Measure specifying device, measure specifying method, and recording medium

Abstract

A measure specifying device according to the present invention comprises: a regular-customer proportion acquisition means for acquiring the proportion of regular customers in each of a plurality of shops; a shop identification means for identifying, from among the plurality of shops on the basis of the proportion of regular customers in each of the plurality of shops, a shop in which the proportion of regular customers satisfies a prescribed standard; a measure information acquisition means for acquiring measure information regarding measures to be carried out in each of the plurality of shops; a measure specifying means for specifying a recommended measure on the basis of a measure to be carried out in the identified shop in which the proportion of regular customers was high enough to satisfy the prescribed standard; and output means for outputting information related to the specified measure.

Description

Measure specifying device, measure specifying method, and recording medium

[0001] The present disclosure relates to a measure specifying device, a measure specifying method, and a recording medium.

[0002] There is a technology that proposes measures for store management in order to improve store performance.

[0003] Patent document 1 describes a store support server that detects stores among multiple stores whose transaction volume has changed by more than a standard, and transmits measures implemented by those stores to other stores that have attributes related to those stores.

[0004] JP 2018-101260 A

[0005] By the way, there is a need for stores to increase the number of regular customers who visit the store frequently. This is because increasing the number of regular customers can be expected to lead to stable sales. However, it is difficult to find store management measures to increase the number of regular customers.

[0006] An example of an objective of the present disclosure is to provide a technology for providing information for more appropriate store management.

[0007] A policy identification device in one aspect of the present disclosure includes a regular customer ratio acquisition means for acquiring the ratio of regular customers at each of a plurality of stores, a store identification means for identifying, based on the ratio of regular customers at each of the plurality of stores, stores among the plurality of stores whose regular customer ratio meets a predetermined standard, a policy information acquisition means for acquiring policy information on policies being implemented at each of the plurality of stores, a policy identification means for identifying recommended policies based on policies being implemented at stores whose identified regular customer ratio meets the predetermined standard, and an output means for outputting information related to the identified policies.

[0008] A method for identifying measures in one aspect of the present disclosure obtains the proportion of regular customers at each of a plurality of stores, identifies stores among the plurality of stores where the proportion of regular customers meets a predetermined standard based on the proportion of regular customers at each of the plurality of stores, obtains measure information for measures being implemented at each of the plurality of stores, identifies recommended measures based on the measures being implemented at stores where the identified proportion of regular customers meets the predetermined standard, and outputs information about the identified measures.

[0009] A program in one aspect of the present disclosure causes a computer to perform the following processing: obtain the proportion of regular customers at each of a plurality of stores; identify, based on the proportion of regular customers at each of the plurality of stores, stores among the plurality of stores whose proportion of regular customers meets a predetermined standard; obtain policy information on policies being implemented at each of the plurality of stores; identify recommended policies based on the policies being implemented at stores whose identified proportion of regular customers meets the predetermined standard; and output information on the identified policies.

[0010] Each program may be stored in a non-transitory computer-readable recording medium.

[0011] One example of the effect of the present disclosure is that it is possible to provide information on more appropriate store management.

[0012] It is a diagram showing the configuration of a system including a measure specifying device. It is a block diagram showing the functional configuration of the measure specifying device. It is a flowchart showing the operation of the measure specifying device. It is a diagram showing the hardware configuration for realizing the measure specifying device in the present disclosure by a computer and its peripheral devices.

[0013] Embodiments of the present disclosure will be described in detail with reference to the drawings.

[0014] [Embodiment] FIG. 1 is a diagram illustrating an example of a system including a measure identification device 100 according to the present disclosure.

[0015] In FIG. 1, the measure identification device 100 is a device that identifies measures to be recommended to a store in order to increase the proportion of regular customers, using the proportion of regular customers at multiple stores and measure information on measures being implemented at the stores.

[0016] A regular customer is a customer who frequently visits a store. For example, a regular customer is a customer who visits a store a predetermined number of times or more within a predetermined period. Also, for example, a frequent customer is a customer who visits a store and spends a predetermined amount or more within a predetermined period. The criteria for determining whether a customer is a regular customer are not particularly limited and may be set appropriately depending on the characteristics of the store. Also, using a store may be entering the store or purchasing a product or the like at the store.

[0017] Measures are matters related to store operations. Measures may include not only sales promotion plans such as special sales and campaigns, but also various matters such as procedures and rules related to store operations. Measures may be, for example, the product lineup in the store, the store's shelf layout, the time required for checkout, and the presence or absence of self-checkouts. Shelf layout includes the location of products and the number of identical products to be displayed across the entire shelf.

