Product recommendation device, product recommendation method, and recording medium

Abstract

A product recommendation device according to the present invention comprises: an acquisition means that acquires attribute information, behavior history, and purchase history of a customer in a plurality of retailers which use a common business format with different customer segments; an analysis means that analyzes the interests and the preferred price range of the customer on the basis of the attribute information, the behavior history, and the purchase history; and a determination means that determines, on the basis of the analysis result, a recommended product in a business format different from the common business format and in a price range different from the price range preferred by the customer.

Description

Product recommendation device, product recommendation method, and recording medium

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

[0002] In some cases, products recommended to a customer are determined using information collected by multiple retailers. In this case, products in a price range different from the price range of the product purchased by the customer may be recommended.

[0003] Patent document 1 describes that if the product prices resulting from an analysis of a customer's overall affiliated store usage information frequently match with high prices, information is generated that indicates that the customer has a preference for high-priced products.

[0004] JP 2009-512100 A

[0005] A retailer may lose customers by recommending products or services of a common business type that are offered by multiple retailers to customers. In addition, a retailer may offer multiple business types.

[0006] One of the objects of the present disclosure is to provide a product recommendation device etc. that can reduce the possibility that a retailer will lose customers when recommending products in different price ranges.

[0007] A product recommendation device according to one aspect of the present disclosure includes an acquisition means for acquiring attribute information, behavioral history, and purchase history of customers from multiple retailers that operate a common business format and have different customer segments; an analysis means for analyzing the customer's interests and price range preferences based on the attribute information, behavioral history, and purchase history; and a determination means for determining recommended products in a different price range from the customer's preferred price range, in a different business format from the common business format, based on the analysis results.

[0008] A product recommendation method in one aspect of the present disclosure acquires customer attribute information, behavioral history, and purchase history from multiple retailers that operate a common business format and have different customer segments, analyzes the customer's interests and price range preferences based on the attribute information, behavioral history, and purchase history, and, based on the analysis results, determines recommended products in a business format different from the common business format and in a price range different from the customer's preferred price range.

[0009] A product recommendation program in one aspect of the present disclosure causes a computer to execute a process of acquiring attribute information, behavioral history, and purchase history of customers from multiple retailers that operate a common business format but have different customer segments, analyzing the customer's interests and price range preferences based on the attribute information, behavioral history, and purchase history, and determining, based on the analysis results, recommended products in a business format different from the common business format and in a price range different from the customer's preferred price range.

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

[0011] One example of the effect of the present disclosure is that it allows retailers to reduce the possibility of losing customers when recommending products in different price ranges.

[0012] 1 is a block diagram showing an example of the configuration of a product recommendation system including a product recommendation device; FIG. 2 is a block diagram showing an example of the configuration of a product recommendation device; FIG. 3 is a table showing an example of information acquired by the product recommendation device; FIG. 4 is a table showing another example of information acquired by the product recommendation device; FIG. 5 is a flowchart showing the operation of the product recommendation device; and FIG. 6 is a diagram showing an example of the hardware configuration of the product recommendation device.

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

[0014] [Embodiment] An application example of a product recommendation device 10 will be described with reference to Fig. 1. Fig. 1 is a block diagram showing an example of the configuration of a product recommendation system including the product recommendation device 10.

[0015] The product recommendation device 10 is connected to a database 90 via a wired or wireless network. Referring to FIG. 1 , the product recommendation device 10 is connected to a company A database 90A, a company B database 90B, a company C database 90C, and a company D database 90D. Companies A, B, C, and D are examples of retailers from which the product recommendation device 10 acquires information. Companies A, B, C, and D may be, for example, retailers in a collaborative relationship. The company A database 90A, the company B database 90B, the company C database 90C, and the company D database 90D are examples of databases 90 managed by each retailer. Each database 90 stores information about the respective retailer's customers. Each database 90 stores, for example, attribute information, behavioral history, and purchase history of the respective retailer's customers. In the example shown in FIG. 1 , the product recommendation device 10 can acquire information from four companies. The number of retailers from which the product recommendation device 10 can acquire information is not limited to four. Furthermore, information on a plurality of retailers acquired by the product recommendation device 10 may be aggregated in a common database 90 .

[0016] The configuration of the product recommendation device 10 will be described with reference to Fig. 2. Fig. 2 is a block diagram showing an example of the configuration of the product recommendation device 10. The product recommendation device 10 includes an acquisition unit 101, an analysis unit 102, and a determination unit 103.

[0017] The acquisition unit 101 is one aspect of an acquisition means for acquiring attribute information, behavioral history, and purchase history of customers from a plurality of retailers that operate in a common business format and have different customer segments. Here, a retailer refers to a retailer.

[0018] A business type is a classification of retailers according to the products they handle or the method of selling the products. Examples of business types include clothing specialty stores, shoe and footwear specialty stores, restaurants, amusement facilities, and electronics mass retailers. Examples of business types are not limited to these. For example, a clothing specialty store may be further classified into business types such as men's clothing specialty stores and women's clothing specialty stores. Furthermore, an amusement facility may be further classified into business types such as karaoke booths, bowling alleys, and game centers. Examples of business types are not limited to these. Furthermore, a business type may be a business that operates in that business type.

[0019] Here, a product is a product sold by a retailer. Examples of a product are suits and sneakers. A product may also include a type of product. Examples of a type of product are clothing and shoes. Examples of products are not limited to these. A product includes a service provided by a retailer. Examples of services are using a restaurant or a karaoke booth. Examples of services are not limited to these.

[0020] A customer segment is a classification of customers targeted by a retailer. One example of a customer segment is a classification of customers based on the price range of the products they purchase. Customers are classified by the price range of the products they purchase. Retailers with different customer segments are retailers that target different classifications of customers. In other words, retailers with different customer segments are retailers with different customer demographics. If customer segments are classified by the price range of the products customers purchase, retailers with different customer segments are retailers whose target customers purchase products in different price ranges. In other words, retailers with different customer segments are retailers whose products are sold in different price ranges. Customer segments are not limited to this. For example, customer segments may be classified by at least one of the customer's age, gender, family structure, and occupation.

[0021] A price range is a ranking of the same product according to price. The same product may be a product of the same type. Examples of price ranges are a high price range, a mid-price range, and a low price range. Product prices belonging to each rank of the high price range, the mid-price range, and the low price range are set in advance. The product prices belonging to each rank may differ depending on the product. For example, if the product is a suit, the product price belonging to the high price range may be set to 100,000 yen or more, while if the product is sneakers, the product price belonging to the high price range may be set to 30,000 yen or more. Examples of price ranges are not limited to these. The price range may be expressed numerically. The price range may be expressed as rank 1, rank 2, and rank 3, for example. The price range itself may be expressed by monetary amount. Furthermore, the number of ranks is not limited to three.

[0022] The attribute information, behavior history, and purchase history acquired by the acquisition unit 101 will be described below.

