JP7395834B2 - Assortment recommendation device, assortment recommendation method, and program - Google Patents

Description

The present disclosure relates to an assortment recommendation device, an assortment recommendation method, and a program.

In order to improve sales at retail stores (convenience stores, supermarkets, etc.), it is important to stock products that are needed by customers using the store.

A technique for recommending product assortments in stores is disclosed in, for example, Patent Document 1. The assortment recommendation device described in Patent Document 1 calculates sales amount composition information for products that have a sales record at a store and predicts sales amount composition information for products that have no sales record, and calculates sales amount from these products. Decide which products to recommend based on the price composition ratio.

International Publication No. 2018/056220

The technique described in Patent Document 1 does not describe that products needed by customers using the store are taken into consideration when determining products to be recommended for assortment. For this reason, even if a customer using a store goes shopping at the store, the products necessary for the customer may not be available and the customer may not be able to purchase the product.

One of the purposes of the present disclosure is to provide an assortment recommendation device, an assortment recommendation method, and a program that can solve the above-mentioned problems and recommend the assortment of products needed by customers using a store. It is to be.

An assortment recommendation device according to an aspect of the present disclosure includes an acquisition means for acquiring an identifier of a customer who visited a target store, an identifier of a customer who purchased a product at another store, and an identifier of the product; A determining means is provided for determining recommended products to be ordered at the target store based on a purchase history of products purchased at the other store by a customer visiting the store.

An assortment recommendation method in one aspect of the present disclosure acquires an identifier of a customer who visited a target store, an identifier of a customer who purchased a product at another store, and an identifier of the product,
Recommended products to order at the target store are determined based on the purchase history of products purchased at the other store by a customer visiting the target store.

A program according to an aspect of the present disclosure acquires, in a computer, an identifier of a customer who visited a target store, an identifier of a customer who purchased a product at another store, and an identifier of the product, and acquires an identifier of a customer who visited the target store. A process is executed to determine recommended products to be ordered at the target store based on the purchase history of products purchased at the other store at the other store.

The effect of the present disclosure is that it is possible to recommend an assortment of products needed by customers using the store.

FIG. 1 is a block diagram showing the configuration of an assortment recommendation system 10 in the first embodiment. It is a figure showing an example of purchase data in a 1st embodiment. It is a figure showing an example of store visit data in a 1st embodiment. It is a diagram showing an example of store visit history in the first embodiment. It is a figure showing an example of product information in a 1st embodiment. It is a figure showing an example of ordering information in a 1st embodiment. It is a figure showing an example of purchase history in a 1st embodiment. It is a flowchart which shows the store visit process in 1st Embodiment. It is a flowchart showing purchase processing in the first embodiment. It is a flowchart which shows product decision processing in a 1st embodiment. It is a figure showing an example of product decision processing in a 1st embodiment. FIG. 3 is a diagram showing an example of an order recommendation screen in the first embodiment. It is a figure showing an example of an arrival notification screen in a 1st embodiment. It is a block diagram showing the composition of store server 120 in a 2nd embodiment. 5 is a block diagram showing an example of the hardware configuration of a computer 500. FIG.

Embodiments will be described in detail with reference to the drawings. In addition, in each drawing and each embodiment described in the specification, similar components are given the same reference numerals, and explanations are omitted as appropriate.

(First embodiment)
A first embodiment will be described.

First, the configuration of the assortment recommendation system 10 in the first embodiment will be described. FIG. 1 is a block diagram showing the configuration of an assortment recommendation system 10 in the first embodiment. Referring to FIG. 1, the product assortment recommendation system 10 includes store systems 100A, B, ... (hereinafter collectively referred to as the store system 100), a headquarters system 200, and customer terminals 300a, b, ... (hereinafter collectively referred to as store system 100). (also referred to as customer terminal 300). The store system 100 is installed, for example, in each of stores 1A, B, etc. (hereinafter collectively referred to as stores 1) such as convenience stores and supermarket chains in the retail industry. The headquarters system 200 is installed, for example, at the headquarters 2 of the chain. The store system 100, the headquarters system 200, and the customer terminal 300 are connected by a communication network 400, for example.

The store 1 may include a regular store, and a labor-saving store or an unmanned store. In order to improve business efficiency and expand into small-scale commercial areas, manpower-saving stores and unmanned stores use computer systems to register purchased items, pay for purchases, provide customer service support, in-store monitoring, inventory management, and equipment management. This is a small-sized store that reduces the work of store staff regarding such matters, and reduces the number of store staff on-site at all times compared to a regular store, or eliminates it altogether.

Referring to FIG. 1, a store system 100 includes a POS (Point of Sale) device 110, a store server 120, and a store terminal 180. The POS device 110, the store server 120, and the store terminal 180 are connected, for example, by an in-store network.

A card reader/writer 140, a barcode reader 150, and a camera 160 may be connected to the POS device 110 and the store server 120 through an in-store network or the like. The card reader/writer 140, the barcode reader 150, and the camera 160 are installed at arbitrary positions within the store 1, such as the entrance and exit of the store 1, for example.

Further, a card reader/writer 141, a barcode reader 151, a camera 161, and a tag reader/writer 171 may be connected to the POS device 110. The card reader/writer 141, barcode reader 151, camera 161, and tag reader/writer 171 are installed near the POS device 110, for example.

The card reader/writers 140 and 141 are devices that read and write information to and from magnetic cards and non-contact IC (Integrated Circuit) cards. Barcode readers 150 and 151 are devices that read barcodes. The cameras 160 and 161 are imaging devices that capture images of the inside of the store 1 and of customers and products inside the store 1. The tag reader/writer 171 is a device that reads and writes information to and from an IC tag.

The POS device 110 includes a purchaser identification section 111, a registration section 112, a payment section 113, and a purchase data generation section 114.

The purchasing customer identification unit 111 identifies the customer ID (Identifier) of the customer who purchases the product. The customer ID is an identifier for identifying the customer. In the first embodiment, a member ID of a convenience store or supermarket chain is used as the customer ID. The purchasing customer identifying unit 111 identifies the customer ID (member ID) using the customer's membership card, for example. The purchasing customer identification unit 111 uses the card reader/writer 141 to read out the customer ID from the membership card in the form of a magnetic card or non-contact IC card presented by the customer.

Further, the purchasing customer identification unit 111 may identify the customer ID using the unique ID of the credit card or electronic money card. In this case, the purchasing customer identification unit 111 uses the card reader/writer 141 to read the unique ID from the magnetic or contactless IC card type credit card or electronic money card presented by the customer.

