Customer analysis system and customer analysis method

Abstract

To provide a technique capable of performing customer analysis by timely grasping a change in a value that a customer or a commodity appeals.SOLUTION: The customer analysis system includes a computer having at least an arithmetic device and a storage device. The storage device stores a customer feature score in which a score of a feature of a customer is associated with the customer, a sales target feature appeal score in which a score at which the sales target appeals to the feature of the customer is associated with the sales target, and customer feedback from the customer on the sales target. The computing device updates the customer feature score and the sales target feature appeal score using the customer feedback, and outputs a feature of a customer whose score increases and a sales target that appeals to the feature.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a customer analysis system and a customer analysis method. [Background technology]

[0002] In recent years, in response to the diversification of end-user needs and corporate services, a fundamental review of traditional business operations and systems has become one of the key factors in becoming a company of choice for customers. As competition between companies intensifies, improving customer experience is necessary to ensure customer loyalty. Here, customer experience refers to the value experienced by customers at all points of contact with a company. In order to improve customer experience, it is necessary to understand customers more deeply, at the level of their values. In today's world, where customer values ​​are rapidly changing, the challenge is to estimate customer values ​​and then provide products that, for example, keep up with those changes in values.

[0003] As an example of a technology for providing products based on customer values, the technology described in Patent Document 1 has been proposed. Patent Document 1 states, "According to the customer analysis system and method of the present invention, it is possible to create a model that accurately integrates a model relating to values ​​and lifestyles in a person's life in general with a model relating to customer needs for products or services, and by using this model, it is possible to deepen ties with customers while providing products and services that suit the customer." and "The present invention is a customer analysis system that analyzes customers using a model, comprising: a first model acquisition means for acquiring a first model that indicates general characteristics of a person, including at least values ​​and lifestyles in a person's life in general; a second model acquisition means for acquiring a second model that indicates customer needs for products or services; and a third model creation means for creating a third model that integrates the first model and the second model, wherein the general characteristics that define the first model are values ​​and lifestyles expressed independently of demographic characteristics indicating at least age and gender, the first model includes a correlation between the general characteristics and the values ​​regarding products or services, the second model includes a correlation between the customer's values ​​regarding products or services and the details of the products or services selected by the customer, and the third model creation means creates the third model by integrating the first model and the second model by using parts of the first and second models that indicate the same or similar values ​​regarding products or services. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2022-150503 Summary of the Invention [Problem to be solved by the invention]

[0005] By using a model that can be created using the technology described in Patent Document 1, it is possible to offer products based on customer values. However, it is stated that the model is created "based on one or more of the following: questionnaire results, psychological test results, brain response test results, and physiological response test results." As customer values ​​change rapidly, it is difficult to update the model by conducting questionnaires and psychological tests, for example, every week. Therefore, when using the technology described in Patent Document 1, it is not possible to capture changes in customer values ​​in a timely manner.

[0006] Furthermore, the technology described in Patent Document 1 cannot capture changes in the customer values ​​that a product appeals to. In other words, if the technology described in Patent Document 1 is used, for example, when the values ​​appealing to chocolate change from "hedonism" to "achievement," that change cannot be captured.

[0007] The present invention has been made in consideration of the above-mentioned problems, and aims to provide a technology that enables customer analysis that captures changes in the values ​​that customers, products, etc. appeal to in a timely manner. [Means for solving the problem]

[0008] A customer analysis system according to the present invention includes a computer having at least an arithmetic unit and a storage device. The storage device stores customer characteristic scores that associate customers with scores of their characteristics, sales object characteristic appeal scores that associate sales objects with scores of the sales objects' appeal to the customer characteristics, and customer feedback from customers regarding the sales objects. The arithmetic unit uses the customer feedback to update the customer characteristic scores and sales object characteristic appeal scores, and outputs customer characteristics whose scores are increasing and sales objects that appeal to those characteristics.

[0009] Other problems and solutions disclosed in the present application will be made clear in the detailed description and drawings. [Effects of the Invention]

[0010] According to the present invention, it is possible to carry out customer analysis that captures changes in the values ​​that customers, products, etc., appeal to in a timely manner. [Brief explanation of the drawings]

[0011] [Figure 1] 1 is a diagram illustrating an example of the configuration of an entire system including a customer analysis system according to an embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of a hardware configuration of a customer analysis AP server. [Figure 3] FIG. 2 is a diagram illustrating an example of a hardware configuration of a customer analysis DB server. [Figure 4] FIG. 2 illustrates an example of a hardware configuration of a client terminal. [Figure 5] FIG. 2 is a diagram illustrating an example of a hardware configuration of a customer terminal. [Figure 6] FIG. 10 is a diagram illustrating an example of a configuration of a purchase record table. [Figure 7] FIG. 2 is a diagram illustrating an example of a table configuration of a product master. [Figure 8] FIG. 2 is a diagram illustrating an example of a table configuration of a customer master. [Figure 9] FIG. 10 is a diagram illustrating an example of a configuration of a product value appeal score table. [Figure 10] FIG. 10 is a diagram showing an example of the configuration of a customer value score table. [Figure 11] FIG. 10 is a diagram illustrating an example of a table configuration of a value master. [Figure 12] FIG. 10 is a diagram illustrating an example of a configuration of a product review table. [Figure 13] FIG. 2 is a sequence diagram illustrating an example of the overall flow of processing executed in the customer analysis system. [Figure 14] 10 is a flowchart showing an example of the flow of a product value appeal score update process. [Figure 15] 10 is a flowchart showing an example of the flow of a customer value score update process. [Figure 16] 10 is a flowchart showing an example of the flow of a customer value change / appeal product presentation process. [Figure 17] FIG. 10 is a diagram illustrating an example of an input screen displayed by a client terminal. [Figure 18] FIG. 10 is a diagram illustrating an example of an output screen displayed by a client terminal. [Figure 19] FIG. 10 is a diagram illustrating another example of output information. DETAILED DESCRIPTION OF THE INVENTION

[0012] In the following description, an "interface unit" refers to one or more interface devices. The one or more interfaces may be one or more interface devices of the same type (for example, one or more NICs (Network Interface Cards)) or two or more interface devices of different types (for example, a NIC and an HBA (Host Bus Adapter)).

[0013] In the following description, a "storage unit" refers to one or more memories. At least one memory may be a volatile memory or a non-volatile memory. The storage unit may include one or more PDEVs in addition to one or more memories. A "PDEV" refers to a physical storage device, and may typically be a non-volatile storage device (e.g., an auxiliary storage device). A PDEV may be, for example, an HDD (Hard Disk Drive) or an SSD (Solid State Drive).

