Experience-value analysis system and experience-value analysis method

Abstract

To provide an experience value analysis system for enhancing an experience value of a consumer in a series of purchase experiences including before and after purchase behavior by analyzing a factor inhibiting purchase of merchandise for a customer and proposing a measure for reducing the factor based on an analysis result.SOLUTION: The system 100 for analyzing an experience value obtained by a customer through a purchase experience comprises a processor and a storage device, and acquires external information through a network, and acquires customer information including behavior information of the customer in a store. An action process which is a flow of a series of actions performed by a customer in a store is analyzed on the basis of external information and customer information, a pain index obtained by indexing a purchase action inhibition factor is calculated for each action constituting the action process and each connection before and after each action, an improvement point of the action process is estimated on the basis of the pain index, and a measure for realizing another action process for reducing the total value of the pain index is recommended.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to an experience value analysis system and an experience value analysis method. [Background technology]

[0002] In recent years, the needs of consumers who are the customers of companies have become more diverse. Therefore, in order to build loyalty, it is important for companies to provide consumers with not only the value provided directly through the sale of products and services, but also the value gained from the series of purchasing experiences (hereinafter referred to as "purchase experience") including those before and after the purchase (hereinafter referred to as "experiential value").

[0003] In this context, various technologies have been proposed that can provide consumers with experience value that meets their needs (for example, Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 7347000 Summary of the Invention [Problem to be solved by the invention]

[0005] The technology in Patent Document 1 identifies store visitors, predicts the products that the identified customers will be interested in, and changes the sales floor layout to suit the customers in order to effectively display the products, thereby providing the customers with a purchasing experience that meets their needs.However, there is a problem in that if the customers do not purchase the products, it is difficult to accurately understand the reasons for this as long as the focus is only on purchasing behavior.

[0006] The present invention has been made in consideration of the above-mentioned problems, and aims to provide technology that can accurately analyze factors that hinder a potential consumer from purchasing a product and, based on the analysis results, propose measures to reduce those factors, thereby enhancing the experiential value of the consumer throughout the entire purchasing experience, including both before and after the purchasing behavior. [Means for solving the problem]

[0007] The experiential value analysis system according to the present invention is a system for analyzing the experiential value that a consumer customer gains through a purchasing experience, in which a computer having at least a processor and a storage device acquires external information, which is information obtained from outside via a network, acquires customer information, which is information about the customer, including at least behavioral information, which is information that represents the customer's behavior within the store, analyzes the behavioral process, which is the flow of a series of actions taken by the customer within the store, based on the acquired external information and customer information, calculates a pain index, which indexes factors that hinder purchasing behavior, for each action that constitutes the analyzed behavioral process of the customer and for each connection between each action, estimates points for improvement in the behavioral process based on the calculated pain index, and recommends measures to realize a different behavioral process that reduces the total pain index value based on the estimated points for improvement. [Effects of the Invention]

[0008] According to the present invention, by accurately analyzing factors that hinder a potential consumer from purchasing a product and proposing measures to reduce those factors based on the analysis results, it is possible to increase the experiential value of the consumer throughout the entire purchasing experience, including both before and after the purchasing behavior.

[0009] Problems other than those mentioned above and solutions thereto will become apparent from the following detailed description of the preferred embodiments and the accompanying drawings. [Brief explanation of the drawings]

[0010] [Figure 1]1 is a diagram illustrating an example of the overall configuration of a system including an experience value analysis system according to an embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of the hardware configuration of an experience value analysis system and a user terminal. [Figure 3] FIG. 2 is a diagram illustrating an example of functional blocks of an experience value analysis system. [Figure 4] 10 is a diagram showing an example of the configuration of a weather information table stored in an external information storage unit. FIG. [Figure 5] 10 is a diagram illustrating an example of a configuration of an event information table stored in an external information storage unit. FIG. [Figure 6] 10 is a diagram illustrating an example of a configuration of a region information table stored in an external information storage unit. FIG. [Figure 7] FIG. 4 is a diagram showing an example of the configuration of a traffic information table stored in an external information storage unit. [Figure 8] 10 is a diagram illustrating an example of a configuration of a travel information table stored in a customer information storage unit. FIG. [Figure 9] 10 is a diagram illustrating an example of a configuration of a behavior information table stored in a customer information storage unit. FIG. [Figure 10] 10 is a diagram illustrating an example of a configuration of a purchase information table stored in a customer information storage unit. FIG. [Figure 11] 10 is a diagram showing an example of the configuration of a value table stored in a value information storage unit; FIG. [Figure 12] FIG. 10 is a diagram illustrating an example of behavior process information. [Figure 13] 10 is a diagram showing an example of the configuration of a behavior process table stored in a behavior process storage unit. FIG. [Figure 14] FIG. 10 illustrates an example of a process transition state. [Figure 15] FIG. 10 is a diagram illustrating an example of the configuration of a process transition state table stored in an improvement point storage unit. [Figure 16] FIG. 10 is a diagram illustrating an example of the configuration of a pain index table stored in an improvement point storage unit. [Figure 17] 10 is a flowchart showing an example of the flow of processing (external information acquisition processing) executed by an external information acquisition unit. [Figure 18] 10 is a flowchart showing an example of the flow of a process (customer information acquisition process) executed by a customer information acquisition unit. [Figure 19] 10 is a flowchart showing an example of the flow of processing (value analysis processing) executed by a value analysis unit. [Figure 20] 10 is a flowchart showing an example of the flow of processing (behavior process analysis processing) executed by a behavior process analysis unit. [Figure 21] 10 is a flowchart showing an example of the flow of a process (improvement point estimation process) executed by an improvement point estimation unit. [Figure 22] 10 is a flowchart showing an example of the flow of a process (policy recommendation process) executed by a policy recommendation unit. [Figure 23] FIG. 10 is a diagram illustrating an example of a policy recommendation screen. DETAILED DESCRIPTION OF THE INVENTION

[0011] Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. However, these embodiments are merely examples for realizing the present invention and do not limit the technical scope of the present invention.

[0012] In the following description, the same or similar components will be denoted by common reference numerals, and redundant description may be omitted.

[0013] 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.

[0014] In the following description, an "interface unit" refers to one or more interface devices. The one or more interface devices 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, an NIC and an HBA (Host Bus Adapter)).

[0015] In the following description, a "storage unit" includes at least one memory. The at least one memory may be a volatile memory or a non-volatile memory. The storage unit may also include one or more PDEVs in addition to the 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).

[0016] 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.

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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.

[0021] 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.

[0022] Furthermore, in the following description, the "experience value 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 experience value 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).

[0023] <Configuration example of experience value analysis system 100> First, an example of the configuration of an experience value analysis system 100 according to this embodiment will be described with reference to Figures 1 to 3. Figure 1 is a diagram that schematically shows an example of the configuration of the entire system including the experience value analysis system 100. Figure 2 is a diagram that schematically shows an example of the hardware configuration of the experience value analysis system 100 and the user terminal 110, and Figure 3 is a diagram that shows an example of the functional blocks of the experience value analysis system 100.