Further, the purchasing customer identification unit 111 may identify the customer ID using a member application running on the customer's terminal. In this case, the purchasing customer identification unit 111 uses the barcode reader 151 to read a barcode indicating the customer ID displayed on the member application running on the customer's terminal. Similarly, the purchaser identification unit 111 may use the camera 161 to read a two-dimensional barcode indicating the customer ID displayed on the member application. Further, the purchasing customer identification unit 111 may identify the customer ID through facial image authentication. In this case, the purchasing customer identification unit 111 uses, for example, the camera 161 to acquire a facial image of the customer and performs facial image authentication. Similarly, the purchaser identifying unit 111 may identify the customer ID using biometric authentication means other than facial image authentication, such as iris authentication, fingerprint authentication, vein authentication, etc.

The registration unit 112 registers products purchased by customers. The registration unit 112 identifies the product ID using, for example, a barcode attached to the product. In this case, the registration unit 112 uses the barcode reader 151 to read a barcode indicating a product ID assigned to the product purchased by the customer. Similarly, the registration unit 112 may use the camera 161 to read a two-dimensional barcode indicating the product ID.

Furthermore, the registration unit 112 may identify the product ID using an IC tag. In this case, the registration unit 112 uses the tag reader/writer 171 to read information indicating the product ID from the tag attached to the product. Further, the registration unit 112 may identify the product ID by image recognition. In this case, the registration unit 112 uses the camera 161 to acquire a product image and performs image recognition of the product.

The payment unit 113 performs payment for products purchased by customers. The settlement unit 113 performs settlement using, for example, a credit card or an electronic money card. In this case, the payment unit 113 uses the card reader/writer 141 to read the information necessary for payment from the credit card or electronic money card in magnetic format or contactless IC card format presented by the customer, and uses the card reader/writer 141 to read the information necessary for payment from the credit card or electronic money card in magnetic format or contactless IC card format presented by the customer. Perform payment (settlement).

Further, the settlement unit 113 may perform settlement using cash. In this case, the payment unit 113 performs payment by, for example, receiving and receiving cash by a clerk or using an automatic change machine (not shown) connected to the POS device 110.

Further, the payment section 113 may perform the payment using a payment application running on the customer's terminal. In this case, the payment unit 113 uses the barcode reader 151 to read a barcode indicating information necessary for payment, which is displayed on a payment application running on the customer's terminal, and performs payment. Similarly, the payment unit 113 may use the camera 161 to read a two-dimensional barcode that is displayed on the payment application and indicates information necessary for payment.

Further, the payment unit 113 may perform payment using facial image authentication. In this case, the payment unit 113 uses, for example, the camera 161 to acquire a facial image of the customer, identifies the customer through facial image authentication, and uses the credit card, electronic money, bank account, etc. registered in association with the customer ID. Payment will be made by. Similarly, the payment unit 113 may perform payment using biometric authentication means other than facial image authentication, such as iris authentication, fingerprint authentication, vein authentication, etc.

Note that product registration and payment may be performed, for example, by an operation by a clerk at the store 1 or by a customer. Alternatively, a configuration may be adopted in which product registration is performed by an operation by a clerk at the store 1, and payment is performed by a customer's operation.

Purchase data generation section 114 generates purchase data and transmits it to store server 120. FIG. 2 is a diagram showing an example of purchase data in the first embodiment. In the purchase data, as shown in FIG. 2, purchase date and time, purchaser ID, purchase product ID, and number of purchases are set in association with each other. The purchase date and time indicates the date and time when the product was purchased. The purchaser ID indicates the customer ID of the customer who purchased the product. The purchased product ID and the number of purchases indicate the product ID of the purchased product and the number of purchased products, respectively. Note that instead of the number of purchases, a purchase amount indicating the amount purchased may be used.

The store server 120 includes a customer identification section 121, a store visit history storage section 122, a product information storage section 123, an update section 124, a transmission section 125, an acquisition section 126, a product determination section 127, a display control section 128, an order reception section 129, It includes a product ordering section 130, an ordering information storage section 131, and a notification section 132. The store server 120, the acquisition unit 126, and the product determination unit 127 are embodiments of an assortment recommendation device, an acquisition unit, and a determination unit in the present disclosure, respectively. Further, the display control unit 128, the product ordering unit 130, and the notification unit 132 are embodiments of display control means, product ordering means, and notification means in the present disclosure, respectively.

The customer identification unit 121 identifies the customer ID of the customer who visited the own store 1. For example, when a customer enters the store 1, the customer identification unit 121 uses the above-described card reader/writer 140, barcode reader 150, and camera 160 to identify the customer who has visited the store in the same manner as the purchasing customer identification unit 111. Identify the customer's customer ID. It should be noted that the customer identification unit 121 may identify the customer ID at any timing during the customer's visit, such as when the customer leaves the store 1, as long as it can identify the customer ID of the customer who visited the store.

Further, the customer identification unit 121 may use the customer ID of the customer who purchased the product, identified by the purchasing customer identification unit 111, as the customer ID of the customer.

The customer identification unit 121 further generates store visit data and stores it in the store visit history storage unit 122. FIG. 3 is a diagram showing an example of store visit data in the first embodiment. In the store visit data, as shown in FIG. 3, the store visit date and time and store visitor ID are set in association with each other. The visit date and time indicates the date and time when the customer visited the store. The visitor ID indicates the customer ID of the visitor.

The store visit history storage unit 122 stores store visit history. The store visit history represents the customer's store visit history. FIG. 4 is a diagram showing an example of store visit history in the first embodiment. In the store visit history, for example, as shown in FIG. 4, store visit data is set in order of visit date and time.

The product information storage unit 123 stores product information. The product information is information regarding products handled (to be sold) in the own store 1. FIG. 5 is a diagram showing an example of product information in the first embodiment. In the product information, for example, as shown in FIG. 5, the product ID, product unit price, inventory quantity, sales volume, and sales amount are set in association with each other. The handled product ID indicates the product ID of the product handled at the store 1. The unit price of the product and the number of items in stock indicate the unit price and number of items in stock, respectively, of the item indicated by the product ID. The number of products sold and the amount of sales are the total number of products sold and the total amount of sales, respectively, during a predetermined period (for example, the most recent year, month, or week). . Note that the sales amount may be used instead of the sales number.

The update unit 124 updates product information based on the purchase data received from the POS device 110.

The transmitter 125 transmits the purchase data received from the POS device 110 to the headquarters system 200.

