Information processing device, information processing method, and program

Abstract

To make it easier to formulate sales strategies. [Solution] The memory unit 10 stores each of multiple products in association with psychological attributes, which are attributes related to consumer purchasing and indicate the consumer's purchasing values. The identification unit 121 refers to sales information related to product sales in stores and identifies sales performance by psychological attribute, which is the sales performance of products associated with each psychological attribute in a first period, and sales performance by psychological attribute in a second period different from the first period. The analysis unit 122 analyzes changes in consumer purchasing values ​​in stores based on the sales performance by psychological attribute in the first period and the sales performance by psychological attribute in the second period. The output unit 123 outputs the results analyzed by the analysis unit 122.

Description

[Technical Field]

[0001] The present invention relates to an information processing apparatus, an information processing method, and a program. [Background Art]

[0002] Conventionally, systems that support store sales strategies are known. Patent Document 1 discloses a technique of disclosing sales data of a store, which is additionally updated daily, to the store. [Prior Art Documents] [Patent Documents]

[0003] [Patent Document 1] Japanese Unexamined Patent Application Publication No. 2007-102745 [Summary of the Invention] [Problems to be Solved by the Invention]

[0004] A person in charge of a store analyzes the above sales data to formulate a sales strategy, and aims to improve sales by executing the formulated sales strategy. However, since analyzing sales data is not easy for the person in charge of the store, there has been a problem that the load of formulating a sales strategy is high.

[0005] The present invention has been made in view of these points, and an object of the present invention is to provide a technique for facilitating formulation of a sales strategy. [Means for Solving the Problems]

[0006] A first aspect of the present invention is an information processing device. This information processing device includes: a storage unit that stores a plurality of products in association with psychological attributes, which are attributes relating to consumer purchasing and indicating consumer purchasing values; a specification unit that refers to sales information relating to the sale of the products in a store and identifies sales performance by psychological attribute, which is the sales performance of the products associated with each psychological attribute in a first period, and sales performance by psychological attribute in a second period different from the first period; an analysis unit that analyzes changes in consumer purchasing values ​​in the store based on the sales performance by psychological attribute in the first period and the sales performance by psychological attribute in the second period; and an output unit that outputs the results of the analysis performed by the analysis unit.

[0007] The memory unit may further associate and store each of the plurality of products with sub-attributes, which are other attributes different from the main attribute, which is a psychological attribute; the identification unit may, by referring to the sales information, identify the sales performance by sub-attribute, which is the sales performance of the products associated with each sub-attribute during the first period, and the sales performance by sub-attribute during the second period; and the analysis unit may analyze the changes in consumers' values ​​at the store based on the sales performance by psychological attribute and the sales performance by sub-attribute during the first period and the sales performance by psychological attribute and the sales performance by sub-attribute during the second period.

[0008] The identification unit may further select period-variable products, which are products in which the difference between sales performance in the first period and sales performance in the second period is relatively large, by referring to the sales information. The analysis unit may identify the attribute in which the difference between sales performance in the first period and sales performance in the second period is relatively large, based on the sales performance by psychological attribute in the first period and the sales performance by psychological attribute in the second period. The analysis unit may further analyze the relationship between the identified attribute and the period-variable products based on whether or not the product information indicating the period-variable products includes content relating to the identified attribute.

[0009] The identification unit may refer to the sales information to further identify the number of times a combination of the products has been purchased, and the analysis unit may further analyze the consumer's values ​​for at least one of the products included in the combination based on the number of purchases and the attributes associated with each of the products included in the combination.

[0010] The storage unit may further store user data including attributes indicating consumer purchasing values ​​for each user and the user's behavioral history; the identification unit may select the first period from among the weekdays, or select the second period from among the holidays, and may refer to the sales information to identify attribute-specific sales performance, which is the sales performance of the products associated with each attribute in the first period, and attribute-specific sales performance in the second period; the analysis unit may (1) refer to the user data to calculate the user attribute ratio, which is the ratio of each of the attributes of a plurality of users who are estimated to have visited the store in the first period, and the user attribute ratio in the second period; and (2) compare the user attribute ratio and attribute-specific sales performance in the first period with the user attribute ratio and attribute-specific sales performance in the second period; and the output unit may output the results of the comparison made by the analysis unit.

[0011] The analysis unit may estimate the ratio of the daily sales quantity of the planned order product on weekdays to the daily sales quantity of the planned order product on holidays, based on the sales performance by attribute during the first period, the sales performance by attribute during the second period, and the attributes associated with the planned order product, which is the product that the store plans to order. The output unit may output information showing the ratio of the sales quantities of the planned order product.

[0012] The information processing device may further include an acquisition unit that acquires external environment information indicating the external environment of the location where the store is located at the time the sale corresponding to the sales information was made, the identification unit may refer to a plurality of sales information and the external environment information corresponding to each of the sales information to identify environmentally volatile products which are products whose sales performance fluctuates relatively large in response to changes in the external environment, the analysis unit may determine at least one of the order quantity of the environmentally volatile product and the priority of the placement location of the environmentally volatile product in the store's sales area based on the changes in the external environment, and the output unit may output at least one of the determined order quantity and the determined priority.

[0013] The analysis unit may, based on the sales information, extract long-term fluctuations for each attribute from the time-series changes in attribute-specific sales performance, which is the sales performance of the products associated with each attribute, and the output unit may output information showing the long-term fluctuations for each attribute.

[0014] The storage unit may further store user data including attributes indicating consumer purchasing values ​​for each user and the user's behavioral history; the identification unit may identify sales performance by psychological attribute in a third period by referring to the sales information; the analysis unit may compare the ratio of the total sales quantity or sales amount of the products associated with each psychological attribute with the ratio of each of the multiple users in the store's trading area by referring to the sales performance by psychological attribute and the user data; and the output unit may output the results of the comparison made by the analysis unit.

[0015] The memory unit may further associate and store each of the multiple products with an attribute value indicating the degree of the value indicated by the attribute associated with the product. The analysis unit may, if there is a product that is out of stock, compare for each product sold by the store that is different from the out-of-stock product, a first deviation degree indicating the degree of discrepancy between the attribute value associated with the out-of-stock product and the type attribute value defined for the type of the out-of-stock product, and a second deviation degree indicating the degree of discrepancy between the attribute value corresponding to the product sold and the type attribute value of the product sold. The analysis unit may select a substitute product from among the products sold in which the difference between the two is relatively small, and the output unit may output information indicating the substitute product.

[0016] The analysis unit may select the substitute product from among the products for sale whose type is different from the out-of-stock product.

[0017] A second aspect of the present invention is an information processing method. This information processing method includes the steps of: a processor acquiring information relating each of a plurality of products to psychological attributes, which are attributes relating to consumer purchasing and indicating consumer purchasing values; referring to sales information relating to the sale of the products in a store to identify sales performance by psychological attribute, which is the sales performance of the products associated with each psychological attribute in a first period, and sales performance by psychological attribute in a second period different from the first period; analyzing changes in consumer purchasing values ​​in the store based on the sales performance by psychological attribute in the first period and the sales performance by psychological attribute in the second period; and outputting the results of the analysis performed in the analysis step.

[0018] A third aspect of the present invention is a program. The program causes a computer to execute the steps of: acquiring information in which each of a plurality of products is associated with a psychological attribute that is an attribute related to consumer purchasing and represents values related to consumer purchasing; referring to sales information related to sales of said products in a store, identifying sales results by psychological attribute, which are sales results of said products associated with each said psychological attribute for each psychological attribute in a first period, and said sales results by psychological attribute in a second period different from said first period; analyzing a change in values related to consumer purchasing at said store based on said sales results by psychological attribute in said first period and said sales results by psychological attribute in said second period; and outputting a result analyzed in said analyzing step.

[0019] In order to provide this program or update a part of the program, a computer-readable recording medium having this program recorded thereon may be provided, and this program may be transmitted via a communication line.

[0020] Note that any combination of the above components, and conversions of the expression of the present invention between methods, apparatuses, systems, computer programs, data structures, recording media and the like are also effective as aspects of the present invention. Effects of the Invention

[0021] According to the present invention, it is possible to facilitate the formulation of sales strategies. Brief Description of the Drawings

[0022] [Figure 1] It is a schematic diagram for explaining the outline of processing executed by the information processing apparatus according to the embodiment. [Figure 2] It is a diagram schematically showing the functional configuration of the information processing apparatus according to the embodiment. [Figure 3] It is a diagram schematically showing an example of the configuration of a database stored in a storage unit. [Figure 4] It is a diagram schematically showing an example of sales results classified by psychological attribute for each of a first period and a second period specified by a specifying unit. [Figure 5] It is a schematic diagram for explaining long-term fluctuations extracted by an analysis unit. [Figure 6] It is a flowchart for explaining the flow of information processing executed by the information processing apparatus according to the embodiment.