[0024] (Example of overall system configuration) The experience value analysis system 100 is a computer system capable of analyzing the experience value of customers gained through their purchasing experiences, and is realized by one or more computers or server devices having the components described below. In this embodiment, the experience value analysis system 100 is connected to an unmanned product display shelf equipped with various sensor devices (e.g., location information sensors using satellite positioning systems such as GPS, gravity sensors, LiDAR (Light Detection and Ranging) sensors, cameras, sonar, etc.) that can detect the position, distance, body orientation, behavior, facial expression, etc. of the consumer, and a large touch screen provided as an input / output device, so that data can be communicated between them via an appropriate network 120 such as the Internet or a dedicated line, and the case will be described as an example in which the consumer purchases a product displayed on the product display shelf.

[0025] As shown in FIG. 1, the experience value analysis system 100 is connected to user terminals 110a, 110b, 110c, . . . , 110n (hereinafter, collectively referred to as "user terminals 110") such as laptop PCs, tablets, and smartphones owned by users of the experience value analysis system 100 via an appropriate network 120 such as the Internet or a dedicated line, so that data can be communicated between them. The experience value analysis system 100 and the network 120 are connected via a wired connection using well-known communication equipment (not shown), but may also be connected wirelessly. Each user terminal 110 and the network 120 are connected wirelessly, but may also be connected via a wired connection.

[0026] Furthermore, for example, various sensor devices and various devices related to digital signage installed in a store, other computer devices, server devices, etc. (hereinafter also referred to as "other devices") may be connected to this experience value analysis system 100 via the network 120 so as to be able to communicate data with each other. In this case, the other devices and the network 120 may be connected via a wired connection via well-known communication equipment (not shown) or may be connected wirelessly.

[0027] In this embodiment, the experience value analysis system 100 has been described as being made up of one device. However, for example, the experience value analysis system 100 may be made up of a plurality of devices.

[0028] In addition, in this embodiment, the experience value analysis system 100 and the user terminal 110 and other devices have been described as being separate devices. However, the experience value analysis system 100 and the user terminal 110 and other devices may be configured as the same device. In this case, the experience value analysis system may be configured as a system including, for example, the user terminal 110 and other devices. Furthermore, for example, the experience value analysis system may be configured to include some or all of the functions performed by the user terminal 110 and other devices.

[0029] (Example of hardware configuration of the experience value analysis system 100) Next, an example of the hardware configuration of the experience value analysis system 100 will be described with reference to FIG.

[0030] The experience value analysis system 100 according to this embodiment is realized by a single general-purpose computer device, as shown in Fig. 2. The following description will be given assuming that the experience value analysis system 100 is realized by a single general-purpose computer device including one or more processors 101, one or more memories 102, one or more storage devices 103, one or more communication interface devices 104, one or more input devices 105, one or more output devices 106, one or more sensing devices 107, and a wired or wireless communication line 108 connecting them together.

[0031] That is, the experience value analysis system 100 has a storage device including a memory 102 and a storage device 103, an interface device including a communication interface device 104, an input device 105 and an output device 106, a sensing device 107, and a processor 101 connected thereto.

[0032] The storage device 103 is an auxiliary storage device made up of a nonvolatile storage element such as a flash memory. Specific examples of the storage device 103 include a solid state drive (SSD) and a hard disk drive (HDD). The storage device 103 stores at least an experience value analysis program. The experience value analysis program is a computer program for implementing the functions required for the experience value analysis system 100.

[0033] In other words, when the experiential value analysis program is executed by the processor 101, various processes are performed, including processing related to the acquisition of external information (various types of information acquired from the outside via the communication unit) described later in relation to Figures 3 and 17 (hereinafter referred to as ``external information acquisition processing''), processing related to the acquisition of various information about customers (hereinafter also referred to as ``customer information'') described later in relation to Figures 3 and 18 (hereinafter referred to as ``customer information acquisition processing''), processing related to the analysis of values ​​described later in relation to Figures 3 and 19 (hereinafter referred to as ``value analysis processing''), processing related to the analysis of behavioral processes described later in relation to Figures 3 and 20 (hereinafter referred to as ``behavioral process analysis processing''), processing related to the estimation of improvement points described later in relation to Figures 3 and 21 (hereinafter referred to as ``improvement point estimation processing''), and processing related to the recommendation and / or proposal of measures described later in relation to Figures 3 and 22 (hereinafter referred to as ``measure recommendation processing'').

[0034] The value of experience analysis program may be installed from a program source. The program source may be, for example, a computer that distributes the program or a computer-readable recording medium. The value of experience analysis program may also be configured by a device driver, an operating system, various application programs located at higher levels than these, and a library that provides common functions to these programs. Furthermore, two or more programs may be realized as one value of experience analysis program, and one value of experience analysis program may be realized as two or more programs.

[0035] The memory 102 is a primary storage device mainly made up of volatile storage elements such as RAM (Random Access Memory). The memory 102 temporarily stores data representing various information read from the storage device 103 and various data acquired via the communication interface device 104 and / or the input device 105.

[0036] The processor 101 is a processor device such as a CPU (Central Processing Unit) and various co-processors. The processor 101 loads an experience value analysis program into the memory 102 and executes it, thereby performing overall control of the experience value analysis system 100 itself and also managing a control unit (not shown) that performs various processes such as calculation processing and determination processing.

[0037] The interface devices include a communication interface device 104 that connects to a network 120 and communicates with a user terminal 110 or other devices, an input device 105, and an output device 106.

[0038] The sensing device 107 is any of various sensing devices capable of detecting the position, orientation, and behavior of customers within a store, such as a camera, sonar, LiDAR (Light Detection And Ranging) sensor, human presence sensor, or location information sensor using a satellite positioning system such as GPS.

[0039] (Example of hardware configuration of user terminal 110) Next, an example of the hardware configuration of the user terminal 110 will be described with reference to FIG.

[0040] The user terminal 110 of this embodiment is realized by a single general-purpose computer device, as shown in Fig. 2. The following description will be given assuming that the user terminal 110 is realized by a single general-purpose computer device including one or more processors 111, one or more memories 112, one or more storage devices 113, one or more communication interface devices 114, one or more input devices 115, one or more output devices 116, one or more sensing devices 117, and a wired or wireless communication line 118 connecting them.

[0041] That is, the user terminal 110 has a storage device including a memory 112 and a storage device 113, an interface device including a communication interface device 114, an input device 115 and an output device 116, a sensing device 117, and a processor 111 connected thereto.

[0042] The storage device 113 is an auxiliary storage device made up of a nonvolatile storage element such as a flash memory. Specific examples of the storage device 113 include a solid state drive (SSD) and a hard disk drive (HDD). The storage device 113 stores various computer programs, including programs for implementing functions required by the user terminal 110.

[0043] The program may be installed from a program source. The program source may be, for example, a computer that distributes the program or a computer-readable recording medium. The program may also be composed of a device driver, an operating system, various application programs located at higher levels than these, and a library that provides common functions to these programs. Furthermore, two or more programs may be realized as one program, or one program may be realized as two or more programs.

[0044] The memory 112 is a main storage device mainly made up of volatile storage elements such as RAM (Random Access Memory). The memory 112 temporarily stores data representing various information read from the storage device 113 and various data acquired via the communication interface device 114 and / or the input device 115.

[0045] The processor 111 is a processor device such as a CPU (Central Processing Unit) and various co-processors. The processor 111 loads the above programs into the memory 112 and executes them, thereby performing overall control of the user terminal 110 itself and also managing a control unit that performs various processes such as calculation processing and determination processing.