The acquisition unit 126 acquires the store visit history of its own store 1 from the store visit history storage unit 122 . Further, the acquisition unit 126 acquires purchase data of each other store 1 other than the own store 1 from the store system 100.

The product determining unit 127 determines products recommended to be ordered at the own store 1 (hereinafter also referred to as recommended order products). The product determining unit 127 determines products recommended for ordering at the own store 1 based on the purchase history of each product purchased at another store 1 by a customer visiting the own store 1 (hereinafter also referred to as a target store). Here, the product determination unit 127 determines the recommended ordering products, for example, from among the products that are not handled at the own store 1 and which the customer who visited the own store 1 purchased at another store 1 . Furthermore, the product determining unit 127 determines the recommended number of ordered products for recommended order products based on the above-mentioned purchase history. Note that the recommended order quantity may be used instead of the recommended order quantity.

The display control unit 128 transmits the recommended products to be ordered at the own store 1, the recommended number of orders to be ordered, and the recommended amount to the store terminal 180, and displays the recommended products to the store clerk.

The order receiving unit 129 receives a request from a store clerk to order a recommended product from the store terminal 180.

The product ordering unit 130 performs ordering processing for recommended products. The ordering process is, for example, a process of transmitting product order information to the headquarters system 200 and requesting delivery of the product to the own store 1.

The ordering information storage unit 131 stores ordering information. The ordering information is information regarding ordering recommended products. FIG. 6 is a diagram showing an example of ordering information in the first embodiment. For example, as shown in FIG. 6, the ordering information includes a recommended ordering product ID, an ordering date and time, the number of orders, and an arrival notification customer ID. The recommended order product ID indicates the product ID of the ordered recommended product. The order date and time and the number of orders indicate the date and time when the product indicated by the recommended order product ID was ordered, and the number of orders, respectively. The order quantity may be used instead of the order quantity. The arrival notification customer ID indicates the customer ID of the customer who sends the arrival notification when the product is received at the own store 1.

The notification unit 132 transmits a notification of the arrival of the recommended order product to the customer terminal 300 of the customer when the recommended order product is delivered and arrived at the own store 1.

The store terminal 180 is a terminal used by a clerk at the store 1. The store terminal 180 displays the recommended order products received from the store server 120, as well as the recommended order quantity and quantity. Further, the store terminal 180 transmits an order request for recommended order products to the store server 120.

The headquarters system 200 includes a headquarters server 210.

The headquarters server 210 includes a purchase history storage section 211 , a purchase history update section 212 , a purchase history transmission section 213 , and a delivery instruction section 214 .

The purchase history storage unit 211 stores the purchase history of each store 1. The purchase history represents the purchase history of products by customers who visited the store.

FIG. 7 is a diagram showing an example of purchase history in the first embodiment. For example, as shown in FIG. 7, the purchase history storage unit 211 stores the purchase history for each store. In each purchase history, purchase data received from the store system 100 of each store 1 is set in order of purchase date and time.

The purchase history update unit 212 updates the purchase history stored in the purchase history storage unit 211 with the purchase data received from the store system 100.

The purchase history transmitting unit 213 extracts the purchase history of the customer requested by the store system 100 from the purchase history stored in the purchase history storage unit 211 and transmits it to the store server 120.

The delivery instruction unit 214 instructs a delivery center (not shown) or the like to deliver the products ordered from the store system 100 of each store 1 to the store 1 from which the order was placed.

Note that some of the components of the store server 120 may be included in the headquarters server 210. For example, at least one of the acquisition unit 126 and the product determination unit 127 may be included in the headquarters server 210.

The customer terminal 300 is, for example, a terminal device owned by the customer, such as a mobile phone or a smartphone. The customer terminal 300 displays the notification of the arrival of the recommended order product received from the store system 100 of the store 1.

Next, the operation of the first embodiment will be explained.

First, store visit processing will be explained.

FIG. 8 is a flowchart showing the store visit process in the first embodiment. The store visit process is executed in each store 1 every time a customer visits the store.

In the store system 100 of each store 1, the customer identification unit 121 of the store server 120 identifies the customer ID of the customer who visited the store (step S101).

The customer identification unit 121 generates visit data and stores it in the visit history storage unit 122 (step S102).

For example, the customer identification unit 121 uses the card reader/writer 140 installed at the entrance/exit of the store 1 to read the customer ID from the membership card of the customer entering the store 1 . The store visitor identification unit 121 then generates store visit data as shown in FIG.

In the store 1, the processes of steps S101 and S102 are repeated every time a customer visits the store.

Thereby, for example, store visit data is stored in the store visit history storage unit 122 of the store server 120 of the store 1A as shown in FIG.

Next, the purchase process will be explained.

FIG. 9 is a flowchart showing purchase processing in the first embodiment. The purchase process is executed in each store 1 each time a customer purchases a product.

In the store system 100 of each store 1, the purchaser identification unit 111 of the POS device 110 identifies the customer ID of the customer who purchases the product (step S201).

For example, the purchasing customer identification unit 111 uses a card reader/writer 141 installed near the POS device 110 to read the customer ID from the customer's membership card.

The registration unit 112 registers the products purchased by the customer (step S202).

For example, the registration unit 112 uses a barcode reader 151 installed near the POS device 110 to read the product ID of the product purchased by the customer.

The payment unit 113 performs payment for the products purchased by the customer (step S203).

For example, the payment unit 113 uses the card reader/writer 141 to perform payment using an electronic money card.

Purchase data generation unit 114 generates purchase data and transmits it to store server 120 (step S204).

For example, the purchase data generation unit 114 generates purchase data as shown in FIG.

The update unit 124 of the store server 120 updates the product information in the product information storage unit 123 based on the received purchase data (step S205). Here, the updating unit 124 subtracts the number of purchases included in the purchase data from the number of items in stock associated with the handled product ID that matches the purchased product ID included in the purchase data in the product information. Furthermore, the updating unit 124 adds the number of purchases included in the purchase data to the number of sales associated with the product ID. Further, the updating unit 124 adds a value obtained by multiplying the sales amount associated with the product ID by the unit price associated with the product ID and the number of purchases included in the purchase data.

The transmitter 125 transmits the received purchase data to the headquarters system 200 (step S206).

The purchase history update unit 212 of the headquarters server 210 updates the purchase history in the purchase history storage unit 211 with the purchase data received from the store system 100 (step S207).

In store 1, each time a customer purchases a product, the processes from steps S201 to S207 are repeated.

