purchase number C4 and N_D4 representing the revived member average purchase total amount D4; a branch R8 branches from the node N_B5 representing the non-member sales amount B5 to nodes N_C5 representing the non-member purchase number C5 and N_F5 representing the non-member average purchase unit price F5; a branch R14 branches from the node N_D2 representing the new member average purchase total amount D2 to nodes N_F2 representing the new member average purchase unit price F2 and N_E2 representing the new member average purchase frequency E2; a branch R15 branches from the node N_D3 representing the continuing member average purchase total amount D3 to nodes N_F3 representing the continuing member average purchase unit price F3 and N_E3 representing the continuing member average purchase frequency E3; and a branch R16 branches from the node N_D4 representing the revived member average purchase total amount D4 to nodes N_F4 representing the revived member average purchase unit price F4 and N_E4 representing the revived member average purchase frequency E4.

[0133] In each node N in the tree diagrams T1, T2, and T3, an estimated value of an index is described, and in some nodes N, an estimated value corrected by the process shown in the flowchart of FIG. 9 is described.

[0134] Note that the present invention is not limited to the above-described embodiments and further includes various variations. Those skilled in the art can make various changes and substitutions with respect to the components of the above-described embodiments within the technical scope of the present invention or its equivalent scope.

[0135] In the above-described embodiments, an example of displaying the estimated value of an index using a tree diagram has been given. However, in other embodiments of the present invention, the display step of displaying the tree diagram on the display device may be omitted. According to such an embodiment, it is possible to provide the estimated value of an index that can be expressed in a tree diagram or other formats.

[0136] The screens shown in the above-described respective figures are merely examples, and the components included in each screen and their arrangements can be arbitrarily changed.

