JP2019067263A - Information presentation system - Google Patents

Abstract

To provide a system capable of presenting information concerning customers or goods which are obtained from shopping behaviors of customers at a real store.SOLUTION: An information presentation system 10 comprises: a data acquisition part 220 configured to acquire time series shopping data which is a piece of time series data concerning customers who holds a good in shopping at a real store; an information generation part 230 that is configured to acquire a piece of category information which represents categories of customers or goods on the basis of the time series shopping data and to generate a piece of store analysis information on the basis of the category information of a good which is held by a customer and a piece of good identification information; and a data output part 250 configured to output a piece of store analysis information as a piece of data usable for an administrator of the real store.SELECTED DRAWING: Figure 1

Description

  The present invention relates to a technology for presenting information on retail store customers or products.

  A technology has been proposed that uses information on the behavior of customers who shop at retail real stores such as supermarkets and the like, and that uses time-series information along the order or time (see Patent Document 1).

Patent No. 4620807

  However, in the information disclosed in Patent Document 1 of such information, the quality of the customer's shopping experience is enhanced by reflecting the behavior or mental state of the customer in the sales floor, and the shop is made to be more selected by the customer. I can not

  Therefore, the present invention provides an information presentation system that presents, to a store operator, information that can be used to create a sales floor in which the state of a customer's behavior or psychology is reflected.

  An information presentation system according to an aspect of the present invention is based on a data acquisition unit that acquires time-series shopping data that is data related to retention of time-series products by customers in shopping at a real store, and time-series shopping data. Information generation that acquires classification information indicating classification for a customer or a product, and generates store analysis information based on the acquired classification information and stored product information indicating a product held or returned by the customer in the time-series shopping data And a data output unit that outputs store analysis information as data that can be used by a real store operator.

  Note that these general or specific aspects may be realized by a system, a method, an integrated circuit, a computer program, or a recording medium such as a computer readable CD-ROM, a system, a method, an integrated circuit, a computer program And any combination of recording media.

  The information presentation system of the present invention can present information that makes it possible to reflect the customer's behavior or mental state in the sales floor.

FIG. 1 is a block diagram showing a configuration of an information presentation system in the embodiment. FIG. 2A is a diagram showing an example of a commodity holder provided with a reader in the embodiment. FIG. 2B is a view showing another example of the commodity holder provided with the reader in the embodiment. FIG. 2C is a diagram showing an example of a reader in the embodiment. FIG. 3 is a diagram for explaining the concept from reading of product identification information to acquisition of time-series shopping data in the above-described information presentation system. FIG. 4 is a flowchart showing an example of a procedure of processing by the information generation unit included in the above-mentioned information presentation system. FIG. 5: is a figure which shows an example of the data which the data output part with which said information presentation system is equipped outputs. FIG. 6 is a flow chart showing an example of the procedure of processing by the information generation unit included in the above information presentation system. FIG. 7 is a flow chart showing an example of the procedure of processing by the information generation unit included in the above information presentation system. FIG. 8 is a flow chart showing an example of the procedure of processing by the information generation unit included in the above-mentioned information presentation system. FIG. 9A is a diagram for stating competitive eligibility conditions. FIG. 9B is a diagram for stating the competitive non-eligibility condition. FIG. 10A is a diagram showing an arrangement of products by department in a certain store. FIG. 10B is a diagram showing a part of the held product and a part of the holding start time interval extracted and shown in the time-series shopping data. FIG. 11 is a flowchart illustrating an exemplary procedure of processing by the information generation unit included in the above-described information presentation system.

  The present inventor has found that the problems as described above occur regarding the usage example of the information described in the "Background Art" section.

  In order to solve such a problem, an information presentation system according to an aspect of the present invention acquires data for acquiring time-series shopping data, which is data regarding retention of time-series goods by customers in shopping at a real store. And classification information indicating classification of a customer or a product based on the time-series shopping data, the acquired classification information, and held product information indicating a product held or returned by the customer in the time-series shopping data And a data output unit that outputs the shop analysis information as data that can be used by a real shop operator.

  This makes available, as information on each product, information indicating the classification of the customer or the product based on the analysis of the behavior of the customer who purchased or considered the purchase.

  Such classifications reflect the state of the customer's mind or the like that can be read from the behavior. Therefore, for example, the store manager can acquire information for better grasping the status of the customer of the store who wants to be reflected in the sales floor. The sales floor created using such information can provide the customer with, for example, a convenient shopping experience, and can provide the store operator with improved sales.

  For example, the store analysis information may include classification information indicating a classification for the customer regarding the degree of rush of the customer. In addition, such classification may be quantitative classification for each time width, or may be a qualitative classification divided based on statistically obtained knowledge etc., for example, "a customer who is in a hurry" regarding the degree of hurry. It may be classified as "customer with sufficient time" or "intermediate customer".

  For example, the information generation unit may acquire, from the time-series shopping data, a shopping time which is a time when a customer applies shopping at a real store, and may acquire classification information based on the shopping time.

  For example, the information generation unit may acquire the classification information by determining the classification for the customer based on the magnitude relationship between the shopping time and the first threshold.

  For example, the information generation unit may acquire, from the time-series shopping data, a holding start interval at which holding of products of a plurality of items by the customer is started, and may obtain classification information based on the holding start interval.

  For example, the information generation unit may acquire the classification information by determining the classification for the customer based on the magnitude relationship between the holding start interval and the second threshold.

  The time that a customer spends shopping or the interval at which they take a product changes between the customer who is in a hurry and the customer who is not. For example, the shop owner can grasp the tendency of the products to be purchased for each classification of customers, and the shop operators can reflect on the arrangement or the guidance display of the goods, for example, a sales floor where shopping can be smoothly performed for customers rushing. Making is realized.

  Also, for example, the store analysis information may include classification regarding a product that has been lost depending on whether the customer holds the product or classification information indicating a classification for a product for which the customer took a long time to specify the location of the product.

  For example, the information generation unit acquires, from the time-series shopping data, an elapsed time before holding which is the time from the predetermined action by the customer to the start of holding of the product, acquires sales of the held product, and proceeds before holding The classification information indicating the classification of the product may be acquired based on the time and the acquired sales.

  For example, when the elapsed time before holding exceeds the third threshold, the information generation unit may obtain the classification information by determining the classification of the product based on the size relationship between the sales and the fourth threshold. .

  For example, when the sales exceed the fourth threshold, the information generation unit may obtain the classification information by determining the classification of the product as a product that is lost depending on whether the customer holds or not.

  For example, when the selling is equal to or less than the fourth threshold, the information generation unit may acquire the classification information by determining the classification of the product as the product whose location specification took a long time.

  The time it takes before the customer places a product in the shopping basket can vary with the customer's condition. For example, if you get lost before making a decision to buy the item, the time will be long. Or, if you are willing to buy a product, but the product is not readily found in the store, the same is true. In such a case, each product is classified based on the status of the customer and presented to the store operator, and the store operator reflects the product placement or POP (Point Of Purchase) etc. This makes it possible to create an easy-to-find sales floor where products can obtain information necessary for selection. In addition, for stores, the loss of sales opportunities is reduced.

  Also, for example, the shop analysis information may include the classification information indicating the classification of the product for which the customer has been confused by whether the customer purchases or returns after the start of holding.

  For example, the information generation unit acquires, from the time-series shopping data, the occurrence of return of the product by the customer and the held product information indicating the product, and acquires classification information indicating the classification of the product based on the stored product information. Good.

  For example, the information generation unit calculates the return rate for the product based on the held product information, and determines the classification of the product regarding the degree of customer's easiness to buy or not based on the return rate. You may acquire classification information by doing.

  The occurrence of cancellation of holding of the product by the customer in the shop (return of the product to the sales floor) has conventionally been information that can not be grasped by the store operator. The information presentation system in the present disclosure is capable of presenting such information to the store operator, and the store operator utilizes this information in planning the sales strategy including assortment and price setting to improve sales. Can be On the other hand, for customers, a lined store with more attractive products in terms of assortment or price is realized.