[0023] Attribute information is information about a customer's attributes. The attribute information includes information about at least one of the customer's age, gender, place of residence, family structure, or occupation. The customer's place of residence may be, for example, the customer's home address, or the region, prefecture, or city, ward, town, or village where the customer lives. Information about the customer's family structure includes information about the relationship with the customer. Information about the customer's family structure may include, for example, the ages and genders of the customer's family members. Information about the customer's family structure may also be, for example, information about people living with the customer.

[0024] The attribute information is registered by the customer, and the attribute information registered by the customer is stored in, for example, a database 90 managed by the retailer.

[0025] The attribute information may be stored in association with information that identifies a customer. The information that identifies a customer is, for example, a customer number or ID that is set for each customer. The customer number or ID may be set by the retailer. Alternatively, the customer number or ID may be set by the customer. A customer number or ID set for the same customer may be common to multiple retailers. Alternatively, the information that identifies a customer may be, for example, the customer's name or contact information. The customer's name or contact information is registered by the customer.

[0026] A behavioral history is a behavioral history of a customer who purchases a retailer's products. The behavioral history is, for example, a history of location information. The behavioral history may be, for example, a customer behavioral history recorded by analyzing video footage of the customer. The behavioral history is not limited to these. The behavioral history may be, for example, a combination of a history of location information and a customer behavioral history recorded by analyzing video footage.

[0027] The behavioral history includes behavior that can be used to infer the customer's intended use of the product. The intended use of the product can be inferred from the customer's behavioral history. If the product is a service, the intended use of the product may be the intended use of the service by the customer. The intended use of the product is the purpose for which the customer purchases and uses the product. The intended use of the service is the purpose for which the customer uses the service. For example, the purpose of using the service is to watch live streaming of an idol concert, which is a hobby.

[0028] The customer's use of the product may be detected, for example, by analyzing images captured by a camera (not shown) installed in the retailer's facility. The camera can capture images of the customer's behavior when purchasing the product. The camera may also capture images of the customer's behavior when using a service, for example. The images are then used to analyze the customer's use of the product. The analysis results are, for example, linked to information identifying the customer and stored in the database 90. The image analysis is performed using known technology. The image analysis can be performed by an information processing device (not shown) different from the product recommendation device 10.

[0029] The customer's intended use of the product may be detected, for example, from an input by the customer to a terminal (not shown) installed in the facility. If the service used by the customer is the use of a karaoke booth, an example of the behavioral history is a history of songs sung by the customer in the karaoke booth. Therefore, for example, the song history may be linked to information identifying the customer from the terminal installed in the facility and stored in the database 90. Another example of the behavioral history is the content viewed in the karaoke booth. In this case, too, for example, the content content may be linked to information identifying the customer from the terminal installed in the facility and stored in the database 90.

[0030] The method for detecting the customer's intended use of the product is not limited to these. The customer's intended use of the product may be obtained, for example, from the customer's responses to a questionnaire. The retailer may conduct a questionnaire on the customer's intended use of the product. The responses to the questionnaire may then be stored in the database 90, for example, linked to information identifying the customer.

[0031] The behavior history is not limited to these. For example, the behavior history may be a history of the customer's behavior outside the retailer's premises.

[0032] A purchase history is information about products that a customer has purchased in the past. If the product is a service, the purchase history includes the customer's usage history of the service. The purchase history includes the product names, number of products, and product prices purchased by the customer. If the product is a service, the number of products may be the time the customer used the service. The purchase history may also include the date and time when the customer purchased the product. The purchase history may be stored in database 90, for example, linked to information that identifies the customer.

[0033] The acquisition unit 101 acquires attribute information, behavioral history, and purchase history of customers from multiple retailers that operate in a common business format but have different customer segments. Since the multiple retailers have different customer segments, customers may differ from retailer to retailer. Therefore, the acquisition unit 101 acquires attribute information, behavioral history, and purchase history of customers from the multiple retailers. Furthermore, the acquisition unit 101 acquires attribute information, behavioral history, and purchase history related to a common business format operated by the multiple retailers. The acquisition unit 101 may acquire at least one of the attribute information, behavioral history, and purchase history.

[0034] Here, the information acquired by the acquisition unit 101 will be described with reference to Fig. 3. Fig. 3 is a table showing an example of information acquired by the product recommendation device. Fig. 3 shows retailers operating in various business formats. The retailers operating in various business formats are shown in order according to customer segments, which are classified according to the price range of the products purchased by customers. In other words, in Fig. 3, the higher the row, the higher the price range of the products handled by the retailer, and the lower the row, the lower the price range of the products handled by the retailer. For example, if the business format is a clothing specialty store, the price range of the products handled by companies B, A, C, and D will be highest in this order.

[0035] In the example shown in Fig. 3, the acquisition unit 101 acquires customer attribute information, behavioral history, and purchase history within the area surrounded by a thick line. That is, in the example shown in Fig. 3, the common business format is clothing specialty stores. The multiple retailers that operate the same business format but have different customer segments are Company A, Company B, Company C, and Company D. That is, the acquisition unit 101 acquires customer attribute information, behavioral history, and purchase history related to the clothing specialty stores managed by Company A, Company B, Company C, and Company D.

[0036] The acquisition unit 101 can acquire attribute information, behavioral history, and purchase history from the database 90 managed by each retailer. The acquisition unit 101 can acquire information identifying a retailer and the attribute information, behavioral history, and purchase history managed by the retailer in association with each other. In other words, the acquisition unit 101 acquires the acquired attribute information, behavioral history, and purchase history in a manner that enables identification of which retailer collected the information.

[0037] The acquisition unit 101 may further acquire attribute information, purchase history, and behavior history related to a business type different from the common business type. In other words, the acquisition unit 101 acquires attribute information, purchase history, and behavior history related to a business type different from the common business type operated by the retailer, in addition to attribute information, purchase history, or behavior history related to a common business type operated by multiple retailers with different customer segments. Here, the attribute information related to the common business type and the attribute information related to the different business type may be the same.

[0038] With reference to Fig. 4, the information acquired by the acquisition unit 101 in this modification will be described. Fig. 4 is a table showing another example of information acquired by the product recommendation device. The retailers and business types in Fig. 4 are the same as those in Fig. 3. In the example shown in Fig. 4, the information acquired by the acquisition unit 101 is the information in the area surrounded by a thick line and the information in the area shaded in gray. The information in the area shaded in gray is attribute information, purchase history, and behavior history related to business types different from the common business type operated by retailers operating in a common business type. In other words, the acquisition unit 101 acquires information on business types other than clothing specialty stores operated by companies A, B, C, and D operating in a common business type.

[0039] The method by which the acquisition unit 101 acquires attribute information, purchase history, and behavior history related to a business type different from the common business type is the same as the method described above.