Mode for Carrying Out the Invention

[0023] <Summary of Embodiment> FIG. 1 is a schematic diagram for explaining an overview of processing executed by the information processing apparatus 1 according to the embodiment. The information processing apparatus 1 is an apparatus that provides a service for facilitating formulation of store sales strategies. The store is a retail store that sells products, for example, a convenience store, a supermarket, or the like. The store includes at least chain stores, and may further include independent stores.

[0024] The information processing apparatus 1 is used, for example, by a person in charge of formulating store sales strategies. When formulating a sales strategy for the person's own store (hereinafter referred to as the "target store"), the person in charge uses the information processing apparatus 1 via another terminal. Here, the other terminal is, for example, a personal computer, a smartphone, a tablet terminal, or the like.

[0025] Information Processing Device 1 is a device that manages information processing services, such as a personal computer or server. Information Processing Device 1 stores user data, product data, and store data. User data is data about users who use the communication service, and for each user, it includes attributes corresponding to that user and the user's activity history (e.g., location history of the terminal used by the user, purchase history of products purchased by the user, etc.). Attributes are psychological attributes that indicate the consumer's purchasing values, purchasing motivations, or purchasing psychology. Psychological attributes include, for example, health consciousness, emphasis on convenience, emphasis on quality, enjoyment of cooking, frugality, conservatism, and fondness for new things. Furthermore, attributes are information used in common for both users and products.

[0026] Product data is data about products, including, for example, attributes corresponding to each product. Store data is data about stores, including, for example, information for identifying sales performance for multiple products corresponding to attributes of a store over a specified period (e.g., POS (Point of Sales) data).

[0027] The following outline of the processes performed by the information processing device 1 will be explained in order from (1) to (3), with reference to Figure 1, where these numbers correspond to (1) to (3) in Figure 1. The information processing device 1 may perform the process in response to a request from a store employee, or it may perform the process at predetermined times. These predetermined times may include, for example, weekends, the end and beginning of the month, predetermined times after the end of a quarter, or predetermined times after the implementation of sales measures (e.g., seasonal events or discount campaigns).

[0028] (1) The information processing device 1 refers to sales information such as POS data at the target store to identify sales performance by psychological attribute for the first period and sales performance by psychological attribute for the second period. Sales performance by psychological attribute is the sales performance for each of the multiple products corresponding to each psychological attribute.

[0029] The first and second periods are periods that are compared to each other. The first and second periods may be of the same length or may be of different lengths. These periods may be relatively short, such as a few hours (early morning, evening, etc.), or relatively long, such as a year or more. Furthermore, the first and second periods may include overlapping periods. These periods may consist of continuous periods or of multiple discontinuous periods. In addition, these periods may be predetermined periods or periods specified by the store manager.

[0030] If the first and second periods are predetermined periods, for example, the start of the first period is in the future compared to the start of the second period. Specifically, for example, the first period is the most recent month, and the second period is the same period one year prior to the first period. Also, if the processing of the information processing device 1 is executed at predetermined timings, the first and second periods corresponding to those timings may be predetermined. For example, if the processing of the information processing device 1 is executed at a predetermined timing after the end of a quarter, the first period may be the immediately preceding quarter, and the second period may be the quarter immediately preceding the first period.

[0031] (2) The information processing device 1 analyzes changes in consumer purchasing values ​​at the target store based on sales performance by psychological attribute in the first period and sales performance by psychological attribute in the second period. For example, the information processing device 1 calculates the ratio of each psychological attribute to the sales performance by psychological attribute in the first period and the ratio of each psychological attribute to the sales performance by psychological attribute in the second period. Then, as an analysis of changes in values, the information processing device 1 identifies the psychological attribute whose ratio changed the most in the first period compared to the second period, based on the ratios of each psychological attribute in the first and second periods. The information processing device 1 may also use large language models (LLMs) to analyze changes in consumer purchasing values ​​at the target store.

[0032] (3) The information processing device 1 outputs the results of the analysis. The information processing device 1 transmits the results of the analysis to a terminal operated by the person in charge, for example. The information processing device 1 may also transmit the sales performance by psychological attribute for the first period and the second period to the terminal operated by the person in charge.

[0033] In this way, the information processing device 1 can output information on changes in consumer purchasing values ​​at the target store, analyzed based on sales performance by psychological attribute for each of the first and second periods. This allows the person in charge to formulate a sales strategy by referring to the output. For example, if the first period is the most recent month and the second period is the same period one year prior to the first period, the person in charge can consider increasing the order quantity of products corresponding to the consumer purchasing values ​​that were most prominent in the first period compared to the second period. In this way, the information processing device 1 makes it easier to formulate a sales strategy by outputting information on changes in consumer purchasing values ​​at the target store.

[0034] <Functional configuration of the information processing device 1 according to the embodiment> Figure 2 is a schematic diagram showing the functional configuration of an information processing device 1 according to an embodiment. The information processing device 1 comprises a storage unit 10, a communication unit 11, and a control unit 12. In Figure 2, the arrows indicate the main data flow, and there may be data flows not shown in Figure 2. In Figure 2, each functional block shows a functional unit configuration, not a hardware (device) unit configuration. Therefore, the functional blocks shown in Figure 2 may be implemented in a single device, or they may be implemented separately in multiple devices. Data exchange between functional blocks may be performed via any means, such as a data bus, network, or portable storage medium.

[0035] The storage unit 10 is a large-capacity storage device such as a ROM (Read Only Memory) that stores the BIOS (Basic Input Output System) of the computer that implements the information processing device 1, a RAM (Random Access Memory) that serves as the working area of ​​the information processing device 1, and an HDD (Hard Disk Drive) or SSD (Solid State Drive) that stores the OS (Operating System), application programs, and various information referenced when the application programs are executed. The storage unit 10 stores a user management database 100 that manages user data, a product management database 101 that manages product data, and a store management database 102 that manages store data.

[0036] Figure 3 is a schematic diagram showing an example of the configuration of the database stored in the memory unit 10. In the user management database 100 shown in Figure 3(a), a user ID (Identifier), attributes, and behavioral history are stored associated with each user. The user ID is information that identifies a user, specifically information used to identify a user. For each attribute stored in the user management database 100, an attribute value is associated with the degree to which the user conforms to that attribute, such as health orientation. This attribute value indicates the degree to which the consumer's purchasing values, purchasing motivations, or purchasing psychology are aligned. For example, a larger numerical value indicates a higher degree of conformity to the defined attribute. The user management database 100 may store only one attribute (for example, the attribute that best suits the user), or it may store only one attribute value. The behavioral history is a history of the user's actions, such as the user's location history or purchase history.

[0037] The product management database 101 shown in Figure 3(b) stores a product ID, product name, category, and attributes associated with each product. The product ID is information that identifies the product, specifically, information used to identify the product. The category is information used to classify products by type. In the example shown in Figure 3(b), there are two categories for the product: a major category and a minor category. However, it is not limited to this; there may be one category or three or more categories. The attributes stored in the product management database 101 are associated with attribute values ​​that indicate the degree to which the product conforms to the attribute, such as health consciousness. These attribute values ​​indicate the degree to which consumers value the product, their motivation for purchasing the product, or their psychology regarding purchasing the product. The product management database 101 may store only one attribute (for example, the attribute that best conforms to the product), or it may store attribute values ​​corresponding to only one attribute.

[0038] The store management database 102 shown in Figure 3(c) stores a store ID, related business ID, address, and POS data associated with each store. The store ID is information that identifies the store, specifically information used to identify the store. The related business ID is information that identifies related businesses involved in the operation of the store, specifically information used to identify related businesses. Related businesses are businesses involved in the operation of the store, for example, businesses that operate or manage chain stores. The address is information that identifies the location where the store is located. The POS data is sales information related to the sale of each product at the store, specifically information including the date and time of sale, the product sold, and the quantity of the product sold.

[0039] Returning to Figure 2, the communication unit 11 is a communication interface for the information processing device 1 to communicate with external devices, and is implemented using a known communication module such as a LAN (Local Area Network) module or a Wi-Fi (registered trademark) module. Hereafter in this specification, it is assumed that when the information processing device 1 communicates with external devices, it does so via the communication unit 11, and the description of the communication unit 11 may be omitted.

[0040] The control unit 12 is a processor such as the CPU (Central Processing Unit), GPU (Graphics Processing Unit), or NPU (Neural Network Processing Unit) of the information processing device 1, and functions as an attribute assignment unit 120, a specification unit 121, an analysis unit 122, an output unit 123, and an acquisition unit 124 by executing a program stored in the storage unit 10.