  Also, for example, the store analysis information may include classification information indicating a classification for the product regarding the degree of mutual competition between two or more products.

  For example, from the time-series shopping data, the information generation unit holds the time interval between the occurrence of the retention of the first item by the customer and the occurrence of the return of the second item different from the item and the retention indicating the first item and the second item. The product information may be acquired, and the classification information may be acquired based on the held product information.

  For example, the information generation unit determines the classification information by determining the classification regarding the degree of mutual competition between the first product and the second product based on the magnitude relationship between the time interval and the sixth threshold and the held product information. You may get it.

  For example, the information generation unit acquires the division of the first product and the division of the second product using the product specification information indicating the first product and the second product and the information indicating the division of the product handled in the actual store. Based on the magnitude relationship between the time interval and the sixth threshold, and the similar relationship between the division of the first commodity and the division of the second commodity, the classification regarding the degree of mutual competition for the first commodity and the second commodity is determined You may

  Even for products that are known to be in a competitive relationship, it has been difficult for store operators to know how customers are examining among competing products at the sales floor. The information presentation system in the present disclosure can acquire such information from the substitution of the product held by the customer and present it to the store operator. The store operator can use such information to discover an unknown competitive relationship between products, and a competitive relationship between products that are not one-to-one. Such information on the competitive relationship of products can be used by the store operator for assortment, product arrangement, or presentation of customized advertisements for each customer. Customers can have more opportunities to know new products at the store, broadening the range of product choices.

  Also, for example, the store analysis information may include classification information indicating a classification for a customer regarding whether or not the customer is a group customer.

  For example, the information generation unit indicates, from the time-series shopping data, a time interval between the occurrence of retention of the first item by the customer and the occurrence of retention of the second item different from the item, and the first and second items. Acquires the held product information, calculates the required moving time between the sales floor of the first product and the sales floor of the second product in the actual store, and displays the held product information respectively indicating the first product and the second product If the travel time is equal to or less than the travel time, classification information may be acquired by determining the classification as a group customer for the customer.

  As a result, the store can obtain information for identifying a product that is frequently purchased by a group customer, which was conventionally difficult to grasp. The information on such products can be used by the store operator for assortment, product arrangement, or presentation of customized advertisements for each customer in the shop.

  The information provided by the information presentation system in the present disclosure may be used not only by the store operator but also by a maker of a product. For example, products that are frequently lost can use such information for package renewal, promotion of sales promotion, or development of new products in order to differentiate them from competitive products.

  These general or specific aspects may be realized by a system, a method, an integrated circuit, a computer program, or a recording medium such as a computer readable CD-ROM, a system, a method, an integrated circuit, a computer program Or it may be realized by any combination of recording media.

  Embodiments will be specifically described below with reference to the drawings. The following embodiments all show general or specific examples. Numerical values, shapes, materials, components, arrangement positions and connection forms of components, steps, order of steps, and the like shown below are merely examples, and are not intended to limit the present invention. Further, among the components in the following embodiments, components not described in the independent claim indicating the highest concept are described as arbitrary components.

Embodiment
[1. Configuration example]
An information presentation system in the embodiment will be described with reference to FIGS. 1 to 2C.

  FIG. 1 is a block diagram showing a configuration of an information presentation system in the embodiment.

  The information presentation system 10 according to the embodiment is, for example, based on information related to the behavior of a customer who is shopping at a real service (hereinafter, also simply referred to as a store) in a self-service system in retail business such as a supermarket or convenience store. The store operator is presented with information that can be used to create a sales floor that reflects the state of the customer's behavior or mind.

  It should be understood that the self-service system here is widely adopted in the above-mentioned retail stores and basically satisfies the following conditions. One is that the customer can know the price of each product by a predetermined price setting such as a price tag or uniform all products. Also, one is that the customer can freely select the product to be purchased from among the displayed products, and the selected product is placed and held on a basket, cart, or tray, etc. which the store lends out, or a sales floor You can go back and cancel your selection. Further, one is that the settlement of the purchased goods is carried out by bringing the held goods into a settlement office equipped with a cash register near the exit of the customer.

  However, the idea of the present invention can be applied even to a store where these conditions are not strictly satisfied. These conditions are shown for the purpose of presenting a specific example of a situation in which the information presentation system of the present invention is used, and is not intended to limit the present invention. For example, holding of the product to be purchased may be performed using a bag or the like carried by the customer, or may be simply held by hand. Also, the settlement may be performed by a method that does not use a settlement office equipped with a cash register. Further, any of the above conditions is also included in the concept of the self-service method in the present disclosure when there is assistance or management by a store clerk in the store.

  The information presentation system 10 includes a reader 100 and a shop analysis server 200.

  The reader 100 acquires product specification information indicating a product held by the customer during shopping.

  The reader 100 includes a sensor 110 and a transmitter 120.

  The sensor 110 acquires product specification information indicating the product from the product held by the customer who is shopping in the store.

  The transmitting unit 120 transmits the product specifying information acquired by the reader 100 to the store analysis server 200 described later.

  Such a reader 100 is, for example, carried or worn by a shopper customer and travels with the customer in the store. More specifically, an apparatus for a customer to place and hold a product, such as an in-store shopping basket as shown in FIG. 2A or a cart as shown in FIG. 2B (hereinafter referred to as a product holder ), Or as shown in FIG. 2C, on an information terminal such as a smartphone or a tablet computer separate from the product holder and used by a customer. However, in FIG. 2B, illustration of a portion including the lower frame of the cart and a portion including wheels is omitted. Another example of a product holder is a tray.

  The reader 100 can acquire information for identifying a product more naturally and with higher certainty for a shopper by being provided in the product holder.

  The reader 100 provided in the shopping basket shown in FIG. 2A includes a sensor 111 as an example of the sensor 110. The reader 100 provided in the cart shown in FIG. 2B includes, as an example of the sensor 110, a sensor 112 directed to a place where goods are placed. The reader 100 implemented on the information terminal shown in FIG. 2C includes, as an example of the sensor 110, a sensor (image sensor) 113 that configures a built-in camera.

  The sensor 110 is, for example, an optical sensor for reading a barcode attached to a product. Alternatively, the sensor 110 may be, for example, an RF reader for reading an RFID (Radio Frequency IDentication) from an RF (Radio Frequency) tag attached to a product. The barcode or the RFID indicates a commodity identification code such as, for example, a JAN (Japan Article Number) code. Alternatively, the sensor 110 may be an image sensor for acquiring an appearance image of a product. The information indicated by the barcode or the RFID and the information indicated by the appearance image of the item are examples of item specifying information in the present embodiment.

  For example, every time a customer picks up a product at the counter, the customer directs the sensor 100 to read the barcode attached to the product and causes the reader 100 to read it, and then places the bar code in the shopping basket (start of product holding). That is, the reader 100 sequentially reads the product specifying information indicating the product held by the customer according to the operation of the customer.

  The customer also causes the reader 100 to read the bar code when returning the product once placed in the shopping cart to the counter. In this case, the customer makes an operation to make the reader 100 recognize that the bar code of the product to be returned is read, for example, the user presses the button (not shown) of the reader 100, and then transmits the bar code to the reader 100. Make it read.

  If the sensor 110 of the reader 100 is an RF reader, the product placed on the product holder is periodically scanned, and holding and return of the product are recognized in comparison with the result obtained by the previous scan. May be

  The product specification information read by the reader 100 in this manner can also be used for the settlement of the product price. For example, the commodity identification information is transmitted from the reader 100 to the cash register at the checkout in the store, and the cash register presents the total amount of the commodity indicated by the commodity identification information to the customer. It is not necessary for the sales clerk to find the barcode of each product with a scanner into a POS (Point Of Sales, in Japanese, also referred to as point-of-sale information management) register found in a conventional checkout office. Alternatively, the product specification information may be determined when leaving the store and transmitted to the store's sales management system, and the settlement may then be performed with a credit card or other various payment means.

