Business analysis systems, methods, and programs

Abstract

Based on sales analysis results, the system extracts a small number of elements that account for the majority of sales, and automatically proposes areas for improvement and specific solutions in product, sales, and purchasing based on the profitability and purchasing trends of those elements. [Solution] The business analysis system obtains customer names of a company, shipping data associated with the customer's sales amount, purchase frequency and type of goods for a predetermined period, and the unit price of the goods. It extracts customer groups whose sales amount is above a predetermined value, extracts customer groups from the extracted customer groups whose purchase frequency of the goods is above a predetermined value, extracts customer groups from the extracted customer groups whose number of purchase types of goods is above a predetermined value, and determines that customer groups are targets for in-depth sales if both the purchase frequency and the number of purchase types of goods are above a predetermined value within those customer groups.

Description

Technical Field

[0001] The present invention relates to a system that extracts "minor elements that bring about the majority of sales" based on sales performance data, and proposes improvement targets and specific solutions in product, sales, and purchasing from the profitability and purchase trends of these elements.

Background Art

[0002] In economics, a distribution called the "Pareto principle" is known. Applying this principle to the sales of a company, for example, it can be said that "80% of the company's sales are accounted for by about 20% of the customers". Therefore, Patent Document 1 discloses a program for conducting business analysis by calculating the transaction composition ratio based on the "transaction volume" for each customer using the Pareto distribution.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, with the program of Patent Document 1, it is difficult to conduct sufficient business analysis based only on the "transaction volume", and detailed business analysis from perspectives such as "customers, products, sales representatives, sales channels, suppliers" is required to identify the problems of the company. On the other hand, conducting detailed analysis for all customers, products, etc. is inefficient, and it becomes difficult to extract the items to be improved.

[0005] Therefore, it is desirable to efficiently conduct detailed business analysis by utilizing the Pareto principle, and further, it is desirable to be able to automatically propose measures for improving products, sales, and purchasing based on the results of these analyses.

[0006] To address complex business problems, detailed and efficient analysis applying Pareto's principle is necessary. Furthermore, the development of a system that automatically proposes improvement measures for core business departments such as product development, sales activities, and purchasing processes based on these analysis results is desirable.

[0007] In view of these issues, the present invention aims to provide a management analysis system, method, and program that extracts "a few elements that account for the majority of sales" based on sales performance data, and automatically proposes areas for improvement and specific solutions in product, sales, and purchasing. [Means for solving the problem]

[0008] The present invention provides the following solutions.

[0009] According to the invention relating to the first feature, An acquisition unit acquires shipping data that associates the customer name of a company with the customer's sales amount, purchase frequency and type of goods for a specified period, and the unit price of the goods. A sales Pareto extraction unit that extracts customer groups whose sales amount is equal to or greater than a predetermined value, A frequency Pareto extraction unit extracts customer groups from the extracted customer groups in which the frequency of purchase of the product by those customers is equal to or greater than a predetermined value, A variety Pareto sampling unit extracts from the customer groups extracted by the frequency Pareto sampling unit the customer groups in which the number of types of products purchased by the customer is equal to or greater than a predetermined value, A target customer determination unit determines that the customer group extracted by the aforementioned Pareto extraction unit is a target for in-depth sales, We provide a business analysis system equipped with the following features.

[0010] According to the invention relating to the first feature, based on Pareto's principle, it is possible to automatically extract a small number of elements that account for the majority of the results (for example, the top 20% of customers who account for 80% of the total sales), and in addition, based on detailed sales analysis (for example, the frequency of purchase and the number of types of products purchased by each customer), it is possible to automatically identify target customers from whom hints for developing products with high customer value and information related to improving sales and services can be obtained.

[0011] The invention relating to the second feature is the invention relating to the first feature, The aforementioned shipping data is further associated with the profit margin of the aforementioned customer, The sales Pareto extraction unit further comprises a classification unit that classifies customer groups whose profit margin is below a predetermined value, among the customer groups extracted by the sales Pareto extraction unit, according to the predetermined value. The aforementioned target customer determination unit provides a business analysis system that determines the classified customer group as a target for profit improvement.

[0012] According to the invention relating to the second feature, it becomes possible to automatically identify target customers who should be targeted for profit improvement based on their profit margins, and to improve their profit margins through improvements in product pricing, sales, and purchasing.