[0046] The interface device includes a communication interface device 114 that connects to the network 120 and communicates with the experience value analysis system 100 and other devices, and an I / O (Input / Output) interface consisting of an input device 115 and an output device 116.

[0047] The sensing device 117 is a variety of sensing devices that can detect the location and behavior of a customer holding the user terminal 110, the location and direction of various fixtures in the store, including product display shelves, etc., such as a location information sensor using a satellite positioning system such as GPS, an acceleration sensor, a gravity sensor, a LiDAR (Light Detection And Ranging) sensor, a camera, or a sonar.

[0048] (Example of functional block of the experience value analysis system 100) Next, an example of blocks of various functions provided in the experience value analysis system 100 according to this embodiment will be described with reference to Fig. 3. Note that each block described below does not represent a hardware configuration, but represents a functional block.

[0049] The experiential value analysis system 100 is configured with functional blocks including a control unit (not shown), a memory unit (not shown), a communication unit (not shown), and a user interface unit 410 consisting of an input unit (not shown) and an output unit (not shown).

[0050] The control unit executes various data processing based on the programs and data stored in the memory unit and the data acquired by the communication unit. The control unit also executes various processes, such as the external information acquisition process (described in detail below with reference to FIG. 17), customer information acquisition process (described in detail below with reference to FIG. 18), value analysis process (described in detail below with reference to FIG. 19), behavioral process analysis process (described in detail below with reference to FIG. 20), improvement point estimation process (described in detail below with reference to FIG. 21), and measure recommendation process (described in detail below with reference to FIG. 22). The control unit also functions as an interface between the memory unit and the communication unit.

[0051] As shown in FIG. 3, the control unit has the functional blocks of an external information acquisition unit 210, a customer information acquisition unit 220, a values ​​analysis unit 230, a behavioral process analysis unit 240, an improvement point estimation unit 250, and a measure recommendation unit 260.

[0052] The external information acquisition unit 210 executes an external information acquisition process (described in detail later with reference to FIG. 17).

[0053] The external information acquisition unit 210 includes the following functional blocks: a weather information acquisition unit 211 , an event information acquisition unit 212 , a local information acquisition unit 213 , and a traffic information acquisition unit 214 .

[0054] The weather information acquisition unit 211 executes a process of acquiring information relating to the weather (hereinafter also referred to as "weather information") from among the external information.

[0055] The event information acquisition unit 212 executes a process of acquiring information relating to an event (hereinafter also referred to as "event information") from among the external information.

[0056] The local information acquisition unit 213 executes a process of acquiring information relating to a local area (hereinafter also referred to as "local information") from among the external information.

[0057] The traffic information acquisition unit 214 executes a process of acquiring information relating to traffic (hereinafter also referred to as "traffic information") from among the external information.

[0058] The customer information acquisition unit 220 executes a customer information acquisition process (described in detail later with reference to FIG. 18).

[0059] The customer information acquisition unit 220 includes the following functional blocks: a movement information acquisition unit 221 , a behavior information acquisition unit 222 , and a purchase information acquisition unit 223 .

[0060] The movement information acquisition unit 221 executes a process of acquiring information relating to the movement of the customer (hereinafter also referred to as "movement information") from the customer information.

[0061] The behavior information acquisition unit 222 executes a process of acquiring information on customer behavior (hereinafter also referred to as "behavior information") from the customer information.

[0062] The purchase information acquisition unit 223 executes a process of acquiring information relating to customer purchases (hereinafter also referred to as "purchase information") from the customer information.

[0063] The value analysis unit 230 executes a value analysis process (described in detail below with reference to FIG. 19).

[0064] The value analysis unit 230 includes functional blocks of a value calculation unit 231 from purchase information and a value calculation unit 232 from behavioral information.

[0065] The purchase information based value calculation unit 231 executes a process of calculating a value from the purchase information acquired by the purchase information acquisition unit 223 .

[0066] The behavioral information-based value calculation unit 232 executes a process of calculating a value from the behavioral information acquired by the behavioral information acquisition unit 222 .

[0067] The behavioral process analysis unit 240 executes behavioral process analysis processing (described in detail below with reference to FIG. 20).

[0068] The behavioral process analysis unit 240 includes the following functional blocks: a movement information-based process calculation unit 241, a behavior information-based process calculation unit 242, and an external information-based process generation unit 243.

[0069] The movement information process calculation unit 241 executes a process of calculating a process from the movement information acquired by the movement information acquisition unit 221 .

[0070] The behavioral information process calculation unit 242 executes a process calculation based on the behavioral information acquired by the behavioral information acquisition unit 222 .

[0071] The process generation unit 243 from external information executes processing to generate a process from external information acquired by the external information acquisition unit 210, in other words, weather information acquired by the weather information acquisition unit 211, event information acquired by the event information acquisition unit 212, local information acquired by the local information acquisition unit 213, and traffic information acquired by the traffic information acquisition unit 214.

[0072] The improvement point estimation unit 250 executes an improvement point estimation process (described in detail later with reference to FIG. 21).

[0073] The improvement point estimation unit 250 includes the following functional blocks: a process transition state estimation unit 251 , a pain index calculation unit 252 , and an improvement point recommendation unit 253 .

[0074] The process transition state estimation unit 251 executes a process transition state estimation process.

[0075] The pain index calculation unit 252 executes a process of calculating a pain index, which is an index that indicates the degree of pain caused by various actions of a customer.

[0076] The improvement point recommendation unit 253 executes a process of recommending various improvement points.

[0077] The policy recommendation unit 260 executes a policy recommendation process (described in detail later with reference to FIG. 22).

[0078] The measure recommendation unit 260 includes an improvement point display unit 261 as a functional block.

[0079] The improvement point display unit 261 executes a process of displaying the various improvement points recommended by the improvement point recommendation unit 253 on the output device 106 of the experience value analysis system 100 and / or the output device 116 of the user terminal 110 .

[0080] The control unit is configured using a processor 101, and can realize these functional blocks by executing a predetermined experience value analysis program. Note that the control unit may be configured using a logic circuit such as an FPGA (Field Programmable Gate Array) instead of the processor 101. The control unit may also be configured by combining the processor 101 and a logic circuit.

[0081] The memory unit is configured using a storage device consisting of, for example, a storage device 103 and memory 102, and stores programs that supply various processing commands to the control unit, and data representing various information used in the processing executed by the control unit.

[0082] As shown in FIG. 3, the storage unit has the following functional blocks: an external information storage unit 310, a customer information storage unit 320, a value information storage unit 330, a behavioral process storage unit 340, and an improvement point storage unit 350.

[0083] The external information storage unit 310 stores external information acquired by the external information acquisition unit 210, in other words, weather information acquired by the weather information acquisition unit 211, event information acquired by the event information acquisition unit 212, local information acquired by the local information acquisition unit 213, and traffic information acquired by the traffic information acquisition unit 214.

[0084] Therefore, the external information storage unit 310 stores at least a weather information table 311, an event information table 312, a region information table 313, and a traffic information table 314, as shown in FIG.