[0041] Figure 2 shows an example where the information processing device 1 is composed of a single device. The information processing device 1 may be implemented using multiple computing resources such as processors and memory, for example, as in a cloud computing system. In this case, each part constituting the control unit 12 is implemented by at least one of the multiple different processors executing a program.

[0042] The attribute assignment unit 120 assigns a corresponding psychological attribute to each of the multiple products from a set of psychological attributes based on the product information that represents the product. The product information that represents a product is information about the product, such as the product description, product packaging image, product type, etc. The attribute assignment unit 120 may obtain product information from the storage unit 10, or it may obtain product information from a device managed by an external business, such as a business that supplies the products. The attribute assignment unit 120 assigns psychological attributes to products using, for example, LLM.

[0043] The attribute assignment unit 120 assigns psychological attributes to a product by inputting, for example, product information and a prompt to the LLM that instructs the LLM to calculate an attribute value indicating the degree to which the product indicated by the product information conforms to that psychological attribute for each predetermined psychological attribute, thereby obtaining the attribute value of each psychological attribute output by the LLM.

[0044] The attribute assignment unit 120 may assign the psychological attribute with the highest attribute value among multiple psychological attributes as an attribute of the product. Alternatively, the attribute assignment unit 120 may assign one or more psychological attributes with an attribute value above a predetermined threshold as an attribute of the product. When the attribute assignment unit 120 assigns a psychological attribute to a product, it associates the product ID corresponding to the product with the assigned psychological attribute and stores it in the storage unit 10. The storage unit 10 stores the information associating each of the multiple products with their psychological attributes in the product management database 101.

[0045] The identification unit 121 refers to sales information regarding the sale of products at the target stores and identifies sales performance by psychological attribute, which is the sales performance of products associated with each psychological attribute during the first period, and sales performance by psychological attribute during a second period different from the first period. The identification unit 121 identifies sales performance by psychological attribute during the first period and sales performance by psychological attribute during the second period by performing, for example, the following two steps.

[0046] As the first step, the identification unit 121 acquires information indicating the first period and the second period. The storage unit 10 stores, for example, period setting information which is information that associates the information indicating the first period and the second period with the timing at which the information processing device 1 executes the processing. The identification unit 121 acquires the information indicating the first period and the second period based on the period setting information and the current date and time. Alternatively, the identification unit 121 may acquire the information by receiving, for example, the information indicating the first period and the second period from a terminal operated by a person in charge at the target store.

[0047] As a second step, the identification unit 121 refers to sales information such as POS data for the first and second periods at the target store to identify sales performance by psychological attribute for the first and second periods at the target store. The identification unit 121 refers to, for example, the product management database 101 and the store management database 102 to calculate attribute-specific sales performance for each of the first and second periods, which is the sales performance of multiple products assigned to each psychological attribute. Sales performance is at least one of the sales quantity of multiple products assigned to the attribute and the total sales amount of multiple products assigned to the attribute. For example, for a product assigned to one attribute, the identification unit 121 adds at least one of the sales quantity and the total sales amount of that product to the sales performance of the attribute assigned to that product. Also, for example, for a product assigned to multiple attributes, the identification unit 121 adds at least one of the sales quantity and the total sales amount of that product to the sales performance of the attribute with the highest attribute value among the multiple attributes assigned to that product.

[0048] The analysis unit 122 analyzes changes in consumer purchasing values ​​at the target store based on sales performance by psychological attribute in the first period and sales performance by psychological attribute in the second period. For example, for each psychological attribute, the analysis unit 122 calculates the ratio of that psychological attribute to the sales performance by psychological attribute in the first period and the ratio of that psychological attribute to the sales performance by psychological attribute in the second period. Then, as an analysis of changes in values, the analysis unit 122 identifies the psychological attribute with the highest rate of increase in its ratio and the psychological attribute with the highest rate of decrease in its ratio in the second period compared to the first period, based on the ratios for each psychological attribute in the first and second periods, respectively.

[0049] Figure 4 schematically shows an example of sales performance by psychological attribute for the first and second periods, respectively, as identified by the identification unit 121. In the example shown in Figure 4, the sales performance by psychological attribute for the first and second periods, as identified by the identification unit 121, is represented as a bar graph showing the ratio of sales performance for each psychological attribute in the first and second periods. In the example shown in Figure 4, the psychological attribute of "liking cooking" has the highest rate of increase in ratio in the second period compared to the first period, and the psychological attribute of "prioritizing convenience" has the highest rate of decrease in ratio in the second period compared to the first period. Also, the start of the first period is in the future compared to the start of the second period. In this case, the analysis unit 122 may include in its analysis results, for example, that the characteristics of consumers' purchasing values ​​in the first period are highly correlated with the psychological attribute of "prioritizing convenience," and that the characteristics of consumers' purchasing values ​​in the second period are highly correlated with the psychological attribute of "liking cooking."

[0050] Furthermore, the analysis unit 122 may perform the analysis by inputting, for example, the sales performance by psychological attribute for the first and second periods, along with a prompt, into the LLM and obtaining the content of the analysis output by the LLM. The prompt to be input into the LLM here is, for example, a prompt that instructs the analysis of changes in consumer purchasing values ​​at the target store between the first and second periods.

[0051] The output unit 123 outputs the results of the analysis performed by the analysis unit 122. The output unit 123 transmits the analysis results to a terminal operated by, for example, a staff member at a target store. This allows the staff member to formulate a sales strategy by referring to the output content analyzed using attributes. Specifically, let's explain with an example where the current date is November, the first period is December of the previous year, and the analysis results show that the characteristics of consumer purchasing values ​​in the first period are highly correlated with the psychological attribute of prioritizing convenience. In this case, the staff member can refer to these analysis results when considering ordering products for December of the current year, for example. Based on the analysis results, the staff member can formulate a sales strategy such as increasing the order quantity of products associated with prioritizing convenience. In this way, the output unit 123 makes it easier to formulate sales strategies.

[0052] The output unit 123 may output the results of the analysis using attributes different from psychological attributes. Specifically, the attribute assignment unit 120 assigns sub-attributes to each of the multiple products, based on the product information representing the product, which are other attributes different from the main attribute (hereinafter, psychological attributes may be referred to as "main attribute") which is a psychological attribute. Sub-attributes are attributes that indicate the nature of consumer consumption of the product, and for example, the time of day when consumers consume the product (e.g., early morning, morning, noon, afternoon, late night, etc.), the place (e.g., home, workplace, while traveling, outdoors, in a car, etc.), the timing (e.g., daily, weekend, as a reward, in an emergency, on the spur of the moment, etc.), the target person who consumes the product (e.g., one person, a couple, infants, children, the elderly, pets, etc.), and the purpose for which consumers consume the product (e.g., family gathering, party, guests, gift, SNS (Social Networking)). These factors include service (e.g., visual appeal), ease of preparation (e.g., ready to eat, just add hot water, microwave, requires cooking, etc.), storage format (e.g., room temperature, refrigerated, frozen, individual packs, large capacity, etc.), cleanup after consumption (e.g., less waste, container can be thrown away as is, no cooking utensils required, etc.), consumer motivation for consuming the product (e.g., stress relief, improved concentration, relaxation, fatigue recovery, wakefulness, etc.), flavor profile (e.g., stimulating, mild, rich, refreshing, etc.), and social contribution to product consumption (e.g., local production for local consumption, environmental consideration, fair trade, animal welfare, traditional, etc.).

[0053] The attribute assignment unit 120 assigns sub-attributes to products, for example, using LLM. When the attribute assignment unit 120 assigns sub-attributes to a product, it associates the product ID corresponding to the product with the assigned sub-attributes and stores them in the storage unit 10. The storage unit 10 further associates each of the multiple products with the sub-attributes, which are attributes other than the main attribute, which is a psychological attribute, and stores them in the product management database 101.

[0054] The identification unit 121 refers to the sales information of the target stores and identifies the sales performance by sub-attribute, which is the sales performance of products associated with each sub-attribute during the first period, and the sales performance by sub-attribute during the second period. The identification unit 121 identifies the sales performance by sub-attribute for each of the first and second periods in the same manner as the sales performance by main attribute, which is the sales performance by psychological attribute for each of the first and second periods (hereinafter, sales performance by psychological attribute may be referred to as "sales performance by main attribute").