[0137] In the above-described embodiments, part of the processing executed by the information processing apparatus 1 may be executed by another device (such as the administrator terminal device 5). In this case, it can be said that the processing as the information processing apparatus according to the present embodiment is executed by a system (computer system) including a plurality of computers configured by the information processing apparatus 1 and other devices. The invention described in the claims of the present application at the time of filing is appended below. [1] A method for an information processing apparatus to perform processing related to the analysis of commodity purchases, wherein the information processing apparatus is capable of accessing a storage device, the storage device stores a plurality of purchase information, one of the purchase information includes information related to a date when a purchase transaction was made, customer identification information for identifying the customer, and information related to the amount of the purchase transaction, as information related to a single purchase transaction in which a single customer purchases a commodity, an information extraction step of extracting a plurality of the purchase information related to the purchase transactions made during a target period; an index calculation step of calculating a plurality of indices related to the analysis of the purchase of the commodity based on the plurality of the purchase information extracted in the information extraction step; and a display step of generating a tree diagram representing at least a part of the plurality of the indices calculated in the index calculation step as one node each and displaying the tree diagram on a display device. The index calculation step includes a sales amount calculation step of calculating the sales amount of the commodity during the target period as the index based on the plurality of the purchase information extracted in the information extraction step; a purchase number calculation step of calculating the number of customers who purchased the commodity during the target period as the index based on the plurality of the purchase information extracted in the information extraction step; and an average purchase total amount calculation step of calculating, as the index, the average purchase total amount which is the average of the total amounts of the commodities purchased by a single customer during the target period, for the customers who made the purchase transactions during the target period, based on the plurality of the purchase information extracted in the information extraction step. The display step includes generating the tree diagram including a branch that branches from the node of the sales amount to the nodes of the purchase number and the average purchase total amount. Method. [2] A plurality of the indices calculated for one target period are referred to as an index group. A target value related to the sales amount is referred to as a sales target value. A machine learning model that estimates a numerical value related to one of the indices other than the sales amount when the numerical value related to the sales amount reaches the sales target value, based on the correlation relationship between the sales amount and the one index other than the sales amount, is referred to as an index estimation model. The memory device stores a plurality of the index groups calculated for a plurality of the target periods having equal lengths and different start dates. A model generation step of generating one or more index estimation models for one or more of the indexes other than the sales amount based on the plurality of the index groups stored in the memory device. An estimation step of estimating one or more of the indexes when a numerical value related to the sales amount reaches the sales target value based on a result of applying the set sales target value to the one or more index estimation models. The display step includes generating the tree diagram including, as numerical values representing the nodes, a numerical value corresponding to the set sales target value and numerical values corresponding to the one or more estimated indexes. The method according to [1]. [3] A difference between two corresponding indexes in two of the index groups calculated for two of the target periods, wherein the lengths of the two target periods are equal and the start dates of the two target periods are shifted by a unit period, is called a difference index. The index estimation model for one of the indexes is a machine learning model that estimates the difference index of the one index when the difference index of the sales amount reaches the sales target value based on a correlation relationship between the difference index of the sales amount and the difference index of the one index. The memory device stores (N + 1) index groups calculated for (N + 1) target periods having equal lengths and start dates shifted by the unit period one by one (N represents an integer of 1 or more). Among the (N + 1) target periods, the target period with the latest start date at the i-th (i represents an integer from 1 to N + 1) is called the i-th target period. The start date of the k-th target period (k represents an integer from 1 to N) and the start date of the (k + 1)-th target period are shifted by the unit period. An index included in the index group calculated for the i-th target period is called the i-th index. A difference obtained by subtracting one (k + 1)-th index corresponding to the one k-th index from the one k-th index is called the k-th difference index. When generating the index estimation model for one of the indexes, the model generation step calculates the first difference index to the N-th difference index of the sales amount based on the sales amount included in each of the (N + 1) index groups stored in the memory device. calculating a first difference index to an N-th difference index of the one index based on each of the (N + 1) index groups stored in the memory device; generating the index estimation model based on the first difference index to the N-th difference index calculated for the sales amount and the first difference index to the N-th difference index calculated for the one index; the estimating step, when estimating one of the indexes, estimating the difference index of the one index when the difference index of the sales amount reaches the sales target value based on a result of applying the set sales target value to the index estimation model generated for the one index; calculating an estimated value of the one index based on the estimated difference index of the one index and a first index related to the one index; The method according to [2]. [4] the estimating step, when estimating one of the indexes, converting the target change amount of the sales amount per the target period set according to an input instruction into the target change amount of the sales amount per the unit period; estimating the difference index of the one index when the difference index of the sales amount reaches the sales target value based on a result of applying the converted target change amount of the sales amount per the unit period to the index estimation model generated for the one index as the sales target value; converting the target change amount of the one index per the unit period indicated by the estimated difference index into the target change amount of the one index per the target period; calculating an estimated value of the one index based on the converted target change amount of the one index per the target period and the first index related to the one index; the displaying step includes generating the tree diagram including a numerical value corresponding to the set target change amount of the sales amount per the target period and numerical values corresponding to the estimated one or more indexes as numerical values representing the node; The method according to [3]. [5] calling the customer identified by the customer identification information a member; calling the customer not identified by the customer identification information a non-member; the index calculating step includes A member purchase number calculation step of calculating, as the index, the number of members who purchased the product during the one target period based on the plurality of purchase information extracted in the information extraction step; A non-member purchase number calculation step of calculating, as the index, the number of non-members who purchased the product during the one target period based on the plurality of purchase information extracted in the information extraction step, and The model generation step includes generating, based on the plurality of index groups stored in the storage device, an index estimation model for the member purchase number and an index estimation model for the non-member purchase number, respectively. The estimation step includes estimating, based on the results of applying the set sales target value to the index estimation models for the member purchase number and the non-member purchase number, respectively, the member purchase number and the non-member purchase number when the numerical value regarding the sales amount reaches the sales target value. The display step includes generating the tree diagram including numerical values corresponding to the estimated member purchase number and non-member purchase number, respectively, as numerical values representing the nodes. The method according to [2]. [6] The storage device stores a plurality of customer information. One piece of the customer information includes customer identification information for identifying one of the members, the date on which the one member was registered, and the most recent date on which the one member purchased the product. A member newly registered during one target period is referred to as a new member during the one target period. A member who purchased the product within a predetermined period before the start date of one target period and up to the start date is referred to as a continuing member during the one target period. A member other than the new member and the continuing member during one target period who purchased the product during the one target period is referred to as a revived member during the one target period. The index calculation step includes a new member purchase number calculation step of calculating, as the index, the number of new members who purchased the product during one target period based on the plurality of purchase information extracted in the information extraction step and the plurality of customer information stored in the storage device; A continuing member purchase number calculation step of calculating, as the index, the number of continuing members who purchased the product during the one target period based on the plurality of purchase information extracted in the information extraction step and the plurality of customer information stored in the storage device; A revived member purchase number calculation step of calculating, as the index, the number of revived members who purchased the product during the one target period based on the plurality of purchase information extracted in the information extraction step and the plurality of customer information stored in the storage device, and including: The model generation step includes generating the index estimation models for the number of new member purchases, the number of continuing member purchases, and the number of revived member purchases respectively based on the plurality of index groups stored in the storage device; The estimation step includes estimating the number of new member purchases, the number of continuing member purchases, and the number of revived member purchases when the numerical value regarding the sales amount reaches the sales target value based on the results of applying the set sales target value to the index estimation models for the number of new member purchases, the number of continuing member purchases, and the number of revived member purchases respectively; A correction step of correcting the estimated indexes, including correcting the estimated number of new member purchases, the estimated number of continuing member purchases, and the estimated number of revived member purchases such that the sum of the corrected number of new member purchases, the corrected number of continuing member purchases, and the corrected number of revived member purchases is equal to the estimated number of member purchases, and the ratio of the corrected number of new member purchases, the corrected number of continuing member purchases, and the corrected number of revived member purchases is equal to the ratio of the estimated number of new member