[0018] The policy identification device 100 is connected to various terminal devices provided in the store and a database that manages store information. The various terminal devices provided in the store and the database that manages store information store the store's regular customer ratio and information on policies implemented in the store. The various terminal devices provided in the store and the database that manages store information may store information for calculating the store's regular customer ratio. The policy identification device 100 is also communicably connected to a terminal device used by the store manager to manage the store. The policy identification device 100 outputs information on policies recommended for the store to the terminal device.

[0019] Next, the configuration of the policy identification device 100 in the embodiment will be described.

[0020] Fig. 2 is a block diagram showing the configuration of the policy identification device 100. Referring to Fig. 2, the policy identification device 100 includes a regular customer ratio acquisition unit 101, a store identification unit 102, a policy information acquisition unit 103, a policy identification unit 104, and an output unit 105.

[0021] Next, the configuration of the policy identification device 100 in the embodiment will be described in detail.

[0022] The regular customer ratio acquisition unit 101 is an example of a regular customer ratio acquisition means that acquires the ratio of regular customers at each of a plurality of stores.

[0023] For example, the regular customer ratio acquisition unit 101 acquires the ratio of regular customers from a device that manages the ratio of regular customers for each store. The device that manages the ratio of regular customers for each store is a terminal device such as a store computer installed in the store, a database that manages store information, or the like.

[0024] Here, the multiple stores from which the regular customer ratio acquisition unit 101 acquires the ratios of regular customers may be any set of stores. For example, the multiple stores may be a chain of stores in the retail or service industry. That is, the regular customer ratio acquisition unit 101 may acquire the ratio of regular customers for each of all stores included in a database that stores the ratios of regular customers for each store, or may acquire the ratio of regular customers for each of some stores that meet specific conditions among all stores included in the database. In this case, for example, the regular customer ratio acquisition unit 101 may refer to a database that manages store information, extract target stores from which the ratios of regular customers are to be acquired, and acquire the ratio of regular customers for each of the extracted stores.

[0025] Of all the stores included in the database, some stores that meet certain conditions are multiple stores with similar store characteristics. The store characteristics may be, for example, the store's location. Furthermore, for example, the store characteristics may be trends in the attributes of customers who visit the store. For example, the multiple stores may be stores where the attributes of customers who visit the store are similar, or stores where the attributes of regular customers who visit the store are similar. In this case, information indicating the attributes of customers who visit the store and information indicating whether the customers are regular customers may be stored in a database that manages the above-mentioned store information.

[0026] Furthermore, for example, the regular customer ratio acquisition unit 101 may calculate the ratio of regular customers based on information acquired from each of the multiple stores. The information acquired from each of the multiple stores is information used to calculate the number of visitors and the number of regular customers at the store. In this case, for example, the regular customer ratio acquisition unit 101 counts the number of visitors and the number of regular customers based on the information acquired from each of the multiple stores. Then, the regular customer ratio acquisition unit 101 calculates the ratio of the number of regular customers to the number of visitors. Details of the method for calculating the ratio of regular customers, such as the criteria for determining whether a customer is a regular customer and whether to use the total number or the actual number as the number of visitors and the number of regular customers, may be set appropriately depending on the characteristics of the store.

[0027] For example, the information acquired at each of the multiple stores and used to calculate the percentage of regular customers may be any of images of people detected from images taken at each of the multiple stores, customer identification information provided by customers at the time of payment at each of the multiple stores, location information of customer terminals at each of the multiple stores, etc. Note that the information acquired at each of the multiple stores and used to calculate the percentage of regular customers may be the number of visitors and the number of regular customers at the store. The regular customer percentage acquisition unit 101 may calculate the ratio of the number of regular customers to the acquired number of visitors.

[0028] Furthermore, whether a customer is a regular customer may be determined based on the frequency of the customer's visits to the store, or may be determined by the store clerk who handles the customer's transaction when making a payment.

[0029] For example, the regular customer ratio acquisition unit 101 may determine whether a customer is a regular customer based on the frequency of the customer's store visits. In this case, for example, the regular customer ratio acquisition unit 101 acquires images taken in the store and identifies customers appearing in the images using known image recognition technology. The regular customer ratio acquisition unit 101 may then calculate the number of store visits for each customer in a predetermined period, and determine that customers whose store visit frequency is equal to or greater than a predetermined value are regular customers. In this case, the regular customer ratio acquisition unit 101 may also calculate the number of store visits for each customer identification information provided by the customer at the time of checkout based on POS (Point of Sales) data, and determine that customers whose store visit frequency is equal to or greater than a predetermined value are regular customers. In this case, the number of store visits refers to the number of times the customer purchases a product. Methods for calculating a customer's store visit frequency and determining whether a customer is a regular customer based on the customer's store visit frequency are not limited to these examples.