[0014] In the following description, a "processor unit" refers to one or more processors. At least one processor is typically a CPU (Central Processing Unit). The processor may include a hardware circuit that performs some or all of the processing.

[0015] In the following description, functions are sometimes described using the expression "kkk unit" (excluding the interface unit, storage unit, and processor unit). However, the functions may be realized by one or more computer programs being executed by the processor unit, or by one or more hardware circuits (e.g., a field-programmable gate array (FPGA) or an application-specific integrated circuit (ASIC)). When a function is realized by a program being executed by the processor unit, the specified processing is performed using the storage unit and / or the interface unit, as appropriate, and therefore the function may be considered to be at least a part of the processor unit. Processing described using a function as the subject may be processing performed by the processor unit or a device having the processor unit. A program may be installed from a program source. The program source may be, for example, a program distribution computer or a computer-readable recording medium (e.g., a non-transitory recording medium). The description of each function is an example; multiple functions may be combined into one function, or one function may be divided into multiple functions.

[0016] In the following description, information may be described using expressions such as "xxx table," but the information may be expressed in any data structure. In other words, to indicate that the information does not depend on the data structure, an "xxx table" may be referred to as "xxx information." In the following description, the structure of each table is an example, and one table may be divided into two or more tables, or all or part of two or more tables may be one table.

[0017] In the following description, "time" is expressed in units of year, month, day, hour, minute, and second, but the time unit may be coarser or finer than that, or may be a different unit.

[0018] In the following description, a "dataset" means data (a logical block of electronic data) consisting of one or more data elements, and may be, for example, any of a record, a file, a key-value pair, and a tuple.

[0019] In the following explanation, a process may be described using a "program" as the subject, but the process described using a program as the subject may also be a process performed by a processor or a device having that processor. Two or more programs may be realized as one program, and one program may be realized as two or more programs.

[0020] Furthermore, in the following description, a "customer analysis system" may be a system made up of one or more physical computers, or may include a system (e.g., a cloud computing system) implemented on a group of physical computing resources (e.g., a cloud infrastructure). When the customer analysis system "displays" the display information, it may mean that the display information is displayed on a display device possessed by the computer, or that the computer transmits the display information to a display computer (in the latter case, the display information is displayed by the display computer).

[0021] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

[0022] In the following description, the same or similar components will be designated by common reference numerals, and redundant description may be omitted.

[0023] Furthermore, when there are multiple elements having the same or similar functions, the multiple elements may be described by using the same reference numeral with different subscripts to distinguish between them. On the other hand, when there is no need to distinguish between the multiple elements, the subscripts may be omitted.

[0024] Hereinafter, the "features" in claim 1 will be referred to as "values" and the "items for sale" as "products," and examples will be explained using drawings.

[0025] <System configuration example> Fig. 1 is a diagram showing an example of the overall configuration of a system including a customer analysis system 100 according to this embodiment. The customer analysis system 100 shown in Fig. 1 is a computer system that, in the retail industry, detects increases / decreases in customer values ​​and enables product selection that appeals to the increasing values ​​of customers.

[0026] 1, this customer analysis system 100 is configured by the cooperation of a customer analysis AP server 101 and a customer analysis DB server 102, which are communicatively connected to each other via a network 104. Of course, this configuration of the customer analysis system 100 is just one example, and these servers may also be implemented as an integrated server device.

[0027] On the other hand, the client terminal 103 is a terminal used by a user who wants to recognize products that appeal to rising customer values ​​in order to determine the product lineup, such as in a retail store's product department, and is capable of communicating with, for example, the customer analysis AP server 101 via the above-mentioned network 104.

[0028] Next, we will explain the hardware configuration of each server that makes up the customer analysis system 100. Fig. 2 is a diagram showing an example of the hardware configuration of the customer analysis AP server 101 according to this embodiment.

[0029] The customer analysis AP server 101 in this embodiment includes a storage device 201 configured as an appropriate nonvolatile storage device such as a hard disk drive, a memory 203 configured as a volatile storage device such as RAM, a CPU (Central Processing Unit) 202 that is a computing device that reads and executes a program 210 stored in the storage device 201 into the memory 203, and serves as a control unit (not shown) to perform overall control of the device itself and perform various judgments, calculations, and control processes, and a communication device 206 that communicates with a client terminal 103 operated by a user via a network 104. The program 210 in the storage device 201 is a program that implements the functions required for the customer analysis system 100 of this embodiment, namely, a product value appeal score update function, a customer value score update function, and a customer value change / appeal product presentation function.

[0030] 3 is a diagram showing an example of the hardware configuration of the customer analysis DB server 102 according to this embodiment. The customer analysis DB server 102 according to this embodiment includes: a storage device 301 configured as an appropriate nonvolatile storage device such as a hard disk drive; a memory 303 configured as a volatile storage device such as RAM; a CPU 302, which is a computing device that reads and executes a program 310 stored in the storage device 301 into the memory 303, and serves as a control unit (not shown) for overall control of the device itself and for performing various judgments, calculations, and control processing; and a communication device 306 that is connected to the network 104 and handles communication processing with other devices. In addition to the program 310, the storage device 301 also includes a purchase history table 311, a product master 312, a customer master 313, a product value appeal score table 314, a customer value score table 315, a value master 316, and a product review table 317.

[0031] Next, an example of the hardware configuration of the client terminal 103 according to this embodiment will be described. FIG. 4 is a diagram showing an example of the hardware configuration of the client terminal 103 according to this embodiment. The client terminal 103 includes a storage device 401 configured as a suitable nonvolatile storage device such as a hard disk drive, a memory 403 configured as a volatile storage device such as RAM, a CPU 402 that is a computing device that reads a program 410 stored in the storage device 401 into the memory 403 and executes it as a control unit (not shown) to perform overall control of the device itself and perform various judgments, calculations, and control processes, an input device 404 that accepts key inputs and voice inputs from the user, an output device 405 such as a display that displays processed data, and a communication device 406 that is connected to the network 104 and handles communication processing with the customer analysis AP server 101 and the like. In addition to the program 410, the storage device 401 also stores screen data for an input screen 450 and an output screen 451. The screen data for the input screen 450 and the output screen 451 may be part of the program 410.

[0032] Next, an example of the hardware configuration of the customer terminal 105 according to this embodiment will be described. FIG. 5 is a diagram showing an example of the hardware configuration of the customer terminal 105 according to this embodiment. The customer terminal 105 includes a storage device 501 configured as a suitable nonvolatile storage device such as a hard disk drive, a memory 503 configured as a volatile storage device such as RAM, a CPU 502 that is a computing device that reads a program 510 stored in the storage device 501 into the memory 503 and executes it as a control unit (not shown) to perform overall control of the device itself and perform various judgments, calculations, and control processes, an input device 504 that accepts key inputs and voice inputs from the user, an output device 505 such as a display that displays processed data, and a communication device 506 that is connected to the network 104 and handles communication processing with the customer analysis AP server 101. In addition to the program 510, the storage device 501 also stores screen data for an input screen 550. The screen data for the input screen 550 may be part of the program 510.