purchases, the estimated number of continuing member purchases, and the estimated number of revived member purchases; The display step includes generating the tree diagram including, as numerical values representing the nodes, the numerical value corresponding to the corrected number of new member purchases, the numerical value corresponding to the corrected number of continuing member purchases, and the numerical value corresponding to the corrected number of revived member purchases respectively; The method according to [5]. [7] Of the sales amount during the target period, the total amount of the products purchased by the members is called the member sales amount; Of the sales amount during the target period, the total amount of the goods purchased by the non - members is referred to as the non - member sales amount, Of the member sales amount during the target period, the total amount of the goods purchased by the new members is referred to as the new - member sales amount, Of the member sales amount during the target period, the total amount of the goods purchased by the continuing members is referred to as the continuing - member sales amount, Of the member sales amount during the target period, the total amount of the goods purchased by the revived members is referred to as the revived - member sales amount, The index calculation step is, Based on the plurality of purchase information extracted in the information extraction step and the plurality of customer information stored in the storage device, a new - member sales amount calculation step of calculating the new - member sales amount during one target period as the index, Based on the plurality of purchase information extracted in the information extraction step and the plurality of customer information stored in the storage device, a continuing - member sales amount calculation step of calculating the continuing - member sales amount during one target period as the index, Based on the plurality of purchase information extracted in the information extraction step and the plurality of customer information stored in the storage device, it includes a revived - member sales amount calculation step of calculating the revived - member sales amount during one target period as the index, The model generation step includes generating respective index estimation models for the new - member sales amount, the continuing - member sales amount, the revived - member sales amount, and the non - member sales amount based on the plurality of index groups stored in the storage device, The estimation step includes estimating the new - member sales amount, the continuing - member sales amount, the revived - member sales amount, and the non - member sales amount when the numerical values related to the sales amount reach the sales target value, based on the results of applying the set sales target value to the index estimation models for the new - member sales amount, the continuing - member sales amount, the revived - member sales amount, and the non - member sales amount respectively. The correction step includes correcting the estimated new member sales amount, the estimated continuing member sales amount, the estimated revived member sales amount, and the estimated non-member sales amount such that the sum of the corrected new member sales amount, the corrected continuing member sales amount, the corrected revived member sales amount, and the corrected non-member sales amount is equal to the set sales target value, and the ratio of the corrected new member sales amount, the corrected continuing member sales amount, the corrected revived member sales amount, and the corrected non-member sales amount is equal to the ratio of the estimated new member sales amount, the estimated continuing member sales amount, the estimated revived member sales amount, and the estimated non-member sales amount. The display step includes generating the tree diagram including, as numerical values representing the nodes, a numerical value corresponding to the corrected new member sales amount, a numerical value corresponding to the corrected continuing member sales amount, a numerical value corresponding to the corrected revived member sales amount, and a numerical value corresponding to the corrected non-member sales amount. The method according to [6]. [8] The average of the customers who conducted the purchase transactions during the target period, which is the average number of times one customer conducted the purchase transactions during the target period, is referred to as the average purchase frequency. The average purchase frequency of the new members is referred to as the new member average purchase frequency. The average purchase frequency of the continuing members is referred to as the continuing member average purchase frequency. The average purchase frequency of the revived members is referred to as the revived member average purchase frequency. The average of the customers who conducted the purchase transactions during the target period, which is the average total amount of the goods purchased by one customer in one purchase transaction during the target period, is referred to as the average purchase unit price. The average purchase unit price of the new members is referred to as the new member average purchase unit price. The average purchase unit price of the continuing members is referred to as the continuing member average purchase unit price. The average purchase unit price of the revived members is referred to as the revived member average purchase unit price. The average total purchase amount of the new members is referred to as the new member average total purchase amount. The average total purchase amount of the continuing members is referred to as the continuing member average total purchase amount. The average total purchase amount of the revived members is referred to as the revived member average total purchase amount. The index calculation step is Based on the plurality of the purchase information extracted in the information extraction step and the plurality of the customer information stored in the storage device, an average purchase frequency calculation step of calculating the average purchase frequency of new members, the average purchase frequency of continuing members, and the average purchase frequency of reactivated members in one of the target periods as the respective indicators, Based on the plurality of the purchase information extracted in the information extraction step and the plurality of the customer information stored in the storage device, an average purchase unit price calculation step of calculating the average purchase unit price of new members, the average purchase unit price of continuing members, and the average purchase unit price of reactivated members in one of the target periods as the respective indicators, and The model generation step includes generating respective indicator estimation models for the average purchase frequency of new members, the average purchase frequency of continuing members, the average purchase frequency of reactivated members, the average purchase unit price of new members, the average purchase unit price of continuing members, and the average purchase unit price of reactivated members based on the plurality of the indicator groups stored in the storage device, The estimation step includes estimating the average purchase frequency of new members, the average purchase frequency of continuing members, the average purchase frequency of reactivated members, the average purchase unit price of new members, the average purchase unit price of continuing members, and the average purchase unit price of reactivated members when the numerical value regarding the sales amount reaches the sales target value, based on the results of applying the set sales target value to the indicator estimation models for the average purchase frequency of new members, the average purchase frequency of continuing members, the average purchase frequency of reactivated members, the average purchase unit price of new members, the average purchase unit price of continuing members, and the average purchase unit price of reactivated members, The correction step includes setting a target value of the total average purchase amount of new members according to the value obtained by dividing the corrected sales amount of new members by the corrected number of new member purchases, correcting the estimated average purchase unit price of new members and the estimated average purchase frequency of new members such that the product of the corrected average purchase unit price of new members and the corrected average purchase frequency of new members is equal to the set target value of the total average purchase amount of new members, and the ratio of the corrected average purchase unit price of new members to the corrected average purchase frequency of new members is equal to the ratio of the estimated average purchase unit price of new members to the estimated average purchase frequency of new members, Setting a target value for the average total purchase amount of the continuous members according to the value obtained by dividing the corrected continuous member sales amount by the corrected number of continuous member purchases; Correcting the estimated average purchase unit price of the continuous members and the estimated average number of purchases of the continuous members respectively so that the product of the corrected average purchase unit price of the continuous members and the corrected average number of purchases of the continuous members is equal to the set target value of the average total purchase amount of the continuous members, and the ratio of the corrected average purchase unit price of the continuous members to the corrected average number of purchases of the continuous members is equal to the ratio of the estimated average purchase unit price of the continuous members to the estimated average number of purchases of the continuous members; Setting a target value for the average total purchase amount of the revived members according to the value obtained by dividing the corrected revived member sales amount by the corrected number of revived member purchases; Including correcting the estimated average purchase unit price of the revived members and the estimated average number of purchases of the revived members respectively so that the product of the corrected average purchase unit price of the revived members and the corrected average number of purchases of the revived members is equal to the set target value of the average total purchase amount of the revived members, and the ratio of the corrected average purchase unit price of the revived members to the corrected average number of purchases of the revived members is equal to the ratio of the estimated average purchase unit price of the revived members to the estimated average number of purchases of the revived members; The display step includes generating the tree diagram including, as numerical values representing the nodes, numerical values corresponding to the corrected average number of purchases of the new members, numerical values corresponding to the corrected average number of purchases of the continuous members, numerical values corresponding to the corrected average number of purchases of the revived members, numerical values corresponding to the corrected average purchase unit price of the new members, numerical values corresponding to the corrected average purchase unit price of the continuous members, and numerical values corresponding to the corrected average purchase unit price of the revived members; The method according to [7]. [9] A plurality of the indicators calculated for one of the target periods are called an indicator group; The storage device stores one or more of the indicator groups calculated for one or more of the target periods; A performance comparison value calculation step of calculating one or more of the performance comparison values for comparing one or more of the indicators in one of the indicator groups calculated for one of the target periods in the indicator calculation step with one or more of the indicators in the past indicator group calculated for the past target period having the same length as the one target period, wherein each of the performance comparison values represents the difference between one of the indicators in the one indicator group and the past indicator corresponding to the one indicator in the past indicator group. When generating the tree diagram for one of the indicator groups, the display step includes generating the tree diagram including one or more of the performance comparison values calculated for a plurality of the indicators in the one indicator group as the numerical values representing the nodes. The method according to [1].