  Even in the conventional POS system, from the sales information obtained via the POS register for settlement, sales of stores by time zone, product trends, and associations between products and the gender and age of the purchaser, etc. are grasped. It is used for management. In the information presentation system in the present disclosure, the product identification information is also used for applications other than settlement. Details will be described later.

  The acquired item specifying information is wirelessly transmitted to the shop analysis server 200 by the transmitting unit 120 (not shown in FIGS. 2A to 2C) included in the item holder such as the shopping basket.

  A series of processes from reading to transmission of such item specific information is performed by a processor (not shown) included in the reader 100 reading and executing a predetermined program stored in a storage device (not shown). To be done. When the information terminal shown in FIG. 2C is owned by the user, the user installs a program (application) provided by the store to the store analysis server 200 to transmit the product specification information to the store analysis server 200 in the information terminal and starts up. You may use in the store.

  The reader 100 as described above is realized by what each customer uses through the behavior of shopping, such as a product holder or an information terminal, and the product identification information transmitted by the reader 100 corresponds to the product of each customer. It shows time-series information on retention.

  The store analysis server 200 acquires time-series data, which is data relating to the time-series holding of the product by the customer, based on the product specification information acquired from the reader 100. The realization of the store analysis server 200 that processes such information is, for example, a server computer installed in the store, a cloud server outside the store, or, if the store is one of the chain stores, This is done using a host computer used by the headquarters, or a combination thereof (hereinafter referred to as a server computer etc.).

  The store analysis server 200 has a receiver 210, a data acquisition unit 220, an information generation unit 230, and a store master as functional components realized by the processor reading and executing a program from the storage device using the above server computer or the like. 240 and a data output unit 250.

  The store analysis server 200 acquires the product specification information transmitted from the reader 100 via the reception unit 210.

  The data acquisition unit 220 acquires data on holding of a product based on the product specification information acquired by the reception unit 210. As described above, the product specification information indicates information on holding of products of each customer in time series, and data on holding of products is data on holding of products of time series of each customer. FIG. 3 is a diagram for explaining the concept from reading of product identification information in the information presentation system 10 to acquisition of time-series shopping data.

  The product specification information acquired by the reader 100 is transmitted to the shop analysis server 200. The character string in the left balloon in FIG. 3 indicates log data including a series of product specifying information that the reader 100 acquires and transmits. The data acquisition unit 220 acquires time-series shopping data as exemplified in the balloon on the right of FIG. 3, which indicates in order of time the products for which the customer has started or returned holding indicated by the acquired product specification information.

  In the time-series shopping data in this example, the product identification code of each product included in the product specification information and the data acquisition unit 220 collate the product identification code with the database (product master included in the store master 240). The acquired product name, the state of each product, and the time difference between the occurrence of the state are included as mutually associated records. The product identification code or the product name is an example of held product information in the present embodiment.

  When the product specification information is an appearance image of a product, for example, the data acquisition unit 220 executes character recognition on this image, or executes image recognition using a model obtained by machine learning, and performs product name. You may get

  Further, the state of each product in the time-series shopping data indicates whether the customer has started or released holding of the product in each record. In this example, "hold" indicates the start of hold, and "return" indicates the release of hold.

  The time lag indicates, for example, the time elapsed from the time when the customer first started holding any product in the sales department until the start or cancellation of holding of each product. Calculation of this time is performed based on goods specification information, for example. For example, in the reader 100, a time stamp of the time when the item specifying information is read is added to the item specifying information to be transmitted to the shop analysis server 200. The data acquisition unit 220 that has received the product specification information via the reception unit 210 acquires this time difference based on this time stamp. This time difference is included in the "time-series" item of time-series shopping data as time-series information of data relating to product retention.

  In addition, the format of time-sequential information is not limited to the time difference as illustrated. For example, the starting point of the time difference may be a time difference from the time of start or release of holding of the product shown in the immediately preceding record, or may be the actual time when start or release of holding of the product has occurred. Also, in the case of a time lag, it may be earlier than the customer first gets any goods, for example at the start of holding the goods holder or at the time of activation of the reader 100. In this case, the reader 100 is provided with a switch or sensor for using such a point of time as a time difference origin. In addition, when an information terminal separate from the product holder is used as the reader 100, the time of activation of the application that causes the information terminal to function as the reader 100 or check-in to the store performed by the user with this application The timing etc. may be the starting point of the time difference.

  The time-series shopping data acquired in this manner is next to be processed by the information generation unit 230.

  The information generation unit 230 acquires classification information indicating the classification of the customer or the product based on the time-series shopping data, and the acquired classification information and stored product information indicating the product held or returned by the customer in the time-series shopping data. And store analysis information based on

  The classification of the customer or the product based on the time-series shopping data is a classification regarding the behavior of the customer who is shopping at a real store, or the psychological state or the like appearing in the behavior. Such a classification can not be obtained by a POS system that relies on information at the time of registration of sales results in cash registers. Further, it is further advanced from the time-series information shown in Patent Document 1, and it is useful for the store to further improve the sales, efficiency, etc. of the store through improvement of customer satisfaction, convenience, etc. . Details of the processing procedure for generation of such classification and store analysis information will be exemplified in the description of the operation of the information presentation system 10 described later.

  The store analysis information generated by the information generation unit 230 is output by the data output unit 250 as data usable by the store operator. Data that can be used by the store operator is, for example, data indicating the correlation between the above classification and the product, and the store operator can change the assortment, arrangement and display of the product, display guidance, or sales promotion measures, and measure the effect It can be used to

  In addition, the representation format of the data to be output is not limited, and various formats such as data tables, lists, graphs, maps, etc. can be used, and display on a monitor or a portable terminal connected to the information presentation system 10 Is output as a printable data file as needed. The data file is stored, for example, in a storage device provided in a server computer or the like that implements the information presentation system 10. The store operator can access and use the data file at any time.

  The store master 240 is a set of basic information on a real store that uses the information presentation system 10, and is stored in a storage device provided in a server computer or the like that implements the information presentation system 10. The store master 240 also includes a product master and a sales floor master.

  The product master is a collection of product information handled at the actual store, and for example, the product code of each product, the product name, and the department (category) are included in the information. The sales floor master includes, for example, information such as the product arrangement of a real store and shelf allocation.

  The store master 240 is referred to by the data acquisition unit 220 and the information generation unit 230 realized by the processor executing a predetermined program in the information presentation system 10, and is used for acquisition and generation of information. Details will be illustrated in the description of the operation of the information presentation system 10 described later. The store master 240 may also be used in other systems installed in retail stores, such as an ordering system, an inventory management system, and a sales management system.

  The configuration of the information presentation system 10 described so far is an example of the row configuration of the information presentation system according to the present disclosure, and the present invention is not limited to this.

  For example, the reader 100 may not be provided as part of an information presentation system.

  In addition, the reader 100 is not a device that each customer carries and uses individually as described above, but senses a series of customer shopping behavior installed on a wall, ceiling, or fixture in a store, etc. It may be a group of devices capable of Specifically, cameras installed at a plurality of locations in a store may be used.

  In this case, the customer's action on holding the product is performed by image recognition of the product and the image of the customer, which are photographed by these cameras.

  Alternatively, the device group may further include sensors other than the image sensor provided in the camera, such as an RF reader for acquiring information for identifying a product or a customer. That is, the reader 100 further acquires information for identifying each customer so that the shopping behavior of each customer can be handled individually and captured in time series. The store analysis server 200 that receives this information further includes a customer identification unit for performing identification of each customer using this information.

  The store analysis server 200 obtains time-series shopping data as shown in FIG. 3 by mutually associating each identified customer, the action of holding or returning the goods of each customer, and the goods which each customer has started or canceled holding. Be done.

[2. Operation]
Hereinafter, the operation of the information presentation system 10 will be described by taking a plurality of examples according to the type of data to be presented.

  Note that the procedure until the data acquisition unit 220 obtains time-series shopping data based on the product specification information acquired from the reader 100 is basically the same regardless of which type of data is presented. Also, for any type of data, the format of the output can be determined by the user of the data such as the store operator according to the application. The following description focuses on the operation of the information generation unit 230 which differs depending on the type of data.