[0030] Also, for example, if a store clerk handling a customer's transaction determines that the customer is a regular customer, the POS terminal may display a button on the transaction screen for inputting that the customer is a regular customer, and when the button is pressed, record the fact that the customer is a regular customer in association with the transaction information.

[0031] The process for calculating the ratio of regular customers may be performed by the regular customer ratio acquisition unit 101 or by another device.

[0032] The store identification unit 102 is an example of a store identification means that identifies, from among a plurality of stores, stores whose proportion of regular customers meets a predetermined standard based on the proportion of regular customers at each of the plurality of stores. For example, the store identification unit 102 identifies stores with a high proportion of regular customers. It is believed that a high proportion of regular customers can affect the performance of a store. Therefore, there are cases where stores are required to increase the number of regular customers. Furthermore, in stores with a high proportion of regular customers, it is believed that measures implemented at the store will contribute to the proportion of regular customers.

[0033] The predetermined criterion used by the store identification unit 102 may be any criterion for determining that the proportion of regular customers is high. For example, the predetermined criterion may be that the store is included in a predetermined number of stores among multiple stores with the highest proportions of regular customers, or that the proportion of regular customers is equal to or greater than a predetermined value. Further, for example, the predetermined criterion may be that the proportion of regular customers has increased by more than a predetermined value over a predetermined period, or that the proportion of regular customers has changed from less than a predetermined value to equal to or greater than a predetermined value over a predetermined period. The predetermined value and the predetermined period may be set as appropriate.

[0034] The store identification unit 102 may further identify stores for which the measures are to be recommended. For example, the store identification unit 102 may identify stores for which the proportion of regular customers does not meet a predetermined standard as stores for which the measures are to be recommended. Note that the stores for which the measures are to be recommended are not limited to this example, and the stores for which the measures are to be recommended may be all stores or some stores, such as stores that are not implementing the identified measures. For example, after the measures are identified by the measure identification unit 104 described below, the store identification unit 102 may identify stores that are not implementing the identified measures as stores for which the measures are to be recommended.

[0035] The policy information acquisition unit 103 is an example of a policy information acquisition means that acquires policy information on policies being implemented at each of a plurality of stores. The policy information is stored, for example, in a terminal device at the store or in a database that manages store information. In this case, the policy information acquisition unit 103 acquires the policy information from the terminal device at the store or the database that manages store information. For example, the policy information acquisition unit 103 may acquire the policy information from a database that stores inventory information or a database that stores POS data.

[0036] The policy information acquisition unit 103 may acquire policy information for each of a plurality of stores, or may acquire policy information for a store identified by the store identification unit 102 from the policy information for a plurality of stores. Furthermore, when a predetermined period is set as the predetermined criterion used by the store identification unit 102 to identify a store, the policy information acquisition unit 103 may acquire policy information for a predetermined period.

[0037] The measure identification unit 104 is an example of a measure identification means that identifies a recommended measure based on measure information of measures implemented in stores where the identified proportion of regular customers is high enough to satisfy a predetermined standard. Measures implemented in stores where the proportion of regular customers is high enough to satisfy a predetermined standard are considered to have the potential to affect the high proportion of regular customers. Therefore, the measure identification unit 104 identifies measures based on the measure information of measures implemented in stores where the identified proportion of regular customers is high enough to satisfy a predetermined standard, as information regarding more appropriate store operation.

[0038] The policy identification unit 104 may identify, as a recommended policy, a policy implemented in a store where the proportion of regular customers is at a height that satisfies a predetermined standard. Furthermore, for example, the policy identification unit 104 may identify, as a recommended policy, some of the policies implemented in a store where the proportion of regular customers is at a height that satisfies a predetermined standard. Furthermore, for example, when there are multiple stores where the proportion of regular customers is at a height that satisfies a predetermined standard, the policy identification unit 104 may identify, as a recommended policy, a policy implemented in stores where the proportion of regular customers is at a height that satisfies a predetermined standard or more. Furthermore, the policy identification unit 104 identifies one or more recommended policies.

[0039] For example, the policy identification unit 104 may identify a policy that is implemented in a store where the height at which the identified proportion of regular customers satisfies a predetermined standard, but is not implemented in a store where the height at which the proportion of regular customers does not meet the predetermined standard. A policy that is implemented only in a store where the height at which the proportion of regular customers meets the predetermined standard is considered to be highly likely to affect the high proportion of regular customers. Therefore, the policy identification unit 104 identifies, as information regarding more appropriate store operation, a policy that is implemented in a store where the height at which the identified proportion of regular customers meets the predetermined standard, but is not implemented in a store where the height at which the proportion of regular customers does not meet the predetermined standard. In this way, the policy identification unit 104 may be able to identify a policy that has an effect on the high proportion of regular customers.