[0013] The invention relating to the third feature is the invention relating to the first feature, The aforementioned shipping data further includes the purchase date and quantity of the aforementioned product, The target customer determination unit provides a business analysis system that determines the target for competitive countermeasures from among the customer groups extracted by the sales Pareto extraction unit, based on changes in the purchase timing and changes in the purchase quantity.

[0014] According to the invention relating to the third feature, by understanding customer needs and circumstances based on changes in purchase timing and quantity, and by detecting the impact on market share and the risk of customer churn, it becomes possible to automatically propose measures to improve product pricing, sales, and purchasing.

[0015] Although the present invention belongs to the category of computer systems, it also exhibits similar operations and effects according to the category in other categories such as methods and programs.

Advantages of the Invention

[0016] According to the present invention, it is possible to provide a business analysis system, method, and program that extract "a small number of elements that bring about the majority of sales" based on sales performance data and automatically propose improvement targets and specific solutions in product, sales, and purchasing.

Brief Description of the Drawings

[0017] [Figure 1] It is a diagram for explaining the outline of the business analysis system 1 which is an embodiment of the present invention. [Figure 2] It is a configuration diagram of the business analysis system 1 of the present embodiment. [Figure 3] It is a flowchart of the target customer determination process executed by the business analysis system 1 of the present embodiment. [Figure 4] It is a diagram for explaining the shipping list 100 in the business analysis system 1 of the present embodiment. [Figure 5] It is a diagram for explaining the product master 110 in the business analysis system 1 of the present embodiment. [Figure 6] It is a diagram for explaining the customer group 300 extracted by the sales Pareto extraction unit 22 in the shipping data 200 in the business analysis system 1 of the present embodiment. [Figure 7] It is a diagram for explaining the customer group 400 extracted by the frequency Pareto extraction unit 23 in the shipping data 200 in the business analysis system 1 of the present embodiment. [Figure 8] It is a diagram for explaining the customer group 500 extracted by the item number Pareto extraction unit 24 in the shipping data 200 in the business analysis system 1 of the present embodiment and the determined intensive sales target 510. [Figure 9]This figure illustrates the profit improvement target 520 determined in the shipment data 200 of the management analysis system 1 of this embodiment. [Figure 10] This figure illustrates the competitive countermeasure target 540 determined in the shipment data 200 of the business analysis system 1 of this embodiment. [Modes for carrying out the invention]

[0018] The best mode for carrying out the present invention will be described below with reference to the figures. However, this is merely one example, and the technical scope of the present invention is not limited thereto.

[0019] [Overview of Management Analysis System 1] An overview of a business analysis system 1, which is one embodiment of the present invention, will be described based on Figure 1. Figure 1 is a diagram illustrating the overview of a business analysis system 1, which is one embodiment of the present invention. The business analysis system 1 consists of a computer 2 and is a computer system for performing business analysis.

[0020] Computer 2 of the business analysis system 1 includes, for example, computers such as desktop PCs, laptops, and servers; mobile devices such as smartphones and tablet devices; and wearable devices such as head-mounted displays like smart glasses and smartwatches.

[0021] Furthermore, the computer 2 of the management analysis system 1 may be implemented as, for example, a single terminal device, multiple terminal devices, or a virtual device such as a cloud computer.

[0022] Furthermore, the management analysis system 1 may consist of the terminal device described above instead of the computer 2.

[0023] Computer 2 of the management analysis system 1 is connected to the aforementioned terminal devices and other terminals and devices via a public network, etc., enabling data communication and enabling the transmission and reception of necessary data and information.

[0024] Next, we will explain the overview of the processes performed by the business analysis system 1. First, the computer 2 of the business analysis system 1 acquires shipment data 200, which associates the customer name of the company with the customer's sales amount, purchase frequency and type of goods for a predetermined period, and the unit price of the goods (step S1). Specifically, the computer 2 acquires shipment data 200, which associates the customer name and code of the company with the customer's sales amount, purchase frequency and type of goods for a predetermined period, and the unit price of the goods, based on the shipment list 100 and various masters such as the product master 110. The shipment list 100 and the product master 110 will be described in detail later. If the information in the shipment list 100 is insufficient, the computer 2 may further rely on various masters such as the customer master and supplier master in addition to the product master 110.