[10] A plurality of the indicators calculated for one of the target periods are referred to as an indicator group. The pre-planned indicators are referred to as budget indicators. A plurality of the budget indicators planned for one of the target periods are referred to as a budget indicator group. The storage device Stores one or more of the indicator groups calculated for one or more of the target periods And one or more of the budget indicator groups planned for the one or more of the target periods. A budget comparison value calculation step of calculating one or more of the budget comparison values for comparing one or more of the indicators in one of the indicator groups calculated for one of the target periods in the indicator calculation step with one or more of the budget indicators in one of the budget indicator groups planned for the one of the target periods, wherein each of the budget comparison values represents the difference between one of the indicators in the one indicator group and the budget indicator corresponding to the one indicator in the one budget indicator group. When generating the tree diagram for one of the indicator groups, the display step includes generating the tree diagram including one or more of the budget comparison values calculated for a plurality of the indicators in the one indicator group as the numerical values representing the nodes. The method according to [1].

[11] When the target period is set according to the input instruction, the information extraction step includes extracting a plurality of the purchase information regarding the purchase transactions conducted during the set target period. The method according to [1].

[12] The indicator calculation step An average purchase frequency calculation step of calculating, as the index, an average purchase frequency that is an average of the number of times each customer who conducted the purchase transaction during the target period made a purchase transaction during the target period, based on the plurality of purchase information extracted in the information extraction step; An average purchase unit price calculation step of calculating, as the index, an average purchase unit price that is an average of the total amount of the goods purchased by each customer in one purchase transaction, based on the plurality of purchase information extracted in the information extraction step, and The display step includes generating the tree diagram including a branch that branches from the node of the average total purchase amount to the nodes of the average purchase unit price and the average purchase frequency. The method according to [1].

[13] One piece of the purchase information includes information regarding the number of the goods purchased in one purchase transaction. The index calculation step includes: An average number of purchased items calculation step of calculating an average number of purchased items that is an average of the number of items purchased by the customer in one purchase transaction, based on the plurality of purchase information extracted in the information extraction step; and An average unit price of purchased items calculation step of calculating an average unit price of purchased items that is an average of the unit prices of the goods purchased by the customer in one purchase transaction, based on the plurality of purchase information extracted in the information extraction step. The display step includes generating the tree diagram including a branch that branches from the node of the average purchase unit price to the nodes of the average unit price of purchased items and the average number of purchased items. The method according to

[12] .

[14] A program including instructions for causing an information processing apparatus to perform processing related to analysis of purchase of goods, wherein the processing performed by the information processing apparatus according to the instructions includes each step of the method described in any one of [1] to

[13] . Program.

[15] An information processing apparatus for performing processing related to analysis of purchase of goods, comprising a processing unit and a storage unit storing instructions for causing the processing unit to perform processing, wherein the processing performed by the processing unit according to the instructions includes each step of the method described in any one of [1] to

[13] . Information processing apparatus.

[16] An information processing apparatus for performing processing related to analysis of purchase of goods, comprising means for performing each step of the method described in any one of [1] to

[13] . Information processing apparatus.