[0055] The analysis unit 122 analyzes changes in consumer values ​​at the target store based on the sales performance by main attribute and sub-attribute in the first period and the sales performance by main attribute and sub-attribute in the second period. For example, the analysis unit 122 performs analyses based on sales performance by main attribute, analyses based on sales performance by sub-attribute, and analyses based on sales performance by main attribute and sub-attribute. For example, the analysis unit 122 analyzes changes in consumer values ​​at the target store by identifying the main attribute with the highest rate of increase in the second period compared to the first period, and the sub-attribute with a relatively high degree of relevance, which is the degree of relationship with that main attribute.

[0056] Specifically, for example, the analysis unit 122 identifies that the main attribute of "emphasis on convenience" has the highest growth rate, and that the main attribute of "emphasis on convenience" is relatively highly correlated with the sub-attributes of "late night" and "range." The analysis unit 122 performs an analysis based on sales performance by main attribute and sales performance by sub-attributes by executing the following five steps, for example.

[0057] As the first step, the analysis unit 122 calculates, for each main attribute, the ratio of that main attribute to the total sales performance by that main attribute in the first period and the ratio of that main attribute to the total sales performance by that main attribute in the second period. As the second step, based on the ratios for each main attribute in the first and second periods, the analysis unit 122 identifies the main attribute with the highest percentage increase in its ratio in the second period compared to the first period, and the main attribute with the highest percentage decrease in its ratio.

[0058] In the third step, the analysis unit 122 calculates, for each sub-attribute, the ratio of that sub-attribute to the total sales performance by sub-attribute in the first period and the ratio of that sub-attribute to the total sales performance by sub-attribute in the second period. In the fourth step, based on the ratios for each main attribute in the first and second periods, the analysis unit 122 identifies sub-attributes whose ratios increased relatively more in the second period compared to the first period, and sub-attributes whose ratios decreased more significantly.

[0059] As the fifth step, the analysis unit 122 identifies sub-attributes that have a relatively high correlation with the identified main attribute from among the identified sub-attributes. For example, the analysis unit 122 refers to the product management database 101 and compares the degree of correlation between the main attribute and the sub-attribute with several thresholds, such as the number of products to which the main attribute and the sub-attribute are associated, and evaluates based on the comparison results. Specifically, the analysis unit 122 refers to the product management database 101 and the store management database 102 and calculates the number of products sold in the first and second periods, respectively, to which both the main attribute and the sub-attribute are associated. The analysis unit 122 may compare the calculated number with several thresholds and evaluate the degree of correlation between the main attribute and the sub-attribute based on the comparison results. The analysis unit 122 may also identify the sub-attributes first and then identify the main attribute that has a relatively high correlation with the identified sub-attribute. Furthermore, the analysis unit 122 may use LLM to perform analysis based on sales performance by main attribute and sales performance by sub-attribute.

[0060] The output unit 123 outputs the results of the analysis performed by the analysis unit 122. The output unit 123 transmits the analysis results to a terminal operated by, for example, a person in charge of the target store. This allows the output unit 123 to output analysis results with higher accuracy based on sub-attributes, making it easier for the person in charge to formulate sales strategies.

[0061] The identification unit 121 further selects period-variable products, which are products where the difference between sales performance in the first period and sales performance in the second period is relatively large, by referring to the sales information of the target stores. The identification unit 121 selects period-variable products by performing, for example, the following two steps:

[0062] As the first step, the identification unit 121 refers to, for example, the product management database 101 and the store management database 102 to calculate the degree of change between the sales performance of a product in the first period and the sales performance of a product in the second period for each of the multiple products sold at the target store in either the first period or the second period. Here, the amount of change is an indicator that shows the degree of change, such as the difference, rate of change, or percentage increase or decrease between the sales performance of the first period and the sales performance of the second period.

[0063] In the second step, the identification unit 121 selects products from among the products whose change amount is greater than a predetermined value as period-variable products. The predetermined value is a value related to the amount of change that the identification unit 121 uses to determine whether or not to select a product as a period-variable product. This value can be determined experimentally by taking into account the trend of changes in the sales performance of the products, the number of products, the proportion of products selected as period-variable products, etc.

[0064] The analysis unit 122 identifies attributes where the difference between sales performance in the first and second periods is relatively large, based on sales performance by psychological attribute in the first period and sales performance by psychological attribute in the second period. It then further analyzes the relationship between the identified attributes and period-variable products based on whether or not the product information indicating period-variable products includes content related to the identified attributes. The analysis unit 122 analyzes the relationship between the identified attributes and period-variable products by performing, for example, the following three steps.

[0065] As the first step, the analysis unit 122 calculates the difference in sales performance for each of several psychological attributes between the first and second periods, based on the sales performance by psychological attribute in the first period and the sales performance by psychological attribute in the second period. The difference in sales performance is, for example, the difference, rate of change, or rate of increase or decrease between the sales performance in the first period and the sales performance in the second period.

[0066] In the second step, the analysis unit 122 identifies psychological attributes that are within a predetermined rank when the psychological attributes are arranged in descending order of the difference in sales performance. The analysis unit may also identify psychological attributes whose difference in sales performance is greater than a predetermined threshold. The predetermined rank and threshold are the criteria used by the analysis unit 122 when identifying attributes with relatively large differences in sales performance. These ranks and thresholds can be determined experimentally, taking into account the distribution of differences in sales performance for each psychological attribute, the purpose of the analysis, etc.

[0067] As a third step, the analysis unit 122 analyzes the relationship between the identified attribute and the period-variable product by calculating the semantic similarity between the identified attribute and the product information of the period-variable product. More specifically, the analysis unit 122 converts the identified attribute and the product information of the period-variable product into vectors and calculates the similarity (e.g., cosine similarity) with the converted vectors. Alternatively, the analysis unit 122 may analyze the relationship between the identified attribute and the period-variable product based on the frequency with which words related to the identified attribute appear in the product information of the period-variable product.

[0068] Furthermore, if the analysis unit 122 finds that the correlation between the identified attribute and the period-variable product is relatively high, and the identified attribute is not associated with the period-variable product, it may associate the product ID corresponding to the period-variable product with the identified attribute and store it in the product management database 101 of the storage unit 10. If the correlation between the identified attribute and the period-variable product is relatively high, and the identified attribute is already associated with the period-variable product, the analysis unit 122 may update the attribute value of the identified attribute associated with the product ID corresponding to the period-variable product stored in the product management database 101 to a higher value.

[0069] Furthermore, the analysis unit 122 may analyze the relationship between a specified attribute and a product that fluctuates over time by inputting, for example, sales performance by psychological attribute for the first and second periods, product information indicating the product that fluctuates over time, attributes associated with the product that fluctuates over time and the attribute values ​​of those attributes, and prompts into the LLM, thereby obtaining the content of the analysis output by the LLM. The prompts input into the LLM here are, for example, prompts that instruct the analysis of the relationship between the specified attribute and the product that fluctuates over time, whether or not to assign the specified attribute to the product that fluctuates over time, and, if the specified attribute is assigned to the product that fluctuates over time, to calculate the attribute value of that attribute. In this way, the analysis unit 122 is expected to improve the accuracy of the analysis in accordance with sales performance in order to facilitate the formulation of sales strategies. In addition, the output unit 123 may, for example, transmit the analysis results to a terminal operated by a person in charge at the target store. This allows the person in charge to further consider the content output by the output unit 123 when formulating sales strategies, for example, regarding the necessity of placing orders.

[0070] The identification unit 121 further identifies the number of purchases, which is the number of times multiple product combinations were purchased, by referring to sales information of the target store over a predetermined period. The predetermined period is the period covered by the sales information referenced by the identification unit 121 for purposes such as forecasting product demand and analyzing consumer demand trends. This period can be determined experimentally, taking into account trends in product sales volume, the periodicity of product sales, the purpose of the analysis, etc., but examples include the most recent week, one month, one year, etc.

[0071] The analysis unit 122 further analyzes the consumer's values ​​towards at least one of the multiple products included in the combination, based on the identified number of purchases and the attributes associated with each of the multiple products included in the combination related to that purchase count. Hereinafter, in this specification, the attribute may be a main attribute, which is a psychological attribute, or a sub-attribute. The analysis unit 122, for example, may analyze each of the multiple products included in the combination (hereinafter, the product to be analyzed will be referred to as the "target product"). For example, if the analysis unit 122 identifies that the identified number of purchases is above a predetermined number, and that a certain number (majority, all, etc.) or more of products other than the target product included in the combination related to the identified number of purchases are associated with the same attribute, it will output an analysis result indicating that the correlation between the target product and the attribute is relatively high. The predetermined number is the number of purchases that the analysis unit 122 uses as a criterion when determining whether the correlation between the target product and the attribute is relatively high. This number may be determined experimentally, taking into account the distribution of the number of purchases for each combination of multiple products, the purpose of the analysis, etc. The analysis unit 122 may also perform the analysis using LLM.