[0025] Next, computer 2 extracts customer group 300 whose sales amount is above a predetermined value (step S2). Specifically, computer 2 extracts customers whose sales amount is above a predetermined percentage of the total sales amount of the company as customer group 300. This extracts customers whose sales amount is above a predetermined percentage of the total sales amount of the company. The predetermined value of sales amount can be set by the user to any value, such as 80%.

[0026] Next, computer 2 extracts customers from the extracted customer group 300 whose purchase frequency of the product is above a predetermined value (step S3). Specifically, computer 2 extracts customers from the customer group 300 extracted in step S2 above whose purchase frequency of the product is above a predetermined percentage of the entire customer group, and identifies them as customer group 400. In this way, customers whose purchase frequency of the product is above a predetermined percentage of customer group 300 are identified as customer group 400. The predetermined value of customer frequency can be set by the user to any value such as 20%.

[0027] Next, computer 2 extracts customer groups 500 from the extracted customer groups 400 in which the number of different types of products purchased by the customers is equal to or greater than a predetermined value (step S4). Specifically, computer 2 extracts customer groups in which the number of different types of products purchased by customers in the customer group 400 extracted in step S3 above is equal to or greater than a predetermined percentage. The predetermined value for the number of different types of products purchased can be set by the user to any value, such as 10% of all product types.

[0028] Next, computer 2 determines the extracted customer group 500 as a target for in-depth sales 510 (step S5). Specifically, computer 2 determines the customer group 500 extracted in step S4 above, that is, the customer group 500 in which the sales amount, purchase frequency, and number of types of products included in the shipment data 200 each exceed a predetermined percentage, as a target for in-depth sales 510. An in-depth sales target is a target to which solutions such as upselling, cross-selling, understanding the company's strengths and weaknesses, and gathering information for developing products with high customer value are proposed based on Pareto analysis. Solutions may be assigned evaluations such as A to D according to their importance. Specifically, a number of types of products purchased exceeding a specified upper value may be evaluated as A, a number of orders exceeding a specified upper value as B, and sales exceeding a specified upper value as C.

[0029] In step S5 described above, the shipping data 200 is further associated with the profit margin of the customers, and the computer 2 may classify the customer groups 300 extracted in step S2 above whose profit margins are below a predetermined value according to that predetermined value, and determine the classified customer groups as profit improvement targets 520. Specifically, the computer 2 may classify the customer groups 300 extracted in step S2 above, that is, the customer groups 300 that account for a predetermined percentage or more of the total sales amount of the company, among the customer groups 200 whose profit margins included in the shipping data 200 are below a predetermined value, according to that predetermined value, and determine each classified customer group as a profit improvement target 520. The profit margin may be the gross profit margin or the operating profit margin. The predetermined value of the profit margin may be set by the user to any value. The profit improvement target 520 is a target for which solutions such as price design, sales, and procurement improvement measures are proposed. As with the in-depth sales targets, the solutions may be assigned evaluations such as A to D according to their importance. Specifically, a profit margin of 20% or less below the average could be evaluated as A, a profit margin of 10-20% below the average as B, a profit margin of 0-10% below the average as C, and a profit margin of 0% or more above the average as D.

[0030] Furthermore, in step S5 described above, the shipping data 200 may further include the purchase date and quantity of the goods, and competitive action targets 540 may be set from among the customer groups 300 extracted in step S2 described above, based on the changes in purchase date and the changes in purchase quantity. Specifically, the computer 2 may determine that the customer groups 300 extracted in step S2 described above, that is, customer groups 300 that account for a predetermined percentage or more of the total sales amount of the company, are competitive action targets 540 based on the changes in purchase date and the changes in purchase quantity included in the shipping data 200.

[0031] The above is an overview of the processes performed by Management Analysis System 1.

[0032] [System Configuration of Management Analysis System 1] Based on Figure 2, the system configuration of the management analysis system 1 of this embodiment will be described. The management analysis system 1 consists of computer 2 and is a computer system that extracts "a few elements that contribute to the majority of sales" based on sales performance data, and proposes areas for improvement and specific solutions in products, sales, and purchasing based on the profitability and purchasing trends of those elements.