[0085] The weather information table 311 is a table for managing weather information. The weather information table 311 has a record for each date and time. A record represents, for example, a date and time, a region, precipitation, and temperature. That is, the weather information table 311 manages weather information for each time period by linking and recording, for each record, a date and time, a region, and the precipitation and temperature for that region for the time period represented by the date and time. In the example shown in FIG. 4, the weather information table 311 records that the precipitation for one hour from "07:00:00 on April 1, 2024" in "City A" was "0 mm" and the temperature was "10°C."

[0086] The event information table 312 is a table for managing event information. The event information table 312 has a record for each date. The record indicates, for example, the date on which the event is held, the time period during which the event is held, the name of the event, the location where the event is held, and the number of participants of the event. That is, the event information table 312 manages event information by date and time period by linking the date and time period during which the event is held with the name, location, and scale of the event for each record. According to the example shown in FIG. 5, the event information table 312 records that "Event 1," which will be held on "April 1, 2024" from "9:00 AM to 5:00 PM," will be held in "Square B," and that the event is expected to have approximately "50" participants.

[0087] The region information table 313 is a table for managing region information. The region information table 313 has a record for each time period. The record indicates, for example, a time period, the name of the region, the population of the region at that time period, and the number of households in the region at that time period. In other words, the region information table 313 manages region information for each time period by linking and recording, for each record, the time period, the name of the region, and the population and number of households in the region at that time period. According to the example shown in FIG. 6, the region information table 313 records that the population of "City A" in "April 2024" is "18,000" people and the number of households is "5,000."

[0088] The traffic information table 314 is a table for managing traffic information. The traffic information table 314 has a record for each date. A record represents, for example, a date, a time period, a location name, and the congestion status at that location during that time period on that date. That is, the traffic information table 314 manages traffic information for each day and time period by linking and recording, for each record, the date, time period, the location name, and the congestion status at that location during that time period on that date. According to the example shown in FIG. 7, the traffic information table 314 records that a traffic congestion of about "3 km" occurred "near the entrance" of the expressway from "9:00 to 10:00" on "April 1, 2024."

[0089] The customer information storage unit 320 stores customer information acquired by the customer information acquisition unit 220, in other words, movement information acquired by the movement information acquisition unit 221, behavior information acquired by the behavior information acquisition unit 222, and purchase information acquired by the purchase information acquisition unit 223.

[0090] Therefore, the customer information storage unit 320 stores at least a movement information table 321, a behavior information table 322, and a purchase information table 323, as illustrated in FIG.

[0091] The movement information table 321 is a table for managing movement information. The movement information table 321 has a record for each date and time. A record indicates, for example, a date and time, a location label indicating the destination location, and a user ID indicating the user who moved to that location at that date and time. In other words, the movement information table 321 manages movement information by time by linking and recording the date and time, location label, and user ID for each record. According to the example shown in FIG. 8, the movement information table 321 records that a user with user ID "0001" moved to "Area A" at "7:01:00" on "April 1, 2024."

[0092] The behavior information table 322 is a table for managing behavior information. The behavior information table 322 has a record for each date and time. A record indicates, for example, a date and time, an behavior label representing the user's behavior, and a user ID representing the user who performed the behavior at that date and time. In other words, the behavior information table 322 manages behavior information by time by linking and recording the date and time, the behavior label, and the user ID for each record. In the example shown in FIG. 9, the behavior information table 322 records that a user with user ID "0001" "stopped in front of product shelf A" at "7:02:00" on "April 1, 2024."

[0093] The purchase information table 323 is a table for managing purchase information. The purchase information table 323 has a record for each date and time. For example, a record indicates the date and time, the name of the product purchased at that date and time, the purchase amount of that product, and the user ID representing the user who purchased that product at that date and time for that amount. In other words, the purchase information table 323 manages purchase information by time by linking and recording the date and time, the purchased product, the purchase amount, and the user ID for each record. In the example shown in FIG. 10, the purchase information table 323 records that a user with user ID "0001" purchased "Product A" for "100" yen at "7:20:00" on "April 1, 2024."

[0094] The value information storage unit 330 stores various information related to values ​​(hereinafter also referred to as "value information").

[0095] Therefore, the value information storage unit 330 stores at least a value table 331, as shown in FIG.

[0096] The value table 331 is a table for managing value information. The value table 331 has a record for each user ID. The record represents, for example, a user ID representing the user who owns the value, the user's age group, the user's gender, the last update date of the record, the user's value 1 score, and the user's value 2 score. That is, the value table 331 manages value information for each user by linking and recording, for each record, the user ID, the user's age group, gender, value 1 score, and value 2 score of the user identified by the user ID, and the last update date of the record. In the example shown in FIG. 11, the value table 331 records that the user with user ID "0001" is a "male" in his teens, that the user's value 1 score is "0.3", that the value 2 score is "0.1", and that the last update date of the record is "April 1, 2024".

[0097] The behavioral process storage unit 340 stores various information related to the behavioral process of a customer (hereinafter also referred to as "behavioral process information"). The behavioral process information is information that represents the flow of a series of behaviors (hereinafter also referred to as "behavioral process" or "journey") performed by a customer consumer, as exemplified in FIG. 12. In other words, the behavioral process information is information that records all behaviors performed by a customer consumer within a certain period of time, along with the connections between each behavior, as exemplified in FIG. 12. The behavioral process information illustrated in Figure 12 represents a series of actions taken by a customer in the store: "enter area A (S1201), arrive near product display shelf B (S1202), stop in front of product display shelf B (S1203), reach for product C displayed on product display shelf B (S1204), acquire product C (S1205), move away from product display shelf B (S1206), exit area A (S1207), and purchase product C (S1208)."

[0098] Therefore, the behavior process storage unit 340 stores at least a behavior process table 341, as shown in FIG.

[0099] The behavior process table 341 is a table for managing behavior process information. The behavior process table 341 has a record for each process. The record indicates, for example, the name of the process, the name of the previous process representing the process immediately preceding the process, the name of the subsequent process representing the process immediately following the process, and the process ID of the process. In other words, the behavior process table 341 manages behavior process information for each process by linking and recording the process name, the previous process and subsequent process of the process, and the process ID of the process for each record. According to the example shown in FIG. 13, the behavior process table 341 records that for a process named "Shelf A Purchasing Process," the previous process is "Area A," there is no subsequent process, and the process ID is "P0001."

[0100] The improvement point storage unit 350 stores various information (hereinafter also referred to as "improvement point information") relating to improvement points when recommending and / or proposing measures to a user.

[0101] Therefore, the improvement point storage unit 350 stores at least a process transition state table 351 and a pain index table 352, as shown in FIG.

[0102] The process transition state table 351 is a table for managing information about process transition states. A process transition state refers to a process transition state in behavioral process information, as illustrated in FIG. 14 . The process transition state table 351 has a record for each process ID. The record represents, for example, a process ID, a transition probability from the process represented by the process ID to another process, a user's stay time in the process, and the date and time the process was performed. That is, the process transition state table 351 manages the process transition state for each process by linking and recording the process ID, the transition probability from the process represented by the process ID to another process, the user's stay time in the process, and the date and time the process was performed. In the example illustrated in FIG. 15 , the process transition state table 351 records that the transition probability from the process represented by the process ID “P0001” to another process is “1.0,” the user's stay time in the process is “30” seconds, and the date and time the process was performed is “April 1, 2024.”