[2-1. Classification based on shopping time etc.]
It is known from previous experiments that there is a difference in the tendency of purchased goods at stores between those who are in a hurry and those who are not. Therefore, in each store, for example, if the characteristic of the purchase characteristic characteristic of the customer who is rushing in the store (hereinafter referred to as a rush customer) is known, for example, such an item may be on the shortest route connecting the store entrance and the settlement. By arranging in the store, it is possible to create a convenient sales floor for rush customers.

  FIG. 4 is a flowchart showing a procedure example of processing by the information generation unit 230 for the information presentation system 10 to output shop analysis information using the classification regarding the degree of rush for each customer.

  In this example, the degree of rush of the customer is determined using the time spent for one shopping at the store (the shopping time) and the interval between the start of holding each of the items of different items (holding start interval). The information generation unit 230 can acquire the shopping time and the holding start interval from time-series shopping data as shown in FIG. 3.

  The information generation unit 230 that has acquired the shopping time and the holding start interval of a certain customer determines whether the shopping time is less than or equal to the first threshold (step S41). When the shopping time is equal to or less than the first threshold (Yes in step S41), the information generation unit 230 determines that the classification of the customer is a rush customer (step S45).

  The first threshold is, for example, a time length set based on an average of shopping times of all customers of this store. The average of the shopping times is also calculated and obtained from the time-series shopping data in the information presentation system 10. The first threshold is set to a time shorter than this average shopping time, for example, a time length of 1⁄4 of the average shopping time.

  The reason that the classification of the customer is determined to be a hurry customer using step S41 is, for example, an action or mental state in which only a pre-decided purchase item is put in the shopping basket and moved quickly so as to settle quickly. Because there is a high possibility of

  If the shopping time exceeds the first threshold (No in step S41), the information generation unit 230 determines whether the holding start interval of the customer is equal to or less than the second threshold (step S42). If the shopping time is equal to or less than the second threshold (Yes in step S42), the information generation unit 230 determines that the classification of the customer is a rush customer (step S45).

  The second threshold is, for example, a time length set based on an average of holding start intervals of all customers of this store. The average of the holding start intervals is also calculated and obtained from the time-series shopping data in the information presentation system 10. The second threshold is set to a time shorter than the average holding start interval, for example, a half of the average holding start interval.

  The reason that the classification of a customer is determined to be a rush customer using steps up to step S42 is, for example, an action that a predetermined purchased item is rushed into the shopping basket but the shopping time is relatively long because the number of items is large. Or because there is a high possibility of being in a mental state.

  If the holding start interval exceeds the second threshold (No in step S42), the information generation unit 230 determines whether the shopping time of the customer exceeds the eighth threshold (step S43). If the shopping time exceeds the eighth threshold (Yes in step S43), the information generation unit 230 determines that the classification of the customer is a customer who can afford the time (step S47).

  The eighth threshold is also, for example, a length of time set based on the average of shopping times for all customers in this store, and also for a time longer than the first threshold set based on the average of shopping times for all customers, For example, it is set to the same length of time as the average shopping time.

  There is no reason to rush the classification of the customer to the customer with enough time by step S43, for example, there is no reason to rush, so it is not necessary to hurry and search for the product to be purchased and put it in the shopping basket etc. The reason is that there is a high possibility that you will be in a behavior or mental state that the time of your stay is also relatively long.

  If the shopping time is equal to or less than the eighth threshold (No in step S43), the information generation unit 230 determines whether the holding start interval of the customer exceeds the ninth threshold (step S44). If the holding start interval exceeds the ninth threshold (Yes in step S44), the information generation unit 230 determines that the classification of the customer is a customer with ample time (step S47).

  The ninth threshold is also, for example, a length of time set based on an average of retention start intervals of all customers in this store, and is longer than a second threshold similarly set based on an average of retention start intervals of all customers. The time is set to, for example, the same time length as the average holding start interval.

  There is no need to rush to search for items to be purchased and place them in the shopping basket etc., as there is no reason for the customer classification to be determined as a customer with sufficient time by step S44. This is because there is a high possibility that the time of stay in the store is not particularly long, because of the small number of items purchased.

  If the shopping time is equal to or less than the ninth threshold (No in step S44), the information generation unit 230 determines that the classification of the customer is an intermediate customer who is not in a hurry but does not have enough time (step S46). ).

  The information generation unit 230 that has determined the classification of the customer in the steps up to this point uses the determined classification as classification information. The information generation unit 230 further generates store analysis information based on the classification information and the held product information indicating the product held by the customer (step S48). Store analysis information obtained in this manner is, for example, information obtained by statistically processing the results of the process shown in FIG. 4 executed for a large number of customers, and more specifically, purchase by a rush customer at the store Show products that tend to be The store analysis information is output by the data output unit 250 as data usable by the store operator. FIG. 5 is a diagram showing an example of data output by the data output unit 250. As shown in FIG.

  In this example, information is presented for the top 10 customers in the category of customers who are in a hurry, customers who have time, and items purchased by the entire customer. Also, the same divisions are connected by line between customer classifications. Each section is acquired by the information generation unit 230 with reference to the product master included in the store master 240, to which the product indicated by the held product information belongs.

  Using such information, the store operator changes the shelf allocation so that, for example, the shelves of the top three divisions will be placed along the route between the entrance and the checkout office, and the shopping experience of rushing customers Improve the quality of the store, encourage revisiting, and increase sales.

  In addition, said process sequence and the information shown are an example, and the following modifications are considered.

  For example, the classification determination as described above may be performed based on only one of the shopping time and the holding start interval.

Further, the determination regarding the holding start interval may be performed after being normalized according to the distance of the path between the commodities. For example, the holding start interval may be corrected according to the distance of the physical space between items. More specifically,
L: The shortest path length between the product to be processed and the item whose holding has been started immediately before that (This shortest path length is included as information in, for example, the sales master of the store master 240, and the information generation unit 230 You may refer and get it)
V: Average walking speed (time zone, store industry, aisle width, if the information of each customer can be obtained, a value according to gender and age may be used)
K: Correction coefficient (a value of 1.0 or more. A time other than the time required for movement, such as stopping in front of other products, for including time other than the consideration of purchase of the product to be processed coefficient)
As
Correction holding start interval = actual elapsed time-(L / V) × K
This makes it possible to present more accurate information.

  Moreover, the classification of the customer may be classified into two or four or more types instead of three, or may be a quantitative classification for each time width. In addition, the qualitative classification like the above-mentioned example divides time width based on knowledge etc. which were obtained statistically, for example. Alternatively, more subdivided classification may be performed based on continuous values such as an index value (a hurry index) indicating the degree of hurry calculated from the shopping time and the holding start interval.

  Also, in the above example, the classification for the customer is determined based on the magnitude relationship between the shopping time or the holding start interval and the threshold, but the classification of the customer as described above is the shopping time or the holding start interval While based, thresholds may be determined using techniques that do not use. For example, the classification may be determined using the percentiles of all customer shopping times or retention initiation intervals of a customer's shopping times or retention initiation intervals.

  In addition, the shopping time or holding start interval used to calculate the above threshold or percentile may not be obtained from the time-series shopping data of all the customers. For example, a shopping time or holding start interval obtained from time-series shopping data of customers in the latest fixed period, customers in the same time zone, or customers with the same or similar number of purchased items may be used.

  Also, instead of the percentile, the above classification may be determined using the order in ascending or descending order.

  Further, the products shown in the shop analysis information may not be divisions as in the above example, but may be individual products. Moreover, the shop analysis information limited to the goods etc. which exist in a specific division, a specific area in a shop, etc. may be shown by filtering. The example shown in FIG. 5 may be store analysis information in which the department focuses on food and beverages.