[0072] Furthermore, if the correlation between an attribute and a target product is relatively high, the analysis unit 122 may associate the attribute with the product ID corresponding to the target product and store it in the product management database 101 of the storage unit 10 if the attribute is not already associated with the target product. If the correlation between an attribute and a target product is relatively high, the analysis unit 122 may update the attribute value of the attribute associated with the product ID corresponding to the target product stored in the product management database 101 to a higher value if the attribute is already associated with the target product. In this way, the analysis unit 122 is expected to improve the accuracy of the analysis of target products based on products purchased together in order to facilitate the formulation of sales strategies. In addition, the output unit 123 may transmit the analysis results to a terminal operated by a person in charge at the target store, for example. This allows the person in charge to further consider the output content analyzed using attributes, for example, when formulating sales strategies regarding the necessity of placing orders.

[0073] The identification unit 121 may refer to sales information of the target store for a predetermined period and identify each of the multiple products present in the sales information of the target store for a predetermined period as a target product. The identification unit 121 may further identify, for each target product, combinations of multiple products that include the target product and the number of times that combination is purchased. The analysis unit 122 may further analyze, for each target product, the consumer's values ​​for the target product based on the target product, the combinations that include the target product, the number of times that combination is purchased, and the attributes associated with the products included in at least one of the combinations.

[0074] The analysis unit 122 calculates the degree of association between the target product and each product purchased simultaneously with it, for example, using known analytical methods such as basket analysis. The analysis unit 122 calculates the association between the target product and each of several attributes, for example, based on the attribute value of each product associated with that attribute and the degree of association between that product and the target product. The analysis unit 122 may also use LLM to calculate the degree of association between the target product and each product purchased simultaneously with it. Furthermore, the analysis unit 122 may also use LLM to calculate the association between the target product and each of several attributes. This allows the analysis unit 122 to further improve the accuracy of its analysis of the target product based on products purchased simultaneously, making it easier to formulate sales strategies.

[0075] The memory unit 10 further stores user data, including attributes that indicate a consumer's purchasing values ​​for each user, and the user's behavioral history. Specifically, the memory unit 10 stores a user management database 100 that manages user data. The identification unit 121 selects a first period from among the weekdays and a second period from among the holidays, and by referring to the sales information of the target store, it identifies attribute-specific sales performance, which is the sales performance of products associated with each attribute in the first period, and attribute-specific sales performance in the second period.

[0076] The analysis unit 122 refers to user data and calculates the user attribute ratio, which is the ratio of the attributes of multiple users who are estimated to have visited the target store during the first period, and the user attribute ratio for the second period. The user attribute ratio is the ratio of the attributes of users who were present within a predetermined range including the target store. The predetermined range is a range defined to determine the number of users who may have used the store or who have used the store. The predetermined range may be defined numerically, such as a radius of 100 meters from the store, or it may be the road area adjacent to the store, or it may be inside the store. The analysis unit 122 calculates the user attribute ratio for the first and second periods, respectively, by performing the following two steps, for example.

[0077] As the first step, the analysis unit 122 refers to the user's activity history (e.g., user location history) stored in the user management database 100 and the location and map information of the target store stored in the store management database 102 to identify users who were within a predetermined range including the target store during the first and second periods, respectively.

[0078] As a second step, the analysis unit 122 refers to the user management database 100 and calculates the ratio of the attributes of each of the multiple users identified in the first and second periods as the user attribute ratios corresponding to the first and second periods, respectively. The user attribute ratio may be the ratio of one attribute assigned to each user, the ratio of the attribute with the highest attribute value among multiple attributes assigned to each user, the ratio of the sum of the attribute values ​​of each of the multiple attributes assigned to each user, or the ratio of the statistical values ​​(e.g., mean, mode, median, etc.) of each of the multiple attributes assigned to each user. For example, a threshold may be set for each attribute, and the analysis unit 122 may assign "1" to the attribute if the attribute value of that attribute exceeds the threshold corresponding to that attribute, and assign "0" to the attribute if the attribute value does not exceed the threshold corresponding to that attribute, and calculate the ratio of the sum of the values ​​assigned to each attribute for each user as the user attribute ratio.

[0079] The analysis unit 122 compares the user attribute ratio and sales performance by attribute in the first period with the user attribute ratio and sales performance by attribute in the second period. The analysis unit 122 compares by, for example, calculating the difference or ratio between the sales performance of that attribute in the first period and the sales performance of that attribute in the second period for each attribute. The analysis unit 122 further compares by, for example, calculating the difference or ratio between the ratio of the attribute corresponding to that attribute in the user attribute ratio of the first period and the ratio of the attribute corresponding to that attribute in the user attribute ratio of the second period.

[0080] The analysis unit 122 may, for example, output a result indicating that, if sales of health-conscious products increased in the first period compared to the second period, the main factor behind the increase was, for example, a higher proportion of health-conscious users visiting the store. The analysis unit 122 compares the user attribute ratio and sales performance by attribute in the first period with the user attribute ratio and sales performance by attribute in the second period using known analytical methods such as regression analysis, multiple regression analysis, and correlation analysis. As an example of comparison, the analysis unit 122 may, for example, use information indicating the user attribute ratio and period as explanatory variables and sales performance for each attribute as the dependent variable, and use known analytical methods to calculate the degree of impact of changes in the user attribute ratio and the degree of impact of differences in periods.

[0081] Changes in user attribute ratios refer to changes such as the percentage increase or decrease in the ratio of that attribute to the user attribute ratio in the first period, the ratio of that attribute to the user attribute ratio in the second period, and the percentage of that attribute to the user attribute ratio in the second period. The impact of changes in user attribute ratios is an indicator that shows the degree of change in sales performance caused by changes in user attribute ratios. The impact of differences in periods is an indicator that shows the degree of change in sales performance caused by differences in periods. The analysis unit 122 calculates the impact of changes in user attribute ratios and the impact of differences in periods by performing the following three steps, for example.

[0082] As the first step, the analysis unit 122 calculates the difference between the sales performance of an attribute in the first period and the sales performance of that attribute in the second period for each attribute. As the second step, the analysis unit 122 calculates the degree of influence of the change in the user attribute ratio in the first and second periods on the difference in sales performance of that attribute for each attribute.

[0083] In the third step, the analysis unit 122 calculates the impact of the difference in time period on the difference in sales performance for each attribute, based on the impact of the change in the user attribute ratio calculated in the second step and the difference in sales performance calculated in the first step. The analysis unit 122 may also perform the comparison using LLM.

[0084] The output unit 123 outputs the results of the comparison performed by the analysis unit 122. The output unit 123 then transmits the analyzed results to a terminal operated by, for example, a staff member at a target store. This allows the staff member to use the output, which was analyzed using attributes, as a reference to formulate a sales strategy.

[0085] The analysis unit 122 estimates the ratio of the daily sales volume of planned ordered products on weekdays to the daily sales volume of planned ordered products on holidays. When estimating the ratio of sales volumes, the analysis unit 122 estimates the ratio of sales volumes based on the sales performance by attribute in the first period, the sales performance by attribute in the second period, and the attributes associated with the planned ordered products, which are the products that the target stores plan to order. The analysis unit 122 estimates the ratio of sales volumes by performing, for example, the following five steps.

[0086] As the first step, the analysis unit 122 identifies the average daily sales performance by attribute on weekdays based on the sales performance by attribute in a first period selected from among the weekday periods. As the second step, the analysis unit 122 identifies the average daily sales performance by attribute on holidays based on the sales performance by attribute in a second period selected from among the holiday periods. As the third step, the analysis unit 122 calculates the ratio of the average daily sales performance on weekdays to the average daily sales performance on holidays for each attribute.

[0087] In the fourth step, the analysis unit 122 identifies the products to be ordered. The storage unit 10 stores, for example, a database that manages the products to be ordered at the target store. The analysis unit 122 may identify the products to be ordered by referring to this database. In the fifth step, the analysis unit 122 estimates the ratio of the average daily sales performance on weekdays to the average daily sales performance on holidays corresponding to the attribute associated with the product to be ordered, calculated in the third step, as the ratio of the average daily sales quantity on weekdays to the average daily sales quantity on holidays for the product to be ordered.

[0088] The output unit 123 outputs information showing the ratio of sales quantities of the planned order products estimated by the analysis unit 122. The output unit 123 transmits the analysis results to a terminal operated by, for example, the person in charge of the target store. This allows the person in charge to refer to the output content analyzed using attributes to determine the order quantities on weekdays and holidays, and to formulate a sales strategy. For example, if the ratio of sales quantities of the planned order products shows that the sales quantity on holidays is five times that of weekdays, the person in charge will consider increasing the order quantity of the planned order products on holidays compared to weekdays.