[0103] The pain index table 352 is a table for managing pain indices. The pain index table 352 has a record for each process ID. The record represents, for example, a process ID, an estimated pain index for the process represented by the process ID, the user's stay time in the process, and the date and time the process was performed. That is, the pain index table 352 manages pain indices for each process by linking and recording, for each record, the process ID, the estimated pain index for the process represented by the process ID, the user's stay time in the process, and the date and time the process was performed. In the example shown in FIG. 16, the pain index table 352 records that the estimated pain index for the process with process ID "P0001" is "0.1," that the user's stay time in the process is "30" seconds, and that the date and time the process was performed is "April 1, 2024."

[0104] By reading and writing this information to the memory unit, the control unit can execute various processes such as the external information acquisition process (described in detail below in relation to Figure 17), customer information acquisition process (described in detail below in relation to Figure 18), value analysis process (described in detail below in relation to Figure 19), behavioral process analysis process (described in detail below in relation to Figure 20), improvement point estimation process (described in detail below in relation to Figure 21), and measure recommendation process (described in detail below in relation to Figure 22).

[0105] The communication unit is responsible for communication processing with the user terminal 110 and other devices via the Internet (an example of the network 120). The communication unit is configured using, for example, a NIC (Network Interface Card) or an HBA (Host Bus Adapter).

[0106] The user interface unit 410 is configured to include functional blocks of an input unit and an output unit.

[0107] The input unit is responsible for input-related processing, such as accepting input operations from the user, among other processes related to the user interface. The input unit is configured using input devices 105, such as a keyboard, pointing device, or touch panel, and detects various operations from the user.

[0108] The output unit is responsible for output-related processes, such as displaying various screens on a display device, outputting audio, etc. The output unit is configured using an output device 106, such as a liquid crystal display or a touch screen.

[0109] That is, each component of the experiential value analysis system 100 is realized by hardware including a processor 101, storage devices such as a memory 102 and a storage device 103, wired or wireless communication lines 108 and interface devices (104, 105, 106) that connect them, and software stored in the storage device 103 that supplies processing instructions to the computing unit.

[0110] In this embodiment, the functions of the experience value analysis system 100 have been described as being integrally realized by a single computer device. However, these functions may also be realized by multiple interconnected computers or server devices. Furthermore, the experience value analysis system 100 may be configured to include a general-purpose computer device such as a laptop PC and a web browser installed thereon, or may be configured to include a web server and various mobile devices.

[0111] Furthermore, the experience value analysis system 100 may have other functions in addition to the various functions described above.

[0112] <Processing flow example> Next, each process executed by the experience value analysis system 100 according to this embodiment will be described with reference to FIGS.

[0113] (External information acquisition processing) FIG. 17 is a flowchart 1700 showing an example of the flow of the external information acquisition process executed by the external information acquisition unit 210 in the experience value analysis system 100 according to this embodiment.

[0114] In step S1701, the control unit of the experience value analysis system 100 executes a process in which the external information acquisition unit 210 receives an input operation from the user regarding various information such as the region and period. This acquires the various information such as the region and period entered by the user. Upon completing the process in step S1701, the control unit of the experience value analysis system 100 proceeds to step S1702.

[0115] In step S1702, the control unit of the experience value analysis system 100 executes processing in which the external information acquisition unit 210 acquires weather information, event information, local information, and traffic information that meets the conditions, as external information, via the network 120, using the various information input by the user in step S1701 as conditions. As a result, various types of external information such as weather information, event information, local information, and traffic information that meet the conditions are acquired via the network 120. Upon completing the processing in step S1702, the control unit of the experience value analysis system 100 proceeds to step S1703.

[0116] In step S1703, the control unit of the experience value analysis system 100 executes processing in which the external information acquisition unit 210 stores, among the various types of external information acquired in step S1702, the weather information in the weather information table 311, the event information in the event information table 312, the region information in the region information table 313, and the traffic information in the traffic information table 314, each as data for each time period. As a result, among the various types of information acquired in step S1702, the weather information in the weather information table 311, the event information in the event information table 312, the region information in the region information table 313, and the traffic information in the traffic information table 314, each as data for each time period. Upon completing the processing in step S1703, the control unit of the experience value analysis system 100 ends the external information acquisition processing shown in flowchart 1700 of FIG. 17.

[0117] (Customer information acquisition process) FIG. 18 is a flowchart 1800 showing an example of the flow of the customer information acquisition process executed by the customer information acquisition unit 220 in the experience value analysis system 100 according to this embodiment.

[0118] In step S1801, the control unit of the experience value analysis system 100 executes processing to acquire GPS information or floor movement information linked to the user ID of the user in question, using the customer information acquisition unit 220. As a result, the GPS information or floor movement information linked to the user ID of the user in question is acquired. Upon completing the processing in step S1801, the control unit of the experience value analysis system 100 proceeds to step S1802.

[0119] In step S1802, the control unit of the experience value analysis system 100 executes processing in which the customer information acquisition unit 220 divides the GPS information or floor movement information acquired in step S1801 into block information defined on a map, and stores the movement label data as time-series information in the movement information table 321. As a result, the GPS information or floor movement information acquired in step S1801 is divided into block information defined on a map, and the movement label data is stored as time-series information in the movement information table 321. When the processing in step S1802 is completed, the control unit of the experience value analysis system 100 proceeds to step S1803.

[0120] In step S1803, the control unit of the experience value analysis system 100 executes processing to detect the position and orientation from the product display shelf or the like based on sensor information from the product display shelf or the like using the customer information acquisition unit 220. As a result, the position and orientation from the sales shelf or the like is detected from the sensor information from the product display shelf or the like. When the processing in step S1803 is completed, the control unit of the experience value analysis system 100 proceeds to step S1804.

[0121] In step S1804, the control unit of the experience value analysis system 100 executes processing in which the customer information acquisition unit 220 calculates patterns of various posture changes, such as sitting, wandering, etc., assigns predefined state labels to the patterns, and stores the patterns in the behavior information table 322. As a result, patterns of various posture changes, such as sitting, wandering, etc., are calculated, assigned predefined state labels, and stored in the behavior information table 322. Upon completing the processing in step S1804, the control unit of the experience value analysis system 100 proceeds to step S1805.

[0122] In step S1805, the control unit of the experience value analysis system 100 executes processing to acquire purchase data linked to the user using the customer information acquisition unit 220, and to store various information such as the user ID and the amount of purchased items in the purchase information table 323. As a result, purchase data linked to the user is acquired, and various information such as the user ID and the amount of purchased items is stored in the purchase information table 323. Upon completing the processing in step S1805, the control unit of the experience value analysis system 100 proceeds to step S1806.

[0123] In step S1806, the control unit of the experience value analysis system 100 executes processing by the customer information acquisition unit 220 to link the survey information with the purchased item and store it when the user has responded to the survey information at an online store, app, etc. As a result, when the user has responded to the survey information at an online store, app, etc., the survey information is linked with the purchased item and stored. Upon completing the processing in step S1806, the control unit of the experience value analysis system 100 ends the customer information acquisition processing shown in the flowchart 1800 of FIG. 18.

[0124] (Value analysis processing) FIG. 19 is a flowchart 1900 showing an example of the flow of the value analysis process executed by the value analysis unit 230 in the experience value analysis system 100 according to this embodiment.