  Further, the form of use of the presented shop analysis information is not limited to the above example. For example, based on the hurry index and the list of products held or purchased by each customer, the shelving of the entire store may be determined so as to optimize the shopping time at the store. This optimization refers not only to shortening the rushing customer's shopping time, but, for example, to customers who are more aware of time-consuming customers, enjoy shopping itself, and encourage so-called "next buying" Also refers to changes in

  Also, in the above, the customers are classified into the "hurry" classification according to the shopping time or the holding start interval, but the way of the customer classification according to the similar time is not limited to this. For example, in accordance with these times, it is also possible to classify the product to be purchased as a "purpose buy" customer who has decided before arrival to the store. Such a customer often moves in the store along a route that efficiently travels the sales floor where the desired product is located, and shows a tendency similar to a “hurry customer”, such as a short holding start interval or short shopping time. .

  Further, in the above example, the store analysis information is used by the store operator as information on sales results, but may be used to classify a customer in shopping.

  For example, the information generation unit 230 may provisionally determine the classification regarding the degree of rush of the customer in shopping based on the combination of the held products, the holding start interval, and the like. The data output unit 250 recommends a product to the customer through digital signage or information equipment possessed by the customer, and store analysis information indicating the customer, the classification determined for the customer, and the product already held by the customer. Output to the system And by this system, the goods with the high possibility that the customer of this classification will buy later may be shown to the customer.

  Further, the store analysis information may be presented to and used by a maker of a product handled in the store. As a more specific example, a manufacturer is provided with information that, for example, a product of the manufacturer tends to be purchased by a rush customer based on store analysis information. The maker who obtained this information can change the design of the package so that rush customers can easily find it at the store, create a POP advertisement, and distribute it. For manufacturers, it is easier to obtain information on the behavior or mental state of the customer in the shop, which can be finally grasped over time and money by observation in the shop, confirmation of recording, questionnaire, etc. It becomes possible to develop products in line with the behavior or mental state.

[2-2. Classification based on elapsed time before the start of product retention]
Next, the process of classification based on the time taken before putting the product into the shopping basket will be described.

  For example, if the customer has a doubt before making a decision to purchase a product, the product will be put in the shopping basket from the entry until the product is put in the shopping basket or immediately after (or returned to the store) It takes a long time to put the item in the shopping basket. Also, although there is an intention to buy a product, the same is true if the product is not readily found in the sales floor. If a product that is likely to cause such a situation is known, the store operator can provide information that encourages the purchase of the product at the store, or change the product to a position that is more easily found, etc. Loss of opportunity can be reduced.

  FIG. 6 is a flowchart showing an example of a procedure of processing by the information generation unit 230 for outputting information on the shop analysis information in which the information presentation system 10 classifies items corresponding to the above-mentioned items for each item.

  In this example, it is determined whether or not the product has a doubt or difficulty in identifying the place for the purchase of the product for the customer, the elapsed time before the start of holding of each product in the counter (elapsed time before holding), and each product It is determined using the sales of The information generation unit 230 can acquire this elapsed time before holding from time-series shopping data as shown in FIG. 3. In addition, information on sales can be acquired by referring to sales record data acquired by a store using a POS system for each product indicated in the time-series shopping data. Here, the selling is, for example, the number of sales, but is not limited thereto.

  The information generation unit 230 that has acquired the elapsed time before holding of a certain customer determines whether the elapsed time before holding is less than or equal to the third threshold (step S61). If the elapsed time before holding is less than or equal to the third threshold (Yes in step S61), the information generation unit 230 determines that the classification of the item is a normal item (step S63).

  The third threshold is, for example, a time length set based on the average of the pre-holding start times of the entire store. The average of the holding start intervals can be calculated and obtained from the time-series shopping data in the information presentation system 10.

  If the elapsed time before holding exceeds the third threshold (No in step S61), the information generation unit 230 determines the magnitude relationship between the sale of the product and the fourth threshold (step S62). The fourth threshold is, for example, a value of the number of pieces set based on the average of sales of products of the division to which the product belongs, from the sales record data described above.

  If the sale of this item is less than or equal to the fourth threshold (No in step S62), the information generation unit 230 determines that the category of the item is the item whose location specification took a long time (step S64). If the sale of this item exceeds the fourth threshold (No in step S62), the information generation unit 230 determines that the category of the item is the lost item depending on whether the customer holds it for purchase (step S62). Step S65).

  The product is classified as described above using step S62, because the product with high sales is time to decide the product to be purchased by the customer for some reason although the location in the store is already known to the customer Because there is a high possibility of The reason is that, for example, when it is difficult for the customer to recognize differences between products because the number of items handled in the same department is large, the frequency of new product introduction is relatively high or the number of items is large. There is a possibility that the customer is likely to get lost between the new product and the conventional product. Also, for products with low sales, it is possible that the location of the product sales area may not be clear, or the customer knows the location of the product sales area where the product you intend to buy, but the product is in the shelf or This is because there is a high possibility that the display condition will make it less noticeable.

  The information generation unit 230 that has determined the classification of the product in the steps up to this point uses the determined classification as classification information. The information generation unit 230 further generates store analysis information based on the classification information and the held product information of the product (step S66).

  Store analysis information obtained in this manner is, for example, information obtained by statistically processing the result of the process shown in FIG. 6 executed over a predetermined period, and products belonging to each category determined as described above Show. The store analysis information is output by the data output unit 250 as data usable by the store operator. For example, it may be presented to the store operator as a list of products belonging to each category.

  Using such information, the store operator executes, for example, changing the number of places or faces of each item on a shelf including items that have taken a long time to specify a place, and promoting sales using a POP advertisement. be able to. This makes it easier for the customer to find this product, makes it easier to shop, and reduces the loss of sales opportunities for the store. In addition, for products that are confused by whether or not they are held by the customer, the store operator may display a POP advertisement to promote information on products that they want to sell more or provide information on differences between products, or change the pricing etc. Can encourage them to pick up and hold them.

  In addition, since the customer may choose not to purchase at the same time or purchase both after losing the purchase, the store operator can thereby reduce the loss of sales opportunities and lead to an increase in sales.

  In addition, said process sequence, the information used, and the information shown are an example, and a modification is considered.

For example, as in the case of the above-mentioned holding start interval, the elapsed time before holding may be corrected according to the distance of the physical space between the articles. More specifically,
L: The shortest path length between the product to be processed and the product whose holding has been started or released immediately before that V: Average walking speed (time zone, store industry, passage width, information of each customer can be obtained For example, numbers matched to gender and age may be used)
K: Correction coefficient (a value of 1.0 or more. A time other than the time required for movement, such as stopping in front of other products, for including time other than the consideration of purchase of the product to be processed coefficient)
As
Elapsed time before correction holding = actual elapsed time-(L / V) x K
This makes it possible to present more accurate information.

  For example, if it is a chain store, it is not restricted to the average of the sales in a self-store as a 4th threshold, the number based on the sales record in other stores with similar scale and location may be used.

  Moreover, the determination in step S62 performs clustering by unsupervised machine learning such as the k-means method instead of using the fourth threshold, and products included in an appropriate cluster according to the elapsed time before holding and sales are each It may be determined to belong to the classification.

  Further, in the above example, the classification of the product is determined based on the magnitude relationship between the elapsed time before holding or sales and the threshold value, but the classification of the product as described above is the elapsed time before holding or sales While based, thresholds may be determined using techniques that do not use. For example, the classification may be determined using a percentile for the pre-holding elapsed time of all customers of the pre-holding elapsed time of a certain customer. Also, the classification may be determined using the percentile of the sale of a certain item in the sale of each item in the same department.

  In addition, the pre-holding elapsed time or sales used to calculate the above threshold or percentile may not be obtained from the time-series shopping data of all the customers. For example, a pre-holding elapsed time or sales obtained from time-series shopping data of customers in a recent fixed period or customers in the same time zone may be used.

  Also, instead of the percentile, the above classification may be determined using the order in ascending or descending order.

  Also, the classification may be made on a division of goods rather than on individual goods. In this case, as the average of sales, a value calculated according to, for example, the numerical values of other stores as described above or the composition ratio of sales between other stores or the division of the same store may be used.

  In addition, store analysis information indicating such a classification may be presented to a maker of a product handled in the store and used. For example, a maker of a product classified as a product whose location is not easy to understand can add, to the package of the product, information on the feature of the product or a change for improving the visibility.