[0040] The analysis unit 102 is one aspect of an analysis means that analyzes a customer's interests and price range preferences based on attribute information, behavioral history, and purchase history. The analysis unit 102 analyzes a customer's interests and price range preferences using the attribute information, behavioral history, and purchase history acquired by the acquisition unit 101. The analysis by the analysis unit 102 may analyze trends in customer interests. The analysis by the analysis unit 102 may also analyze trends in customer price range preferences. In other words, the analysis by the analysis unit 102 does not need to be a highly accurate analysis. For example, the analysis unit 102 may analyze a customer's interests and price range preferences using a trained model of customer interests and price range preferences. The analysis unit 102 may also analyze a customer's interests and price range preferences using statistical data. For example, the analysis unit 102 may analyze a customer's interests based on statistics regarding the likelihood of the customer being interested. For example, the analysis unit 102 may analyze a customer's price range preferences based on statistics regarding the likelihood of the customer preferring something.

[0041] A customer's interests are subjects in which the customer is interested. A customer's interests include, for example, the customer's hobbies. A customer's hobbies may also include subjects in which the customer's family is interested. The analysis unit 102 then analyzes the customer's interests based on the attribute information, behavioral history, and purchase history.

[0042] The analysis unit 102 can analyze the interests of the customer by using, for example, attribute information. As described above, the attribute information includes information on at least one of the customer's age, gender, place of residence, family structure, and occupation. The analysis unit 102 can analyze the interests of the customer by using at least one piece of attribute information.

[0043] The analysis unit 102 can analyze the interests of a customer based on, for example, family structure. Information about the customer's family structure may include, for example, the age, gender, and relationship of the customer's family members to the customer. If the customer has elderly parents, the analysis unit 102 can analyze that the customer is interested in nursing care products. The analysis unit 102 may also analyze the interests of a customer based on life events of the customer or the customer's family. If the customer has a child in his / her third year of high school, the analysis unit 102 can analyze that the customer is interested in books about university entrance exams and home appliances necessary for a new life. In addition, the analysis unit 102 can analyze that if the customer has a child who is about to become an adult, the customer is interested in furisode kimono and furisode dressing services.

[0044] The analysis unit 102 can analyze the interests of a customer based on, for example, the customer's occupation. If the customer is a housewife, the analysis unit 102 may analyze that baking tends to be a customer's hobby. In other words, the analysis unit 102 can analyze that the customer tends to be interested in products related to baking.

[0045] The analysis unit 102 may analyze the interests of a customer based on, for example, the gender and age of the customer. If the customer is a female high school student, the analysis unit 102 may analyze that the customer is interested in popular idol groups.

[0046] The analysis using the attribute information by the analysis unit 102 is not limited to these. The analysis unit 102 uses the attribute information to analyze the objects of interest and hobbies of the customer.

[0047] The analysis unit 102 can analyze the customer's interests using, for example, the behavioral history. As described above, the behavioral history includes behavior that can infer the customer's intended use of a product. If the product is a service, the intended use of the product may be the customer's intended use of the service. Thus, the analysis unit 102 can analyze the customer's interests using the intended use of the product that can be inferred from the behavioral history. For example, if a customer uses a karaoke box to watch an idol concert, the analysis unit 102 can analyze that the customer's hobby is supporting the idol. The analysis unit 102 may also analyze that the customer is interested in attending idol events. The analysis unit 102 may also analyze that the customer is interested in gadgets if the customer uses a cafe for teleworking. In this case, as described below, the analysis unit 102 may analyze the customer's interests using purchase history in addition to the behavioral history.

[0048] The analysis using the behavioral history by the analysis unit 102 is not limited to these. The analysis unit 102 uses the behavioral history to analyze the subjects of interest and hobbies of the customer.

[0049] The analysis unit 102 can analyze the interests of a customer using, for example, a purchase history. The analysis unit 102 can analyze the interests of a customer based on, for example, the contents of products included in the purchase history. For example, if the customer's purchase history includes walking shoes, the analysis unit 102 can analyze that the customer's hobby is walking. The analysis unit 102 can also analyze the customer's hobbies based on the type of clothing included in the customer's purchase history. For example, the analysis unit 102 may analyze that the customer's hobby is dancing based on the type of clothing. For example, the analysis unit 102 may analyze that the customer is interested in overseas artists based on the type of clothing.

[0050] The analysis unit 102 may analyze customer interests based on the product purchase frequency calculated from the purchase history. The purchase frequency is calculated from the purchase history for a predetermined period. The predetermined period may be any period suitable for analyzing customer interests. The analysis unit 102 can analyze customer preferences based on the contents of products with high purchase frequencies. Here, the method for determining whether a product is purchased frequently is not particularly limited. The analysis unit 102 may determine that a product is purchased frequently if the purchase frequency is equal to or greater than a threshold. The analysis unit 102 may also determine that a product is purchased frequently if the purchase frequency is equal to or greater than the average of other customers.

[0051] The analysis using the purchase history by the analysis unit 102 is not limited to these. The analysis unit 102 uses the behavior history to analyze the objects of interest and hobbies of the customer.

[0052] The analysis unit 102 may analyze the interests of a customer by combining attribute information, behavioral history, and purchase history. By using multiple pieces of information, the analysis unit 102 can improve the accuracy of the analysis of customer interests. Furthermore, by using multiple pieces of information, the analysis unit 102 can analyze the interests of a customer in more detail. For example, the analysis unit 102 can analyze the preferences of a customer from the attribute information and purchase history. The analysis unit 102 can analyze that a customer is interested in idols from the attribute information, and may further analyze which idols the customer is particularly interested in from the purchase history.

[0053] Known techniques can be used for analyzing the interests of customers by the analysis unit 102. For example, information in which the objects or hobbies in which the customer is interested are associated with the contents of the attribute information, behavioral history, and purchase history can be used.

[0054] The analysis unit 102 analyzes customers' price range preferences in a common business format based on attribute information, purchase history, and behavioral history related to the common business format. Price range preferences are the price ranges that customers prefer when purchasing products. Preferred price ranges may vary depending on the customer. Therefore, the analysis unit 102 analyzes customers' price range preferences based on attribute information, purchase history, and behavioral history related to the common business format. Here, the analysis unit 102 analyzes customers' price range preferences in a common business format operated by multiple retailers. In other words, the analysis unit 102 can analyze price range preferences for products in a common business format.

[0055] The analysis unit 102 can analyze price range preferences using, for example, the purchase history. The analysis unit 102 can analyze that the price range to which the prices of the products included in the purchase history belong is the customer's preferred price range. If the purchase history includes multiple products, the products included in the purchase history may belong to multiple price ranges. In this case, the analysis unit 102 may analyze the price range with the largest number of products belonging to it as the customer's preferred price range. Furthermore, the analysis unit 102 may analyze the highest price range among the price ranges to which the products belong as the customer's preferred price range. The analysis unit 102 may analyze the lowest price range among the price ranges to which the products belong as the customer's preferred price range.

[0056] The analysis unit 102 can analyze price range preferences using, for example, the attribute information. The analysis unit 102 can analyze price range preferences according to, for example, the age included in the attribute information. The analysis unit 102 may analyze that if the customer is in their teens, the customer prefers products in a low price range. The analysis unit 102 can also analyze price range preferences according to the occupation included in the attribute information. The analysis unit 102 may analyze that if the customer has a high-income occupation, the customer prefers products in a high price range.