Explanation of Symbols

[0138] 1... Information processing device, 11... Communication unit, 12... Storage unit, 121... Program, 13... Processing unit, 2... Storage device, 21... Purchase information DB, 22... Customer information DB, 23... Performance index DB, 24... Budget index DB, 3... E-commerce server, 4... Store management device, 5... Administrator terminal device, 9... Communication network, 70... Graphic, 71 - 75... Input elements, S1 - S6... Screens, T1 - T4... Tree diagrams

Claims

1. A method for performing a process related to analysis of product purchases by an information processing device, comprising: the information processing device is capable of accessing a storage device; The storage device stores a plurality of pieces of purchase information; The one of the purchase information includes, as information regarding one purchase transaction in which one customer purchases a product, information regarding the date on which the one purchase transaction was made, customer identification information for identifying the one customer, and information regarding the amount of the one purchase transaction, An information extraction step of extracting a plurality of pieces of purchase information relating to the purchase transactions carried out during a target period; an index calculation step of calculating a plurality of indexes related to an analysis of purchases of the products based on the plurality of purchase information extracted in the information extraction step; a display step of generating a tree diagram in which at least some of the indices calculated in the index calculation step are represented as one node each, and displaying the tree diagram on a display device; The index calculation step includes: a sales amount calculation step of calculating, as the index, the sales amount of the product during the target period based on the plurality of purchase information extracted in the information extraction step; a number-of-purchasers calculation step of calculating, as the index, a number of purchasers, which is the number of customers who purchased the product during the target period, based on the plurality of purchase information extracted in the information extraction step; an average total purchase amount calculation step of calculating, as the index, an average total purchase amount which is an average of the total amounts of the products purchased by one customer during the target period, the average of the customers who conducted the purchase transactions during the target period, based on the multiple purchase information extracted in the information extraction step; the display step includes generating the tree diagram including branches branching from a node of the sales amount to a node of the number of purchasers and a node of the average purchase total amount; The plurality of indicators calculated for one of the target periods is called an indicator group, The target value for the sales amount is called the sales target value, A machine learning model that estimates a numerical value related to one of the indicators when the numerical value related to the sales amount reaches the sales target value based on a correlation between the one of the indicators other than the sales amount and the sales amount is called an indicator estimation model; The storage device stores a plurality of the index groups calculated for a plurality of the target periods having the same length and different start dates, a model generation step of generating one or more index estimation models related to one or more indexes other than the sales amount based on the plurality of index groups stored in the storage device; and estimating one or more of the indicators in a case where a numerical value relating to the sales amount reaches the sales target value based on a result of applying the set sales target value to the one or more indicator estimation models, the display step includes generating the tree diagram including, as values ​​representing the nodes, values ​​corresponding to the set sales target value and values ​​corresponding to one or more estimated indicators, method.

2. The difference between two corresponding indices in the two groups of indices calculated for the two target periods, where the lengths of the two target periods are equal and the start dates of the two target periods are shifted by a unit period, is called a difference index, the indicator estimation model for one of the indicators is a machine learning model that estimates the difference indicator of the one indicator when the difference indicator of the sales amount reaches the sales target value, based on a correlation between the difference indicator of the sales amount and the difference indicator of the one indicator; the storage device stores the (N+1) sets of indices calculated for (N+1) target periods (N is an integer equal to or greater than 1) having equal lengths and start dates shifted by the unit period; Among the (N+1) target periods, the target period with the latest start date i (i is an integer from 1 to N+1) is called the i-th target period, the start date of the kth target period (k is an integer from 1 to N) and the start date of the (k+1)th target period are shifted by the unit period, An index included in the set of indexes calculated for the i-th target period is referred to as the i-th index, The difference obtained by subtracting the (k+1)th index corresponding to the kth index from the kth index is called the kth difference index, In the model generation step, when the index estimation model for one of the indexes is generated, Calculating a first difference indicator through an N-th difference indicator of the sales amount based on the sales amount included in each of the (N+1) index groups stored in the storage device; Calculating a first difference index through an N-th difference index of the one index based on the one index included in each of the (N+1) index groups stored in the storage device; generating the index estimation model based on a first difference index through an N-th difference index calculated for the sales amount and a first difference index through an N-th difference index calculated for the one index; In the estimation step, when estimating one of the indicators, estimating the differential indicator of the one indicator when the differential indicator of the sales amount reaches the sales target value based on a result of applying the set sales target value to the indicator estimation model generated for the one indicator; Calculating an estimate of the one index based on the estimated difference index of the one index and a first index related to the one index; The method of claim 1.

3. In the estimation step, when estimating one of the indicators, converting the target change in the sales amount per target period, which is set in response to an input instruction, into a target change in the sales amount per unit period; estimating the difference indicator of the one indicator when the difference indicator of the sales amount reaches the sales target value based on a result of applying the converted target change amount of the sales amount per unit period to the indicator estimation model generated for the one indicator; converting a target change amount of the one index per unit period indicated by the estimated difference index into a target change amount of the one index per target period; calculating an estimated value of the one index based on the converted target change amount of the one index per target period and the first index related to the one index; the display step includes generating the tree diagram including, as values ​​representing the nodes, values ​​corresponding to a target change in the amount of sales per the set target period and values ​​corresponding to one or more of the estimated indicators; The method of claim 2.

4. The customer identified by the customer identification information is called a member; The customer who is not identified by the customer identification information is called a non-member; The index calculation step includes: a member purchase number calculation step of calculating, as the index, a member purchase number which is the number of the members who purchased the product during the one target period, based on the plurality of purchase information extracted in the information extraction step; a non-member purchaser number calculation step of calculating, as the index, the number of non-member purchasers, which is the number of non-members who purchased the product during the one target period, based on the plurality of purchase information extracted in the information extraction step; the model generation step includes generating the index estimation models for the number of member purchasers and the number of non-member purchasers based on the plurality of index groups stored in the storage device, The estimation step includes: and estimating the number of member purchasers and the number of non-member purchasers in the case where the numerical value relating to the sales amount reaches the sales target value based on a result of applying the set sales target value to the index estimation model relating to the number of member purchasers and the number of non-member purchasers, respectively; The display step includes generating the tree diagram including, as values ​​representing the nodes, values ​​corresponding to the estimated number of member purchasers and the estimated number of non-member purchasers. The method of claim 1.