[0033] Furthermore, the business analysis system 1 may include other terminals and devices. For example, each user may use a separate computer 2, in which case the business analysis system 1 will execute each of the processes described later using one or more combinations of computer 2 and the other included terminals and devices.

[0034] Computer 2 may be implemented as, for example, a single terminal device, multiple terminal devices, or a virtual device such as a cloud computer.

[0035] Computer 2 includes, for example, computers such as desktop PCs, laptops, and servers; mobile devices such as smartphones and tablet devices; and wearable devices such as head-mounted displays like smart glasses and smartwatches.

[0036] Computer 2 includes a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), RAM (Random Access Memory), ROM (Read Only Memory), and other components as its control unit.

[0037] Computer 2 includes data storage as a memory unit, such as a hard disk, semiconductor memory, recording medium, or memory card. The data storage may be internal data storage and / or external data storage. The data may be stored in a cloud service or database.

[0038] Computer 2 includes a communication unit equipped with devices to enable communication with other terminals and equipment. The communication method may be wireless or wired.

[0039] Computer 2 shall have the necessary functions for operating Computer 2 as an input unit. Examples of input methods include a liquid crystal display for touch panel functionality, a keyboard, a mouse, a pen tablet, hardware buttons on the device, a microphone for voice recognition, etc. The present invention is not particularly limited by the input method.

[0040] Computer 2 shall have the necessary functions for a user of the business analysis system 1 to operate Computer 2 as an output unit. Examples of output methods include display and audio output such as projection to an LCD display, PC display, or projector. The present invention is not particularly limited in function by the output method.

[0041] The control unit, in cooperation with the processing unit, implements a sales Pareto extraction unit 22, a frequency Pareto extraction unit 23, a type count Pareto extraction unit 24, a target customer determination unit 25, and a classification unit 26. The control unit, in cooperation with the processing unit and the storage unit, implements an acquisition unit 21 and a determination unit 28. The control unit, in cooperation with the processing unit and the input unit, implements a reception unit 27.

[0042] The above describes the system configuration of Management Analysis System 1.

[0043] [Target Customer Identification Process] Based on Figure 3, the target customer determination process performed by computer 2 will be explained. Figure 3 is a flowchart of the target customer determination process performed by computer 2. As shown in Figure 3, the target customer determination process consists of steps S11 to S15, which are the processes performed in steps S1 to S5 described above.

[0044] First, the acquisition unit 21 of computer 2 acquires shipping data 200, which associates the company's customer name, the customer's sales amount, purchase frequency and type of goods for a predetermined period, and the unit price of the goods (step S11). Specifically, the acquisition unit 21 acquires shipping data 200, which associates the company's customer name and code, the customer's sales amount, purchase frequency and type of goods for a predetermined period, and the unit price of the goods, based on the shipping list 100 and various masters such as the product master 110. If the information in the shipping list 100 is insufficient, the acquisition unit 21 may further base its acquisition on various masters such as the customer master and supplier master in addition to the product master 110.

[0045] The aforementioned shipping list 100 is a shipping detail used in the company's accounting and includes sales performance data for all customers. As shown in Figure 4, the shipping list 100 includes, but is not limited to, customer code, customer name, product code, product name, quantity, unit price, shipping date, sales representative, etc. "Customer code" is a unique code assigned to the company's trading customers. "Customer name" is the name of the company's trading customer. "Product code" is a unique code assigned to the company's products and is the code for the products shipped to the trading customer. "Product name" is the name of the company's products and is the name of the products shipped to the trading customer. "Quantity" is the number of products shipped to the trading customer. "Unit price" is the unit price of the products shipped to the trading customer. "Shipping date" is the date the products were shipped to the trading customer. "Sales representative" is the company's sales representative and is the person in charge of shipping the company's products to the trading customer.

[0046] The aforementioned product master 110 contains data that shows the details of a company's products. As shown in Figure 5, the product master 110 includes, but is not limited to, product code, product name, supplier code, supplier name, product type, etc. "Product code" is a unique code assigned to a product. "Product name" is the name of the product. "Supplier code" is a unique code assigned to the supplier of the product. "Supplier name" is the name of the supplier of the product. "Product type" is a name used to categorize products by type.

[0047] The aforementioned shipping data 200 is obtained by associating customer code and customer name with the customer's sales amount, purchase frequency and number of product types for a specified period, and the unit price of the product, based on various master data such as the shipping list 100 and product master 110.