[0057] The analysis results regarding price range preferences by the analysis unit 102 are not limited to these. The analysis unit 102 can also analyze price range preferences using behavioral history. For example, first, the acquisition unit 101 acquires the behavioral history of a customer recorded by analyzing video footage of the customer. Then, the analysis unit 102 can analyze that the customer prefers low-price ranges if the customer has been looking at a discounted product corner in a retailer's store for a predetermined period of time or more.

[0058] The analysis unit 102 may analyze customer interests by combining attribute information, behavioral history, and purchase history. By using multiple pieces of information, the analysis unit 102 can improve the accuracy of the analysis of price range preferences. Furthermore, by using multiple pieces of information, the analysis unit 102 can perform a detailed analysis of price range preferences. Publicly known techniques can be used for the analysis of price range preferences by the analysis unit 102.

[0059] Here, an analysis by the analysis unit 102 when the acquisition unit 101 further acquires attribute information, purchase history, and behavior history related to a business type different from the common business type will be described. In this case, the analysis unit 102 can analyze the customer's interests and price range preferences in different business types based on the attribute information, behavior history, and purchase history related to the common business type and the attribute information, behavior history, and purchase history related to a business type different from the common business type.

[0060] The analysis unit 102 analyzes price range preferences in different business formats based on attribute information, purchase history, and behavior history related to the different business formats. Customers may have different price range preferences depending on the business format. Therefore, by using attribute information, purchase history, and behavior history related to the different business formats to analyze price range preferences in different business formats, it may be possible to accurately analyze price range preferences in different business formats.

[0061] The analysis unit 102 analyzes customers' price range preferences in different business formats based on attribute information, purchase history, and behavioral history related to the different business formats. The method by which the analysis unit 102 analyzes price range preferences in different business formats is the same as the method for analyzing price range preferences described above. In other words, the method for analyzing price range preferences in different business formats is the same as the method for analyzing price range preferences in a common business format. Because the acquisition unit 101 acquires attribute information, purchase history, and behavioral history related to the different business formats, the analysis unit 102 can analyze customers' price range preferences in different business formats. The analysis unit 102 may analyze price range preferences in different business formats based on attribute information, purchase history, and behavioral history related to the common business format and attribute information, purchase history, and behavioral history related to the different business formats.

[0062] The analysis unit 102 can analyze customer interests based on attribute information, behavioral history, and purchase history related to a common business type and attribute information, behavioral history, and purchase history related to a different business type. The accuracy of the analysis may be improved by the analysis unit 102 analyzing customer preferences using attribute information, behavioral history, and purchase history related to a different business type.

[0063] The determination unit 103 is one aspect of a determination means that determines recommended products in a business format different from the common business format and in a price range different from the price range preferred by the customer based on the analysis results. The determination unit 103 determines recommended products for the customer using the analysis results by the analysis unit 102. In this case, the recommended products are products in a business format different from the common business format operated by multiple retailers. Furthermore, the recommended products are products in a price range different from the price range preferred by the customer in the common business format.

[0064] The determination unit 103 determines a business type that matches the customer's interests as a different business type based on the analysis results of the customer's interests. The determination unit 103 can determine a different business type based on information associating the customer's interests with business types that match the interests. An example of a method for determining a different business type by the determination unit 103 will be described. For example, a case will be described in which the acquisition unit 101 acquires attribute information, behavioral history, and purchase history related to a clothing specialty store as a common business type. For example, the analysis unit 102 can analyze that the customer is interested in an idol group based on the attribute information, behavioral history, and purchase history related to the clothing specialty store. In this case, the determination unit 103 can determine, for example, an amusement facility as a different business type from the common business type. As a specific example, the determination unit 103 can determine karaoke boxes as a different business type among amusement facilities. In karaoke boxes, customers may be able to watch live streaming of idol group concerts. Therefore, an amusement facility determined as a different business type may match the customer's interests.

[0065] Furthermore, the analysis unit 102 can analyze, for example, from the attribute information, behavioral history, and purchase history related to clothing specialty stores, that the customer is interested in home appliances that children will use in their new life. In this case, the determination unit 103 can determine, for example, a home appliance mass retailer as a different business type from the common business type. A home appliance mass retailer determined as a different business type may match the customer's interests.

[0066] The determination unit 103 determines recommended products in a price range different from the customer's preferred price range based on the analysis results regarding price range preferences. Here, the customer's preferred price range refers to the customer's preferred price range in a common business format operated by multiple retailers. The analysis unit 102 analyzes price range preferences in a common business format operated by multiple retailers. Therefore, the determination unit 103 determines a price range for recommended products in a business format different from the common business format, using the analysis results of the price range preferences in the common business format analyzed by the analysis unit 102. Here, a price range different from the customer's preferred price range refers to a price range that is higher or lower than the customer's preferred price range.

[0067] The method by which the determining unit 103 determines the price range will be described.

[0068] The determination unit 103 can determine the price range of recommended products by assuming that the price range preferences in a common business category and the price range preferences in a business category different from the common business category tend to be similar. For example, if a customer prefers high-price range products in a common business category, the customer may also prefer high-price range products in a business category different from the common business category. Also, if a customer prefers low-price range products in a common business category, the customer may also prefer low-price range products in a business category different from the common business category.

[0069] Therefore, when a customer prefers a higher price range in a common business category, the determination unit 103 determines recommended products in a higher price range in a business category different from the common business category. That is, the determination unit 103 determines recommended products in a higher price range than the customer's preferred price range in the common business category. Furthermore, when a customer prefers a lower price range in the common business category, the determination unit 103 determines recommended products in a lower price range in a business category different from the common business category. That is, the determination unit 103 determines recommended products in a lower price range than the customer's preferred price range in the common business category.

[0070] Here, the high price range and the low price range may be set in advance. For example, each price range rank may be set as a high price range or a low price range. Also, for example, a threshold value for product prices may be set. In this case, if a price range is higher than the threshold value, the price range is a high price range, and if a price range is lower than the threshold value, the price range is a low price range.

[0071] Furthermore, a price range higher than the price range preferred by a customer is a price range that is higher than the price range preferred by a customer in the same business category by a predetermined amount or more. A price range lower than the price range preferred by a customer is a price range that is lower than the price range preferred by a customer in the same business category by a predetermined amount or more. The number of price ranges to be changed may be set in advance. If price ranges are ranked, a price range that is higher than the predetermined amount or more is, for example, a price range that is one or more ranks higher. Furthermore, a price range that is lower than the predetermined amount or more is, for example, a price range that is one or more ranks lower.

[0072] For each price range preferred by customers in a common business type, a price range of recommended products in a business type different from the common business type may be set in advance. For example, if price ranges are divided into rank 1, rank 2, rank 3, rank 4, and rank 5, and the price range preferred by customers in the common business type is rank 3, the price range of recommended products in a different business type may be set to rank 2. The price range of recommended products in a different business type for each price range preferred by customers in the common business type may be set by, for example, a retailer. The price range of recommended products in a different business type for each price range preferred by customers in the common business type may be set by agreement of, for example, multiple retailers.