[0033] (System features) Next, we will explain the functions provided in the customer analysis system 100 of this embodiment. The functions described below are functions that are implemented, for example, by executing a program 210 provided in the customer analysis AP server 101 in the customer analysis system 100. Of course, other servers may also have similar functions, or the functions may be divided and held between servers to cooperate with each other.

[0034] (Product master registration) The customer analysis system 100 of this embodiment has a function of accepting product information in which a user has described the characteristics of a product from the client terminal 103 and storing the accepted information in the storage device 301 as a product master 312 .

[0035] (Customer master registration) The customer analysis system 100 of this embodiment has the function of accepting customer information from a customer terminal 105, which contains customer information from customers who use a retail store, and storing the accepted information in a memory device 301 as a customer master 313.

[0036] (Product review registration) The customer analysis system 100 of this embodiment has the function of accepting product review information written by customers who use retail stores, etc., from the customer terminal 105, and storing the accepted information in the storage device 301 as a product review table 317.

[0037] (Product value appeal score update function) The customer analysis system 100 has the function of calculating a score of the values ​​appealing to a product using a value master 316 that defines the values ​​used in analyzing customer values ​​and values ​​appealing to a product, and a product review table 317 that stores information on product reviews by customers who use retail stores, and then calculating a weighted average of the calculated score and scores calculated in the same way in the past, and storing the result in a product value appeal score table 314.

[0038] (Customer Value Score Update Function) The customer analysis system 100 also has the function of calculating a customer's value score using a purchase history table 311 that stores the customer's product purchase history and a product value appeal score table 314 that stores the scores of the values ​​that products appeal to, taking a weighted average of the calculated score and scores calculated in the same way in the past, and storing the result in a customer value score table 315.

[0039] (Customer values ​​change and product appeal function) In addition, the customer analysis system 100 has the function of displaying on the client terminal 103 a screen that presents changes in customer values ​​and products that appeal to rising customer values, using a product value appeal score table 314 that stores scores of values ​​that products appeal to, and a customer value score table 315 that stores scores of customer values.

[0040] (Data configuration example) Next, an example of the configuration of data held by the server that constitutes the customer analysis system 100 of this embodiment will be described.

[0041] 6 is a diagram showing an example of the configuration of a purchase history table 311 provided in the customer analysis DB server 102. This purchase history table 311 is a table generated by storing POS data acquired from the POS systems of each store that sells products, for example, by the customer analysis DB server 102, and has the following items as table items.

[0042] That is, the customer code, product code, date, selling price, number of sales, and sales amount. In the configuration illustrated in Figure 6, the customer code is defined as "C111111," the product code purchased by the customer with this customer code is defined as "P111111," the purchase date is defined as "20230101," the selling price of this product to this customer is defined as "100," the number of sales is defined as "3," and the sales amount is defined as "300." When the customer analysis AP server 101 issues an information acquisition instruction using the customer value score update function or the customer value change / appeal product presentation function, the customer analysis DB server 102 will send this purchase history table 311 to the customer analysis AP server 101.

[0043] 7 is a diagram showing an example of the table configuration of the product master 312 provided in the customer analysis DB server 102. This product master 312 is a table that the customer analysis DB server 102 generates by storing user input values ​​received from the client terminal 103, for example, and has the following items as table items.

[0044] 7, the product code, product name, product category, and product description are defined as "P111111," the product name "Chocolate A," the product category "Confectionery," and the product description "Sweet and energizing chocolate." When the user inputs the conditions via the input screen on the client terminal 103, the customer analysis DB server 102 will send this product master 312 to the customer analysis AP server 101.

[0045] 8 is a diagram showing an example of the table configuration of the customer master 313 provided in the customer analysis DB server 102. This customer master 313 is a table that the customer analysis DB server 102 generates by storing input values ​​received from the customer terminal 105 by customers who use retail stores, for example, and has the following items as table items.

[0046] That is, the customer code, age group, and gender. In the configuration illustrated in FIG. 8, the customer code is defined as "C111112," the customer's age group as "10," and the gender as "no response." When the customer analysis AP server 101 issues an information acquisition instruction using the customer value score update function, the customer analysis DB server 102 will send this customer master 313 to the customer analysis AP server 101.

[0047] 9 is a diagram showing an example of the configuration of the product value appeal score table 314 provided in the customer analysis DB server 102. This is the table structure of the product value appeal score table 314. This product value appeal score table 314 is a table that is generated by, for example, the customer analysis DB server 102 by storing calculation results received from the customer analysis AP server 101, and has the following items as table items.

[0048] These are the product code, update date, and value appeal score. Here, the value appeal score is the score of a product with this product code that appeals to a customer's values. It is expressed as a 10-dimensional vector, with each dimension representing a value and the value representing the product's appeal score for each value. In the configuration illustrated in FIG. 9 , the product code is "P111111," the update date of the value appeal score for this product code is "20230131," and the value appeal score is defined as each dimension: value 1 appeal score, value 2 appeal score, value 3 appeal score, ..., with values ​​of 0.8, 0.1, 0.0, etc. When the customer analysis AP server 101 issues an information acquisition command using the product value appeal score update function, customer value score update function, or customer value change / appeal product presentation function, the customer analysis DB server 102 sends this product value appeal score table 314 to the customer analysis AP server 101.

[0049] 10 is a diagram showing an example of the configuration of a customer value score table 315 provided in the customer analysis DB server 102. This customer value score table 315 is a table that the customer analysis DB server 102 generates by storing calculation results received from the customer analysis AP server 101, for example, and has the following items as table items.

[0050] These are age group, gender, update date, and value score. Here, the value score is the value score of a customer of the current age group and gender, and is expressed as a 10-dimensional vector, with each dimension representing a value and each value representing the score of each value held by the customer. In the configuration illustrated in FIG. 10 , the customer's age group is "10," the gender is "male," the value score update date for the current product code is "20230101," and the value score dimensions are defined as value 1 score, value 2 score, value 3 score, ..., with values ​​of 0.3, 0.1, 0.1, etc. Note that the initial value scores for each age group and gender are predetermined by the administrator of the customer analysis DB server 102. When the customer analysis AP server 101 issues an information acquisition command using the customer value score update function or the customer value change / promotional product presentation function, the customer analysis DB server 102 transmits this customer value score table 315 to the customer analysis AP server 101.