[2-3. Classification based on return after the start of product retention]
Next, the process of classification based on the incidence rate of return of goods (hereinafter referred to as return rate) will be described.

  For example, in the case of a product for which the customer has lost purchase, it is considered that the situation where the product is once held but returned to the counter before settlement occurs more frequently than the product that is not. As mentioned above, the store may have lost a sales opportunity, as the customer may have the option of buying and not buying at the same time, or buying both. Also, for a customer, for example, if he / she does not buy one in order to be undecided, it takes time and labor to consider buying something or what to buy instead, and if it is necessary to buy both of them inevitably. Is needless expense and the quality of the shopping experience is reduced.

  If information for grasping such a product is obtained, the store manager can reduce loss of sales opportunities and improve sales by, for example, presenting the product information to the customer by POP advertisement. In addition, it is possible to provide the customer with a high quality shopping experience place with less time or money, by obtaining information about the product and deciding on the product to be purchased.

  FIG. 7 is a flowchart showing a procedure example of processing by the information generation unit 230 for outputting information on the store analysis information using the classification of each product based on the incidence rate of return by the information presentation system 10.

  In this example, the classification of the degree of customer's confusion about whether to purchase or not for a product that is lost whether to buy or return is determined based on the return rate of the product. The information generation unit 230 can calculate and acquire the return rate of each product from the time-series shopping data as shown in FIG. 3. For example, the incidence rate of a certain product is calculated as a ratio of the total number of occurrences of return to the total number of occurrences of start of holding of the product.

  The information generation unit 230 that has acquired the return rate for a certain product determines whether the rate of occurrence is greater than a predetermined threshold (step S71). If the occurrence rate is greater than the predetermined threshold (Yes in step S71), the information generation unit 230 determines that the classification of the product is a product that the customer is easily lost as to whether or not to purchase (step S72).

  The predetermined threshold is a probability set in the information presentation system 10 based on, for example, the return rate for the entire section to which the item belongs, which is calculated from the above-described time-series shopping data.

  If the return rate is equal to or less than the threshold (No in step S71), the classification of the product is determined as a product that is not particularly easily lost by the customer (step S73).

  The information generation unit 230 that has determined the classification of the product in the steps up to this point uses the determined classification as classification information. The information generation unit 230 further generates store analysis information based on the classification information and the held product information of the product (step S74).

  Store analysis information obtained in this manner is, for example, information obtained by statistically processing the result of the process shown in FIG. 7 executed over a predetermined period, and products belonging to each category determined as described above Show. The store analysis information is output by the data output unit 250 as data usable by the store operator. For example, it may be presented to the store operator as a list of products belonging to each category.

  Using such information, the store manager can publish a POP advertisement for providing information on sales promotion of a product he / she wishes to sell or a difference between products, or changing the price setting. In this way, the store operator can improve the quality of the customer's shopping experience and reduce the loss of sales opportunities, leading to an increase in sales.

  In addition, said process sequence, the information used, and the information shown are an example, and a modification is considered.

For example, the shop analysis information may include classification based on continuous values such as an index value (stray index) indicating the degree of susceptibility to stray based on the return rate. As a more specific example,
Lost index = exp (return rate-threshold) / (1 + exp (return rate-threshold))
It may be calculated as

  Also, classification may be based on descending or ascending return rates or percentiles of return rates within the division of goods, rather than comparison to thresholds.

  In addition, store analysis information indicating such a classification may be presented to a maker of a product handled in the store and used. For example, a manufacturer of easily lost products can use such information for package renewal, differentiation of competitive products, sale of different size products, development of sales promotion methods, or development of new products.

  In addition, above-mentioned 2-2. The classification of lost in “A” differs from that in shopping in terms of whether it is lost until holding a product or after being held once. Users of store analysis information can formulate a sales strategy conscious of this point.

[2-4. Classification of competition among products]
For store operators, competition among products can be grasped to some extent from general knowledge, sense and rule of thumb. However, it has been difficult to grasp how customers are considering among competing products in the sales floor, except for special surveys such as observation at stores, confirmation of recordings, and questionnaires. Also, finding competition between non-one-to-one products is particularly difficult.

  Here, the products held by the customer may be switched between competing products. In the information presentation system 10, such information can be acquired and presented to the store operator.

  FIG. 8 is a flowchart showing a procedure example of processing by the information generation unit 230 for the information presentation system 10 to output shop analysis information using the classification related to the competition relationship between products.

  In this example, the presence or absence of a competitive relationship between products is the occurrence of retention in a product, the occurrence of return of different products from the product and the item, and the relationship between departments of each product obtained based on the product information of each product. It is determined using The information generation unit 230 can acquire such occurrence of holding and return of the product from the time-series shopping data as shown in FIG. 3 (step S81). In addition, the relationship between the divisions of each product can be acquired with reference to the product master for the product indicated by the held product information.

  Occurrence of retention of a certain product (hereinafter referred to as the first product) and return of a product different from this product and the product (hereinafter referred to as the second product) from the time-series shopping data of one customer's shopping The information generation unit 230, which has detected the occurrence of the event, determines whether the information regarding the occurrence of the event conforms to the condition indicating the presence or absence of the competition. More specifically, the information generation unit 230 is information on time intervals of occurrence of holding of the first product and occurrence of return of the second product acquired from the time-series shopping data, each of the first commodity and the second commodity described above It is determined whether the information of the division of each of the first product and the second product acquired using the held product information and the product master of each of the items matches a predetermined condition. These conditions will be described with reference to the examples shown in FIGS. 9A and 9B.

  FIG. 9A is a table in which examples (a) to (e) of conditions (hereinafter, also referred to as competition eligibility conditions) indicating the existence of a competition relationship are tabulated. The arrows indicate the order of occurrence in time series, the solid line surrounding each product name (product A etc.) indicates the occurrence of holding of each product, and the broken line indicates return. Each example will be described below.

  Condition example (a) is an example of a condition for determining the presence of a competition relationship between one-to-one commodities.

  In this example, if the product B is held at a time interval equal to or less than the threshold from the return of the product A after the product A once held is returned, it is determined that the product A and the product B compete with each other.

  Alternatively, if the product B belongs to a section similar to the product A, it is determined that the product A and the product B compete with each other. The plurality of departments being similar means that, for example, if the parent departments (divisions one hierarchy level) of a plurality of departments set in a tree structure are common, these departments are similar.

  Note that the number of layers, which is a criterion of determination that sections are similar, is not limited to one layer, and may be set as appropriate. Also, when the divisions of the product are not set in a tree structure or are set in a tree structure, an exceptional similar relationship between divisions is separately defined, and a table etc. to which the information generation unit 230 can refer The information presentation system 10 may be provided as data of Depending on the criteria for determining that the departments are similar to each other, it is possible to determine the existence of a competition between products that are conventionally determined not to compete. For example, even when the product A is almond chocolate and the product B is a cafe latte, it can be determined that a competition relationship exists.

  Condition example (b) is an example of a condition for determining the presence of a competition relationship between a pair of products.

  In this example, after the products A and C once held are returned in order, if the product B is held at a time interval equal to or less than the threshold from the return of the product A, the products A and C and the product B compete with each other. It is determined that.

  Alternatively, if the return of the product A, the return of the product C, and the holding of the product B occur at time intervals equal to or less than a predetermined threshold, it is determined that the products A and C and the product B compete.

  Alternatively, if the product B belongs to a section similar to each of the product A and the product C, it is determined that the products A and C and the product B compete with each other.

  For example, even if the product A is a rice ball, the product C is a plum rice ball, the product B is a boxed lunch, and the customer selects one boxed lunch instead of two rice balls, it can be determined that a competition exists.

  The condition example (c) is also an example of the condition for determining the existence of a competitive relationship between one-to-one products, and the condition example (a) is the order of occurrence of return of product A and retention of product B It is the case that was replaced.

  In this example, when the return of the product A occurs at a time interval equal to or less than the threshold value from the occurrence of the holding of the product B, it is determined that the product A and the product B compete with each other.