[0073] The determining unit 103 may determine the price range of the recommended products depending on whether the common business type and the different business type are similar. Similar business types mean that products in the business type have similar characteristics. For example, clothing and shoes / footwear both have the characteristic of being worn. Therefore, clothing specialty stores and shoe / footwear specialty stores may have similar business types. Here, whether the common business type and the different business type are similar may be set in advance.

[0074] In addition, in similar business types, customers' price range preferences may also be similar. For example, price preferences for clothing may be similar to price range preferences for shoes and footwear, which are similar to clothing. Therefore, the determination unit 103 can determine the price range of recommended products depending on whether the common business type and the different business type are similar.

[0075] When a common business type and a different business type are similar, the determination unit 103 can determine the price range of recommended products by assuming that the price range preferences in the common business type and the price range preferences in the different business type tend to be similar. In other words, if a customer prefers a high price range in the common business type, the customer may also prefer a high price range in a business type different from the common business type. Therefore, when a customer prefers a high price range in the common business type, the determination unit 103 determines recommended products in a higher price range in a business type different from the common business type. In other words, the determination unit 103 determines recommended products in a higher price range than the price range preferred by the customer in the common business type. Furthermore, when a customer prefers a low price range in the common business type, the customer may also prefer a low price range in a business type different from the common business type. Therefore, when a customer prefers a low price range in the common business type, the determination unit 103 determines recommended products in a lower price range in a business type different from the common business type. That is, the determination unit 103 determines recommended products in a price range lower than the price range preferred by the customer in the common business category.

[0076] When the common business type and the different business type are not similar, the determination unit 103 may determine the price range of the recommended products assuming that the price range preferences in the common business type and the price range preferences in the different business type tend to differ. For example, a customer who pays high prices for personal items such as clothing and shoes may prefer inexpensive restaurants for meals. In other words, if a customer prefers a high price range in the common business type, the customer may prefer a lower price range in a business type different from the common business type. Therefore, if a customer prefers a high price range in the common business type, the determination unit 103 determines recommended products in a lower price range in a business type different from the common business type. In other words, the determination unit 103 determines recommended products in a price range lower than the price range preferred by the customer in the common business type. Furthermore, if a customer prefers a low price range in the common business type, the customer may prefer a higher price range in a business type different from the common business type. Therefore, when a customer prefers a lower price range in a common business category, the determination unit 103 determines recommended products in a higher price range in a business category different from the common business category. In other words, the determination unit 103 determines recommended products in a higher price range than the price range preferred by the customer in the common business category.

[0077] The method of determining the price range by the determination unit 103 is not limited to these. The determination unit 103 may determine the price range based on, for example, attribute information. That is, the determination unit 103 may determine whether to recommend products in a higher price range than the customer's preferred price range or products in a lower price range, depending on the attribute information. Furthermore, the determination unit 103 may determine the price range based on, for example, the purchase frequency of products included in the purchase history. That is, the determination unit 103 may determine whether to recommend products in a higher price range than the customer's preferred price range or products in a lower price range, depending on the purchase frequency.

[0078] The determination unit 103 may determine the price range of the recommended products based on the analysis result of the price range preference as well as the characteristics of the products in different business categories. The price range preferred by customers may differ depending on the products in different business categories. Therefore, the determination unit 103 determines the price range of the recommended products based on the analysis result of the price range preference as well as the characteristics of the products in different business categories.

[0079] The determination unit 103 may determine a price range higher than the price range preferred by the customer as the price range of the recommended product based on the characteristics of the product in the different business type. For example, if the product in the business type different from the common business type determined by the determination unit 103 is not a frequently purchased product, the customer may pay a high price for a single purchase. For example, although the purchase frequency of school bags is not high compared to daily necessities, the customer may purchase an expensive product when purchasing a school bag. Similarly, although the purchase frequency of travel package tours is not high, the customer may purchase an expensive product when purchasing a package tour. In other words, when a customer purchases a product that is not purchased frequently, the customer may prefer a higher price range. Therefore, the determination unit 103 may determine a price range higher than the price range preferred by the customer based on the characteristics of the product in the business type of the recommended product.

[0080] On the other hand, the determination unit 103 may determine a price range for the recommended product that is lower than the price range preferred by the customer, based on the characteristics of the product in a different business type. For example, if the product in a business type different from the common business type determined by the determination unit 103 is not a frequently purchased product, the customer may prefer a lower price range. For example, the customer may not want to spend money on a product that is used infrequently. Therefore, the determination unit 103 may determine a price range that is lower than the price range preferred by the customer, based on the characteristics of the product in the business type of the recommended product.

[0081] The method of determining the price range according to the characteristics of the product in the business category of the recommended product is not limited to these. For example, if the product in the different business category is a frequently purchased product, the determination unit 103 may determine a price range lower than the price range preferred by the customer. Furthermore, if the product in the different business category is a product that is easily damaged, the determination unit 103 may determine a price range lower than the price range preferred by the customer. The characteristics of the product may be registered in advance.

[0082] Here, the determination of recommended products when the acquisition unit 101 further acquires attribute information, purchase history, and behavior history related to a business type different from the common business type will be described. As described above, the analysis unit 102 can analyze customer preferences and price range preferences in different business types based on the attribute information, behavior history, and purchase history related to the common business type and the attribute information, behavior history, and purchase history related to the different business type. Therefore, the determination unit 103 determines a price range different from the price range preferred by the customer in the different business type as the price range of the recommended products.

[0083] For example, if a customer prefers a higher price range in a business type different from the common business type, the determination unit 103 determines recommended products in a higher price range in the different business type. That is, the determination unit 103 determines recommended products in a higher price range than the price range preferred by the customer in the different business type. Also, if a customer prefers a lower price range in a business type different from the common business type, the determination unit 103 determines recommended products in a lower price range in the different business type. That is, the determination unit 103 determines recommended products in a lower price range than the price range preferred by the customer in the different business type.

[0084] The method for determining the price range by the determination unit 103 is not limited to these. The determination unit 103 may determine the price range based on price range preferences and product characteristics in the business format of the recommended product. The method for determining the price range according to the product characteristics in the business format of the recommended product is as described above. Furthermore, the method for determining different business formats by the determination unit 103 is the same as the determination method described above.

[0085] The recommended products determined by the determination unit 103 are recommended to the customer. Information about the recommended products is transmitted, for example, to a customer terminal (not shown) used by the customer. The information about the recommended products is then displayed on the customer terminal in a format that the customer can view. The information about the recommended products may also be displayed on the customer terminal in a format that the customer can order. The information about the recommended products may also be transmitted to a terminal installed in the retailer's facility. In this case, the information about the recommended products may be transmitted in association with information identifying the customer who recommends the recommended products. By transmitting the information about the recommended products to a terminal installed in the retailer's facility, for example, a retailer staff member can confirm the information about the recommended products. Then, for example, when the customer visits the store, the staff member can recommend the recommended products to the customer. In order to realize such processing of recommending recommended products to customers, the product recommendation device 10 may include an output unit that outputs information about the recommended products. For example, the output unit outputs the information about the recommended products to a customer terminal, a terminal installed in the retailer's facility, or the like.