[0040] For example, the policy identification unit 104 identifies, as a recommended policy, the difference between a policy implemented in a store where the identified percentage of regular customers satisfies a predetermined standard and a policy implemented in a store where the percentage of regular customers does not satisfy the predetermined standard. In this case, for example, the policy identification unit 104 identifies the difference between the product assortment in a store where the identified percentage of regular customers satisfies the predetermined standard and the product assortment in a store where the percentage of regular customers does not satisfy the predetermined standard. Here, the product assortment may be the type of product or the number of each product in stock. Furthermore, the product assortment may be more detailed, such as the brand, size, color, pattern, and price of the product. For example, even though multiple stores in a chain store, such as convenience stores, sell similar products, the percentage of regular customers at each store is expected to vary depending on the product assortment. As a specific example of the product assortment, a store with a high percentage of regular customers may determine the number of specific products in stock and the number and type of other products in stock so as to display a larger number of specific products. Furthermore, in relation to the product lineup, stores with a high percentage of regular customers may determine shelf allocations for specific products so that a large number of specific products are displayed. Note that although product lineup has been described as an example of a policy, the policy is not limited to this example.

[0041] In addition, if a specified period is set as the specified criterion used by the store identification unit 102 to identify a store, the policy identification unit 104 may identify a policy based on the policy being implemented at a store where the proportion of regular customers during the specified period is high enough to meet the specified criterion.

[0042] Furthermore, the target stores for which the measure identification unit 104 recommends measures are not particularly limited. As described above, the target stores may be all stores or some stores. The target stores for which the measure is recommended may be, for example, stores with a high percentage of regular customers that does not meet a predetermined standard, stores that are not implementing the identified measures, etc.

[0043] Furthermore, the measure identification unit 104 may identify a recommended measure based on measure information of multiple stores with similar store characteristics. This is because stores with similar characteristics are expected to have similar customer demographics, and measures taken at stores with a high proportion of regular customers are likely to be effective for similar customer demographics at stores with similar characteristics. For example, a store in an office district and a store in a resort area have different customer demographics, and it is conceivable that the measures that are in demand from customers will differ.

[0044] When extracting a plurality of stores with similar store characteristics as the plurality of stores handled by the policy identification device 100, the timing for extracting the plurality of stores with similar store characteristics is not limited.

[0045] In addition, at this time, the measure identification unit 104 may identify measures for each store that is the target store for which measures are recommended and has a proportion of regular customers that does not meet a specified standard, based on the measure information of stores that have similar characteristics and a high proportion of regular customers.

[0046] The output unit 105 is an example of an output means that outputs information about the identified measures.

[0047] For example, the output unit 105 outputs information about the identified measures to a terminal device provided in a store where the ratio of regular customers does not satisfy a predetermined standard. Also, for example, the output unit 105 may associate information about the identified measures with information indicating stores where the ratio of regular customers does not satisfy a predetermined standard, and output the information to a terminal device used by a person in charge of managing and operating multiple stores.

[0048] Furthermore, the output destination and the output mode to which the output unit 105 outputs the information about the policy may differ depending on the content of the policy. In this case, the output destination and the output mode to which the output unit 105 outputs the information about the policy may be set in advance depending on the content of the policy.

[0049] For example, if the measure is related to the product lineup, the output unit 105 may output information about the measure, such as a recommended order quantity, on an ordering screen to a terminal device used to order products in the store. Furthermore, if the measure is related to the availability of self-checkout registers, the output unit 105 may output information about the measure to a terminal device used by a person in charge of deciding on the introduction of equipment in the store. Furthermore, if the measure is related to shelf layout, the output unit 105 may output an image of the recommended shelf layout as information about the measure to a terminal device installed in the store. Here, the recommended shelf layout image may be an image of the shelf layout of a store with a high proportion of regular customers.

[0050] The output unit 105 may further output, as the information related to the measure, information indicating the grounds for recommending the measure. The information indicating the grounds for recommending the measure may be, for example, the number of stores implementing the specified measure, the ratio of regular customers at the stores implementing the specified measure, sales at the stores implementing the specified measure, etc.

[0051] The output unit 105 may also output information regarding the proportion of regular customers of the store for which the measure is recommended. The information regarding the proportion of regular customers of the store for which the measure is recommended may be the proportion of recent regular customers of the store for which the measure is recommended, the proportion of regular customers of stores with characteristics similar to those of the store for which the measure is recommended, or the trend in the proportion of regular customers of the store for which the measure is recommended.

[0052] The operation of the policy specifying device 100 configured as above will be described with reference to the flowchart of FIG.

[0053] As shown in FIG. 3, first, the regular customer ratio acquisition unit 101 acquires the ratio of regular customers at each of a plurality of stores (step S101).