[0089] The acquisition unit 124 acquires external environment information indicating the external environment of the location where the target store is located at the time the sale corresponding to the sales information of the target store was made. The external environment includes, for example, meteorological information such as temperature, precipitation, weather, humidity, and wind speed. The external environment may also include seasonal information, holiday information, and information on surrounding events. The acquisition unit 124 may acquire external environment information from an external device that provides the external environment, or it may acquire external environment information that has been stored in the storage unit 10 in advance.

[0090] The identification unit 121 refers to multiple sales records of the target store and the corresponding external environmental information for each of those sales records to identify environmentally volatile products from among the products, which are products whose sales performance fluctuates relatively large in response to changes in the external environment. Here, "relatively large fluctuation in sales performance" means, for example, that the magnitude of the change in sales performance in response to changes in the external environment is greater than or equal to a predetermined value. The predetermined value is a value related to the change in sales performance that the identification unit 121 uses to identify environmentally volatile products from among the products. This value can be determined experimentally by taking into account the trend of fluctuations in the sales performance of the products, the number of products, the proportion of products selected as environmentally volatile products, etc. The identification unit 121 may perform an analysis of the change in sales performance in response to changes in the external environment using known analytical methods such as regression analysis, multiple regression analysis, and correlation analysis.

[0091] Furthermore, the identification unit 121 identifies environmentally volatile products based, for example, on each piece of information included in the external environmental information. The external environmental information used to identify environmentally volatile products may be information corresponding to a higher-level concept such as weather information, or it may be individual pieces of information included in that higher-level concept, such as temperature.

[0092] The analysis unit 122 determines at least one of the following based on changes in the external environment: the order quantity for the environmentally volatile product, and the priority of the placement location of the environmentally volatile product in the sales area of ​​the target store. Here, the priority of placement location is an indicator used when determining the placement location of the product, and the higher the priority, the more likely the product is to be placed in a location that is easily seen by consumers. The priority of placement location is expressed by a number such as 1, 2, or 3, or by letters such as "high," "medium," or "low." The analysis unit 122 may, for example, determine the priority of placement location according to the order quantity of the product. The analysis unit 122 may, for example, compare the order quantity of the product with several thresholds and determine the priority of placement location based on the comparison result.

[0093] The analysis unit 122, for example, uses a regression model to determine at least one of the order quantity and placement priority of environmentally variable products. The storage unit 10 may store a regression model that predicts at least one of the order quantity and priority of environmentally variable products based on external environmental information as input. The regression model is pre-trained using known machine learning techniques with multiple sales records of the target store and the corresponding external environmental information for each of those sales records.

[0094] The acquisition unit 124 acquires, for example, external environmental information about the timing when the environmentally volatile product to be ordered will be placed on the sales floor of the target store, from an external device or storage unit 10. The analysis unit 122 inputs the acquired external environmental conditions into a regression model and acquires at least one of the order quantity and placement priority of the environmentally volatile product output by the regression model, thereby determining at least one of the order quantity and placement priority of the environmentally volatile product.

[0095] The output unit 123 outputs at least one of the determined order quantity and the determined priority of the placement location. The output unit 123 transmits the analysis results to a terminal operated by, for example, the person in charge of the target store. This allows the person in charge to refer to the output content analyzed using attributes and determine the order quantity or the priority of the placement location of the fluctuating product as part of their sales strategy. The output unit 123 may also execute a process to order the fluctuating product at the determined order quantity.

[0096] Furthermore, if the identification unit 121 identifies an environmentally volatile product, the identification unit 121 may associate the sub-attributes related to the external environmental information used to identify the environmentally volatile product with the product ID corresponding to the environmentally volatile product and store them in the product management database 101. This allows the analysis unit 122 to perform analysis related to the external environmental information as part of the attribute-based analysis, increasing the likelihood of performing a more consistent and effective analysis.

[0097] The analysis unit 122 extracts long-term fluctuations in sales performance for each attribute from the time-series changes in sales performance by attribute, based on sales information of the target stores. Here, long-term fluctuations indicate the trend of changes in statistical sales performance over a long period. Furthermore, the sales performance by attribute may be sales performance by the main attribute or sales performance by sub-attributes. For example, the identification unit 121 refers to the product management database 101 and the store management database 102 to identify sales performance by attribute for each predetermined period (e.g., 1 day, 1 week, 1 month, etc.) within a predetermined period, and identifies the time-series changes in sales performance by attribute. The predetermined period is the target period of sales information that the analysis unit 122 refers to in order to extract long-term fluctuations of attributes. This period can be determined experimentally by considering the trend in product sales volume, the periodicity of product sales related to attributes, the purpose of the analysis, etc., but it is a relatively long period such as the most recent 1 year, 5 years, or 10 years.

[0098] The analysis unit 122 may extract long-term fluctuations by removing temporary and seasonal fluctuations included in the identified time-series changes. The analysis unit 122 may also extract long-term fluctuations using known time-series analysis methods such as moving averages or regression analysis. Figure 5 is a schematic diagram illustrating the long-term fluctuations extracted by the analysis unit 122. In the example shown in Figure 5, the horizontal axis represents elapsed time, and the vertical axis represents the sales performance of attribute 1. The solid line shows the time-series change in the sales performance of that attribute, and the dashed line shows the extracted long-term fluctuations.

[0099] The output unit 123 outputs information showing the long-term fluctuations for each extracted attribute. The output unit 123 transmits the analysis results to a terminal operated by a staff member at the target store, for example. Alternatively, the output unit 123 may transmit information showing the long-term fluctuations for each attribute and information indicating products associated with that attribute to the terminal operated by the staff member. Products associated with that attribute are identified, for example, by the identification unit 121. This allows the staff member to formulate a sales strategy by referring to the output content of the output unit 123. The long-term fluctuations of an attribute may have a periodicity corresponding to the demand cycle, which includes the introduction, growth, maturity, and decline phases of that attribute. For example, in response to the aging of users visiting the target store, the attribute of "health consciousness" may shift from the introduction phase to the growth phase. By referring to the output content of the output unit 123, the staff member can determine the shift in the demand cycle phase of the attribute and use this as an opportunity to revise the long-term policy of the sales strategy.

[0100] The identification unit 121 may identify attributes whose long-term fluctuation magnitude, extracted by the analysis unit 122, is greater than or equal to a predetermined value. The predetermined value is the magnitude of the fluctuation in sales amount used by the identification unit 121 to determine whether or not there is a transition in the demand cycle. This magnitude can be determined experimentally, taking into account the amount of change in sales quantity, the length of the demand cycle, the duration of each stage of the demand cycle, etc. Furthermore, if the identification unit 121 identifies an attribute whose extracted long-term fluctuation magnitude is greater than or equal to a predetermined value, it may also identify the source stage and destination stage of the transition in the demand cycle for that attribute.

[0101] The identification unit 121 may identify products associated with the identified attributes from among multiple products based on the product management database 101. The output unit 123 outputs information indicating the products identified by the identification unit 121. The output unit 123 transmits information indicating the identified products to, for example, a terminal operated by a person in charge at the target store. The output unit 123 may also transmit the source and destination stages in the demand cycle. By referring to the output content from the output unit 123, the person in charge can use it as an opportunity to review the sales strategy for the products identified by the identification unit 121.

[0102] The identification unit 121 refers to the sales information of the target store to identify sales performance by psychological attribute during the third period. The third period may be a predetermined period (for example, the most recent week, one month, one year, etc.) or a period specified by the store manager.

[0103] The analysis unit 122 refers to the identified sales performance by psychological attribute and user data, and compares the ratio of the total sales quantity or sales amount of products associated with each psychological attribute with the ratio of each attribute of multiple users in the target store's trading area. The trading area is, for example, the geographical area where users expected to use the target store mainly reside or stay, and is stored in the store management database 102 in association with the store ID that identifies the target store. The analysis unit 122 performs the comparison by executing, for example, the following four steps.

[0104] As the first step, the analysis unit 122 refers to the sales performance by psychological attribute during the identified third period and calculates the ratio of sales performance for each psychological attribute. As the second step, the analysis unit 122 refers to the user behavior history (e.g., user location history) stored in the user management database 100 and the trade area and map information of the target store stored in the store management database 102 to identify users who were present in the target store's trade area during the third period. As the third step, the analysis unit 122 refers to the user management database 100 and calculates the ratio of each attribute of the identified users. As the fourth step, the analysis unit 122 compares the ratio calculated in the first step with the ratio calculated in the third step. Specifically, the analysis unit 122 calculates, for example, the magnitude relationship, difference, or ratio of the ratios of corresponding attributes.