[0086] With reference to FIG. 5, the operation of the product recommendation device 10, which includes an acquisition unit 101, an analysis unit 102, and a determination unit 103, will be described. FIG. 5 is a flowchart showing the operation of the product recommendation device 10. In step S101, the acquisition unit 101 acquires attribute information, behavioral history, and purchase history of customers from multiple retailers that operate a common business type and have different customer segments. In step S102, the customer's interests and price range preferences are analyzed based on the attribute information, behavioral history, and purchase history. In step S103, recommended products in a business type different from the common business type and in a price range different from the customer's preferred price range are determined based on the analysis results. Then, the product recommendation device 10 ends its operation. In step S101, if the acquisition unit 101 acquires information on multiple customers, the processes of steps S102 and S103 may be repeated for each customer.

[0087] In this embodiment, the product recommendation device 10 includes an acquisition unit 101 that acquires attribute information, behavioral history, and purchase history of customers from multiple retailers that operate in a common business format but have different customer segments. Then, the determination unit 103 determines recommended products in a business format different from the common business format and in a price range different from the customer's preferred price range based on an analysis of the customer's interests and price range preferences using the attribute information, behavioral history, and purchase history. Multiple retailers may use collected information to recommend products in a price range different from the price range of the products purchased by the customer. However, recommending products or services in a common business format operated by multiple retailers to customers may result in the retailer losing customers. Therefore, the determination unit 103 determines recommended products in a business format different from the common business format and in a price range different from the customer's preferred price range. This reduces the possibility that retailers will lose customers in a common business format when recommending products in different price ranges.

[0088] Furthermore, a retailer may operate in multiple business categories. Therefore, the determining unit 103 determines recommended products in a business category different from the common business category, thereby enabling the retailer to increase sales in the different business categories.

[0089] Furthermore, the multiple retailers from which the acquisition unit 101 acquires information may operate in a common business format but have different customer segments. Here, a customer segment is a classification of customers targeted by a retailer based on the price range of the products purchased by the customers. In other words, retailers with different customer segments are retailers with different customer demographics. Therefore, the customers from which information can be collected may differ for each retailer. Therefore, the acquisition unit 101 acquires information from multiple retailers that operate in a common business format but have different customer segments. As a result, the acquisition unit 101 may be able to acquire customer information that would be difficult to obtain from a single retailer. Therefore, the retailer may be able to acquire new customers. And, as a result of acquiring new customers, the retailer may be able to increase sales.

[0090] In the present embodiment, when a customer prefers a high price range, the determination unit 103 of the product recommendation device 10 determines recommended products in a price range higher than the customer's preferred price range. Furthermore, when a customer prefers a low price range, the determination unit 103 determines recommended products in a price range lower than the customer's preferred price range. The determination unit 103 determines the price range of the recommended products based on the analysis results of the price range preferences by the analysis unit 102. A customer who prefers a high price range may purchase the recommended product if a product in a price range higher than the customer's preferred price range is recommended. Furthermore, a customer who prefers a low price range may purchase the recommended product if a product in a price range lower than the customer's preferred price range is recommended. In other words, since the determination unit 103 determines the price range based on the analysis results, it is possible to determine recommended products in a price range that suits the customer's price range preferences. As a result, the product recommendation device 10 may be able to increase the likelihood that the customer will purchase the recommended products. Here, the recommended products are products in a business type different from the common business type operated by multiple retailers, allowing the retailer to increase sales in a business type different from the common business type. Furthermore, retailers can reduce the possibility of losing customers in the same business category. As a result, retailers can maintain sales in the same business category. For customers, it is possible to increase the number of product choices they can purchase by recommending products in a price range different from their preferred price range.

[0091] In this embodiment, the determining unit 103 of the product recommendation device 10 determines the price range of the recommended products depending on whether the common business type and the different business type are similar. When the common business type and the different business type are similar, the price range preferences in the common business type and the price range preferences in the different business type tend to be similar. On the other hand, when the common business type and the different business type are not similar, the price range preferences in the common business type and the price range preferences in the different business type tend to be different. Therefore, the determining unit 103 determines the price range of the recommended products depending on whether the common business type and the different business type are similar, thereby making it possible to determine recommended products in a price range that suits the customer's price range preferences. As a result, the product recommendation device 10 may be able to increase the likelihood that the customer will purchase the recommended products. When customers purchase the recommended products, retailers can increase their sales.

[0092] In this embodiment, the determining unit 103 of the product recommendation device 10 determines the price range of the recommended products based on the analysis results regarding price range preferences and the characteristics of products in different business categories. The price range preferred by customers may differ depending on the product in different business categories. Therefore, the determining unit 103 may determine a price range for the recommended products that is higher than the price range preferred by the customer based on the characteristics of the products in different business categories. Furthermore, the determining unit 103 may determine a price range for the recommended products that is lower than the price range preferred by the customer based on the characteristics of the products in different business categories. Determining the price range of the recommended products based on the characteristics of the products in different business categories may increase the likelihood that customers will purchase the recommended products. When customers purchase the recommended products, retailers can increase their sales.

[0093] In the product recommendation device 10 according to this embodiment, the customer's interests include the objects or hobbies that the customer is interested in. Then, the determination unit 103 determines, based on the analysis results of the customer's interests, a business type that matches the customer's interests as a different business type. By determining, by the determination unit 103, a business type that matches the customer's interests as a different business type, it is possible to determine recommended products that are suited to the customer's interests. Because the customer's interests include the objects or hobbies that the customer is interested in, the product recommendation device 10 may be able to increase the likelihood that the customer will purchase the recommended products. Because the recommended products are products in a business type that is different from the common business type, retailers can increase sales in business types that are different from the common business type. Furthermore, retailers can reduce the possibility of losing customers in the common business type. As a result, retailers can maintain sales in the common business type.

[0094] In the product recommendation device 10 according to this embodiment, the attribute information includes the customer's family structure. The analysis unit 102 then analyzes the customer's interests based on the family structure. The analysis unit 102 also analyzes the customer's interests based on, for example, life events of the customer or the customer's family. A customer may be interested in products related to their family. A customer may be particularly interested in products related to life events of the customer or the customer's family. Thus, the acquisition unit 101 can acquire the customer's family structure as attribute information. The determination unit 103 determines recommended products using the customer's interests analyzed based on the family structure. As a result, recommended products suited to the customer's interests can be determined. The product recommendation device 10 may then increase the likelihood that the customer will purchase the recommended products.

[0095] In the product recommendation device 10 according to this embodiment, the behavioral history includes behavior by a customer that can be used to estimate the intended use of a product. The analysis unit 102 then analyzes the customer's interests using the intended use. A customer may purchase a product related to their interests. In this case, the customer's purpose for purchasing a product may reflect the customer's interests or hobbies. Therefore, the analysis unit 102 can determine recommended products that are suited to the customer's interests by analyzing the customer's interests using the intended use. The product recommendation device 10 may then be able to increase the likelihood that the customer will purchase the recommended products.