[0054] Next, the store identification unit 102 identifies, from among the plurality of stores, a store whose proportion of regular customers is high enough to satisfy a predetermined standard, based on the proportion of regular customers in each of the plurality of stores (step S102).

[0055] Next, the policy information acquisition unit 103 acquires the policies being implemented in each of the multiple stores (step S103).

[0056] Next, the measure identifying unit 104 identifies a recommended measure based on measures being implemented in stores where the proportion of regular customers is high enough to meet a predetermined standard (step S104).

[0057] Then, the output unit 105 outputs information about the identified measure (step S105).

[0058] This completes the series of operations of the policy identification device 100.

[0059] Note that there are no particular limitations on the timing of identifying measures in steps S101 to S104 and the timing of outputting measures in step S105. The measure identification device 100 may identify measures in steps S101 to S104 and output measures in step S105 in response to an instruction from a store manager or the like, or may perform these operations periodically.

[0060] Furthermore, the order in which the process of step S103 is performed does not necessarily have to be after the process of step S102. For example, the process of step S103 may be performed in parallel with the process of step S101.

[0061] The policy identification device in the above-described embodiment includes a regular customer ratio acquisition unit, a store identification unit, a policy information acquisition unit, a policy identification unit, and an output unit. The regular customer ratio acquisition unit acquires the ratio of regular customers at each of a plurality of stores. The store identification unit identifies, from the plurality of stores, a store where the ratio of regular customers satisfies a predetermined standard based on the ratio of regular customers at each of the plurality of stores. The policy information acquisition unit acquires policy information on policies implemented at each of the plurality of stores. The policy identification unit identifies a recommended policy based on the policies implemented at the stores where the identified ratio of regular customers satisfies the predetermined standard. The output unit outputs information on the identified policies.

[0062] The policy identification device in this embodiment can identify policies that may contribute to a high proportion of regular customers and output information about the identified policies. As a result, the policy identification device in this embodiment can provide information about more appropriate store management. This is because a high proportion of regular customers is desirable in a store, and policies implemented in a store where the proportion of regular customers meets a predetermined standard are likely to affect the high proportion of regular customers. Furthermore, the policy identification device in this embodiment allows managers and others involved in store management to understand policies that may contribute to a high proportion of regular customers and use this information to improve store management.

[0063] [Modification 1] In Modification 1, the policy identification device 100 may identify a policy to be recommended to a specific store.

[0064] An application example of Modification 1 will be described. For example, the specific store is a store designated by a user, such as a store manager. The user, for example, issues a request to the policy identification device 100 via a terminal device to provide information on policies recommended for the specific store. The policy identification device 100 identifies and outputs policies recommended for the specific store.

[0065] Next, each functional configuration of the policy specifying device 100 in the first modification will be described.

[0066] The regular customer ratio acquisition unit 101 acquires the ratios of regular customers of a plurality of stores including a specific store. As in the embodiment, the plurality of stores including the specific store may be all stores included in a database that stores the ratios of regular customers of each store, or may be a portion of all stores included in the database that meet specific conditions.

[0067] The store identification unit 102 identifies a store having a higher proportion of regular customers than the proportion of regular customers of the specific store. Note that a store having a higher proportion of regular customers than the proportion of regular customers of the specific store may be a store having a proportion of regular customers that is greater than the proportion of regular customers of the specific store by a predetermined value or more, and may be identified using criteria for determining that a store has a higher proportion of regular customers than the specific store, as in the embodiment.

[0068] The policy information acquisition unit 103 acquires policy information on policies being implemented in each of the multiple stores.

[0069] The measure identification unit 104 identifies a measure to be recommended to the specific store based on measure information of measures implemented at stores with a higher ratio of regular customers than the ratio of regular customers of the specific store. The method of identifying the measure may be the same as in the embodiment, and the "store with a ratio of regular customers that does not meet a predetermined standard," which is the target store for the measure recommendation in the embodiment, may be read as the "specific store."

[0070] The output unit 105 outputs information about the identified measures. For example, the output unit 105 outputs information about the measures to a terminal device used by a user who has made a request to provide information about measures recommended for a specific store. Furthermore, for example, the output unit 105 may output information about the measures to an output destination specified in the request to provide information about measures recommended for a specific store.

[0071] According to the first modification, it is possible to provide a specific store with information on more appropriate store management in response to a user request.

[0072] [Modification 2] In Modification 2, the measure identification device 100 may identify measures that should be avoided in order to increase the number of regular customers. The measures that should be avoided can be used to identify measures for increasing the proportion of regular customers, for example.