[0105] The output unit 123 outputs the results of the comparison performed by the analysis unit 122. The output unit 123 transmits the analysis results to a terminal operated by, for example, the person in charge of the target store. This allows, for example, the person in charge to verify or correct the validity of the ratio of attributes of multiple users in the target store's trading area using the ratio of sales performance. The person in charge can then formulate a sales strategy based on, for example, the ratio of users in the trading area that has been verified or corrected. In this way, the output unit 123 makes it easier to formulate a sales strategy.

[0106] The attribute assignment unit 120 further assigns attribute values ​​to each of the multiple products, based on the product information representing the product, indicating the degree of value indicated by the attribute associated with that product. The attribute assignment unit 120 associates the product ID corresponding to the product with the attribute value of the assigned attribute and stores it in the storage unit 10. The storage unit 10 further associates the product ID with the attribute value and stores it in the product management database 101.

[0107] The analysis unit 122, if there are out-of-stock items, calculates a first deviation degree based on the product management database 101, which indicates the degree of discrepancy between the attribute value associated with the out-of-stock item and the type attribute value, which is the attribute value of that attribute defined for the type of out-of-stock item. The type attribute value of an attribute is, for example, the average value of the attribute values ​​of each of several products belonging to the product type to which the type attribute value pertains. The type attribute value is not limited to the average value, but may also be other statistical measures such as the mode or median. The analysis unit 122 calculates the first deviation degree based, for example, on the attribute value of the out-of-stock item and the type attribute value.

[0108] Specifically, the analysis unit 122 calculates the first deviation by, for example, subtracting the type attribute value of a missing product from the attribute value of the missing product. Alternatively, the analysis unit 122 may calculate the deviation score of the attribute value of a missing product by using the attribute value of the missing product and the average value (type attribute value) and standard deviation of the attribute values ​​of products whose type is the same as that of the missing product, as the first deviation score. If there are multiple attributes associated with a missing product, the analysis unit 122 calculates the first deviation score for each of the multiple attributes, for example.

[0109] The analysis unit 122 calculates a second deviation degree for each sales product that is different from the out-of-stock product and is sold by the target store, based on the product management database 101. This second deviation degree indicates the degree of discrepancy between the attribute value corresponding to the sales product and the type attribute value of the sales product. The analysis unit 122 calculates the second deviation degree for each sales product in the same way as the first deviation degree for the out-of-stock product.

[0110] The analysis unit 122 compares the first deviation of the out-of-stock product with the second deviation of the product being sold for each product sold. The analysis unit 122 converts both the first deviation of the out-of-stock product and the second deviation of the product being sold into vectors. Specifically, the analysis unit 122 represents the first deviation of the out-of-stock product as a vector by arranging the first deviations corresponding to each of a predetermined set of attributes in a predetermined order. The analysis unit 122 treats the first deviation of attributes not assigned to the out-of-stock product as 0 when converting them into vectors. The analysis unit 122 also converts the second deviation of each product being sold into vectors in the same way as the first deviation of the out-of-stock product. Finally, the analysis unit 122 calculates the similarity (e.g., cosine similarity) between the converted first deviation and the converted second deviation of the product being sold for each product sold. The similarity score is calculated numerically, for example, by the analysis unit 122, and a higher value indicates that the comparison subjects are more similar.

[0111] The analysis unit 122 selects a substitute product from among the products for sale that has a relatively small difference from the original product, based on the comparison results. For example, the analysis unit 122 selects the product for sale with the highest similarity calculated as a result of the comparison as the substitute product. The analysis unit 122 may also select products for sale that are within a predetermined rank from the highest similarity when the products for sale are arranged in descending order of similarity as substitute products. This predetermined rank is the ranking that the analysis unit 122 uses as a selection criterion when selecting a substitute product. This rank can be determined through experimentation, taking into account consumer purchasing trends, etc.

[0112] The output unit 123 outputs information indicating alternative products. This allows the person in charge to refer to the output content analyzed using attributes, and increases the likelihood of ordering alternative products to reduce consumer dissatisfaction caused by shortages.

[0113] The analysis unit 122 may select a substitute product from among the products for sale that are of a different type than the out-of-stock product. The output unit 123 outputs information indicating the substitute product. This allows the person in charge to refer to the output content analyzed using attributes, and increases the likelihood of ordering a substitute product that will reduce consumer dissatisfaction due to the out-of-stock item and has a relatively high probability of creating new demand.

[0114] The analysis unit 122 may calculate the second deviation for each product sold based on the attribute value of the product sold and the type attribute value of the out-of-stock product. For example, the analysis unit 122 calculates the second deviation by subtracting the type attribute value of the out-of-stock product from the attribute value of the attribute of the attribute of the product sold for each predetermined set of attributes. Alternatively, the analysis unit 122 may calculate the deviation score of the attribute value of the product sold for each predetermined set of attributes, using the attribute value of the product sold and the mean value (type attribute value) and standard deviation of the attribute values ​​of products whose type is the same as that of the out-of-stock product. Here, the deviation score of the attribute value of the product sold calculated is a deviation score calculated using products whose type is the same as that of the out-of-stock product as the population. As a result, the analysis unit 122 can use the second deviation calculated in this way to calculate the similarity and select alternative products from different perspectives.

[0115] The analysis unit 122 may also select an alternative product from the products for sale without calculating similarity. The analysis unit 122 selects an alternative product by performing, for example, the following three steps.

[0116] As the first step, the analysis unit 122 selects from the available products the same type as the out-of-stock product. In the second step, the analysis unit 122 compares the first deviation of the out-of-stock product with the second deviation of the product selected in the first step. For example, the analysis unit 122 calculates the absolute value of the value obtained by subtracting the second deviation from the first deviation for each of several predetermined attributes, and calculates the sum of these absolute values. The smaller the sum of the calculated absolute values, the more similar the comparison targets are. Note that for attributes that are not assigned to out-of-stock products or products, the analysis unit 122 treats the first or second deviation of that attribute as 0 and calculates the absolute value.

[0117] In the third step, the analysis unit 122 selects a substitute product from the products selected in the first step, based on the results of the comparison in the second step. For example, the analysis unit 122 selects the product with the smallest sum of absolute values ​​calculated as a result of the comparison as a substitute product. The analysis unit 122 may also select products within a predetermined rank from the smallest sum of absolute values ​​when the products are arranged in ascending order of their sum of absolute values ​​as substitute products. This predetermined rank is a threshold or rank that serves as the selection criterion for the analysis unit 122 when selecting a substitute product. This rank can be determined experimentally, taking into account consumer purchasing trends, etc. This allows the analysis unit 122 to increase the probability of selecting a substitute product with less computational load compared to calculating similarity.

[0118] <Processing flow of the information processing method executed by the information processing device 1> Figure 6 is a flowchart illustrating the flow of information processing performed by the information processing device 1 according to this embodiment. The processing in this flowchart starts, for example, when the information processing device 1 is started up.

[0119] The identification unit 121 acquires product data (S1). The identification unit 121 refers to sales information for the first and second periods at the target store (S2). The identification unit 121 identifies sales performance by psychological attribute for the first period at the target store (S3). The identification unit 121 identifies sales performance by psychological attribute for the second period at the target store (S4).

[0120] The analysis unit 122 analyzes changes in consumer purchasing values ​​at the target store based on sales performance by psychological attribute in the first period and sales performance by psychological attribute in the second period (S5). The output unit 123 outputs the results analyzed by the analysis unit 122 (S6). Once the output unit 123 outputs the results, the process in this flowchart is completed.

[0121] <Variation> In the above example, the store is described as a retail store that sells goods, but it is not limited to this. For example, the store may be a restaurant, accommodation facility, beauty salon, cram school, entertainment facility, training gym, beauty salon, fitness club, karaoke, internet cafe, locker room, meeting room rental, cooking class, laundromat, coin parking, gas station, rental space, charging station, train, bus, taxi, rental car, car sharing, house cleaning, housekeeping, babysitting, repair / maintenance, etc., which is a store that provides at least one of the goods and services. In this case, the storage unit 10 may store a service management database that associates a service ID, service name, category, and attributes for each service provided. Examples of services provided include food and beverage menus in a restaurant, accommodation plans in an accommodation facility, treatment plans in a beauty salon, exam preparation plans in a cram school, and equipment in an entertainment facility or training gym.

[0122] Furthermore, the POS data (sales information or usage history) stored in the store management database 102 includes information such as the date and time of service provision, the service provided, the quantity, time, number of people, and amount of service provided. In addition, the store management database 102 may also store the user's reservation history.