[0096] In this embodiment, the analysis unit 102 analyzes the customer's interests based on the product details included in the purchase history. A customer may decide which product to purchase based on their own interests. Therefore, by analyzing the customer's interests based on the product details included in the purchase history, the analysis unit 102 can determine recommended products that match the customer's interests. Then, the product recommendation device 10 may be able to increase the likelihood that the customer will purchase the recommended products.

[0097] In the present embodiment, the product recommendation device 10 has an acquisition unit 101 that acquires attribute information, behavioral history, and purchase history related to the common business category. The amount of information acquired by the acquisition unit 101 can be reduced compared to, for example, when the acquisition unit 101 acquires information about a business category other than the common business category. As a result, for example, it may be possible to reduce the amount of communication required for the acquisition of information by the acquisition unit 101. Furthermore, the analysis unit 102 analyzes price range preferences in the common business category based on the attribute information, behavioral history, and purchase history related to the common business category. The analysis unit 102 can reduce the amount of information used in the analysis compared to, for example, when the analysis unit 102 also uses information about a business category other than the common business category. As a result, it may be possible to reduce the amount of analysis processing by the analysis unit 102.

[0098] In this embodiment, the product recommendation device 10 includes an acquisition unit 101 that further acquires attribute information, behavioral history, and purchase history related to business types operated by the retailer that are different from the common business type. The analysis unit 102 then analyzes price range preferences in different business types based on the attribute information, behavioral history, and purchase history related to the common business type and the attribute information, behavioral history, and purchase history related to the business types that are different from the common business type. This may improve the accuracy of the analysis of customer preferences for price ranges in different business types. The determination unit 103 then determines a price range for the recommended products that is different from the price range preferred by the customer in the different business type, thereby making it possible to determine recommended products in a price range that suits the customer's price range preferences. As a result, the product recommendation device 10 may be able to increase the likelihood that the customer will purchase the recommended products. When customers purchase the recommended products, the retailer can increase its sales.

[0099] [Hardware Configuration Example] Fig. 6 is a diagram showing a hardware configuration example of the product recommendation device 20 in the present disclosure. The product recommendation device 20 is realized by a computer. The product recommendation device 20 is an example of the product recommendation device 10 realized by a computer.

[0100] The product recommendation device 20 includes a processor 201, a ROM (Read Only Memory) 202, a RAM (Random Access Memory) 203, a storage device 204 such as a hard disk for storing programs, an input / output interface 205 for inputting and outputting data, and a communication interface 206 for network connection. Each component is connected via a bus 207.

[0101] The processor 201 runs an operating system to control the entire computer. Examples of the processor 201 include a CPU (Central Processing Unit), a DSP (Digital Signal Processor), and a GPU (Graphics Processing Unit). The processor 201 loads programs stored in, for example, the ROM 202 or the storage device 204. The processor 201 then executes each process coded in the program. The processor 201 may execute processes or instructions in the illustrated flowchart based on the program.

[0102] The ROM 202 stores application programs, programs according to the embodiments, etc. The RAM 203 is used as a work area for the processor 201.

[0103] The storage device 204 may be, for example, a semiconductor memory such as a flash memory, a hard disk driver (HDD), etc. The storage device 204 stores, for example, an operating system (OS) program, application programs, programs according to each embodiment, etc.

[0104] The input / output interface 205 is connected to peripheral devices (not shown) via a wired network or a wireless network.

[0105] The communication interface 206 is connected to a communication network (not shown) such as a LAN (Local Network) or a WAN (Wide Area Network) via a wireless or wired network. The communication network may be configured by a plurality of communication networks. This allows the computer to be connected to an external device via the communication network. The product recommendation device 20 may have components other than those shown in FIG. 6. For example, the product recommendation device 20 may have a drive device or the like. For example, the processor 201 may be attached to a drive device or the like and read out programs and data stored in a non-transitory tangible recording medium into the RAM 203.

[0106] Although the present disclosure has been described above with reference to the embodiments, the present disclosure is not limited to the above-described embodiments. Various modifications that can be understood by those skilled in the art can be made to the configuration and details of the present disclosure within the scope of the present disclosure. Furthermore, the configurations in the respective embodiments can be combined with each other without departing from the scope of the present disclosure.

[0107] Some or all of the above-described embodiments can be described as, but are not limited to, the following supplementary notes.

[0108] (Supplementary Note 1) A product recommendation device comprising: an acquisition means for acquiring attribute information, behavioral history, and purchase history of customers at multiple retailers that operate a common business format and have different customer segments; an analysis means for analyzing the interests and price range preferences of the customers based on the attribute information, behavioral history, and purchase history; and a determination means for determining recommended products in a business format different from the common business format and in a price range different from the price range preferred by the customers based on the analysis results.

[0109] (Supplementary Note 2) The product recommendation device according to Supplementary Note 1, wherein the determining means determines, as the different business type, a business type that matches the interest of the customer based on an analysis result regarding the interest of the customer.

[0110] (Supplementary Note 3) The product recommendation device according to Supplementary Note 1 or 2, wherein the customer segment is a classification of the customers targeted by the retailer according to the price range of the products purchased by the customers.

[0111] (Supplementary Note 4) The product recommendation device according to any one of Supplementary Notes 1 to 3, wherein, when the customer prefers a higher price range, the determination means determines the recommended products to be in a price range higher than the price range preferred by the customer.

[0112] (Supplementary Note 5) The product recommendation device according to any one of Supplementary Notes 1 to 4, wherein, when the customer prefers a lower price range, the determination means determines the recommended products to be in a price range lower than the price range preferred by the customer.

[0113] (Supplementary Note 6) The product recommendation device according to any one of Supplementary Notes 1 to 5, wherein the determining means determines the price range of the recommended product depending on whether the common business type and the different business type are similar to each other.

[0114] (Supplementary Note 7) The product recommendation device according to any one of Supplementary Notes 1 to 6, wherein the determining means determines the price range of the recommended products based on the analysis results regarding preferences for the price range and characteristics of products in the different business categories.

[0115] (Supplementary Note 8) The product recommendation device according to any one of Supplementary Notes 1 to 7, wherein the attribute information includes a family structure of the customer, and the analysis means analyzes the interests of the customer based on the family structure.

[0116] (Supplementary Note 9) The product recommendation device according to Supplementary Note 8, wherein the analysis means analyzes the interests of the customer based on life events of the customer or a family of the customer.

[0117] (Supplementary Note 10) The product recommendation device according to any one of Supplementary Notes 1 to 9, wherein the interests of the customer include a subject or hobby in which the customer is interested.

[0118] (Supplementary Note 11) The product recommendation device according to any one of Supplementary Notes 1 to 10, wherein the behavioral history includes behavior by the customer that can be used to estimate a use of the product, and the analysis means analyzes the interest of the customer using the use.