[0051] 11 is a diagram showing an example of the table configuration of the value master 316 provided in the customer analysis DB server 102. This value master 316 is a table stored and generated in the customer analysis DB server 102 by, for example, an administrator of the server, and has the following items as table items.

[0052] In other words, it is a value and a definition. In the configuration illustrated in FIG. 11, the value is "value 1," and the definition of this value is defined as "personal pleasure and sensual satisfaction," etc. When a user registers a product and inputs conditions via the input screen on the client terminal 103, the customer analysis DB server 102 sends this value master 316 to the customer analysis AP server 101.

[0053] 12 is a diagram showing an example of the configuration of the product review table 317 provided in the customer analysis DB server 102. This product review table 317 is a table that is generated by the customer analysis DB server 102 by storing input values ​​received from the customer terminal 105 by customers who use retail stores, and has the following items as table items.

[0054] 12, the product code, customer code, response date, and review text are defined as "P111111," the customer code "C111112," and the response date and review text provided by the customer regarding the product as "20230103," "I was satisfied with the rich flavor," etc. When the customer analysis AP server 101 issues an information acquisition instruction using the product value appeal score update function, the customer analysis DB server 102 will send this product review table 317 to the customer analysis AP server 101.

[0055] <System operation example> The actual procedure of the customer analysis method according to this embodiment will be explained below with reference to the drawings. The various operations corresponding to the customer analysis method explained below are realized by a program that is read into each memory and executed by each server constituting the customer analysis system 100. This program is made up of code for carrying out the various operations explained below.

[0056] (Processing procedure example 1) Figure 13 is a sequence diagram showing an example of the overall flow of processing executed in customer analysis system 100 as example 1 of the processing procedure of the customer analysis method of this embodiment. Specifically, it is a sequence diagram showing the flow in which customer analysis AP server 101 and customer analysis DB server 102, which constitute customer analysis system 100, receive various requests from client terminal 103 and customer terminal 105 via input screens, and finally output the analysis results to the output screen of client terminal 103.

[0057] (Product registration request, product registration processing) First, a predetermined user who manages a retail store or the like sends product information describing the characteristics of the product as information about the products they handle from client terminal 103 to customer analysis AP server 101. Upon receiving the product information from client terminal 103, customer analysis AP server 101 executes a product registration function and first instructs customer analysis DB server 102 to acquire information necessary for product registration processing. Meanwhile, customer analysis DB server 102 receives this acquisition instruction and transmits information from value master 316 to customer analysis AP server 101 in response to the request.

[0058] The customer analysis AP server 101 receives the information sent in the above steps from the customer analysis DB server 102 and executes product registration processing using the product registration function. The results of the product registration processing are sent to the customer analysis DB server 102 and are registered in the product master 312 and the product value appeal score table 314.

[0059] Here, the result registered in the product value appeal score table 314 is the initial value of the value appeal score of the product to be registered. This initial value can be calculated from the linguistic similarity between the product description of the product registered in the product master 312 and the definition of each value in the value master 316, and can be calculated by normalizing the numerical sequence of each similarity so that the sum is 1. The linguistic similarity can be calculated, for example, by vectorizing each sentence using an embedding model and calculating the cosine similarity.

[0060] (Customer registration requests, customer registration processing) First, customer information describing the characteristics of a customer who has purchased a product handled by a retail store or the like is sent from customer terminal 105 to customer analysis AP server 101. Having received the customer information from customer terminal 105, customer analysis AP server 101 executes customer registration processing using a customer registration function. The results of the customer registration processing are sent to customer analysis DB server 102 and are registered in customer master 313.

[0061] (Product review registration request, product review registration process) First, product review information in which a customer using a retail store or the like has responded to a product sold at the store is sent from customer terminal 105 to customer analysis AP server 101. Upon receiving the relevant customer information from customer terminal 105, customer analysis AP server 101 executes product review registration processing using a product review registration function, and the results of the product review registration processing are sent to customer analysis DB server 102 and registered in product review table 317.

[0062] (Information acquisition instruction for product value appeal score update processing, information acquisition instruction reception) The customer analysis AP server 101 executes the product value appeal score update function periodically or when a customer registers a product review, and first instructs the customer analysis DB server 102 to acquire information necessary for the product value appeal score update process. On the other hand, the customer analysis DB server 102 receives this acquisition instruction and transmits each piece of information stored in its own storage device 301, namely the product value appeal score table 314, the value master 316, and the product review table 317, to the customer analysis AP server 101. Here, "periodically" may mean, for example, once at the end of each month.

[0063] (Product value appeal score update process) The customer analysis AP server 101 receives the information transmitted in the above steps from the customer analysis DB server 102 and executes product value appeal score update using the product value appeal score update function. The product value appeal score update result is transmitted to the customer analysis DB server 102 and registered in the product value appeal score table 314.

[0064] (Update purchase history table) It is assumed that the purchase records of products and customers registered in the product master 312 and customer master 313 are sent from the POS system of the store to the customer analysis DB server 102 and stored in the purchase record table 311 .

[0065] (Information acquisition instruction for customer value score update processing, information acquisition instruction reception) The customer analysis AP server 101 executes a customer value score update function periodically or when an operation is performed by a predetermined user who manages a retail store, and first instructs the customer analysis DB server 102 to acquire information necessary for the customer value score update process. On the other hand, upon receiving this acquisition instruction, the customer analysis DB server 102 transmits the information of the purchase history table 311, product value appeal score table 314, and customer value score table 315 stored in its own storage device 301 to the customer analysis AP server 101. Here, "periodically" may mean, for example, once every weekend.

[0066] (Customer Value Score Update Process) The customer analysis AP server 101 receives the information transmitted in the above steps from the customer analysis DB server 102 and executes a customer value score update using the customer value score update function. The results of the customer value score update are transmitted to the customer analysis DB server 102 and registered in the customer value score table 315.

[0067] (Customer values ​​change, product presentation conditions acceptance) The client terminal 103 accepts condition input from the user on the input screen and sends it to the customer analysis AP server 101. As the conditions accepted on this input screen, as shown in the example of Fig. 17, the following items can be envisioned: the customer's age, gender, the year and month for comparing value scores, and the number of products to display that appeal to specific values.

[0068] (Information acquisition instructions for customer value changes and product presentation processing, information acquisition instruction reception) The customer analysis AP server 101, which has received the above-mentioned input conditions from the client terminal 103, executes the customer value change / appeal product presentation function, and first instructs the customer analysis DB server 102 to acquire information necessary for processing the customer value change / appeal product presentation. On the other hand, the customer analysis DB server 102 receives this acquisition instruction and transmits each piece of information stored in its own storage device 301, namely the product master 312, product value appeal score table 314, and customer value score table 315, to the customer analysis AP server 101.