[0125] In step S1901, the control unit of the experience value analysis system 100 executes processing to acquire all user information for each user or for a defined user category from the purchase information table 323 using the value of experience analysis unit 230. As a result, the target user information is acquired from the purchase information table 323. Upon completing the processing in step S1901, the control unit of the experience value analysis system 100 proceeds to step S1902.

[0126] In step S1902, the control unit of the experience value analysis system 100 executes processing to extract the defined value information from the text information of the purchased product or the questionnaire information, etc., using the value analysis unit 230. As a result, the defined value information is extracted from the text information of the purchased product or the questionnaire information, etc. When the processing in step S1902 is completed, the control unit of the experience value analysis system 100 proceeds to step S1903.

[0127] In step S1903, the control unit of the experience value analysis system 100 executes processing in which the value analysis unit 230 aggregates value information for each user or user category at defined time intervals and stores each aggregated piece of value information in the value table 331. As a result, value information is aggregated for each user or user category at defined time intervals and each aggregated piece of value information is stored in the value table 331. When the processing in step S1903 is completed, the control unit of the experience value analysis system 100 proceeds to step S1904.

[0128] In step S1904, the control unit of the experience value analysis system 100 executes a process in which the value analysis unit 230 sets the value of products not selected by the user from the behavioral information to negative values ​​and sums them up. As a result, the value of products not selected by the user from the behavioral information is set to negative values ​​and summed up. When the process in step S1904 is completed, the control unit of the experience value analysis system 100 proceeds to step S1905.

[0129] In step S1905, the control unit of the experience value analysis system 100 executes processing in which the value analysis unit 230 acquires purchase data linked to the user and stores each value, such as the user ID and the amount of purchased items, in the purchase information table 323. As a result, purchase data linked to the user is acquired, and each value, such as the user ID and the amount of purchased items, is stored in the purchase information table 323. Upon completing the processing in step S1905, the control unit of the experience value analysis system 100 ends the value analysis processing shown in the flowchart 1900 of FIG. 19.

[0130] <Calculation of values ​​score> Next, calculation of the value score that a product appeals to will be described.

[0131] The value score that a product appeals to is calculated, for example, using the following (Equation 1), (Equation 2), (Equation 3), and (Equation 4).

[0132]

number

[0133]

number

[0134]

number

[0135]

number

[0136] 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 summed up for the number N of review sentences for product i, and becomes the left-hand term, P' feedback.

[0137] In (Equation 2), P' feedback is normalized so that the sum of the values ​​is 1. This vector can be interpreted as the value score at time t that product i appeals to, calculated from customer product reviews (feedback) for product i.

[0138] P on the right hand side of (Equation 3) is the value score at time a that product i appeals to, 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 the past value scores used to calculate the value score that product i appeals to. Using the right hand side of (Equation 3), the value score P from time tT to time t-1 and the value 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.

[0139] (Equation 4) normalizes the vector calculated by (Equation 3) so that the sum becomes 1.

[0140] From the above, the value score P at time t when product i is appealing can be calculated.

[0141] Next, calculation of a customer's value score will be described.

[0142] The customer's value score is calculated, for example, using the following (Equation 5), (Equation 6), (Equation 7), and (Equation 8).

[0143]

number

[0144]

number

[0145]

number

[0146]

number

[0147] 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 score for the time closest to time t that product i appeals to, and the product of Sales and P is added together for the M products purchased by customer segment j to obtain C' feedback, the left-hand term.

[0148] 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).

[0149] C on the right hand side of (Equation 7) is the value score of customer segment j 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 the past value scores used to calculate the value score of customer segment j. Using the right hand side of (Equation 7), the value score C of customer segment j from time tT to time t-1 and the 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.

[0150] (Equation 8) normalizes the vector calculated by (Equation 7) so that the sum of the values ​​becomes 1.

[0151] As a result of the above, the value score C of customer segment j at time t can be calculated.

[0152] (Behavioral Process Analysis Processing) FIG. 20 is a flowchart 2000 showing an example of the flow of behavioral process analysis processing executed by the behavioral process analysis unit 240 in the experience value analysis system 100 according to this embodiment.

[0153] In step S2001, the control unit of the experience value analysis system 100 executes a process in which the behavioral process analysis unit 240 receives an input operation for definition information related to the purchasing process from the user. This acquires the definition information related to the purchasing process input by the user. When the process in step S2001 is completed, the control unit of the experience value analysis system 100 proceeds to step S2002.

[0154] In step S2002, the control unit of the experience value analysis system 100 causes the behavioral process analysis unit 240 to execute processing to acquire, for each user, various data representing customer information from the movement information table 321, the behavior information table 322, and the purchase information table 323. As a result, various data representing customer information is acquired for each user from the movement information table 321, the behavior information table 322, and the purchase information table 323. Upon completing the processing in step S2002, the control unit of the experience value analysis system 100 proceeds to step S2003.

[0155] In step S2003, the control unit of the experience value analysis system 100 causes the behavioral process analysis unit 240 to execute a process of chronologically arranging the various data representing the customer information of each user acquired in step S2002 and linking it to various information such as predefined locations and actions. As a result, the various data acquired in step S2002 is chronologically arranged and linked to various information such as predefined locations and actions. However, if a narrower range or more detailed action is linked, the data is overwritten with that label. Upon completing the process in step S2003, the control unit of the experience value analysis system 100 proceeds to step S2004.

[0156] In step S2004, the control unit of the experience value analysis system 100 executes a process in which the behavioral process analysis unit 240 determines whether or not there is data representing registered behavioral process information (hereinafter also referred to as "behavioral process data"). If it is determined that there is registered behavioral process data (step S2004: YES), the process proceeds to step S2005. On the other hand, if it is determined that there is not registered behavioral process data (step S2004: NO), the process proceeds to step S2007.

[0157] In step S2005, the control unit of the experience value analysis system 100 executes processing to call behavioral process data relating to related processes from among the registered behavioral process data determined to exist in step S2004 by the behavioral process analysis unit 240. As a result, the behavioral process data relating to the related processes is called. Upon completing the processing in step S2005, the control unit of the experience value analysis system 100 proceeds to step S2006.

[0158] In step S2006, the control unit of the experience value analysis system 100 executes processing in which, if there is a pattern in which the user's behavior differs from the registered behavior process, the behavior process analysis unit 240 adds a new process and saves it in the behavior process table 341. As a result, if there is a pattern in which the user's behavior differs from the registered behavior process, a new process is added and saved in the behavior process table 341. When the processing in step S2006 is completed, the control unit of the experience value analysis system 100 ends the behavior process analysis processing shown in the flowchart 2000 of FIG. 20.

[0159] In step S2007, the control unit of the experience value analysis system 100 executes processing in which the behavioral process analysis unit 240 generates behavioral process data representing connections along the time axis of changes in place labels and stores the data in the behavioral process table 341. As a result, behavioral process data representing connections along the time axis of changes in place labels is generated and stored in the behavioral process table 341. When the processing in step S2007 is completed, the control unit of the experience value analysis system 100 proceeds to step S2008.