[0073] In a second modification, the policy identification unit 104 may identify a policy to be avoided based on a policy implemented in a store where the height of the store is such that the proportion of regular customers does not satisfy a predetermined standard. Furthermore, the policy identification unit 104 may identify a policy to be avoided based on a policy implemented in a store where the height of the store is such that the proportion of regular customers does not satisfy a predetermined standard but is not implemented in a store where the height of the store is such that the proportion of regular customers satisfies the predetermined standard. For example, the policy identification unit 104 identifies, as a policy to be avoided, a policy implemented in a store where the height of the store is such that the proportion of regular customers does not satisfy a predetermined standard but is not implemented in a store where the height of the store is such that the proportion of regular customers satisfies the predetermined standard.

[0074] Here, it is assumed that measures implemented in stores where the percentage of regular customers is below a predetermined standard but not implemented in stores where the percentage of regular customers is above the predetermined standard are measures that may not contribute to a high percentage of regular customers. Therefore, by avoiding such measures, it is possible to implement measures that may contribute to a high percentage of regular customers.

[0075] For example, the policy identification unit 104 may exclude the identified policy to be avoided from the policies identified as recommended policies, which may allow the policy identification device 100 to provide information on policies that are more likely to contribute to a high proportion of regular customers.

[0076] Furthermore, for example, the output unit 105 may further output information about measures that should be avoided. This allows the policy identification device 100 to provide information about measures that are unlikely to contribute to a high proportion of regular customers, in addition to information about recommended measures. As a result, the policy identification device 100 can provide the user with information about better store management, which allows the user to consider measures to increase the proportion of regular customers.

[0077] The embodiment, the first modification, and the second modification may be combined as appropriate.

[0078] [Hardware Configuration] Some or all of the components of each device or system in each embodiment of the present disclosure described above are realized by any combination of an information processing device 1000 and a program, for example, as shown in Fig. 4. The information processing device 1000 includes, as an example, the following configuration.

[0079] - CPU (Central Processing Unit) 1001 - ROM (Read Only Memory) 1002 - RAM (Random Access Memory) 1003 - Program 1004 loaded into RAM 1003 - Storage device 1005 for storing program 1004 - Drive device 1007 for reading and writing from and to recording medium 1006 - Communication I / F 1008 for connecting to communication network 1009 - Input / output I / F 1010 for inputting and outputting data - Bus 1011 for connecting each component. Note that I / F is an abbreviation for Interface.

[0080] Each component of each device or system in each embodiment is realized by the CPU 1001 acquiring and executing a program that realizes the function of that component. The program that realizes the function of each component of each device is stored in the storage device 1005 or RAM 1003 in advance, for example, and is read by the CPU 1001 as needed. The program 1004 may be supplied to the CPU 1001 via a communication network, or may be stored in the recording medium 1006 in advance, and the drive device 1007 may read the program and supply it to the CPU 1001.

[0081] There are various variations in the method of realizing each device. For example, each device or system may be realized by any combination of a separate information processing device 1000 and a program for each component. Furthermore, multiple components included in each device may be realized by any combination of a single information processing device 1000 and a program.

[0082] Furthermore, some or all of the components of each device or system may be realized by general-purpose or dedicated circuits (circuitry) including a processor or the like, or a combination of these. Examples of circuits include a CPU, a GPU (Graphics Processing Unit), an FPGA (Field Programmable Gate Array), and an AI (Artificial Intelligence) processing LSI (Large Scale Integration). These may be configured by a single chip or by multiple chips connected via a bus. Some or all of the components of each device may be realized by a combination of the above-mentioned circuits and a program.

[0083] When some or all of the components of each device or system are realized by multiple information processing devices, circuits, etc., the multiple information processing devices, circuits, etc. may be centrally or decentralized. For example, the information processing devices, circuits, etc. may be realized as a client-server system, a cloud computing system, or the like, in which they are connected via a communication network.

[0084] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure.

[0085] Furthermore, although the operations are described in a sequential order in the form of a flowchart, the order of description does not limit the order in which the operations are performed. Therefore, when implementing each embodiment, the order of the operations may be changed as long as it does not affect the content.

[0086] Some or all of the above-described embodiments can be described as, but are not limited to, the following supplementary notes.

[0087] [Supplementary Note 1] A policy identification device comprising: a regular customer ratio acquisition means for acquiring the ratio of regular customers at each of a plurality of stores; a store identification means for identifying, from among the plurality of stores, a store whose regular customer ratio satisfies a predetermined standard based on the ratio of regular customers at each of the plurality of stores; a policy information acquisition means for acquiring policy information on policies being implemented at each of the plurality of stores; a policy identification means for identifying a recommended policy based on the policies being implemented at the stores whose identified regular customer ratios satisfy the predetermined standard; and an output means for outputting information on the identified policies.

[0088] [Appendix 2] The policy identification device described in Appendix 1, wherein the policy identification means identifies policies that are implemented in stores where the identified proportion of regular customers is high enough to satisfy the specified criterion, and policies that are not implemented in stores where the identified proportion of regular customers is high enough not to satisfy the specified criterion.