As a result, for example, the product information of the own store 1A is stored in the product information storage unit 123 of the store server 120 of the store 1A, as shown in FIG. Furthermore, the purchase history storage unit 211 of the headquarters server 210 stores the purchase history of each store 1A to 1C, as shown in FIG.

Next, the product determination process will be explained.

FIG. 10 is a flowchart showing product determination processing in the first embodiment. The product determination process is executed, for example, when a store clerk performs an operation on the store terminal 180 of each store 1 to display a product ordering screen.

The store terminal 180 requests the store server 120 for recommended order products (step S301).

The acquisition unit 126 of the store server 120 acquires product information of its own store 1 from the product information storage unit 123 (step S302).

For example, the acquisition unit 126 of the store 1A acquires product information as shown in FIG.

The acquisition unit 126 acquires the visit history of its own store 1 from the visit history storage unit 122 (step S303).

For example, the acquisition unit 126 of the store 1A acquires store visit history as shown in FIG.

The acquisition unit 126 selects one customer ID of a customer who has visited the own store 1 from the store visit history (step S304). Here, the acquisition unit 126 selects, for example, customers who have visited the store within a predetermined period (for example, the most recent one year, one month, or one week). Below, the selected customers will also be referred to as target customers.

For example, the acquisition unit 126 of the store 1A selects target customer M001 from the store visit history shown in FIG.

The acquisition unit 126 requests the headquarters server 210 of the headquarters system 200 for the target customer's purchase history at each other store 1 other than the own store 1 (step S305).

The purchase history transmission unit 213 of the headquarters server 210 extracts the purchase history of each other store 1 for the target customer from the purchase history stored in the purchase history storage unit 211, and transmits it to the store server 120 (step S306).

For example, the acquisition unit 126 of the store 1A acquires the purchase history of the stores 1B and 1C in FIG. 7 for the target customer M001.

The product determination unit 127 refers to the purchase history of each store 1 and extracts products that the target customer has purchased at other stores 1 (step S307). Below, the extracted products are also referred to as target products.

FIG. 11 is a diagram illustrating an example of product determination processing in the first embodiment. For example, based on the purchase history of target customer M001 at stores 1B and 1C in FIG. 7, the product determination unit 127 of store 1A determines target products X001, X002, X101, X102, And extract X103.

The product determination unit 127 refers to the product information of the own store 1 and determines whether the target product is a product handled (sold) in the own store 1 (step S308). Here, the product determining unit 127 determines that a product whose product ID matches the handled product ID included in the product information is a product handled at its own store 1.

If the target product is a product handled at the own store 1 (step S308/YES), the process advances to step S312.

If the target product is a product that is not handled in the own store 1 (step S308/NO), the product determining unit 127 calculates the order recommendation level indicating the degree of recommendation for ordering the target product (step S309).

Here, the product determination unit 127, for example, refers to the purchase history of each other store 1 and determines the frequency of purchase of the target product by the target customer in a predetermined period (for example, the most recent one year, one month, or one week). Calculate the number of purchases and purchase amount. The purchase frequency is, for example, the average value of the number of purchases in each unit period (for example, each month, each week, or each day) within a predetermined period. For example, the product determining unit 127 calculates the order recommendation level such that the order recommendation level increases as the purchase frequency, number of purchases, and purchase amount in a predetermined period increase. In this case, the product determining unit 127 may determine the order recommendation level based on a recommendation level determination table in which the order recommendation level is defined in advance for the values of purchase frequency, number of purchases, and purchase amount.

For example, among the target products in FIG. 11, products X101, X102, and X103 are products that are not included in the product information in FIG. 4 and are not handled in the own store 1. As shown in FIG. 11, the product determining unit 127 calculates the purchase frequency of products X101, X102, and X103, and determines the order recommendation level.

Note that the product determining unit 127 may calculate the order recommendation degree by comparing the purchase frequency, number of purchases, and purchase amount of the target product in each unit period within a predetermined period. In this case, the product determining unit 127 calculates the order recommendation degree so that the order recommendation degree becomes high, for example, when the purchase frequency, number of purchases, and purchase amount in a unit period increase.

The product determining unit 127 determines recommended products to order based on the order recommendation level (step S310). Here, the product determining unit 127 determines the target product as the recommended product to order, for example, when the order recommendation degree is equal to or higher than a predetermined threshold.

For example, when the predetermined threshold value is the order recommendation level "1", the product determination unit 127 extracts products X101 and X102 whose order recommendation level is "1" or higher, as shown in FIG.

Note that, irrespective of the order recommendation level, if the target product is not handled in the own store 1, the product determination unit 127 may determine the target product as the order recommended product.

Furthermore, the product determining unit 127 determines the recommended number of ordered products and the recommended amount of the recommended products (step S311). Here, too, the product determining unit 127 refers to the purchase history of each other store 1 and determines the recommended number of orders and the recommended amount using the purchase frequency, number of purchases, and purchase amount of the target product in a predetermined period by the target customer. calculate. The product determining unit 127 calculates the recommended number of items to be ordered and the recommended amount, such that, for example, the larger the purchase frequency, number of purchases, and amount of purchases in a predetermined period, the larger the recommended number of items to order and the recommended amount. In this case, the product determining unit 127 determines the recommended number to order and the recommended amount based on the recommended number and recommended amount determination table that defines the recommended number to order and recommended amount for the values of purchase frequency, number of purchases, and amount purchased. may be determined.

For example, the product determining unit 127 determines the recommended order quantities of products X101 and X102, as shown in FIG.

The product determining unit 127 compares the purchase frequency, number of purchases, and purchase amount of the target product in each unit period within the predetermined period, and if the purchase frequency, number of purchases, and purchase amount for the unit period are increasing, the product determination unit 127 places an order. The recommended number of orders and the recommended amount may be calculated so that the recommended number and recommended amount become larger.

Further, the product determining unit 127 may use different data obtained from the purchase history for calculating the order recommendation degree and for calculating the recommended order number and recommended amount. For example, the product determining unit 127 may calculate the order recommendation level using the purchase frequency of the target product in a predetermined period, and may calculate the recommended order number and recommended amount using the number of purchases and the purchase amount.

Furthermore, the product determining unit 127 may determine a predetermined number or amount set in advance for each product as the recommended number or amount to order of the recommended product, regardless of the purchase history.

The store server 120 repeats the process from step S304 for each customer included in the store visit history (step S312).

The product determining unit 127 transmits the product ID, order recommendation degree, recommended order quantity, and recommended quantity of the order recommended product to the store terminal 180 of its own store 1 (step S313).