[0069] (Customer value change / appeal product presentation process execution, customer value change / appeal product presentation information output) The customer analysis AP server 101 receives the information sent from the customer analysis DB server 102 in the above steps and executes the customer value change / appeal product presentation using the customer value change / appeal product presentation function. The customer analysis AP server 101 transmits the results of this customer value change / appeal product presentation to the client terminal 103 via an output screen. The client terminal 103 displays the results of the customer value change / appeal product presentation on this output screen.

[0070] (Processing procedure example 2) FIG. 14 is a flowchart 1400 showing an example of the flow of a process for updating a product value appeal score (hereinafter referred to as "product value appeal score update process") as a second processing procedure example of the customer analysis method according to this embodiment. The product value appeal score update process is specifically executed as a product value appeal score update function possessed by the customer analysis AP server 101. Here, each step of this product value appeal score update process will be described. Details of the calculation of the product value appeal score will be described later using mathematical formulas.

[0071] (Get the latest update date) First, in step S1401, the control unit of the customer analysis AP server 101 executes processing to obtain the latest update date from the update dates in the product value appeal score table 314 obtained from the customer analysis DB server 102 and held in the storage device 201. As a result, the latest update date is obtained from the update dates in the product value appeal score table 314 obtained from the customer analysis DB server 102 and held in the storage device 201. When the processing in step S1401 is completed, the control unit of the customer analysis AP server 101 proceeds to step S1402.

[0072] (Get product reviews since the last update date) Next, in step S1402, the control unit of the customer analysis AP server 101 executes processing to extract all items subsequent to the update date obtained in step S1401 from the product review table 317 already obtained from the customer analysis DB server 102 and stored in the storage device 201. As a result, all items subsequent to the update date obtained in step S1401 are extracted from the product review table 317 already obtained from the customer analysis DB server 102 and stored in the storage device 201. When the processing in step S1402 is completed, the control unit of the customer analysis AP server 101 proceeds to step S1403.

[0073] (Determine whether there are any product reviews since the last update date) Next, in step S1403, the control unit of the customer analysis AP server 101 executes processing to determine the length of the product review table 317 extracted in step S1402. If it is determined that the length is 1 or more, that is, that extracted data exists (step S1403: YES), the process proceeds to step S1404. On the other hand, if it is determined that the length is 0, that is, that extracted data does not exist (step S1403: NO), the product value appeal score update processing shown in flowchart 1400 of FIG. 14 is terminated.

[0074] (Calculating the similarity between product reviews and values) Next, in step S1404, the control unit of the customer analysis AP server 101 executes a process of calculating the similarity between each product review in the product review table 317 extracted in step S1402 and the definition of each value in the value master 316 already obtained from the customer analysis DB server 102 and stored in the storage device 201. This similarity can be calculated as linguistic similarity, and can be, for example, the cosine similarity of the embedding vectors of each sentence. In this way, the similarity between each product review in the product review table 317 extracted in step S1402 and the definition of each value in the value master 316 already obtained from the customer analysis DB server 102 and stored in the storage device 201 is calculated. When the process in step S1404 is completed, the control unit of the customer analysis AP server 101 proceeds to step S1405.

[0075] (The calculated similarity is normalized for each product code.) Next, in step S1405, the control unit of the customer analysis AP server 101 executes a process of averaging the numerical sequence of the similarity with each value calculated for each product review, for each product code, with each value as an axis, and normalizing the process so that the sum of the values ​​of all the values ​​becomes 1. As a result, the numerical sequence of the similarity with each value calculated for each product review is averaged for each product code, with each value as an axis, and normalized the process so that the sum of the values ​​of all the values ​​becomes 1. When the process in step S1405 is completed, the control unit of the customer analysis AP server 101 proceeds to step S1406.

[0076] (Weighted average of value appeal scores) Next, in step S1406, the control unit of the customer analysis AP server 101 acquires, for each product code, from the product value appeal score table 314 that has already been obtained from the customer analysis DB server 102 and stored in the storage device 201, those whose update dates fall within the target period, and then executes a process of calculating a weighted average of the value calculated in step S1405 and the acquired value. Note that this weighted averaging method will be described later using a formula. As a result, for each product code, from the product value appeal score table 314 that has already been obtained from the customer analysis DB server 102 and stored in the storage device 201, those whose update dates fall within the target period are acquired, and then the value calculated in step S1405 and the acquired value are weighted averaged. Upon completing the process in step S1406, the control unit of the customer analysis AP server 101 proceeds to step S1407.

[0077] (The weighted average value is registered as the current product value appeal score) Next, in step S1407, the control unit of the customer analysis AP server 101 executes processing to register the value appeal score calculated in step S1406 in the product value appeal score table 314. As a result, the value appeal score calculated in step S1406 is registered in the product value appeal score table 314. When the processing in step S1407 is completed, the control unit of the customer analysis AP server 101 ends the product value appeal score update processing shown in the flowchart 1400 of FIG.

[0078] (Processing procedure example 3) FIG. 15 is a flowchart 1500 showing an example of the flow of processing for updating a customer value score (hereinafter referred to as "customer value score update processing") as a processing procedure example 3 of the customer analysis method according to this embodiment. Specifically, the customer value score update processing is executed as a customer value score update function possessed by the customer analysis AP server 101. Here, each step of this customer value score update processing will be explained. Details of calculation of the customer value score will be described later using mathematical expressions.

[0079] (Get the latest update date) First, in step S1501, the control unit of the customer analysis AP server 101 executes processing to obtain the latest update date from the update dates in the customer value score table 315 that has already been obtained from the customer analysis DB server 102 and stored in the storage device 201. As a result, the latest update date is obtained from the update dates in the customer value score table 315 that has already been obtained from the customer analysis DB server 102 and stored in the storage device 201. When the control unit of the customer analysis AP server 101 completes the processing in step S1501, the process proceeds to step S1502.

[0080] (Obtain purchase history since the last update date) Next, in step S1502, the control unit of the customer analysis AP server 101 executes processing to extract all items subsequent to the update date obtained in step S1501 from the purchase history table 311 that has already been obtained from the customer analysis DB server 102 and stored in the storage device 201. As a result, all items subsequent to the update date obtained in step S1501 are extracted from the purchase history table 311 that has already been obtained from the customer analysis DB server 102 and stored in the storage device 201. When the processing in step S1502 is completed, the control unit of the customer analysis AP server 101 proceeds to step S1503.