5. The storage device stores a plurality of pieces of customer information; The one of the customer information includes the customer identification information for identifying the one of the members, the date on which the one of the members was registered, and the most recent date on which the one of the members purchased the product; A member who is newly registered during one of the target periods is called a new member during that one target period. A member who purchases the product before the start date of one of the applicable periods and within a specified period leading up to the start date is called a continuing member for that one applicable period; A member other than the new member and the continuing member during a given target period who purchased the product during that given target period is called a reinstated member during that given target period; The index calculation step includes: a new member purchase number calculation step of calculating, as the index, the number of new member purchasers, which is the number of new members who purchased the product during one of the target periods, based on the multiple pieces of purchase information extracted in the information extraction step and the multiple pieces of customer information stored in the storage device; a continuing member purchase number calculation step of calculating, as the index, the number of continuing members who purchased the product during the one target period, based on the plurality of purchase information extracted in the information extraction step and the plurality of customer information stored in the storage device; a resurrected member purchaser number calculation step of calculating, as the index, a resurrected member purchaser number, which is the number of the resurrected members who purchased the product during the one target period, based on the plurality of purchase information extracted in the information extraction step and the plurality of customer information stored in the storage device; the model generation step includes generating the index estimation models for the number of new member purchases, the number of continuing member purchases, and the number of reinstated member purchases based on the plurality of index groups stored in the storage device, the estimation step includes estimating the number of new member purchasers, the number of continuing member purchasers, and the number of resurrected member purchasers in a case where the numerical value relating to the sales amount reaches the sales target value, based on a result of applying the set sales target value to the index estimation model relating to the number of new member purchasers, the number of continuing member purchasers, and the number of resurrected member purchasers; a correction step for correcting the estimated indicators, the step of respectively correcting the estimated number of new member purchases, the estimated number of continuing member purchases, and the estimated number of resurrected member purchases so that the sum of the corrected number of new member purchases, the corrected number of continuing member purchases, and the corrected number of resurrected member purchases is equal to the estimated number of member purchases, and the ratio of the corrected number of new member purchases, the corrected number of continuing member purchases, and the corrected number of resurrected member purchases is equal to the ratio of the estimated number of new member purchases, the estimated number of continuing member purchases, and the estimated number of resurrected member purchases; The display step includes generating the tree diagram including, as values ​​representing the nodes, a value corresponding to the corrected number of new member purchasers, a value corresponding to the corrected number of continuing member purchasers, and a value corresponding to the corrected number of reinstated member purchasers. The method according to claim 4.

6. The total amount of the products purchased by the member out of the sales amount during the target period is called the member sales amount, The total amount of the products purchased by the non-members out of the sales amount during the target period is called the non-member sales amount; The total amount of the products purchased by the new members out of the member sales amount during the target period is called the new member sales amount, The total amount of the products purchased by the continuing members out of the member sales amount during the target period is called the continuing member sales amount, The total amount of the products purchased by the reinstated members out of the member sales amount during the target period is called the reinstated member sales amount; The index calculation step includes: a new member sales amount calculation step of calculating the new member sales amount in one of the target periods as the index based on the plurality of purchase information extracted in the information extraction step and the plurality of customer information stored in the storage device; a continuing member sales amount calculation step of calculating the continuing member sales amount in one of the target periods as the index based on the plurality of purchase information extracted in the information extraction step and the plurality of customer information stored in the storage device; a resurrected member sales amount calculation step of calculating the resurrected member sales amount in one of the target periods as the index based on the plurality of purchase information extracted in the information extraction step and the plurality of customer information stored in the storage device, The model generation step includes generating the index estimation models for the new member sales amount, the continuing member sales amount, the reinstated member sales amount, and the non-member sales amount based on the plurality of index groups stored in the storage device, The estimation step includes estimating the new member sales amount, the continuing member sales amount, the resurrected member sales amount, and the non-member sales amount when the numerical value related to the sales amount reaches the sales target value based on the result of applying the set sales target value to the index estimation model for the new member sales amount, the continuing member sales amount, the resurrected member sales amount, and the non-member sales amount. the correction step includes correcting the estimated new member sales amount, the estimated continuing member sales amount, the estimated resurrected member sales amount, and the estimated non-member sales amount so that a sum of the corrected new member sales amount, the corrected continuing member sales amount, the corrected resurrected member sales amount, and the corrected non-member sales amount is equal to the set sales target value, and a ratio of the corrected new member sales amount, the corrected continuing member sales amount, the corrected resurrected member sales amount, and the corrected non-member sales amount is equal to a ratio of the estimated new member sales amount, the estimated continuing member sales amount, the estimated resurrected member sales amount, and the estimated non-member sales amount; the display step includes generating the tree diagram including, as values ​​representing the nodes, a value corresponding to the corrected new member sales amount, a value corresponding to the corrected continuing member sales amount, a value corresponding to the corrected reinstated member sales amount, and a value corresponding to the corrected non-member sales amount, The method according to claim 5.