For example, the product determining unit 127 transmits the order recommendation level and recommended order quantity of products X101 and X102.

The store terminal 180 displays the product ID, order recommendation level, recommended order quantity, and recommended quantity of the order recommended product received from the store server 120 (step S314).

FIG. 12 is a diagram showing an example of an order recommendation screen in the first embodiment. In the example of FIG. 12, recommended order products X101 and X102 are set with their respective order recommendation degrees and recommended order quantities. There is also an input field for the number of orders for each recommended product. For example, the store terminal 180 of the store 1A displays the order recommendation screen of FIG. 12 to the store clerk.

The store terminal 180 receives an input for ordering recommended products from the store clerk (step S315). The clerk refers to the order recommendation degree, the recommended order quantity, and the recommended quantity on the order recommendation screen, and determines and inputs the order quantity and quantity of the recommended order product. The store terminal 180 accepts input of the product ID of the recommended order product, the number of orders, and the order amount.

The store terminal 180 transmits an order request including the product ID of the recommended order product, the number of orders, and the amount to be ordered to the store server 120 (step S316).

For example, the store terminal 180 transmits an order request for products X101 and X102.

The order receiving unit 129 of the store server 120 receives an order request from a store clerk from the store terminal 180 (step S317).

The product ordering unit 130 performs ordering processing for the recommended ordering product specified in the ordering request (step S318). Here, the product ordering unit 130 transmits to the headquarters system 200 the product ID of the recommended order product specified in the order request, and order processing data including the number of orders and the amount of orders.

For example, the product ordering unit 130 of the store server 120 of the store 1 transmits order processing data for products X101 and X102.

Note that the product ordering unit 130 automatically orders recommended products determined by the product determining unit 127 by using the recommended order quantity and recommended quantity as the order quantity and order quantity, regardless of the order request from the store terminal 180. You may also process the order. In this case, the product determination process (determination of recommended products to be ordered by the product determination unit 127 and order placement by the product ordering unit 130) may be performed periodically (for example, every week or every day).

The product ordering unit 130 updates the ordering information in the ordering information storage unit 131 (step S319). Here, the product ordering unit 130 sets the recommended order product ID of the recommended order product, the order date and time, the number of orders, and the arrival notification customer ID in the order information. The product ordering unit 130 sets, as the arrival notification customer ID, the customer ID of the target customer (the customer who referred to the purchase history) when the recommended order product indicated by the recommended order product ID was determined, for example. The product ordering unit 130 may also set the customer ID of another customer who has purchased the recommended order product at each store 1, in addition to the target customer, as the arrival notification previous customer ID.

For example, the product ordering unit 130 updates the ordering information for products X101 and X102, as shown in FIG.

The delivery instruction unit 214 of the headquarters server 210 in the headquarters system 200 instructs the distribution center or the like to deliver the recommended order product to the store 1 from which the order was placed based on the order processing data received from the store system 100 (step S320). As a result, the recommended order product is delivered to the store 1 from which the order was placed.

For example, the delivery instruction unit 214 instructs delivery of products X101 and X102 to the store 1A.

The notification unit 132 of the store server 120 in the store system 100 sends an arrival notification of the recommended order product to the customer's customer terminal 300 when the recommended order product is delivered and arrived at the own store 1 (step S321). . Here, the notification unit 132 transmits an arrival notification to the customer terminal 300 of the customer whose arrival notification customer ID is associated with the recommended order product ID of the recommended order product in the order information. The notification unit 132 acquires the terminal ID of the customer terminal 300 stored in advance in association with the customer ID of each customer from the customer information storage unit (not shown) of the store server 120 or the head office server 210, and stores the terminal ID. An in-stock notification is sent to the customer terminal 300.

For example, when the product X101 arrives at the store 1A, the store server 120 of the store 1A refers to the ordering information in FIG. 6 and transmits a notice of arrival of the product X101 to the customer terminal 300a of the customer M001 who receives the notice of arrival.

The customer terminal 300 displays the arrival notification received from the store server 120 to the customer (step S322).

FIG. 13 is a diagram showing an example of an arrival notification screen in the first embodiment. The example in FIG. 13 shows that product X101 has been delivered to store 1A. For example, the customer terminal 300a displays the arrival notification screen of FIG. 13 to the customer.

With the above steps, the operation of the first embodiment is completed.

Note that in the first embodiment, a member ID is used as a customer ID for identifying a purchasing customer or a customer visiting the store. However, the present invention is not limited to this, and as long as the customer can be identified, the customer's credit card ID, electronic money ID, or payment application ID using a barcode or two-dimensional barcode may be used as the customer ID.

Next, the effects of the first embodiment will be explained.

According to the first embodiment, it is possible to recommend an assortment of products needed by customers using the store. The reason is that the product determination unit 127 of the store server 120 determines recommended products to order at the own store 1 based on the purchase history of products purchased at other stores 1 by customers who visited the own store 1. It is. As a result, when a customer using store 1 goes shopping at store 1, products necessary for the customer are available at store 1 and can be purchased, increasing sales of store 1. can be expected.

(Modified example of the first embodiment)
Several modifications are possible to the assortment recommendation system 10 of the first embodiment. Each modification will be explained below.

(First modification)
In the product assortment recommendation system 10 of the first embodiment, the product determination unit 127 recommends ordering from among the products that are not handled in the own store 1, among the products that the customer who visited the own store 1 purchased at other stores 1. The product has been decided. However, the product determination unit 127 is not limited to this, and the product determination unit 127 also recommends ordering products from among the products (target products) that the customer visiting the own store 1 has purchased at other stores 1 and which are handled at the own store 1. may be determined.

In this case, as in steps S309 to S311 described above, the product determining unit 127 determines the purchase frequency, number of purchases, and purchase amount of the target product by the target customer in a predetermined period, or the number of purchases of the target product in each unit period within the predetermined period. Based on the comparison results of purchase frequency, purchase number, and purchase amount, recommended products to be ordered, and recommended number and amount to be ordered may be determined.

Furthermore, in addition to these, the product determining unit 127 determines the recommended products to order, the recommended number of orders, and the recommended amount based on at least one of the sales of the target product at its own store 1 and the number of items in stock. Good too. In this case, if the target product is a product handled in own store 1 (step S308/YES), product determination unit 127 determines the target product from the product information of own store 1 stored in product information storage unit 123. Obtain sales figures and inventory numbers. Then, in steps S309 to S311 described above, the product determining unit 127 determines, for example, that the smaller the inventory of the target product or the higher the sales of the target product, the higher the order recommendation level, or the higher the recommended order number. The degree of recommendation for ordering, the number of recommended orders, and the recommended amount are calculated so that the number of orders and the recommended amount become larger. In addition, the product determining unit 127 recommends ordering so that the smaller the stock quantity of the target product and the higher the sales of the target product, the higher the degree of order recommendation, or the larger the recommended number or amount of order. Alternatively, the recommended number of orders or the recommended amount may be calculated.