[0081] (Determine whether there is a purchase record since the last update date) Next, in step S1503, the control unit of the customer analysis AP server 101 executes processing to determine the length of the purchase record table 311 extracted in step S1502. If it is determined that the length is 1 or more, that is, that extracted data exists (step S1503: YES), the process proceeds to step S1504. On the other hand, if it is determined that the length is 0, that is, that extracted data does not exist (step S1503: NO), the customer value score update processing shown in flowchart 1500 of FIG. 15 is terminated.

[0082] (Obtaining the value appeal score of products purchased) Next, in step S1504, the control unit of the customer analysis AP server 101 executes processing to acquire the latest value appeal score of the relevant product code from the product value appeal score table 314 already obtained from the customer analysis DB server 102 and stored in the storage device 201, using each product code present in the extracted purchase history table 311 as a key. As a result, the latest value appeal score of the relevant product code is acquired from the product value appeal score table 314 already obtained from the customer analysis DB server 102 and stored in the storage device 201, using each product code present in the extracted purchase history table 311 as a key. When the control unit of the customer analysis AP server 101 completes the processing in step S1504, it proceeds to step S1505.

[0083] (Total sales by customer segment and product code) Next, in step S1505, the control unit of customer analysis AP server 101 uses the customer code in the extracted purchase history table 311 as a key to obtain the age and gender values ​​from customer master 313 already obtained from customer analysis DB server 102 and stored in storage device 201, and executes a process of aggregating sales amounts for each customer segment based on customer age and gender and for each product code. As a result, using the customer code in the extracted purchase history table 311 as a key, the age and gender values ​​are obtained from customer master 313 already obtained from customer analysis DB server 102 and stored in storage device 201, and sales amounts are aggregated for each customer segment based on customer age and gender and for each product code. When the control unit of customer analysis AP server 101 completes the process in step S1505, it proceeds to step S1506.

[0084] (Calculating customer values ​​from purchasing history) Next, in step S1506, the control unit of the customer analysis AP server 101 multiplies the sales amount for each product code by the value appeal score of the relevant product code, sums the calculated results for each customer segment, and executes processing to normalize the results so that the sum for each value becomes 1. As a result, the sales amount for each product code is multiplied by the value appeal score of the relevant product code, and the calculated results are summed for each customer segment and normalized so that the sum for each value becomes 1. When the control unit of the customer analysis AP server 101 completes the processing in step S1506, it proceeds to step S1507.

[0085] (Weighted average of customer value scores) Next, in step S1507, the control unit of the customer analysis AP server 101 acquires, from the customer value score table 315 already obtained from the customer analysis DB server 102 and stored in the storage device 201, those value scores for each age group and gender whose update date falls within the target period, and executes processing to weightedly average the value calculated in step S1506 and the acquired value of the value score for the relevant age group and gender. As a result, from the customer value score table 315 already obtained from the customer analysis DB server 102 and stored in the storage device 201, those value scores for each age group and gender whose update date falls within the target period are acquired, and then the value calculated in step S1506 and the acquired value of the value score for the relevant age group and gender are weightedly averaged. When the control unit of the customer analysis AP server 101 completes the processing in step S1507, the process proceeds to step S1508.

[0086] (The weighted average value is registered as the current customer value score) Subsequently, in step S1508, the control unit of the customer analysis AP server 101 executes processing to register the customer value score calculated in step S1507 in the customer value score table 315. As a result, the customer value score calculated in step S1507 is registered in the customer value score table 315. Upon completing the processing in step S1508, the control unit of the customer analysis AP server 101 ends the customer value score update processing shown in the flowchart 1500 of FIG.

[0087] (Processing procedure example 4) FIG. 16 is a flowchart 1600 showing an example of the flow of a process for presenting customer value change and appealing products (hereinafter referred to as "customer value change and appealing product presentation process") as a processing procedure example 4 of the customer analysis method according to this embodiment. The customer value change and appealing product presentation process is specifically executed as a customer value change and appealing product presentation function possessed by the customer analysis AP server 101. Here, each step of this customer value change and appealing product presentation process will be described.

[0088] (receiving an analysis request) First, in step S1601, the control unit of the customer analysis AP server 101 executes processing to receive from the client terminal 103 an analysis request including the values ​​of the customer's age group, sex, year and month for comparing the value scores, and the number of products displayed that appeal to specific values, which have been input into the input screen of the client terminal 103. As a result, the analysis request including the values ​​of the customer's age group, sex, year and month for comparing the value scores, and the number of products displayed that appeal to specific values, which have been input into the input screen of the client terminal 103, is received from the client terminal 103. When the processing in step S1601 is completed, the control unit of the customer analysis AP server 101 proceeds to step S1602.

[0089] (Extract customer value scores) Next, in step S1602, the control unit of the customer analysis AP server 101 executes processing to extract the update date and customer value score of the customer value score table 315 using the age and gender of the customer indicated in the analysis request as keys. As a result, the update date and customer value score of the customer value score table 315 are extracted using the age and gender of the customer indicated in the analysis request as keys. When the control unit of the customer analysis AP server 101 completes the processing in step S1602, it proceeds to step S1603.

[0090] (Customer Value Score Aggregation) Furthermore, in step S1603, the control unit of the customer analysis AP server 101 executes a process of dividing the extracted customer value score table 315 by each year and month of the update date described above, averaging the customer value scores in the customer value score table belonging to each year and month, and setting this value as the customer value score for the current year and month. As a result, the extracted customer value score table 315 is divided by each year and month of the update date described above, averaging the customer value scores in the customer value score table belonging to each year and month, and setting this value as the customer value score for the current year and month. When the control unit of the customer analysis AP server 101 completes the process in step S1603, the process proceeds to step S1604.

[0091] (Extract noteworthy customer values) Next, in step S1604, the control unit of the customer analysis AP server 101 acquires the customer's value scores for the months "from" and "to" for which values ​​are compared, as indicated by the analysis request, and the value of the customer's value score for the month corresponding to the time of the analysis request, from among the customer's value scores for each month tallied in the above steps, and executes processing to acquire the value for which the value from "from" to "to" has increased the most and the value for which the value for the month corresponding to the time of the analysis request has decreased the most, respectively. As a result, the control unit of the customer analysis AP server 101 acquires the customer's value scores for the months "from" and "to" for which values ​​are compared, as indicated by the analysis request, and the value for the month corresponding to the time of the analysis request, from among the customer's value scores for each month tallied in the above steps, and the value for which the value from "from" to "to" has increased the most and the value for which the value for the month corresponding to the time of the analysis request has increased the most and the value for which the value for the month corresponding to the time of the analysis request has increased the most. After completing the processing in step S1604, the control unit of the customer analysis AP server 101 proceeds to step S1605.