[0089] [Supplementary Note 3] The policy identification device according to Supplementary Note 1 or 2, wherein the plurality of stores are stores in which attributes of customers visiting the stores are similar.

[0090] [Supplementary Note 4] The policy identification device according to Supplementary Note 3, wherein the plurality of stores are stores in which attributes of regular customers among customers who visit the stores are similar.

[0091] [Supplementary Note 5] The policy identification device according to any one of Supplementary Notes 1 to 4, wherein the predetermined criterion is that the ratio of regular customers has increased by a predetermined value or more in a predetermined period of time.

[0092] [Supplementary Note 6] The policy identification device according to any one of Supplementary Notes 1 to 5, wherein the predetermined criterion is that the ratio of regular customers has changed from less than a predetermined value to equal to or greater than the predetermined value during a predetermined period.

[0093] [Supplementary Note 7] The policy identification device according to Supplementary Note 5 or 6, wherein the policy identification means identifies a policy based on a policy implemented in a store where the proportion of regular customers is at a level that satisfies the predetermined standard during the predetermined period.

[0094] [Supplementary Note 8] The policy identification device described in any of Supplementary Notes 1 to 7, wherein the policy identification means further identifies policies to be avoided based on policies that are implemented in stores where the proportion of regular customers is high enough not to meet the predetermined standard, but are not implemented in stores where the proportion of regular customers is high enough to meet the predetermined standard.

[0095] [Supplementary Note 9] The measure specifying device according to Supplementary Note 8, wherein the output means further outputs information regarding the measure to be avoided.

[0096] [Supplementary Note 10] The measure identification device according to any one of Supplementary Notes 1 to 9, wherein the information related to the measure includes information indicating a basis for recommending the measure.

[0097] [Supplementary Note 11] The policy identification device according to Supplementary Note 10, wherein the information indicating the grounds for recommending the policy is a ratio of regular customers in the store implementing the policy.

[0098] [Supplementary Note 12] The policy identification device according to Supplementary Note 10 or 11, wherein the information indicating the grounds for recommending the policy is the number of stores that are implementing the policy.

[0099] [Supplementary Note 13] The policy identification device according to any one of Supplementary Notes 1 to 12, wherein the regular customers are customers who visit the store a predetermined number of times or more within a predetermined period of time among the customers who use the store.

[0100] [Supplementary Note 14] The policy identification device according to any one of Supplementary Notes 1 to 13, wherein the regular customers are customers who use the store and whose purchase amount per predetermined period is equal to or greater than a predetermined amount.

[0101] [Supplementary Note 15] The policy identification device according to any one of Supplementary Notes 1 to 14, wherein the policy is a product lineup in the store.

[0102] [Supplementary Note 16] The measure specifying device according to Supplementary Note 15, wherein the output means outputs information about the measure to an ordering terminal of a store where the proportion of regular customers is so high that it does not satisfy the predetermined standard.

[0103] [Supplementary Note 17] The measure identifying device according to any one of Supplementary Notes 1 to 16, wherein the measure is a shelf allocation in the store.

[0104] [Supplementary Note 18] The measure identifying device according to Supplementary Note 17, wherein the shelf layout includes the number of identical products to be arranged at the front of the shelf.

[0105] [Supplementary Note 19] A method for identifying measures, comprising: acquiring a proportion of regular customers at each of a plurality of stores; identifying, from among the plurality of stores, stores whose proportion of regular customers satisfies a predetermined standard based on the proportion of regular customers at each of the plurality of stores; acquiring policy information on policies implemented at each of the plurality of stores; identifying recommended policies based on the policies implemented at the identified stores whose proportion of regular customers satisfies the predetermined standard; and outputting information on the identified policies.

[0106] [Supplementary Note 20] A recording medium that records a program that causes a computer to execute the following processes: acquiring the proportion of regular customers at each of a plurality of stores; identifying, based on the proportion of regular customers at each of the plurality of stores, stores among the plurality of stores whose proportion of regular customers satisfies a predetermined standard; acquiring policy information on policies implemented at each of the plurality of stores; identifying recommended policies based on the policies implemented at the identified stores whose proportion of regular customers satisfies the predetermined standard; and outputting information on the identified policies.

[0107] Furthermore, some or all of the configurations described in Supplementary Notes 2 to 18 that are subordinate to Supplementary Note 1 (measure identification device) described above may also be subordinate to Supplementary Note 19 (measure identification method) and Supplementary Note 20 (recording medium) in the same subordinate relationship as Supplementary Note 2 to 18. Furthermore, not limited to Supplementary Note 1, Supplementary Note 19, and Supplementary Note 20, some or all of the configurations described as Supplements may be subordinate to various hardware, software, various recording means for recording software, or systems, within the scope of each of the above-described embodiments.