Thereafter, similarly to steps S313 to S322, recommended products to be ordered are displayed on the store terminal 180, orders are placed on the head office system 200, and arrival notifications are displayed on the customer terminal 300.

This can reduce out-of-stock items needed by customers using the store.

(Second modification)
In the product assortment recommendation system 10 of the first embodiment, the product determination unit 127 determines the recommended products to order, the recommended number of orders, and the recommended amount for customers visiting the own store 1. However, the present invention is not limited to this, and if multiple customers who visited the own store 1 purchased the same product (target product) at another store 1, the product determination unit 127 determines whether the multiple customers purchased the product from other stores Based on the purchase history in 1, the recommended products to order, or the recommended number or quantity to order may be determined.

In steps S309 to S311 described above, the product determination unit 127 determines that, for example, if more customers have purchased the same target product at another store 1, the degree of order recommendation will be higher, or the number of recommended orders or the number of recommended orders will be higher. The degree of recommendation for ordering, or the number of recommended orders or the recommended amount of the target product is calculated so that the quantity becomes larger.

In this case, the product determining unit 127 calculates the order recommendation level, or the recommended order number or recommended amount based on the purchase frequency, purchase number, and total purchase amount of the same target product in a predetermined period by multiple customers. Good too. Further, the product determining unit 127 may calculate the total value of order recommendation degrees of the same target product by a plurality of customers as the order recommendation level of the target product.

This makes it possible to recommend an assortment of products needed by multiple customers using the store.

(Third modification)
In the product assortment recommendation system 10 of the first embodiment, the product determination unit 127 performs product determination processing for customers who have visited the own store 1, and determines recommended products to order. However, the present invention is not limited to this, and the product determination unit 127 may determine whether or not to perform the above-described product determination process for a customer based on the history of visits to the store 1 by customers who have visited the store 1. Good too.

In this case, in step S304 described above, the product determination unit 127 determines the frequency of visits, the number of visits, and the number of days of visits of the customer during a predetermined period (for example, the most recent one year, one month, one week) extracted from the store visit history. calculate. The store visit frequency is, for example, the average value of the number of store visits in each unit period (for example, each month, each week, and each day) within a predetermined period. For example, if the frequency of visits, the number of visits to the store, or the number of days of visits during a predetermined period are greater than or equal to a predetermined value, the product determination unit 127 executes the processes from step S304 onward using the extracted customer as a target customer.

Thereby, it is possible to recommend an assortment of products that the customer needs at the store 1 that the customer uses particularly often.

(Fourth modification)
The product determination unit 127 determines the recommended product to order, or the recommended number or quantity to order.The purchase history of customers who visited the own store 1 at other stores 1 is determined within a predetermined range from the own store 1. The sales history of other existing stores 1 may also be used. Here, the predetermined range is, for example, a range where the distance between stores is within a predetermined distance, or a range where the travel time between stores is within a predetermined time.

For example, when the predetermined range is 500 meters, the product determination unit 127 determines the sales history of other stores 1 within 500 meters from the own store 1 of customers who have visited the own store 1. Determine recommended products to order in step 1.

As a result, it is possible to recommend an assortment of products that the customer purchased at another nearby store 1 because the store 1 did not carry the products that the customer needed.

(Fifth modification)
The purchase history of customers who visited the own store 1 at other stores 1, which is used when the product determination unit 127 determines the recommended product to order, or the recommended number or quantity to order, may be the purchase history of a predetermined time period. good. Here, the predetermined time period may be, for example, a specific time period of the day, a specific day of the week, a specific day or week of the month, a specific day or month of the year, etc. It may be a specific period of time.

In this case, the predetermined time slot may be the same time slot as a time slot in which the customer who visited the own store 1 has visited the own store 1, out of a plurality of predetermined time slots.

For example, if the own store 1 is a store located in an office district, the product determination unit 127 sets the predetermined time period to daytime, such as from 9:00 to 17:00, and uses the daytime purchase history at other stores 1. .

As a result, it is possible to recommend the assortment of products that customers visiting the store 1 need during the day when they are near the office, but have purchased them at other stores 1. In addition, it is possible to prevent the recommendation of product assortments that are needed at night when the customer is in a place not near the office, such as near his or her home, and are unlikely to be purchased at the own store 1.

(Sixth modification)
The product determining unit 127 may further determine products recommended for order withdrawal based on at least the purchase history of products ordered as recommended products at the own store 1 after arrival. Products recommended for order withdrawal are products for which orders should be canceled or the number or amount of orders should be reduced.

For example, when a recommended product is ordered at the own store 1, the product determining unit 127 determines the purchase frequency and number of purchases during a predetermined period (for example, one year, one month, or one week) after the product arrives in stock. , calculate the total purchase amount. Then, if the calculated total value is less than or equal to a predetermined threshold, the product determining unit 127 determines the product as a product recommended for order withdrawal.

For example, when a recommended product is ordered at the own store 1, the product determining unit 127 determines the frequency of purchase, the number of purchases, and the total value of the purchase amount in a predetermined period before and after the product arrives. calculate. Then, the product determining unit 127 compares the calculated total value before and after arrival, and if the total value after arrival has not increased compared to the total before arrival, the product is recommended to be canceled. decided on.

The display control unit 128 transmits the products recommended for order withdrawal at the own store 1 to the store terminal 180 and causes the store terminal 180 to display the products.

Note that if the product determined as a product recommended for order withdrawal has been automatically ordered by the product ordering unit 130, the product ordering unit 130 may cancel the order or reduce the number or amount of orders. . In this case, the order reception unit 129 may accept from the store terminal 180 the clerk's request to cancel the order or to reduce the number of orders or the amount ordered, or the product ordering unit 130 may automatically cancel the order or reduce the number of orders. You may also reduce the amount ordered.

This makes it possible to cancel an order for a product that is not expected to increase sales even though it was ordered as a recommended product.