  Alternatively, if the product B belongs to a section similar to the product A, it is determined that the product A and the product B compete with each other.

  The condition example (d) is also an example of the condition for determining the existence of a competition relationship between a pair of products, and the condition example (b) is for the return of the product A and the product C and the retention of the product B. This is the case where the order of occurrence has changed.

  In this example, it is determined that the product B and the products A and C compete with each other when the products A and C are returned at time intervals equal to or less than the threshold value from the generation of the product B.

  Alternatively, if the holding of the product B, the return of the product A, and the return of the product C occur at time intervals equal to or less than a predetermined threshold, it is determined that the product B and the products A and C compete.

  Alternatively, when each of the product A and the product C belongs to a section similar to the product B, it is determined that the product B and the products A and C compete with each other.

  Further, by using a plurality of the above-described condition examples in combination, it is possible to determine the presence or absence of a competitive relationship even when the occurrence order of purchase or return of a plurality of products is more complicated.

  In the condition example (e), by using the condition example (a) and the condition example (c) in combination, the competitive relationship between the product A and the product B, and the competitive relationship between the product B and the product C It is determined that In light of the shopping scene example, the customer puts the product A and the product C into the shopping basket, and immediately returns the product B into the shopping basket instead of returning the product A. At this point, it is determined that the product A and the product B compete with each other in light of the condition example (a). Here, the customer who has put the product B in the shopping basket and looks at it, immediately returns the product C considering that the product C is unnecessary if the product B is purchased. Here, it is determined that the product B and the product C compete against each other in light of the condition example (c).

  As described above, in the information presentation system 10 according to the present embodiment, it is possible to detect a competitive relationship for the customer's mind that appears in the occurrence of the start and return of holding of a product that normally occurs in shopping behavior.

  FIG. 9B is a table in which an example (f) of a condition (hereinafter referred to as a competition non-qualifying condition) indicating the absence of a competition relationship and a condition example (g) indicating an exception thereof.

  In condition example (f), if return of product A occurs at a time interval equal to or less than the threshold after the second product A is held, product A and product A even if there is a start of holding product B thereafter It is determined that there is no competition with B. The second occurrence of the holding of the product A is highly likely to be due to the customer's erroneous operation such as double scanning of the product barcode, for example, and in such a case, the competition between the product A and the product B It is determined that there is no relationship.

  On the other hand, in the condition example (g), although it is a short time from the occurrence of the second holding of the product A to the occurrence of the return of the product A, when the holding of the product B further occurs during that time. Since there is a high possibility that the second holding of the product A is not due to an erroneous operation as described above, it is determined that there is a competition between the product A and the product B.

  The threshold with which the magnitude relationship is compared with the time interval in the determination using these example conditions is an example of the sixth threshold in the present embodiment.

  In step S82, the information generation unit 230 uses information on time intervals of the occurrence of the holding of the first product and the occurrence of the return of the second product, the held product information of each of the first product and the second product, and the product master. If the information on the divisions of the first commodity and the second commodity acquired by each of the categories is in a competitive non-eligibility condition, that is, whether the condition example (f) of the above example conditions is met, the condition example (g) Determine if it does not match).

  If these pieces of information match the competition non-qualifying condition (Yes in step S82), the information generation unit 230 determines that the classification of the first product and the second product is a product having no conflict with each other (step S85).

  If these conditions do not match the competition ineligibility conditions (No in step S82), the information generation unit 230 further determines that the conditions are competition eligibility conditions, that is, condition examples (a) to (e) of the above example conditions. It is determined whether or not one of the above is matched (step S83).

  If the competitive eligibility condition is not met (No at Step S83), the information generation unit 230 determines that the classification of the first product and the second product is a product having no conflict with each other (Step S85).

  If the competition eligibility condition is met (Yes in step S84), the information generation unit 230 determines that the classification of the first product and the second product is a product that mutually competes (step S84).

  The information generation unit 230 that has determined the classification of the product in the steps up to this point uses the determined classification as classification information. The information generation unit 230 further generates store analysis information based on the classification information and the held product information of the product (step S86).

  Store analysis information obtained in this manner is, for example, information obtained by statistically processing the result of the process shown in FIG. 8 executed over a predetermined period, and products belonging to each category determined as described above Show. The store analysis information is output by the data output unit 250 as data usable by the store operator. For example, it may be a list showing combinations of products determined to be in a competitive relationship with high frequency in order of the frequency.

  In addition, said process sequence and the information shown are an example, and the following modifications are considered.

  For example, based on statistics of frequency of co-occurrence of occurrence of holding of one product and occurrence of return of another item in one-time purchase of a customer without using similar relation of division of goods, A determination regarding competition may be made. Although it is likely to take time until useful store analysis information is obtained, it is expected to obtain store analysis information indicating competition or buying that has not been known at all.

  Further, the competitive relationship between the products may be indicated in the store analysis information as, for example, strong or weak rather than the presence or absence of the competitive relationship as described above. As a more specific example, the index value indicating the strength of the competition between products determined based on the distance between departments within the tree structure of the division or the frequency determined to be in the above-mentioned competition Product classification may be made based on such continuous values.

  Based on the information on the competition among the products, the store operator can consider the assortment, the arrangement of the products, the POP advertisement of the proposal type, and the like, and can improve the sales. Also, for the customer, for example, by receiving a proposal of a new product or a combination of products that can be a substitute product of a product purchased conventionally, it is possible to eliminate the courtesy in a shop where you frequently shop or to buy from a wider range of options. It is expected that the quality of shopping experience will be improved, such as the improvement of customer satisfaction in

  Further, in the above example, the store analysis information is used by the store operator as information on sales results, but may be used during shopping. For example, the data output unit 250 transmits a product to a customer via digital signage or an information device possessed by the customer, which is store analysis information indicating the customer, the product returned by the customer, and the product classified as competing with the returned product. Output to a system that makes The system may then guide the customer to a product that is in competition with the returned product. Alternatively, a product that the store wants to sell may be informed to a customer who has started holding a product classified as a competition with the product that the store wants to sell.

  In addition, such store analysis information may also be presented to a maker of a product handled in the store and used. The newly discovered competition allows, for example, the manufacturer to obtain hints of non-traditional markets or promotional activities.

[2-5. Classification of customer personnel composition]
In the past, it was difficult to link the fact that a customer was a group to sales performance data, and thus information on trends in the purchase of group-specific products was scarce compared to information on individual customers.

  If information of time intervals of occurrence of holding of a plurality of commodities acquired by the information presentation system 10 is used, it can be determined that the customer is a group in a predetermined case. Hereinafter, such a case will be described.

  FIG. 10A is a diagram showing an arrangement of products by department in a certain store. Moreover, in FIG. 10A, the flow line in the shopping which the customer with this store made is shown by the arrow. FIG. 10B is a diagram showing, by extraction, a part of the held product and a time interval of the start of holding shown in the time-series shopping data acquired by the customer at this time by the customer.

  Comparing FIG. 10A and FIG. 10B, for example, although there is a relatively long distance from the meat sales section to the plastic bottle beverage sales section, occurrence of holding of meat products and occurrence of plastic bottle beverage holding The time interval is short for the distance between sales points as compared to the time interval between 3 seconds and other goods. Similarly, the time interval between the occurrence of the produce of the sugar beet product and the occurrence of the retention of the magazine is also shorter relative to the distance between the two seconds and the sales floor. Such a movement between sales sections is impossible in consideration of the movement speed of a person, and it is presumed that there is a high possibility of being a multi-person shopping.

  In the information presentation system 10, in the processing procedure illustrated in FIG. 11, the classification of whether the customer is a group customer or not is determined to generate sales floor analysis information.

  In this example, the time interval at which the customer holds the product, the held product information indicating the product held by the customer, and the time required for moving the route between the sales sections of the product held by the customer (moving required time) are Used. The information generation unit 230 can acquire this time interval and held product information from time-series shopping data as shown in FIG. 3 (steps S111 and S112). In addition, an average time length relating to a route between sales points of products held by the customer can be acquired with reference to the sales floor master for the product indicated by the held product information. For example, the sales floor master indicates the shortest path distance between sales floors of each department. In this case, the information generation unit 230 calculates the movement required time from this distance and the average walking speed of the person. Alternatively, the sales floor master may directly indicate the average travel time required according to the shortest path distance between sales floors of each department.