[0108] REFERENCE SIGNS LIST 100 Measure identification device 101 Regular customer ratio acquisition unit 102 Store identification unit 103 Measure information acquisition unit 104 Measure identification unit 105 Output unit 1000 Information processing device 1001 CPU 1002 ROM 1003 RAM 1004 Program 1005 Storage device 1006 Recording medium 1007 Drive device 1008 Communication I / F 1009 Communication network 1010 Input / output I / F 1011 Bus

Claims

1. A policy identification device comprising: a regular customer ratio acquisition means for acquiring the ratio of regular customers at each of a plurality of stores; a store identification means for identifying, from among the plurality of stores, a store whose regular customer ratio satisfies a predetermined standard based on the ratio of regular customers at each of the plurality of stores; a policy information acquisition means for acquiring policy information on policies being implemented at each of the plurality of stores; a policy identification means for identifying a recommended policy based on the policies being implemented at the identified stores whose regular customer ratio satisfies the predetermined standard; and an output means for outputting information on the identified policy.

2. The policy identification device according to claim 1, wherein the policy identification means identifies policies that are implemented in stores where the identified proportion of regular customers is high enough to satisfy the specified standard, and policies that are not implemented in stores where the identified proportion of regular customers is low enough to not satisfy the specified standard.

3. The policy identification device according to claim 1 or 2, wherein the plurality of stores are stores where the attributes of customers who visit the stores are similar.

4. The policy identification device according to claim 3, wherein the plurality of stores are stores where the attributes of regular customers among the customers who visit the stores are similar.

5. The policy specifying device according to any one of claims 1 to 4, wherein the predetermined criterion is that the ratio of regular customers has increased by a predetermined value or more during a predetermined period.

6. The policy identification device according to any one of claims 1 to 5, wherein the predetermined criterion is that the ratio of regular customers has changed from less than a predetermined value to equal to or greater than the predetermined value during a predetermined period.

7. The policy identification device according to claim 5 or 6, wherein the policy identification means identifies a policy based on a policy implemented in a store where the proportion of regular customers is high enough to satisfy the predetermined standard during the specified period.

8. A policy identification device as described in any one of claims 1 to 7, wherein the policy identification means further identifies policies to be avoided based on policies that are implemented in stores where the proportion of regular customers is high enough not to meet the specified standard, but are not implemented in stores where the proportion of regular customers is high enough to meet the specified standard.

9. The measure specifying device according to claim 8, wherein the output means further outputs information regarding the measures to be avoided.

10. A measure specifying device according to any one of claims 1 to 9, wherein the information relating to the measure includes information indicating the grounds for recommending the measure.

11. The measure specifying device according to claim 10, wherein the information indicating the basis for recommending the measure is the ratio of regular customers in the store implementing the measure.

12. The policy identification device according to claim 10 or 11, wherein the information indicating the basis for recommending the policy is the number of stores implementing the policy.

13. The policy identification device according to any one of claims 1 to 12, wherein the regular customers are customers who visit the store a predetermined number of times or more within a predetermined period.

14. The policy identification device according to any one of claims 1 to 13, wherein the regular customers are customers who use the store and whose purchase amount per predetermined period is equal to or greater than a predetermined amount.

15. A policy identification device according to any one of claims 1 to 14, wherein the policy is the product lineup at the store.

16. The measure specifying device according to claim 15, wherein the output means outputs information about the measure to an ordering terminal of a store where the proportion of regular customers is so high that it does not satisfy the predetermined standard.

17. The measure specifying device according to any one of claims 1 to 16, wherein the measure is shelf allocation in the store.

18. The policy identifying device according to claim 17, wherein the shelf planogram includes the number of identical products to be arranged at the front of the shelf.

19. A method for identifying measures, comprising: acquiring the proportion of regular customers at each of a plurality of stores; identifying, based on the proportion of regular customers at each of the plurality of stores, stores among the plurality of stores whose proportion of regular customers is high enough to satisfy a predetermined standard; acquiring policy information on policies implemented at each of the plurality of stores; identifying recommended policies based on the policies implemented at the identified stores whose proportion of regular customers is high enough to satisfy the predetermined standard; and outputting information on the identified policies.

20. A recording medium that records a program that causes a computer to execute the following processes: acquiring the proportion of regular customers at each of a plurality of stores; identifying, based on the proportion of regular customers at each of the plurality of stores, stores among the plurality of stores whose proportion of regular customers meets a predetermined standard; acquiring policy information for policies implemented at each of the plurality of stores; identifying recommended policies based on the policies implemented at the identified stores whose proportion of regular customers meets the predetermined standard; and outputting information regarding the identified policies.

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