(Seventh modification)
In the product assortment recommendation system 10 of the first embodiment, when the recommended order product is delivered to the own store 1, the notification unit 132 sends an in-stock notification to the customer terminal 300 of the customer indicated by the in-stock notification destination customer ID in the ordering information. Sent notification. However, the notification unit 132 is not limited to this, and may notify by other methods as long as it can notify the customer indicated by the arrival notification customer ID of the arrival of the item. For example, when a customer indicated by the arrival notification customer ID comes to the store 1, the notification unit 132 displays recommended products to order on a digital signage installed in the store 1, a display device connected to the POS device 110, etc. Information indicating that the product has arrived may be displayed.

As a result, at the time when the customer visits store 1, the customer is notified that a product that the customer needed but was not previously available in stock has arrived, It is possible to encourage the purchase of the product.

(Second embodiment)
A second embodiment will be described.

FIG. 14 is a block diagram showing the configuration of the store server 120 in the second embodiment.

Referring to FIG. 14, the store server 120 includes an acquisition section 126 and a product determination section 127. The store server 120, the acquisition unit 126, and the product determination unit 127 are embodiments of an assortment recommendation device, an acquisition unit, and a determination unit in the present disclosure, respectively.

The acquisition unit 126 acquires the identifier of the customer who visited the target store, the identifier of the customer who purchased the product at another store, and the identifier of the product. The product determining unit 127 determines recommended products to order at the target store based on the purchase history of products purchased at other stores by customers who visited the target store.

Next, the effects of the second embodiment will be explained.

According to the second embodiment, similar to the first embodiment, it is possible to recommend an assortment of products needed by customers using the store. The reason is that the product determination unit 127 of the store server 120 determines recommended products to order at the own store 1 based on the purchase history of products purchased at other stores 1 by customers who visited the own store 1. It is.

(Hardware configuration)
In each embodiment described above, each component of each device (POS device 110, store server 120, store terminal 180, headquarters server 210, etc.) represents a functional unit block. A part or all of each component of each device may be realized by any combination of the computer 500 and a program.

FIG. 15 is a block diagram showing an example of the hardware configuration of the computer 500. Referring to FIG. 15, the computer 500 includes, for example, a CPU (Central Processing Unit) 501, a ROM (Read Only Memory) 502, a RAM (Random Access Memory) 503, a program 504, a storage device 505, a drive device 507, and a communication interface 508. , an input device 509, an output device 510, an input/output interface 511, and a bus 512.

The program 504 includes instructions for implementing each function of each device. The program 504 is stored in the RAM 503 or storage device 505 in advance. The CPU 501 implements each function by executing instructions included in the program 504. A drive device 507 reads from and writes to the recording medium 506. Communication interface 508 provides an interface with communication network 400. The input device 509 is, for example, a mouse, a keyboard, or the like, and receives information input from an administrator or the like. The output device 510 is, for example, a display, and outputs (displays) information to an administrator or the like. The input/output interface 511 provides an interface with peripheral devices. In the case of the POS device 110, peripheral devices include the above-described card reader/writer 141, barcode reader 151, camera 161, and tag reader/writer 171. A bus 512 connects each hardware component. Note that the program 504 may be supplied to the CPU 501 via the communication network 40, or may be stored in the recording medium 506 in advance, read by the drive device 507, and supplied to the CPU 501.

Note that the hardware configuration shown in FIG. 15 is an example, and components other than these may be added, or some components may not be included.

There are various variations in how each device is implemented. For example, each device may be realized by any combination of computers and programs that are different for each component. Further, the plurality of components included in each device may be realized by an arbitrary combination of one computer and a program.

Further, a part or all of each component of each device may be realized by a general-purpose or dedicated circuit including a processor or the like, or a combination thereof. These circuits may be configured by a single chip or multiple chips connected via a bus. A part or all of each component of each device may be realized by a combination of the circuits and the like described above and a program.

Further, when a part or all of each component of each device is realized by a plurality of computers, circuits, etc., the plurality of computers, circuits, etc. may be centrally arranged or distributed.

The store server 120 may be placed in the store 1 or may be placed in a location different from the store 1 and connected to the POS 110 and the store terminal 180 via the communication network 400. That is, store server 120 may be realized by a cloud computing system. Similarly, the headquarters server 210 may also be realized by a cloud computing system.

Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above embodiments. Various changes can be made to the structure and details of the present disclosure that can be understood by those skilled in the art within the scope of the present disclosure. Further, the configurations in each embodiment can be combined with each other without departing from the scope of the present disclosure.

A part or all of the embodiments described above may be described as in the following supplementary notes, but the embodiments are not limited to the following.
(Additional note 1)
an acquisition means for acquiring an identifier of a customer who visited the target store, an identifier of a customer who purchased a product at another store, and an identifier of the product;
determining means for determining recommended products to order at the target store based on the purchase history at the other store of products purchased at the other store by a customer visiting the target store;
Assortment recommendation device equipped with
(Additional note 2)
The determining means determines the recommended ordering product from among the products that are not handled at the target store among the products that the customer who visited the target store purchased at the other store.
Product assortment recommendation device described in Appendix 1.
(Additional note 3)
The determining means makes the order recommendation based on at least one of the sales amount and the number of items in stock of products handled at the target store among the products purchased at the other stores by customers visiting the target store. decide on the product,
The product assortment recommendation device described in Appendix 1 or 2.
(Additional note 4)
The determining means further determines the recommended order quantity or recommended order quantity of the recommended order product.
The product assortment recommendation device according to any one of Supplementary Notes 1 to 3.
(Appendix 5)
Further, a display control means is provided for displaying the recommended order product and the recommended order quantity or recommended order amount of the order recommended product on a store clerk's terminal.
Product assortment recommendation device described in Appendix 4.
(Appendix 6)
Further, it includes an ordering means for processing the ordering recommended product.
The product assortment recommendation device according to any one of Supplementary Notes 1 to 5.
(Appendix 7)
Furthermore, when the recommended ordering product is stocked at the target store, a notification means is provided for notifying customers who have purchased the recommended ordering product at the other store of the arrival of the recommended ordering product.
Product assortment recommendation device described in Appendix 6.
(Appendix 8)
The determining means determines the recommended order product based on the purchase history at the other store of products purchased at the other store by a plurality of customers who visited the target store.
The product assortment recommendation device according to any one of Supplementary Notes 1 to 7.
(Appendix 9)
The determining means determines whether or not to determine recommended products to order at the target store based on the history of visits to the target store by customers who visited the target store.
The product assortment recommendation device according to any one of Supplementary Notes 1 to 8.
(Appendix 10)
The determining means determines the recommended product to order based on the purchase history at the other store of the product purchased by the customer visiting the target store at the other store within a predetermined range from the target store.
The product assortment recommendation device according to any one of Supplementary Notes 1 to 9.
(Appendix 11)
The determining means determines the recommended product to order based on the purchase history at the other store of products purchased at the other store during a predetermined time period by a customer visiting the target store.
The product assortment recommendation device according to any one of Supplementary Notes 1 to 10.
(Appendix 12)
The determining means further determines the recommended ordering product as a product recommended for order withdrawal based on the purchase history of the recommended ordering product at least after arrival at the target store.
The product assortment recommendation device according to any one of Supplementary Notes 1 to 11.
(Appendix 13)
Obtaining the identifier of the customer who visited the target store, the identifier of the customer who purchased the product at another store, and the identifier of the product,
determining recommended products to order at the target store based on the purchase history at the other store of products purchased at the other store by a customer visiting the target store;
Recommended assortment method.
(Appendix 14)
to the computer,
Obtaining the identifier of the customer who visited the target store, the identifier of the customer who purchased the product at another store, and the identifier of the product,
determining recommended products to order at the target store based on the purchase history at the other store of products purchased at the other store by a customer visiting the target store;
A program that executes processing.