[0123] <Effects of the information processing device 1 according to the embodiment> As described above, the information processing device 1 according to the embodiment makes it easier to formulate sales strategies.

[0124] Furthermore, this invention will make it possible to contribute to Goal 9 of the United Nations-led Sustainable Development Goals (SDGs), "Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation."

[0125] Although the present invention has been described above using embodiments, the technical scope of the present invention is not limited to the scope described in the above embodiments, and various modifications and changes are possible within the scope of its gist. For example, all or part of the apparatus can be configured by functionally or physically distributing and integrating in any unit. Furthermore, new embodiments resulting from any combination of multiple embodiments are also included in the embodiments of the present invention. The effects of the new embodiments resulting from the combinations are combined with the effects of the original embodiments. [Explanation of Symbols]

[0126] 1. Information Processing Device 10...Storage section 11. Communications Department 12. Control Unit 120...Attribute Assignment Section 121...Specific section 122...Analysis Department Output section of 123... 124...Acquisition part

Claims

1. A memory unit that stores multiple products in association with psychological attributes, which are attributes related to consumer purchasing and indicate consumer values ​​regarding purchasing, A distinguishing unit that, by referring to sales information relating to the sale of the said products in stores, identifies sales performance by psychological attribute, which is the sales performance of the said products associated with each psychological attribute during a first period, and sales performance by psychological attribute during a second period different from the first period. An analysis unit analyzes changes in consumer purchasing values ​​at the store based on the sales performance by psychological attribute during the first period and the sales performance by psychological attribute during the second period. The analysis unit comprises an output unit that outputs the results of the analysis, The storage unit further stores user data for each user, including attributes that indicate the consumer's purchasing values ​​and the user's behavioral history. The analysis unit (1) refers to the user data and calculates the user attribute ratio, which is the ratio of the attributes of each of the multiple users estimated to have visited the store during the first period, and the user attribute ratio during the second period, and (2) compares the user attribute ratio and sales performance by psychological attribute during the first period with the user attribute ratio and sales performance by psychological attribute during the second period. The output unit outputs the results of the comparison performed by the analysis unit. Information processing device.

2. The memory unit further associates and stores each of the multiple products with sub-attributes, which are other attributes different from the main attribute, which is a psychological attribute. The identifying unit, by referring to the sales information, identifies the sales performance by sub-attribute, which is the sales performance of the product associated with each sub-attribute during the first period, and the sales performance by sub-attribute during the second period. The analysis unit analyzes the changes in consumers' values ​​at the store based on the sales performance by psychological attribute and sales performance by sub-attributes during the first period and the sales performance by psychological attribute and sales performance by sub-attributes during the second period. The information processing apparatus according to claim 1.

3. The specified unit further selects period-variable products, which are products in which the difference between the sales performance in the first period and the sales performance in the second period is relatively large, by referring to the sales information. The analysis unit identifies the attribute for which the difference between the sales performance in the first period and the sales performance in the second period is relatively large, based on the sales performance by psychological attribute in the first period and the sales performance by psychological attribute in the second period, and further analyzes the relationship between the identified attribute and the period-variable product based on whether or not the product information indicating the period-variable product includes content related to the identified attribute. The information processing apparatus according to claim 1.

4. The specified unit further identifies the number of times a combination of the multiple products has been purchased by referring to the sales information. The analysis unit further analyzes the consumer's values ​​for at least one of the multiple products included in the combination, based on the number of times and the attributes associated with each of the multiple products included in the combination. The information processing apparatus according to claim 1 or 2.

5. The identifying unit, by referring to the sales information, identifies attribute-specific sales performance, which is the sales performance of the product associated with each attribute during the first period, which is a weekday, and attribute-specific sales performance during the second period, which is a holiday. The information processing apparatus according to claim 1 or 2.

6. The analysis unit estimates the ratio of the daily sales quantity of the planned order product on weekdays to the daily sales quantity of the planned order product on holidays, based on the sales performance by attribute in the first period, the sales performance by attribute in the second period, and the attributes associated with the planned order product which is the product that the store plans to order. The output unit outputs information indicating the ratio of the sales quantity of the planned order product. The information processing apparatus according to claim 5.

7. The information processing device further includes an acquisition unit that acquires external environment information indicating the external environment of the location where the store is located at the time the sale corresponding to the sales information was made, The aforementioned identification unit refers to a plurality of sales information and the corresponding external environment information to identify environmentally volatile products, which are products whose sales performance fluctuates relatively large in response to changes in the external environment. The analysis unit determines, based on the changes in the external environment, at least one of the following: the order quantity for the environmentally volatile product, and the priority of the placement location of the environmentally volatile product in the store's sales area. The output unit outputs at least one of the determined order quantity and the determined priority. The information processing apparatus according to claim 1 or 2.

8. Based on the sales information, the analysis unit extracts long-term fluctuations for each attribute from the time-series changes in attribute-specific sales performance, which is the sales performance of the products associated with each attribute. The output unit outputs information indicating the long-term fluctuations for each attribute. The information processing apparatus according to claim 1 or 2.

9. The storage unit further stores user data for each user, including attributes that indicate the consumer's purchasing values ​​and the user's behavioral history. The specified unit, referring to the sales information, identifies the sales performance by psychological attribute during the third period. The analysis unit refers to the sales performance by psychological attribute and the user data and compares the ratio of the total sales quantity or sales amount of the products associated with each psychological attribute with the ratio of each of the multiple users in the store's trading area. The output unit outputs the results of the comparison performed by the analysis unit. The information processing apparatus according to claim 1.

10. The memory unit further associates and stores each of the multiple products with an attribute value indicating the degree of the value indicated by the attribute associated with that product, If there is a product that is out of stock, the analysis unit compares, for each product sold by the store that is different from the out-of-stock product, a first deviation degree indicating the degree of discrepancy between the attribute value associated with the out-of-stock product and the type attribute value defined for the type of the out-of-stock product, and a second deviation degree indicating the degree of discrepancy between the attribute value corresponding to the product sold and the type attribute value of the product sold, and selects a substitute product from among the products sold in which the difference between the two is relatively small. The output unit outputs information indicating the alternative product. The information processing apparatus according to claim 1.

11. The analysis unit selects the substitute product from among the products for sale whose type is different from the out-of-stock product. The information processing apparatus according to claim 10.

12. The processor, The steps include obtaining information that associates each of multiple products with psychological attributes, which are attributes related to consumer purchasing and indicate consumer values ​​regarding purchasing, and A step of referring to sales information relating to the sale of the said products in stores to identify sales performance by psychological attribute, which is the sales performance of the said products associated with each psychological attribute during a first period, and sales performance by psychological attribute during a second period different from the first period. A step of analyzing changes in consumer purchasing values ​​at the store based on the sales performance by psychological attribute during the first period and the sales performance by psychological attribute during the second period, The analysis step described above includes a step of outputting the results of the analysis, In the aforementioned acquisition step, user data is further acquired for each user, including attributes that indicate the consumer's purchasing values ​​and the user's behavioral history. In the analysis step described above, (1) by referring to the user data, calculate the user attribute ratio, which is the ratio of the attributes of each of the multiple users estimated to have visited the store during the first period, and the user attribute ratio during the second period, and (2) compare the user attribute ratio and sales performance by psychological attribute during the first period with the user attribute ratio and sales performance by psychological attribute during the second period. In the output step, the results of the comparison in the analysis step are output. Information processing methods.

13. On the computer, The steps include obtaining information that associates each of multiple products with psychological attributes, which are attributes related to consumer purchasing and indicate consumer values ​​regarding purchasing, and A step of referring to sales information relating to the sale of the said products in stores to identify sales performance by psychological attribute, which is the sales performance of the said products associated with each psychological attribute during a first period, and sales performance by psychological attribute during a second period different from the first period. A step of analyzing changes in consumer purchasing values ​​at the store based on the sales performance by psychological attribute during the first period and the sales performance by psychological attribute during the second period, The analysis step described above includes a step of outputting the results of the analysis, and the execution of the following steps: In the aforementioned acquisition step, user data is further acquired for each user, including attributes that indicate the consumer's purchasing values ​​and the user's behavioral history. In the analysis step described above, (1) by referring to the user data, calculate the user attribute ratio, which is the ratio of the attributes of each of the multiple users estimated to have visited the store during the first period, and the user attribute ratio during the second period, and (2) compare the user attribute ratio and sales performance by psychological attribute during the first period with the user attribute ratio and sales performance by psychological attribute during the second period. In the output step, the results of the comparison in the analysis step are output. program.

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