[0048] Next, the sales Pareto extraction unit 22 of computer 2 extracts customer groups 300 whose sales amount is equal to or greater than a predetermined value (step S12). Specifically, based on the shipment data 200 obtained in step S11 described above, the sales Pareto extraction unit 22 associates the customer code and customer name with the customer's sales amount, purchase frequency and number of types of products purchased over a predetermined period, and the unit price of the product, and extracts them as customer groups 300. In this way, customers who account for a predetermined percentage or more of the total sales amount of the company are extracted as customer groups 300. Customer groups 300 may also be extracted in order of sales.

[0049] In Figure 6, customers whose sales exceed a predetermined value, namely Company F, Company I, Company E, Company C, and Company M, are extracted as customer group 300. The predetermined sales value may be set by accepting input from the user via the input unit of Computer 2, allowing for arbitrary values ​​such as 80% based on industry, business type, product characteristics, etc.

[0050] In step S12, the sales Pareto extraction unit 22 of computer 2 may extract products whose sales amount is equal to or greater than a predetermined value. Specifically, based on the shipment data 200 acquired in step S11 described above, the sales Pareto extraction unit 22 may extract products whose sales amount is equal to or greater than a predetermined percentage of the total sales of the company as product group 310, associating the product code and product name with the sales amount, purchase frequency, and number of types of products purchased for a predetermined period, and the unit price of the product. In this way, products that are equal to or greater than a predetermined percentage of the total sales of the company are extracted as product group 310. The extraction of product group 310 may also be done by extracting products in order of sales.

[0051] Next, the frequency Pareto extraction unit 23 of computer 2 extracts customers from the extracted customer group 300 whose purchase frequency of the product is equal to or greater than a predetermined value (step S13). Specifically, the frequency Pareto extraction unit 23 extracts customers from the customer group 300 extracted in step S12 above whose purchase frequency of the product is equal to or greater than a predetermined percentage of the entire customer group 300, and identifies them as customer group 400. In this way, customers whose purchase frequency of the product is equal to or greater than a predetermined percentage of the customer group 300 are identified as customer group 400.

[0052] In Figure 7, from customer group 300, customers whose purchase frequency of the product exceeds a predetermined value, namely customers F, I, E, and C, are extracted as customer group 400. The predetermined value for purchase frequency can be set by the user via the input unit of computer 2, depending on the industry, business type, product characteristics, etc., and may be an arbitrary value such as 20%.

[0053] Furthermore, in step S13, the frequency Pareto extraction unit 23 of the computer 2 may extract products from the extracted product group whose purchase frequency is equal to or greater than a predetermined value. Specifically, the frequency Pareto extraction unit 23 may extract products from the product group 310 extracted in step S12 above, in which the purchase frequency of the products is equal to or greater than a predetermined percentage of the entire product group 310, as product group 410. In this way, products whose purchase frequency is equal to or greater than a predetermined percentage of the products in product group 310 are extracted as product group 410.

[0054] Next, the Pareto type extraction unit 24 of computer 2 extracts customer groups 500 from the extracted customer groups 400 in which the number of types of products purchased by the customer is equal to or greater than a predetermined value (step S14). Specifically, as shown in Figure 8, the Pareto type extraction unit 24 extracts customer groups in the customer group 400 extracted in step S13 above in which the number of types of products purchased by the customers is equal to or greater than a predetermined percentage.

[0055] In Figure 8, customers who purchase more than a predetermined number of product types, namely customers I, E, and C, are extracted as customer group 500. The predetermined number of product types can be set by the user to any value, such as 10% of all product types.

[0056] Furthermore, in step S14, the Pareto extraction unit 24 of computer 2 may extract product groups 600 from the extracted product groups 410 in which the number of types of products purchased is equal to or greater than a predetermined value. Specifically, the Pareto extraction unit 24 may extract product groups 600 from the product groups 410 extracted in step S13 in the above-described step 410 in which the number of types of products purchased is equal to or greater than a predetermined percentage.