7. The average number of purchases for the customers who made the purchase transaction during the target period is called the average number of purchases, and The average number of purchases by the new members is referred to as the average number of purchases by the new members, The average number of purchases by the continuing member is referred to as the "average number of purchases by continuing member," The average number of purchases by the reinstated members is referred to as the average number of purchases by the reinstated members; The average of the total amount of the product purchased by one customer in one purchase transaction during the target period is called the average purchase price, The average purchase price of the new member is referred to as the average purchase price of the new member, The average purchase price of the continuing members is referred to as the continuing member average purchase price, The average purchase price of the reinstated member is referred to as the average purchase price of the reinstated member; The average purchase total amount of the new member is referred to as the new member average purchase total amount, The average purchase total amount of the continuing member is referred to as the continuing member average purchase total amount, The average purchase total amount of the reinstated members is referred to as the reinstated member average purchase total amount; The index calculation step includes: an average purchase frequency calculation step of calculating the average purchase frequency of new members, the average purchase frequency of continuing members, and the average purchase frequency of reinstated members in one target period as the indices, based on the plurality of purchase information extracted in the information extraction step and the plurality of customer information stored in the storage device; an average purchase price calculation step of calculating the average purchase price of new members, the average purchase price of continuing members, and the average purchase price of reinstated members in one target period as the indexes, based on the plurality of purchase information extracted in the information extraction step and the plurality of customer information stored in the storage device; the model generation step includes generating the index estimation models for the average number of purchases by new members, the average number of purchases by continuing members, the average number of purchases by resurrected members, the average purchase price of new members, the average purchase price of continuing members, and the average purchase price of resurrected members based on the plurality of index groups stored in the storage device; the estimation step includes estimating the average number of purchases by new members, the average number of purchases by continuing members, the average number of purchases by resurrected members, the average purchase price for new members, the average purchase price for continuing members, and the average purchase price for resurrected members when the numerical value related to the sales amount reaches the sales target value, based on a result of applying the set sales target value to the index estimation models related to the average number of purchases by new members, the average number of purchases by continuing members, the average purchase price for new members, the average purchase price for continuing members, and the average purchase price for resurrected members; The correction step includes: Setting a target value for the average total purchase amount of the new members according to a value obtained by dividing the corrected sales amount of the new members by the corrected number of purchasers of the new members; correcting the estimated new member average unit purchase price and the estimated new member average number of purchases so that the product of the corrected new member average unit purchase price and the corrected new member average number of purchases is equal to the set target value of the new member average total purchase amount, and the ratio of the corrected new member average unit purchase price and the corrected new member average number of purchases is equal to the ratio of the estimated new member average unit purchase price and the estimated new member average number of purchases; Setting a target value for the average total purchase amount of the continuing members according to a value obtained by dividing the corrected sales amount of the continuing members by the corrected number of purchasers of the continuing members; correcting the estimated average continuing member unit purchase price and the estimated average number of purchases of the continuing member so that the product of the corrected average continuing member unit purchase price and the corrected average number of purchases of the continuing member is equal to the set target value of the average continuing member total purchase amount, and the ratio of the corrected average continuing member unit purchase price and the corrected average number of purchases of the continuing member is equal to the ratio of the estimated average continuing member unit purchase price and the estimated average number of purchases of the continuing member; setting a target value for the average total purchase amount of the revived members according to a value obtained by dividing the corrected sales amount of the revived members by the corrected number of purchasers of the revived members; correcting the estimated average unit purchase price of revived members and the estimated average number of purchases of revived members so that a product of the corrected average unit purchase price of revived members and the corrected average number of purchases of revived members is equal to a set target value of the average total purchase amount of revived members, and a ratio of the corrected average unit purchase price of revived members to the corrected average number of purchases of revived members is equal to a ratio of the estimated average unit purchase price of revived members to the estimated average number of purchases of revived members, the display step includes generating the tree diagram including, as values ​​representing the nodes, a value corresponding to the corrected average number of purchases of new members, a value corresponding to the corrected average number of purchases of continuing members, a value corresponding to the corrected average number of purchases of resurrected members, a value corresponding to the corrected average unit purchase price of new members, a value corresponding to the corrected average unit purchase price of continuing members, and a value corresponding to the corrected average unit purchase price of resurrected members; The method according to claim 6.

8. A method for performing a process related to analysis of product purchases by an information processing device, comprising: the information processing device is capable of accessing a storage device; The storage device stores a plurality of pieces of purchase information; The one of the purchase information includes, as information regarding one purchase transaction in which one customer purchases a product, information regarding the date on which the one purchase transaction was made, customer identification information for identifying the one customer, and information regarding the amount of the one purchase transaction, An information extraction step of extracting a plurality of pieces of purchase information relating to the purchase transactions carried out during a target period; an index calculation step of calculating a plurality of indexes related to an analysis of purchases of the products based on the plurality of purchase information extracted in the information extraction step; a display step of generating a tree diagram in which at least some of the indices calculated in the index calculation step are represented as one node each, and displaying the tree diagram on a display device; The index calculation step includes: a sales amount calculation step of calculating, as the index, the sales amount of the product during the target period based on the plurality of purchase information extracted in the information extraction step; a number-of-purchasers calculation step of calculating, as the index, a number of purchasers, which is the number of customers who purchased the product during the target period, based on the plurality of purchase information extracted in the information extraction step; an average total purchase amount calculation step of calculating, as the index, an average total purchase amount which is an average of the total amounts of the products purchased by one customer during the target period, the average of the customers who conducted the purchase transactions during the target period, based on the multiple purchase information extracted in the information extraction step; the display step includes generating the tree diagram including branches branching from a node of the sales amount to a node of the number of purchasers and a node of the average purchase total amount; A plurality of the indicators calculated for one of the target periods is called an indicator group, The storage device stores one or more of the index groups calculated for one or more of the target periods; a performance comparison value calculation step of calculating one or more performance comparison values ​​for comparing one or more of the indicators in one of the index groups calculated for one of the target periods in the indicator calculation step with one or more of the indicators in the past index group calculated for a past target period having the same length as the one of the target periods, each of the performance comparison values ​​representing a difference between one of the indicators in the one of the index groups and a past indicator corresponding to the one of the indicators in the past index group; the display step includes, when generating the tree diagram for one of the index groups, generating the tree diagram including, as values ​​representing the nodes, one or more of the performance comparison values ​​calculated for a plurality of the indexes in the one of the index groups; method.