[0092] (Get the latest value appeal score for each product) Next, in step S1605, the control unit of the customer analysis AP server 101 executes processing to obtain the value appeal score for each product code with the most recent update date from the product value appeal score table 314. As a result, the value appeal score for each product code with the most recent update date is obtained from the product value appeal score table 314. When the processing in step S1605 is completed, the control unit of the customer analysis AP server 101 proceeds to step S1606.

[0093] (Acquiring a product name that appeals to noteworthy customer values) Subsequently, in step S1606, the control unit of the customer analysis AP server 101 executes a process of acquiring, for each value extracted in step S1604, the product codes with the highest value appeal scores acquired in step S1605, in descending order, for the number of displayed products indicated by the analysis request, and acquiring the product names of the acquired product codes from the product master 312. As a result, for each value extracted in step S1604, the product codes with the highest value appeal scores acquired in step S1605 are acquired, in descending order, for the number of displayed products indicated by the analysis request, and the product names of the acquired product codes are acquired from the product master 312. When the process in step S1606 is completed, the control unit of the customer analysis AP server 101 proceeds to step S1607.

[0094] (Screen data generation) Next, in step S1607, the control unit of the customer analysis AP server 101 executes processing to generate screen data for an output screen, as exemplified in FIG. 18. In this example, the changes in customer values ​​are displayed using the age and gender of the customer who was the subject of the customer analysis as keys, and the values ​​that were most "on an upward trend" and "on a downward trend" in the comparison years and months of "from when" and "to when" that were the subject of the customer analysis, the values ​​that were maximum and minimum "in line with the current situation" and "not in line with the current situation" in the year and month at the time of the analysis request, and the products that most appeal to each value are displayed for the number of display products specified in the analysis request. As a result, screen data for the output screen, as exemplified in FIG. 18, is generated. Next, the control unit of the customer analysis AP server 101 executes processing to transmit the generated screen data to the client terminal 103 and display it on the output screen. As a result, the generated screen data is transmitted to the client terminal 103 and displayed on the output screen of the client terminal 103. A user viewing the output screen on the client terminal 103 can recognize at a glance from the screen contents of Fig. 18, for example, products that appeal to values ​​that are on an upward / downward trend, and can select products that appeal to the rising customer values ​​and increase sales. When the control unit of the customer analysis AP server 101 completes the processing in step S1607, it ends the customer value change / appealing product presentation processing shown in flowchart 1600 of Fig. 16.

[0095] Fig. 17 is a diagram specifically showing an example of an input screen displayed by the client terminal 103. The input screen in Fig. 17 allows input of conditions for the customer value change / promotion product presentation process. Conditions can be input such as customer segment (age, gender), comparison date (from when, to when), number of products to display, etc.

[0096] Fig. 18 is a diagram specifically showing an example of an output screen displayed by the client terminal 103. Note that Fig. 18 uses the ten human values ​​classified by Shalom H. Schwartz as examples of values. The output screen of Fig. 18 displays the time series transition of the customer's value score for each year and month (changes in customer values), as well as values ​​that are rising or falling in the comparison year and month, values ​​that are in line with or incompatible with the current situation at the time of the analysis request, and products that appeal to each value, for the number of products displayed.

[0097] FIG. 19 is a diagram showing another example of output information displayed on the output screen. Note that in FIG. 19, the ten human values ​​classified by Shalom H. Schwartz are used as examples of values. The customer analysis AP server 101 can also generate a line graph of changes in values ​​by age group as an output screen, as shown in FIG. 19. The line graph shown in FIG. 19 indicates that, for example, among teenagers, values ​​such as hedonism, universalism, and philanthropy have particularly increased from 2018 to 2023, while values ​​such as tradition and power have particularly decreased. This data can be used as a reference for considering future store initiatives to appeal to teenagers.

[0098] Next, the calculation of the value appeal score of a product will be described.

[0099] The value appeal score of a product can be calculated using, for example, the following (Equation 1), (Equation 2), (Equation 3), and (Equation 4).

[0100]

number

[0101]

number

[0102]

number

[0103]

number

[0104] The right-hand term in (Equation 1), Values, is a list in which each dimension is a definition sentence for each value, and review is the nth review sentence for product i at time t. Similarity is a function that calculates the linguistic similarity between Values ​​and review. Linguistic similarity can be calculated by vectorizing each dimension of Values ​​and review sentences using an embedding model, and then calculating their cosine similarity. The result of this Similarity calculation is added up by the number N of review sentences for product i, and the result becomes the left-hand term, P' feedback.

[0105] In (Equation 2), P' feedback is normalized so that the sum of the values ​​is 1. This vector can be interpreted as the value appeal score at time t calculated from customer product reviews (feedback) for product i.

[0106] P on the right hand side of (Equation 3) is the value appeal score of product i at time a, and τ is the number of days from time a to time t. K is any positive value, and T is the length of the period covered by past product value appeal scores used to calculate the product value appeal score. Using the right hand side of (Equation 3), the product value appeal score P from time tT to time t-1 and the product value appeal score P feedback calculated by (Equation 2) can be added together, with the closer the score calculation time is to the current time, the heavier the weighting.

[0107] (Equation 4) normalizes the vector calculated by (Equation 3) so that the sum becomes 1.

[0108] From the above, the product value appeal score P of the product i at time t can be calculated.

[0109] Next, calculation of a customer's value score will be described.

[0110] The customer's value score is calculated using, for example, the following (Equation 5), (Equation 6), (Equation 7), and (Equation 8).

[0111]

number

[0112]

number

[0113]

number

[0114]

number

[0115] Sales, the right-hand term in (Equation 5), is the total sales amount for customer segment j's purchase of product i at time t. Customer segment j refers to a segment classified by customer age and gender. P is the value appeal score for product i at the time closest to time t, and the product of Sales and P is added together for the M products purchased by customer segment j to form C', the left-hand term, feedback.

[0116] In (Equation 6), C' feedback is normalized so that the sum of the values ​​is 1. This vector can be interpreted as the value score of customer segment j calculated based on the customer's purchasing history (feedback).

[0117] C on the right hand side of (Equation 7) is the customer value score at time a for customer segment j, and τ is the number of days from time a to time t. K is any positive value, and T is the length of the period covered by past customer value scores used to calculate the customer value score. Using the right hand side of (Equation 7), the customer value score C from time tT to time t-1 and the customer value score C feedback calculated in (Equation 6) can be added together, with the closer the score calculation time is to the current time, the heavier the weighting.

[0118] (Equation 8) normalizes the vector calculated by (Equation 7) so that the sum of the values ​​becomes 1.

[0119] As a result of the above, the value score C of customer segment j at time t can be calculated.

[0120] The above-described embodiment of the present invention can be summarized as follows.