[0057] Next, the target customer determination unit 25 of computer 2 determines the extracted customer group 500 as a deep-rooting sales target 510 (step S15). Specifically, the customer group 500 extracted in step S14 above, that is, the customer group 500 in which the sales amount, purchase frequency, and number of types of products included in the shipment data 200 each exceed a predetermined percentage, is determined to be a deep-rooting sales target 510. As described above, a deep-rooting sales target is a target to which solutions such as upselling, cross-selling, understanding the company's strengths and weaknesses, and gathering information for developing products with high customer value are proposed based on Pareto analysis. Note that the extracted customer group 500 may be a product group 600.

[0058] In Figure 8, customers I, E, and C, whose number of product types purchased exceeds a predetermined value, are identified as target customers (510) for in-depth sales. The predetermined value for the number of product types purchased can be set by the user to any value, such as 10% of the total number of product types.

[0059] In step S15 described above, the shipping data 200 is further associated with the profit margins of customers, and the classification unit 26 of computer 2 classifies the customer groups 300 extracted in step S12 above whose profit margins are below a predetermined value according to that predetermined value, and the target customer determination unit 25 of computer 2 may determine each classified customer group as a profit improvement target 520. Specifically, the shipping data 200 acquired by the acquisition unit 21 of computer 2 is further associated with the profit margins of customers, and the classification unit 26 classifies the customer groups 300 extracted in step S12 above, that is, the customer groups 300 that account for a predetermined percentage or more of the total sales amount of the company, according to that predetermined value, and the customer groups included in the shipping data 200 whose profit margins are below a predetermined value, and the target customer determination unit 25 may determine each classified customer group as a profit improvement target 520. As described above, a profit improvement target 520 is a target for which solutions such as considering measures to improve pricing, sales, and procurement are proposed. Furthermore, the profit margin may be either the gross profit margin or the operating profit margin. Also, for target classification, customer groups may be classified according to the order date, shipping date, or main delivery date. Furthermore, customer group 300 may be product group 310.

[0060] In Figure 9, for example, if the predetermined profit margin is 20%, then among the customer group 300, customers whose profit margins are below the predetermined value, i.e., customers named I and C, are determined to be profit improvement targets 520. The predetermined profit margin can be set by the user to any value.

[0061] Furthermore, in step S15 described above, the shipping data 200 may further include the purchase date and quantity of the product, and the target customer determination unit 25 of the computer 2 may determine the competitor countermeasure target 540 from among the customer group 300 extracted in step S12 described above, based on the change in purchase date and the change in purchase quantity. Specifically, the shipping data 200 acquired by the acquisition unit 21 of the computer 2 may further include the purchase date and quantity of the product, and the target customer determination unit 25 may determine the competitor countermeasure target 540 from among the customer group 300 extracted in step S12 described above, that is, customer group 300 that account for a predetermined percentage or more of the total sales amount of the company, based on the change in purchase date and the change in purchase quantity included in the shipping data 200. Note that the extracted customer group 300 may be product group 310.

[0062] In Figure 10, among customer group 300, customer C made three shipments: the first shipment on 2023 / 1 / 23, the second on 2023 / 2 / 23, and the third on 2023 / 4 / 23. The interval between the second and third shipments is longer than the interval between the first and second shipments. Furthermore, the quantity shipped was 35 for the first and second shipments, but decreased to 10 for the third shipment. Based on this, customer C is identified as competitor target 540. Therefore, competitor target 540 is identified as a customer showing signs of change due to changes in purchase timing and quantity. Figure 10 shows an example where the purchase interval lengthens and the purchase quantity decreases, but a customer showing signs of change may also be identified if the purchase interval shortens and the purchase quantity increases.

[0063] The above describes the target customer determination process.

[0064] Therefore, according to the management analysis system 1, based on Pareto's principle, it is possible to automatically extract a small number of elements that account for the majority of results (for example, the top 20% of customers who account for 80% of total sales), and in addition, based on detailed sales analysis (for example, purchase frequency and number of types of products purchased by each customer), it is possible to automatically identify target customers from whom hints for developing products with high customer value and information related to sales and service improvement can be obtained.

[0065] Furthermore, according to the management analysis system 1, it becomes possible to automatically identify target customers for profit improvement based on their profit margins, and to improve their profit margins through improvements in product pricing, sales, and purchasing.

[0066] Furthermore, according to the management analysis system 1, by understanding customer needs and circumstances based on changes in purchase timing and quantity, it becomes possible to automatically propose measures to improve product pricing, sales, and purchasing by detecting the impact on market share and the risk of customer churn.