  The information generation unit 230 that has acquired these pieces of information determines whether the time interval is equal to or less than the movement required time (step S113).

  If the time interval is equal to or less than the required travel time (Yes at step S113), the information generation unit 230 determines that the classification of the customer is a group customer (step S114). If the time interval exceeds the movement required time (No at Step S113), the information generation unit 230 determines that the classification of the customer is a customer who is not a group customer (Step S115).

  The information generation unit 230 that has determined the classification of the customer in the steps up to this point uses the determined classification as classification information. The information generation unit 230 further generates store analysis information based on the classification information and the held product information indicating the product held by the customer (step S116).

  Store analysis information obtained in this manner is, for example, information obtained by statistically processing the result of the process shown in FIG. 11 executed over a predetermined period, and a customer belonging to each category determined as described above Indicates the purchased item. The store analysis information is output by the data output unit 250 as data usable by the store operator. For example, it may be presented to the store operator as a list of products purchased by customers belonging to each category.

  Using such information, the store operator can create a sales floor organized near, for example, a product that is frequently purchased by a group customer, and can also display the guidance of this sales floor. In addition, the area around the sales floor can be designed to have a wide aisle. As a result, the convenience for group customers is increased, and the quality of shopping experience is improved. For the store, the group customer visit frequency can be improved, and the group customer rate is high, which can lead to an increase in sales.

(Other embodiments)
In the above embodiment, each component may be configured by dedicated hardware or implemented by executing a software program suitable for each component. Each component may be realized by a program execution unit such as a CPU (central processing unit) or processor reading and executing a software program recorded in a recording medium such as a hard disk or a semiconductor memory. Here, the software program for realizing the shop analysis server of the above embodiment is the following program.

  That is, this program causes the computer to acquire the product specification information from a reader which sequentially reads the product specification information indicating the product from the product held by the customer who is shopping in a real store by the sensor, and based on the product specification information. To obtain time-series shopping data, which is data relating to holding of time-series goods by the customer, and to obtain classification information indicating classification of the customer or the product based on the time-series shopping data; The store analysis information is generated based on the held product information indicating the product held or returned by the customer, and the store analysis information is output as data usable by the above-mentioned shop owner.

  As mentioned above, although the information presentation system which concerns on one or several aspect was demonstrated based on embodiment, this invention is not limited to this embodiment. The present invention may be included within the scope of the present invention as long as various modifications that can occur to those skilled in the art are applied to the present embodiment without departing from the spirit of the present invention.

  INDUSTRIAL APPLICABILITY The present invention is applicable to a system that provides the operator of this store with information based on the behavior of a customer shopping in the store holding a product as a purchase candidate.

DESCRIPTION OF SYMBOLS 10 Information presentation system 100 Reader 110, 111, 112, 113 Sensor 120 Transmission part 200 Store analysis server 210 Reception part 220 Data acquisition part 230 Information generation part 240 Store master 250 Data output part

Claims (21)

A data acquisition unit for acquiring time-series shopping data, which is data relating to retention of time-series products by customers in shopping at a real store;
The classification information indicating the classification for the customer or the product is acquired based on the time-series shopping data, and the acquired classification information and the held commodity indicating the product held or returned by the customer in the time-series shopping data An information generation unit that generates shop analysis information based on the information;
An information presentation system, comprising: a data output unit that outputs the shop analysis information as data that can be used by an operator of the real shop. The information presentation system according to claim 1, wherein the shop analysis information includes the classification information indicating the classification of the customer regarding the degree of rush of the customer. 3. The information generation unit according to claim 1, wherein the information generation unit acquires, from the time-series shopping data, a shopping time which is a time taken by the customer to shop at the real store, and acquires the classification information based on the shopping time. Information presentation system. The information generation unit
The information presentation system according to claim 3, wherein the classification information is acquired by determining the classification for the customer based on a magnitude relationship between the shopping time and a first threshold. The information generation unit acquires, from the time-series shopping data, a holding start interval at which holding of a plurality of items of goods by the customer is started, and acquires the classification information based on the holding start interval. The information presentation system according to any one of the items. The information generation unit
The information presentation system according to claim 5, wherein the classification information is acquired by determining the classification for the customer based on the magnitude relationship between the holding start interval and the second threshold. The store analysis information includes the classification regarding the product for which the customer has been confused as to whether or not the product is held, or the classification information indicating the classification for the product for which the customer took a long time to specify the location of the product. The information presentation system according to claim 1. The information generation unit
An elapsed time before holding which is a time from the predetermined behavior by the customer to the start of holding of the product is acquired from the time-series shopping data, and sales of the held product is acquired, and the elapsed time before holding The information presentation system according to claim 1, wherein the classification information indicating the classification of the product is acquired based on the sales of the product and the product. The information generation unit
The classification information is acquired by determining the classification of the product based on the size relationship with the fourth threshold when the elapsed time before holding exceeds the third threshold. Information presentation system. The information generation unit
The information according to claim 9, wherein, when the sales exceeds the fourth threshold, the classification information is acquired by determining the classification of the product as a product that is lost depending on whether the customer holds or not. Presentation system. The information generation unit
If the sale is equal to or less than the fourth threshold, the classification information is acquired by determining the classification of the product as a product that takes a long time to specify a place.
The information presentation system according to claim 9 or 10. The information presentation system according to claim 1, wherein the shop analysis information includes the classification information indicating the classification of a product for which the customer is at a loss after purchasing or returning after the start of holding. The information generation unit
The occurrence of the return of the product by the customer and the held product information indicating the product are acquired from the time-series shopping data, and the classification information indicating the classification of the product is acquired based on the stored product information. Or the information presentation system as described in 12. The information generation unit
Calculating a return rate for the product based on the stored product information;
14. The information presentation system according to claim 13, wherein the classification information is acquired by determining the classification of the product regarding the degree of customer's ease of buying or not based on the return rate. The information presentation system according to claim 1, wherein the shop analysis information includes the classification information indicating the classification of the product regarding the degree of mutual competition of two or more products. The information generation unit
The time interval between the occurrence of retention of the first product by the customer and the occurrence of return of the second product different from the product from the time-series shopping data, and the stored product information indicating the first product and the second product, respectively. The information presentation system according to claim 1, wherein the classification information is acquired based on the held commodity information. The information generation unit
The classification information is determined by determining the classification regarding the degree of mutual competition between the first product and the second product based on the magnitude relationship between the time interval and the sixth threshold and the stored product information. The information presentation system according to claim 16. The information generation unit
The division of the first product and the division of the second product are acquired using the product specification information indicating the first product and the second product and the information indicating the division of the product handled in the real store,
Degree of mutual competition for the first product and the second product based on the magnitude relationship between the time interval and the sixth threshold, and the similarity between the category of the first product and the category of the second product The information presentation system according to claim 17, wherein the classification is determined with respect to The information presentation system according to claim 1, wherein the shop analysis information includes the classification information indicating the classification for the customer regarding whether the customer is a group customer. The information generation unit
From the time-series shopping data, the time interval between the occurrence of the retention of the first item by the customer and the occurrence of the retention of the second item different from the item, and the retained item indicating the first item and the second item Get information
The required product information indicating the first product and the second product, and the movement required time between the sales area of the first product and the sales area of the second product in the actual store are calculated,
The information presentation system according to claim 1 or 19, wherein when the time interval is equal to or less than the movement required time, the classification information is acquired by determining the classification as a group customer for the customer. Acquire time-series shopping data, which is data related to the retention of time-series products by customers in shopping at a real store,
Acquiring classification information indicating a classification of the customer or the product based on the time-series shopping data;
Store analysis information based on the acquired classification information and stored product information indicating products stored or returned by the customer;
The information presentation method which outputs the said shop analysis information as data which the operator of the said real shop can use.

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