[0119] (Supplementary Note 12) The product recommendation device according to any one of Supplementary Notes 1 to 11, wherein the analysis means analyzes the interest of the customer based on the content of the product included in the purchase history.

[0120] (Supplementary Note 13) The product recommendation device according to any one of Supplementary Notes 1 to 12, wherein the acquisition unit acquires the attribute information, the behavior history, and the purchase history related to the common business category.

[0121] (Supplementary Note 14) The product recommendation device according to Supplementary Note 13, wherein the analysis means analyzes the preferences for the price range in the common business category based on the attribute information, the behavior history, and the purchase history related to the common business category.

[0122] (Supplementary Note 15) The product recommendation device according to Supplementary Note 14, wherein the determining means determines the recommended products in the price range different from the price range preferred by the customer in the common business category.

[0123] (Supplementary Note 16) The product recommendation device according to any one of Supplementary Notes 13 to 15, wherein the acquisition means acquires the attribute information, the behavior history, and the purchase history related to a business type different from the common business type operated by the retailer.

[0124] (Supplementary Note 17) The product recommendation device according to Supplementary Note 16, wherein the analysis means analyzes the preferences for the price range in the different business type based on the attribute information, the behavior history, and the purchase history related to the different business type from the common business type.

[0125] (Supplementary Note 18) The product recommendation device according to Supplementary Note 17, wherein the determining means determines the price range of the recommended product to be different from the price range preferred by the customer in the different business category.

[0126] (Supplementary Note 19) A product recommendation method comprising: acquiring attribute information, behavioral history, and purchase history of customers at multiple retailers operating in a common business format and with different customer segments; analyzing the interests and price range preferences of the customers based on the attribute information, behavioral history, and purchase history; and determining, based on the analysis results, recommended products in a business format different from the common business format and in a price range different from the price range preferred by the customers.

[0127] (Supplementary Note 20) A recording medium storing a program that causes a computer to execute the following process: acquiring attribute information, behavioral history, and purchase history of customers at multiple retailers that operate a common business format and have different customer segments; analyzing the interests and price range preferences of the customers based on the attribute information, behavioral history, and purchase history; and determining recommended products in a different price range from the price range preferred by the customers in a different business format from the common business format based on the analysis results.

[0128] (Supplementary Note 21) A program that causes a computer to execute the following process: acquiring customer attribute information, behavioral history, and purchase history from multiple retailers that operate a common business format and have different customer segments; analyzing the customer's interests and price range preferences based on the attribute information, behavioral history, and purchase history; and determining recommended products in a different price range from the customer's preferred price range in a different business format from the common business format based on the analysis results.

[0129] Some or all of the configurations described in Supplementary Notes 2-18, which are dependent on Supplementary Note 1, may also be dependent on Supplementary Notes 19-21 in the same dependency relationship as Supplementary Note 2-18. Not limited to Supplementary Notes 1, 19-21, but also to various hardware, software, various recording devices for recording software, or systems, some or all of the configurations described as Supplements may be made dependent on each other within the scope of the above-mentioned embodiments.

[0130] 10, 20 Product recommendation device 90 Database 101 Acquisition unit 102 Analysis unit 103 Decision unit 201 Processor 202 ROM 203 RAM 204 Storage device 205 Input / output interface 206 Communication interface 207 Bus

Claims

1. A product recommendation device comprising: an acquisition means for acquiring customer attribute information, behavioral history, and purchase history from multiple retailers that operate a common business format and have different customer segments; an analysis means for analyzing the customer's interests and price range preferences based on the attribute information, behavioral history, and purchase history; and a determination means for determining recommended products in a business format different from the common business format and in a price range different from the price range preferred by the customer based on the analysis results.

2. The product recommendation device according to claim 1, wherein the determining means determines, as the different business type, a business type that matches the customer's interests based on an analysis result regarding the customer's interests.

3. The product recommendation device according to claim 1 or 2, wherein the customer segment is a classification of the customers targeted by the retailer according to the price range of the products purchased by the customers.

4. The product recommendation device according to any one of claims 1 to 3, wherein, when the customer prefers a higher price range, the determination means determines the recommended products to be in a price range higher than the price range preferred by the customer.

5. The product recommendation device according to any one of claims 1 to 4, wherein, when the customer prefers a lower price range, the determination means determines the recommended products to be in a price range lower than the price range preferred by the customer.

6. The product recommendation device according to any one of claims 1 to 5, wherein the determining means determines the price range of the recommended product depending on whether the common business type and the different business type are similar to each other.

7. The product recommendation device according to any one of claims 1 to 6, wherein the determining means determines the price range of the recommended products based on the analysis results regarding preferences for the price range and the characteristics of products in the different business categories.

8. The product recommendation device according to any one of claims 1 to 7, wherein the attribute information includes a family structure of the customer, and the analysis means analyzes the interests of the customer based on the family structure.

9. The product recommendation device according to claim 8, wherein the analysis means analyzes the interests of the customer based on life events of the customer or the customer's family.

10. The product recommendation device according to any one of claims 1 to 9, wherein the interests of the customer include subjects or hobbies in which the customer is interested.

11. A product recommendation device described in any one of claims 1 to 10, wherein the behavioral history includes behavior by the customer that can be used to estimate the intended use of the product, and the analysis means uses the intended use to analyze the interests of the customer.

12. The product recommendation device according to any one of claims 1 to 11, wherein the analysis means analyzes the interests of the customer based on the contents of the products included in the purchase history.

13. The product recommendation device according to any one of claims 1 to 12, wherein the acquisition means acquires the attribute information, the behavioral history, and the purchase history related to the common business category.

14. The product recommendation device according to claim 13, wherein the analysis means analyzes the preferences for the price range in the common business category based on the attribute information, the behavioral history, and the purchase history related to the common business category.

15. The product recommendation device according to claim 14, wherein the determining means determines the recommended products in the price range different from the price range preferred by the customer in the common business category.

16. A product recommendation device according to any one of claims 13 to 15, wherein the acquisition means acquires the attribute information, the behavioral history, and the purchase history relating to a business type different from the common business type operated by the retailer.

17. The product recommendation device according to claim 16, wherein the analysis means analyzes the preferences for the price range in the different business type based on the attribute information, the behavioral history, and the purchase history relating to the different business type from the common business type.

18. The product recommendation device according to claim 17, wherein the determining means determines the price range of the recommended product to be different from the price range preferred by the customer in the different business category.

19. A product recommendation method comprising: acquiring customer attribute information, behavioral history, and purchase history from multiple retailers operating in a common business format and with different customer segments; analyzing the customer's interests and price range preferences based on said attribute information, behavioral history, and purchase history; and determining, based on the analysis results, recommended products in a business format different from said common business format and in a price range different from the price range preferred by said customer.

20. A recording medium storing a program that causes a computer to execute the following process: acquiring customer attribute information, behavioral history, and purchase history from multiple retailers that operate a common business format and have different customer segments; analyzing the customer's interests and price range preferences based on the attribute information, behavioral history, and purchase history; and determining recommended products in a different price range from the customer's preferred price range in a business format different from the common business format based on the analysis results.

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