[0067] The means and functions described above are realized by a computer (including the CPU, information processing unit, and various terminals) reading and executing a predetermined program. The program is provided, for example, from one or more computers via a network (cloud service, SaaS: Software as a Service). Alternatively, the program may be provided, for example, recorded on a computer-readable recording medium. In this case, the computer reads the program from the recording medium, transfers it to an internal or external recording device, records it, and executes it. Alternatively, the program may be pre-recorded on a recording device (recording medium) such as a magnetic disk, optical disk, or magneto-optical disk, and provided to the computer from that recording device via a communication line.

[0068] Although embodiments of the present invention have been described above, the present invention is not limited to these embodiments. Furthermore, the effects described in the embodiments of the present invention are merely a list of the most preferred effects arising from the present invention, and the effects of the present invention are not limited to those described in the embodiments. [Explanation of Symbols]

[0069] 1 Business analysis system, 2 Computer, 21 Acquisition unit, 22 Sales Pareto extraction unit, 23 Frequency Pareto extraction unit, 24 Variety Pareto extraction unit, 25 Target customer determination unit, 26 Classification unit, 27 Reception unit, 28 Decision unit, 100 Shipping list, 110 Product master, 200 Shipping data, 300, 400, 500 Customer groups, 510 Deepening sales targets, 520 Profit improvement targets, Competitor countermeasure targets, 310, 410, 600 Product groups

Claims

1. An acquisition unit acquires shipping data that associates the customer name of a company with the customer's sales amount, purchase frequency and type of goods for a specified period, and the unit price of the goods. A sales Pareto extraction unit that extracts customer groups whose sales amount is equal to or greater than a predetermined value, A frequency Pareto extraction unit extracts customer groups from the extracted customer groups in which the frequency of purchase of the product by those customers is equal to or greater than a predetermined value, A variety Pareto sampling unit extracts from the customer groups extracted by the frequency Pareto sampling unit the customer groups in which the number of types of products purchased by the customer is equal to or greater than a predetermined value, A target customer determination unit determines that the customer group extracted by the aforementioned Pareto extraction unit is a target for in-depth sales, A management analysis system equipped with the following features.

2. The aforementioned shipping data is further associated with the profit margin of the aforementioned customer, The sales Pareto extraction unit further comprises a classification unit that classifies customer groups whose profit margin is below a predetermined value, among the customer groups extracted by the sales Pareto extraction unit, according to the predetermined value. The management analysis system according to claim 1, wherein the target customer determination unit determines the classified customer group as a profit improvement target.

3. The aforementioned shipping data further includes the purchase date and quantity of the aforementioned product, The business analysis system according to claim 1, wherein the target customer determination unit determines a target for competitive countermeasures from among the customer groups extracted by the sales Pareto extraction unit, based on the change in purchase timing and the change in purchase quantity.

4. A management analysis method performed by a computer, A step of obtaining shipping data that associates the customer name of a company with the customer's sales amount, purchase frequency and type of goods for a specified period, and the unit price of the goods. The steps include: extracting customer groups whose sales amount is equal to or greater than a predetermined value; The steps include: extracting customer groups from the extracted customer groups in which the frequency of purchase of the product is equal to or greater than a predetermined value; The steps include: extracting customer groups from among the customer groups whose purchase frequency of the aforementioned products is above a predetermined value, the number of types of the aforementioned products purchased by those customers is above a predetermined value; The steps include determining the customer group whose purchase frequency of the aforementioned product is above a predetermined value as a target for in-depth sales, A management analysis system equipped with the following features.

5. On the computer, A step of obtaining shipping data that associates the customer name of a company with the customer's sales amount, purchase frequency and type of goods for a specified period, and the unit price of the goods. A step of extracting a customer group whose sales amount is equal to or greater than a predetermined value, A step of extracting customer groups from the extracted customer groups in which the frequency of purchase of the said product is equal to or greater than a predetermined value, A step of extracting customer groups from among the customer groups in which the number of types of products purchased by the customer is equal to or greater than a predetermined value, A step of determining that a customer group whose number of purchase types of the aforementioned products exceeds a predetermined value will be a target for in-depth sales development. A computer-readable program for executing a command.

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