1 Store 2 Headquarters 10 Product assortment recommendation system 100 Store system 110 POS device 111 Purchaser identification unit 112 Registration unit 113 Payment unit 114 Purchase data generation unit 120 Store server 121 Visitor identification unit 122 Visit history storage unit 123 Product information storage unit 124 Update section 125 Transmission section 126 Acquisition section 127 Product determination section 128 Display control section 129 Order reception section 130 Product order section 131 Order information storage section 132 Notification section 140, 141 Card reader/writer 150, 151 Barcode reader 160, 161 Camera 171 Tag reader Writer 180 Store terminal 200 Headquarters system 210 Headquarters server 211 Purchase history storage section 212 Purchase history update section 213 Purchase history transmission section 214 Delivery instruction section 300 Customer terminal 400 Communication network 500 Computer 501 CPU
502 ROM
503 RAM
504 Program 505 Storage device 506 Recording medium 507 Drive device 508 Communication interface 509 Input device 510 Output device 511 Input/output interface 512 Bus

Claims (14)

acquisition means for acquiring the purchase history of products purchased by the customer at another store at the other store based on the identifier of the customer who visited the target store;
By referring to the purchase history, obtain the purchase frequency, number of purchases, or purchase amount of products that the customer purchased at the other store and which are not handled at the target store, and determining means for determining recommended products to order from products not handled at the target store based on the size or increase in purchase amount;
Assortment recommendation device equipped with The determining means determines an order recommendation level based on the purchase frequency, the number of purchases, or the size or increase in the purchase amount, and selects products for which the order recommendation level is equal to or higher than a threshold value among products that are not handled by the target store. The product assortment recommendation device according to claim 1, wherein the device determines a product to be recommended for ordering. In addition to the recommended order products, the determining means determines the sales amount and the number of items in stock of the products handled at the target store among the products purchased at the other stores by customers who visited the target store. determining recommended products to order based on at least one of the
The product assortment recommendation device according to claim 1 or 2. The determining means further determines the recommended number of orders or the recommended amount of the recommended order product based on the purchase frequency, the number of purchases, or the purchase amount.
The product assortment recommendation device according to claim 1 or 2. Further, a display control means is provided for displaying the recommended order product and the recommended order quantity or recommended order amount of the order recommended product on a store clerk's terminal.
The product assortment recommendation device according to claim 4. Further, it includes an ordering means for processing the ordering recommended product.
An assortment recommendation device according to any one of claims 1 to 5. Furthermore, when the recommended ordering product is stocked at the target store, a notification means is provided for notifying customers who have purchased the recommended ordering product at the other store of the arrival of the recommended ordering product.
The product assortment recommendation device according to claim 6. The determining means refers to the purchase history of the plurality of customers, and if the plurality of customers have purchased the same product at the other store, the determination means refers to the purchase history of the plurality of customers, and if the plurality of customers have purchased the same product at the other store, the determination means selects the same product at the other store of the plurality of customers. obtaining the purchase frequency, the number of purchases, and the purchase amount, and determining the recommended order product based on the total value of the purchase frequency, the number of purchases, or the purchase amount within a predetermined period;
An assortment recommendation device according to any one of claims 1 to 7. The acquisition means acquires a visit history of the customer to the target store,
The determining means calculates the frequency of visits, the number of visits, or the number of days of visits of the customer in a predetermined period extracted from the store visit history,
If the frequency of visits, the number of visits, or the number of days of visits is a predetermined value or more, the acquisition means acquires the purchase history of the customer;
An assortment recommendation device according to any one of claims 1 to 8. The determining means refers to the sales history of the other stores existing within a predetermined range from the target store, and determines the purchase frequency, the number of purchases, and the purchase amount of the products purchased by the customer at the other stores. and determining the product to be recommended for order based on the value of the purchase frequency, the number of purchases, or the amount of purchase within a predetermined period.
An assortment recommendation device according to any one of claims 1 to 9. The determining means refers to the purchase history in a predetermined time period at the other store, calculates the purchase frequency, the number of purchases, and the purchase amount of the product purchased by the customer at the other store, and determining the product to be recommended for ordering based on the value of the purchase frequency, the number of purchases, or the amount of purchase within a period;
An assortment recommendation device according to any one of claims 1 to 10. The determining means further calculates the purchase frequency, the number of purchases, and the purchase amount for a predetermined period after the recommended order product arrives in stock by referring to the purchase history of the recommended order product at least at the target store after the product arrives in stock. and determining the recommended product to be ordered as a product recommended to be canceled based on the purchase frequency, the number of purchases, or the total purchase amount;
An assortment recommendation device according to any one of claims 1 to 11. The computer is
Based on the identifier of the customer who visited the target store, obtain the purchase history of the product purchased by the customer at the other store,
Referring to the purchase history, obtaining the purchase frequency, number of purchases, or purchase amount of products that the customer purchased at the other store and which are not handled at the target store;
determining recommended products to order from products not handled at the target store based on the purchase frequency, the number of purchases, or the size or increase in the purchase amount;
Recommended assortment method. to the computer,
Based on the identifier of the customer who visited the target store, obtain the purchase history of the product purchased by the customer at the other store,
Referring to the purchase history, obtaining the purchase frequency, number of purchases, or purchase amount of products that the customer purchased at the other store and which are not handled at the target store;
determining recommended products to order from products not handled at the target store based on the purchase frequency, the number of purchases, or the size or increase in the purchase amount;
A program that executes processing.

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