[0160] In step S2008, the control unit of the experience value analysis system 100 executes processing in which, if behavioral information has been acquired for a specific location or if there is a fixed behavioral pattern, the behavioral process analysis unit 240 creates a label for the target process and a change process of the behavioral pattern as data and saves them in the behavioral process table 341. As a result, if behavioral information has been acquired for a specific location or if there is a fixed behavioral pattern, the label for the target process and a change process of the behavioral pattern are created as data and saved in the behavioral process table 341. Upon completing the processing in step S2008, the control unit of the experience value analysis system 100 ends the behavioral process analysis processing shown in the flowchart 2000 of FIG. 20.

[0161] (Improvement point estimation process) FIG. 21 is a flowchart 2100 showing an example of the flow of the improvement point estimation process executed by the improvement point estimation unit 250 in the experience value analysis system 100 according to this embodiment.

[0162] In step S2101, the control unit of the experience value analysis system 100 executes processing in which the improvement point estimation unit 250 acquires external information such as weather information and event information for the target day from the weather information table 311, event information table 312, local information table 313, and traffic information table 314 stored in the external information storage unit 310. This acquires external information such as weather information and event information for the target day. When the processing in step S2101 is completed, the control unit of the experience value analysis system 100 proceeds to step S2102.

[0163] In step S2102, the control unit of the experience value analysis system 100 executes a process in which the improvement point estimation unit 250 receives an input operation for the target customer information from the user. As a result, the target customer information for which the input operation has been received from the user is acquired. Upon completing the process in step S2102, the control unit of the experience value analysis system 100 proceeds to step S2103.

[0164] In step S2103, the control unit of the experience value analysis system 100 executes processing to acquire behavioral process data linked to the event and the user in question, using the improvement point estimation unit 250. As a result, behavioral process data linked to the event and the user in question is acquired. Upon completing the processing in step S2103, the control unit of the experience value analysis system 100 proceeds to step S2104.

[0165] In step S2104, the control unit of the experience value analysis system 100 executes a process to determine whether or not there is pain index information related to the process, using the improvement point estimation unit 250. If it is determined that there is pain index information related to the process (step S2104: YES), the process proceeds to step S2105. On the other hand, if it is determined that there is no pain index information related to the process (step S2104: NO), the process proceeds to step S2106.

[0166] In step S2105, the control unit of the experience value analysis system 100 acquires the current time and behavioral data using the improvement point estimation unit 250, and executes processing to extract events with large pain indices in the preceding and following processes. As a result, the current time and behavioral data are acquired, and events with large pain indices in the preceding and following processes are extracted. Upon completing the processing in step S2105, the control unit of the experience value analysis system 100 immediately ends the improvement point estimation processing shown in the flowchart 2100 in FIG. 21.

[0167] In step S2106, the control unit of the experience value analysis system 100 executes processing to acquire past user behavior data related to the process in question, using the improvement point estimation unit 250. This acquires past user behavior data related to the process in question. Upon completing the processing in step S2106, the control unit of the experience value analysis system 100 proceeds to step S2107.

[0168] In step S2107, the control unit of the experience value analysis system 100 executes a process in which the improvement point estimation unit 250 calculates the stay time of each process and, if the next process branches, the transition probability, and stores the results in the process transition state table 351. As a result, the stay time of each process and, if the next process branches, the transition probability are calculated and stored in the process transition state table 351. When the process in step S2107 is completed, the control unit of the experience value analysis system 100 proceeds to step S2108.

[0169] In step S2108, the control unit of the experience value analysis system 100 executes a process in which the improvement point estimation unit 250 defines patterns and thresholds that can become pain for the duration of stay and behavioral patterns, and calculates the number of times or duration of stay as a pain index. As a result, patterns and thresholds that can become pain for the duration of stay and behavioral patterns are defined, and the number of times or duration of stay is calculated as a pain index. When the process in step S2108 is completed, the control unit of the experience value analysis system 100 proceeds to step S2109.

[0170] In step S2109, the control unit of the experience value analysis system 100 executes processing to calculate change points in past behavior with respect to places, events, weather, etc., using the improvement point estimation unit 250. At that time, if there is an increase in indecision, stagnation, worry, etc., processing is executed to calculate these as pain. As a result, change points in past behavior with respect to places, events, weather, etc. are calculated. Furthermore, if there is an increase in indecision, stagnation, worry, etc., these are calculated as pain. When the processing in step S2109 is completed, the control unit of the experience value analysis system 100 proceeds to step S2110.

[0171] In step S2110, the control unit of the experience value analysis system 100 executes a process in which the improvement point estimation unit 250 calculates events such as when a purchased product is not repeated or when the product does not match the customer's values ​​as pain. As a result, when a purchased product is not repeated or when the product does not match the customer's values, these events are calculated as pain. Upon completing the process in step S2110, the control unit of the experience value analysis system 100 ends the improvement point estimation process shown in the flowchart 2100 of FIG. 21.

[0172] (Policy recommendation processing) FIG. 22 is a flowchart 2200 showing an example of the flow of the policy recommendation process executed by the policy recommendation unit 260 in the experience value analysis system 100 according to this embodiment.

[0173] In step S2201, the control unit of the experience value analysis system 100 executes a process in which the policy recommendation unit 260 receives an input operation from the user regarding environmental factors for the target day. This acquires the environmental factors for the target day input by the user. When the process in step S2201 is completed, the control unit of the experience value analysis system 100 proceeds to step S2202.

[0174] In step S2202, the control unit of the experience value analysis system 100 executes a process in which the policy recommendation unit 260 determines whether or not there is immediately preceding customer behavior data. If it is determined that there is immediately preceding customer behavior data (step S2202: YES), the process proceeds to step S2203. On the other hand, if it is determined that there is no immediately preceding customer behavior data (step S2202: NO), the process proceeds to step S2205.

[0175] In step S2203, the control unit of the experience value analysis system 100 reads the behavioral data and executes processing to calculate the current process progress using the policy recommendation unit 260. As a result, the behavioral data is read and the current process progress is calculated. When the processing in step S2203 is completed, the control unit of the experience value analysis system 100 proceeds to step S2204.

[0176] In step S2204, the control unit of the experience value analysis system 100 executes processing to read behavioral patterns related to the current process and subsequent processes and purchasing pain data using the policy recommendation unit 260. This reads behavioral patterns related to the current process and subsequent processes and purchasing pain data. Upon completing the processing in step S2204, the control unit of the experience value analysis system 100 proceeds to step S2206.

[0177] In step S2205, the control unit of the experience value analysis system 100 executes a process to read the current behavior pattern data and the purchasing pane data using the policy recommendation unit 260. As a result, the current behavior pattern data and the purchasing pane data are read. Upon completing the process in step S2205, the control unit of the experience value analysis system 100 proceeds to step S2206.

[0178] In step S2206, the control unit of the experience value analysis system 100 causes the policy recommendation unit 260 to execute processing to extract a series of behavioral patterns related to the pain. As a result, a series of behavioral patterns related to the pain are extracted. Upon completing the processing in step S2206, the control unit of the experience value analysis system 100 proceeds to step S2207.

[0179] In step S2207, the control unit of the experience value analysis system 100 executes processing to extract and list processes, products, etc. that cause pain, using the policy recommendation unit 260. As a result, processes, products, etc. that cause pain are extracted and listed. Upon completing the processing in step S2207, the control unit of the experience value analysis system 100 proceeds to step S2208.