9. A method for performing a process related to analysis of product purchases by an information processing device, comprising: the information processing device is capable of accessing a storage device; The storage device stores a plurality of pieces of purchase information; The one of the purchase information includes, as information regarding one purchase transaction in which one customer purchases a product, information regarding the date on which the one purchase transaction was made, customer identification information for identifying the one customer, and information regarding the amount of the one purchase transaction, An information extraction step of extracting a plurality of pieces of purchase information relating to the purchase transactions carried out during a target period; an index calculation step of calculating a plurality of indexes related to an analysis of purchases of the products based on the plurality of purchase information extracted in the information extraction step; a display step of generating a tree diagram in which at least some of the indices calculated in the index calculation step are represented as one node each, and displaying the tree diagram on a display device; The index calculation step includes: a sales amount calculation step of calculating, as the index, the sales amount of the product during the target period based on the plurality of purchase information extracted in the information extraction step; a number-of-purchasers calculation step of calculating, as the index, a number of purchasers, which is the number of customers who purchased the product during the target period, based on the plurality of purchase information extracted in the information extraction step; an average total purchase amount calculation step of calculating, as the index, an average total purchase amount which is an average of the total amounts of the products purchased by one customer during the target period, the average of the customers who conducted the purchase transactions during the target period, based on the multiple purchase information extracted in the information extraction step; the display step includes generating the tree diagram including branches branching from a node of the sales amount to a node of the number of purchasers and a node of the average purchase total amount; A plurality of the indicators calculated for one of the target periods is called an indicator group, The above-mentioned indicators that are planned in advance are called budget indicators, A plurality of the budget indicators planned for one of the target periods is called a budget indicator group, The storage device includes: One or more of the indexes calculated for one or more of the target periods; storing one or more budget indicator groups planned for the one or more target periods; a budget comparison value calculation step of calculating one or more budget comparison values ​​for comparing one or more of the indicators in one of the indicator groups calculated for one of the target periods in the indicator calculation step with one or more budget indicators in one of the budget indicator groups planned for the one of the target periods, each of the budget comparison values ​​representing a difference between one of the indicators in the one of the indicator groups and the budget indicator corresponding to the one of the indicators in the one of the budget indicator groups; The display step includes, when generating the tree diagram for one of the index groups, generating the tree diagram including one or more of the budget comparison values ​​calculated for a plurality of the indexes in the one of the index groups as values ​​representing the nodes. method.

10. the information extraction step includes, when the target period is set in response to an input instruction, extracting a plurality of pieces of purchase information related to the purchase transactions performed during the set target period; The method according to any one of claims 1 to 9.

11. The index calculation step includes: an average purchase frequency calculation step of calculating, as the index, an average purchase frequency, which is an average for the customers who performed the purchase transaction during the target period, and is an average of the number of times that one customer performed the purchase transaction during the target period, based on the multiple purchase information extracted in the information extraction step; an average purchase price calculation step of calculating, as the index, an average purchase price, which is an average for the customers who conducted the purchase transactions during the target period, based on the multiple purchase information extracted in the information extraction step, and is an average of the total amount of the products purchased by one customer in one purchase transaction; the display step includes generating the tree diagram including branches branching from a node of the average purchase total amount to a node of the average purchase price and a node of the average purchase frequency, The method according to any one of claims 1 to 9.

12. The one of the purchasing information includes information regarding the number of the products purchased in one of the purchasing transactions, The index calculation step includes: an average number of purchased items calculation step of calculating an average number of purchased items, which is an average number of the items purchased by the customer in one purchase transaction, based on the plurality of purchase information extracted in the information extraction step; an average product unit price calculation step of calculating an average purchase product unit price, which is an average of the unit prices of the products purchased by the customer in one purchase transaction, based on the plurality of purchase information extracted in the information extraction step; the display step includes generating the tree diagram including branches branching from a node of the average purchase price to a node of the average purchase price and a node of the average number of purchased items; The method of claim 11.

13. A program including instructions for causing an information processing device to perform a process related to an analysis of product purchases, The process performed by the information processing device in accordance with the command includes each step of the method according to any one of claims 1 to 9. program.

14. An information processing device that performs processing related to analysis of product purchases, A processing section; A storage unit that stores an instruction for causing the processing unit to perform a process, The processing performed by the processing unit in accordance with the instructions includes each step of the method according to any one of claims 1 to 9. Information processing device.

15. An information processing device that performs processing related to analysis of product purchases, A method for performing each step of the method according to any one of claims 1 to 9, Information processing device.

Images