[0121] (1) As described above, the customer analysis system 100 according to this embodiment includes a customer analysis AP server 101, which is a computer having at least an arithmetic unit (CPU 302) and a storage device 301. The storage device 301 stores customer feature scores that associate customers with scores of their features, sales object feature appeal scores that associate sales objects with scores of how the sales objects appeal to the customer's features, and customer feedback from customers regarding the sales objects. The arithmetic unit (CPU 302) uses the customer feedback to update the customer feature scores and sales object feature appeal scores, and outputs customer features whose scores are increasing and sales objects that appeal to those features.

[0122] This makes it possible to select sales targets that appeal to the characteristics of customers whose scores are increasing and increase sales.

[0123] (2) Furthermore, the customer feedback includes the customer's purchasing history for the sales object, and the calculation device (CPU 302) updates the customer characteristic score using a value obtained by multiplying the customer's purchasing history for the sales object by the sales object characteristic appeal score. In this way, it is possible to capture changes in customer characteristics in a timely manner using the customer's indirect but frequent feedback on the sales object.

[0124] (3) Furthermore, the customer feedback includes customer reviews of the sales object, and the storage device 301 further stores definitions of customer characteristics. The calculation device (CPU 302) associates a characteristic score with the review based on the customer review of the sales object and the definition of the customer's characteristics, and uses the characteristic score to update the customer characteristic score and the sales object characteristic appeal score. In this way, it is possible to more accurately capture changes in the characteristics appealing to the sales object and the customer characteristics using infrequent but direct feedback from customers about the sales object.

[0125] (4) The storage device 301 further stores information on customer attributes and sales targets, and the calculation device (CPU 302) calculates initial values ​​for the customer feature score and the sales target feature appeal score based on the customer attribute information, the sales target information, and the definition of the customer's features. This allows the initial values ​​to be set more accurately than if they were set randomly.

[0126] (5) Also, as an example, customer characteristics are the values ​​of the customer, that is, the values ​​of a person. Therefore, the 10 human values ​​classified by Shalom H. Schwartz can be used as customer characteristics.

[0127] The best mode for carrying out the present invention has been specifically described above, but the present invention is not limited to this and can be modified in various ways without departing from the spirit of the present invention.

[0128] For example, in the above embodiment, the index value was calculated based on the 10 human values ​​classified by Shalom H. Schwartz, but any index can be used as long as it can define a person's characteristics.

[0129] According to this embodiment, it is possible to ascertain whether a customer's values ​​are rising or falling, and to select products that appeal to the rising values ​​of customers and increase sales. Furthermore, in product demand forecasting for a retail store, for example, by analyzing customers and products at the value level beyond the unit of product or product category, it becomes possible to make product demand forecasts that cannot be made on a product or product category basis.

[0130] The present invention is not limited to the above-described embodiment, and can be implemented using any components without departing from the spirit of the present invention.

[0131] The above-described embodiments, examples, and modifications are merely examples, and the present invention is not limited to these details as long as the features of the invention are not impaired. Furthermore, although various embodiments, examples, and modifications have been described above, the present invention is not limited to these details. Other aspects that can be considered within the scope of the technical idea of ​​the present invention are also included within the scope of the present invention.

[0132] In the above figures, the control lines and information lines shown are those that are considered necessary for explanation, and do not necessarily show all the control lines and information lines that are actually implemented. For example, it can be considered that almost all components are actually connected to each other.

[0133] Furthermore, the layout of each functional unit of customer analysis system 100 described above is merely an example. The layout of each functional unit can be changed to an optimal layout in terms of the performance, processing efficiency, communication efficiency, etc. of the hardware and software provided in customer analysis system 100. [Explanation of symbols]

[0134] 100 Customer Analysis System 101 Customer analysis AP server 102 Customer Analysis DB Server 103 Client terminal (other terminal) 104 Network 201 Storage device 202 CPU 203 memory 206 Communication Equipment 210 Programs 301 Storage device 302 CPU 303 Memory 306 Communication Equipment 310 Program 311 Purchase History Table 312 Product Master 313 Customer Master 314 Product Value Appeal Table 315 Customer Values ​​Table 316 Values ​​Master 317 Product Review Table 401 Storage device 402 CPU 403 Memory 404 Input Device 405 Output Device 406 Communication Equipment 410 Program 450 Input Screen 451 Output Screen 501 Storage device 502 CPU 503 memory 504 Input Device 505 Output Device 506 Communication Equipment 510 Program 550 Input Screen

Claims

1. A customer analysis system, a computer having at least a computing device and a storage device; The storage device includes: a customer feature score in which a customer is associated with a score of the customer's feature; a sales object characteristic appeal score in which the sales object is associated with a score that the sales object appeals to the customer's characteristic; customer feedback from said customers regarding said sales object; Remember, The computing device Using the customer feedback, the customer feature score and the sales object feature appeal score are updated, and the customer feature whose score increases and the sales object that appeals to the feature are output. Customer analysis system.

2. The customer feedback includes customer purchasing history for the sales item; the calculation device updates the customer feature score using a value obtained by multiplying the customer's purchase history for the sales object by the sales object feature appeal score; The customer analysis system according to claim 1 .

3. the customer feedback includes customer reviews of the product; The storage device further stores definitions of the customer characteristics; the computing device associates a feature score with the review based on the customer's review of the sales object and a definition of the customer's features, and updates the customer feature score and the sales object feature appeal score using the feature score; The customer analysis system according to claim 1 .

4. The storage device further stores information on customer attributes and sales targets; the calculation device calculates initial values ​​of a customer feature score and a sales target feature appeal score based on the customer attributes, the sales target information, and the definition of the customer's features; The customer analysis system according to claim 3 .

5. The customer analysis system according to claim 1 , wherein the customer characteristics are values ​​of the customer.

6. A customer analysis method, comprising: The method is carried out using a computer having at least a processing unit and a storage unit, The storage device includes: a customer feature score in which a customer is associated with a score of the customer's feature; a sales object characteristic appeal score in which the sales object is associated with a score that the sales object appeals to the customer's characteristic; customer feedback from said customers regarding said sales object; Remember, The computing device Using the customer feedback, the customer feature score and the sales object feature appeal score are updated, and the customer feature whose score increases and the sales object that appeals to the feature are output. Customer analysis methods.

7. having at least a computing device and a storage device; The storage device a customer feature score in which a customer is associated with a score of the customer's feature; a sales object characteristic appeal score in which the sales object is associated with a score that the sales object appeals to the customer's characteristic; customer feedback from said customers regarding said sales object; In a computer storing The computing device Using the customer feedback, the customer feature score and the sales object feature appeal score are updated, and the customer feature whose score increases and the sales object that appeals to that feature are output. A computer program that executes a process.

Images