[0180] In step S2208, the control unit of the experience value analysis system 100 executes processing in which the policy recommendation unit 260 extracts and presents conditions under which the pain does not occur, given the time, period, etc. under which the pain occurs. This processing executed in step S2208 is performed, for example, by displaying the information to be presented on the policy recommendation screen 2300 shown in FIG. 23. In this way, conditions under which the pain does not occur, given the time, period, etc. under which the pain occurs. When the processing in step S2208 is completed, the control unit of the experience value analysis system 100 proceeds to step S2209.

[0181] In step S2209, the control unit of the experience value analysis system 100 causes the policy recommendation unit 260 to execute processing to suggest actions, products, etc. that will reduce the pain if the pain will be reduced later in time or if the pain will be reduced by selecting another product. This processing executed in step S2209 is performed, for example, by displaying the content of the proposal on the policy recommendation screen 2300 shown in FIG. 23. As a result, actions, products, etc. that will reduce the pain are suggested if the pain will be reduced later in time or if the pain will be reduced by selecting another product. Upon completing the processing in step S2209, the control unit of the experience value analysis system 100 terminates the policy recommendation processing shown in the flowchart 2200 of FIG. 22.

[0182] The experience value analysis system 100 according to this embodiment has been described above.

[0183] The above-described embodiment of the present invention can be summarized as follows.

[0184] (1) The experience value analysis system 100 is a system for analyzing the experience value that a consumer customer gains through a purchasing experience. The system includes a computer having at least a processor 101 and storage devices (102, 103) that acquires external information, which is information obtained from outside via a network 120, acquires customer information, which is information about the customer, including at least behavioral information, which is information describing the customer's behavior in the store, analyzes the customer's behavioral process, which is the flow of a series of actions taken by the customer in the store, based on the acquired external information and customer information, calculates a pain index that indexes factors that inhibit purchasing behavior for each action that constitutes the analyzed customer's behavioral process and the connections between each action, estimates points of improvement in the behavioral process based on the calculated pain index, and recommends measures to realize a different behavioral process that reduces the total pain index based on the estimated points of improvement. In this manner, the experience value analysis system 100 accurately analyzes factors that inhibit the consumer customer from purchasing a product and suggests measures to reduce the factors based on the analysis results, thereby enhancing the consumer's experience value throughout the entire purchasing experience, including before and after the purchasing behavior.

[0185] (2) The customer information further includes at least one of movement information, which is information relating to the customer's movement between floors in the store, and purchase information, which is information relating to the customer's purchase history.

[0186] (3) The external information includes at least one of weather information, which is information about past, present, or future weather conditions at the location of the store; event information, which is information about past, present, or future events being held at the location of the store; local information, which is information about the location of the store; and traffic information, which is information about past or present traffic conditions at the location of the store.

[0187] (4) The pain index is set according to the value score, which quantifies the tendency of each customer's values.

[0188] (5) Value scores are quantified for each customer attribute, which is classified based on age and gender.

[0189] (6) When there are two or more recommended measures, each measure is recommended according to a predetermined priority for each time period and / or each scene.

[0190] (7) When there are two or more recommended measures, the measures are recommended in order of the smallest total pain index in the behavioral process realized by the measures.

[0191] (8) A measure recommendation screen 2300 for recommending measures is displayed on the output device (106, 116).

[0192] (9) The behavioral information and / or movement information is calculated based on sensor information obtained from an external sensor.

[0193] 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.

[0194] The above-described embodiments 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 modified examples 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.

[0195] 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.

[0196] Furthermore, the above-described layout of each functional unit of the experience value analysis system 100 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 included in the experience value analysis system 100. [Explanation of symbols]

[0197] 100: Experience Value Analysis System

Claims

1. An experience value analysis system for analyzing the experience value that a customer consumer gains through a purchasing experience, a computer having at least a processor and a storage device, Acquire external information, which is information acquired from the outside via a network; acquire customer information that is information about the customer, including at least behavioral information that is information that represents the customer's behavior in the store; analyzing a behavioral process, which is a flow of a series of actions taken by the customer in the store, based on the acquired external information and customer information; A pain index, which is an index of factors that inhibit purchasing behavior, is calculated for each behavior that constitutes the analyzed customer's behavioral process and for each connection before and after each behavior, and improvement points in the behavioral process are estimated based on the calculated pain index. recommending measures to realize another behavioral process that reduces the total value of the pain index based on the estimated improvement points; Experience value analysis system.

2. The customer information is Movement information is information regarding the customer's movement through floors within the store; Purchase information, which is information about the customer's purchase history; The experience value analysis system according to claim 1 , further comprising at least one of:

3. The external information is Weather information, which is information about past, present, or future weather conditions at the location of the store; Event information, which is information about past, present, or future events held at the store's location; Regional information, which is information about the location of the store; Traffic information, which is information about past or current traffic conditions at the location of the store; The experience value analysis system according to claim 1 , comprising at least one of the following:

4. The experience value analysis system according to claim 1 , wherein the pain index is set according to a value score that quantifies a tendency of values ​​of each customer.

5. The business improvement system according to claim 4 , wherein the value scores are quantified for each customer attribute classified based on age and gender.

6. The experience value analysis system according to claim 1 , wherein when there are two or more recommended measures, the measures are recommended in accordance with a priority order that is preset for each time period and / or each scene.

7. 2. The experiential value analysis system according to claim 1, wherein when there are two or more recommended measures, the measures are recommended in order of the smallest total value of pain indices in the behavioral processes realized by the measures.

8. The experience value analysis system according to claim 1 , wherein a measure recommendation screen recommending the measure is displayed on an output device.

9. The experience value analysis system according to claim 1 , wherein the behavior information and / or the movement information is calculated based on sensor information acquired from an external sensor.

10. An experience value analysis method for analyzing the experience value that a customer consumer gains through a purchasing experience, comprising: A computer having at least a processor and a storage device, Acquire external information, which is information acquired from the outside via a network; acquire customer information that is information about the customer, including at least behavioral information that is information that represents the customer's behavior in the store; analyzing a behavioral process, which is a flow of a series of actions taken by the customer in the store, based on the acquired external information and customer information; A pain index, which is an index of factors that inhibit purchasing behavior, is calculated for each behavior that constitutes the analyzed customer's behavioral process and for each connection before and after each behavior, and improvement points in the behavioral process are estimated based on the calculated pain index. recommending measures to realize another behavioral process that reduces the total value of the pain index based on the estimated improvement points; Experience value analysis method.

11. An experience value analysis system for analyzing the experience value that a customer consumer obtains through a purchasing experience using a computer having at least a processor and a storage device, Acquire external information, which is information acquired from the outside via a network; acquire customer information that is information about the customer, including at least behavioral information that is information that represents the customer's behavior in the store; analyzing a behavioral process, which is a flow of a series of actions taken by the customer in the store, based on the acquired external information and customer information; A pain index, which is an index of factors that inhibit purchasing behavior, is calculated for each behavior that constitutes the analyzed customer's behavioral process and for each connection before and after each behavior, and improvement points in the behavioral process are estimated based on the calculated pain index. Based on the estimated improvement points, measures are recommended to realize another behavioral process that reduces the total value of the pain index. A computer program that causes the computer to execute a process.

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