JP7685982B2 - Purchasing support device and purchasing support method - Google Patents

Description

The present invention relates to a purchasing support device and a purchasing support method.

Conventionally, trends of customers and the market have been observed and analyzed using various tools such as MA (Marketing Automation) and CRM (Customer Relationship Management), and products have been proposed to customers and the market, and relationships have been built and maintained based on the results.
Furthermore, with the recent rise in online shopping, such technologies have evolved to provide highly accurate product suggestions and purchasing behavior suggestions based on each customer's individual online behavior history.

As mentioned above, conventional technologies that enable product suggestions to customers include, for example, technology that recommends product lineups that customers using a store need (see Patent Document 1).

This technology relates to an assortment recommendation device that includes an acquisition means for acquiring identifiers of customers who visit a target store, identifiers of customers who purchase products at other stores, and identifiers of the products, and a determination means for determining products recommended for ordering at the target store based on the purchase history at the other stores of products purchased at the other stores by customers who visit the target store.

In addition, a technology has been proposed that presents a second product to be combined with the product that the customer has picked up or a product similar to that product (see Patent Document 2).

This technology relates to an information processing device that includes a detection means for detecting the action of a customer grasping a product from an image of the customer, an extraction means for extracting the product that was the subject of grasping detected by the detection means, a second extraction means for extracting a second product to be combined with the product extracted by the extraction means or a product similar to the first product, and a presentation means for presenting the second product extracted by the second extraction means.

In addition, technology has been proposed that provides information to customers at appropriate times (see Patent Document 3).

This technology relates to an information processing method that causes a computer to execute a process of acquiring customer behavior attribute information from sensors including fixture cameras installed in correspondence with fixtures installed in a store, selecting promotional content classified into a plurality of categories based on the behavioral attribute information, and outputting the selected promotional content from an output device including a first display unit installed in correspondence with the fixture.

JP 2020-154510 A JP 2016-62372 A JP 2022-72337 A

Indeed, the number of purchasing opportunities at online shops has increased dramatically, and it is now commonplace for people to purchase products based solely on images and information on a web screen, without actually seeing the actual item.
On the other hand, there are many products for which the elements that can only be sensed in the real world, such as touch, scent, and texture, in addition to the detailed shape, subtle color usage, and size, are still important, and the complex balance of these elements is also important.

Customers who are interested in such products may find it difficult to make a purchasing decision based solely on information available online.

The above points to the limitations of conventional methods that suggest products and services to customers based solely on their behavior in online shops, etc. The inventors therefore focused on the issue that in order to make suitable product suggestions that truly meet customers' desires, it is necessary to also take into account the behavior of customers when confronted with the actual product.

The object of the present invention is to provide technology that can efficiently provide accurate purchasing support information based on customer purchasing behavior in both online and brick-and-mortar stores.

The purchasing support device of the present invention, which solves the above-mentioned problems, is characterized by comprising a storage device that stores customer behavior information in a physical store and an online store, a process that extracts behavior information of a specific customer observed with respect to a specific product in both the physical store and the online store from the behavior information, a process that identifies differences between the behavior of the specific customer with respect to the specific product indicated by the behavior information between the physical store and the online store, and a calculation device that executes a process of identifying customers who are exhibiting a predetermined behavior with respect to the specific product based on the behavior information, and notifying a terminal of the customer of information of the differences.
In addition, the purchasing support method of the present invention is characterized in that an information processing device is equipped with a storage device that stores customer behavior information in a physical store and an online store, and executes a process of extracting behavior information of a specific customer observed with respect to a specific product in both the physical store and the online store from the behavior information, a process of identifying differences between the behavior of the specific customer with respect to the specific product indicated by the behavior information, between the physical store and the online store, and a process of identifying customers who are exhibiting a predetermined behavior with respect to the specific product based on the behavior information, and notifying information of the difference to a terminal of the customer.

The present invention makes it possible to efficiently provide accurate purchasing support information based on customer purchasing behavior in both online and brick-and-mortar stores.

1 is a diagram showing a network configuration including a purchasing support device according to an embodiment of the present invention. 2 is a diagram illustrating an example of a hardware configuration of a purchasing support device according to the present embodiment. FIG. 2 is a diagram illustrating an example of a hardware configuration of an online store server according to the present embodiment. FIG. 2 is a diagram illustrating an example of a hardware configuration of a brick-and-mortar store system in this embodiment. FIG. 2 is a diagram illustrating an example of the configuration of an individual behavior history DB in the present embodiment. 11 is a diagram showing an example of the configuration of a product interest/purchase change DB in the present embodiment. FIG. FIG. 11 is a diagram illustrating an example of behavior transition in this embodiment. FIG. 11 is a diagram illustrating an example of behavior transition in this embodiment. FIG. 11 is a diagram illustrating an example of behavior transition in this embodiment. FIG. 11 is a diagram illustrating an example of behavior transition in this embodiment. FIG. 11 is a diagram illustrating an example of behavior transition in this embodiment. FIG. 11 is a diagram illustrating an example of behavior transition in this embodiment. FIG. 11 is a diagram illustrating an example of behavior transition in this embodiment. FIG. 2 is a diagram illustrating an example of the configuration of a product comment DB in the present embodiment. FIG. 2 is a diagram illustrating an example of the configuration of a product DB in the present embodiment. FIG. 2 is a diagram showing an example of a flow of a purchasing support method according to the present embodiment. FIG. 11 is a diagram illustrating an example of behavior transition in this embodiment. FIG. 11 is a diagram showing an example of output in this embodiment. FIG. 11 is a diagram showing an example of output in this embodiment.

<Network Configuration>
An embodiment of the present invention will be described in detail below with reference to the drawings. Fig. 1 is a network configuration diagram including a purchasing support device 100 according to the present embodiment. The purchasing support device 100 shown in Fig. 1 is a computer capable of efficiently providing accurate purchasing support information based on the purchasing behavior of customers in both online and brick-and-mortar stores.

As shown in FIG. 1, the purchasing support device 100 of this embodiment is communicatively connected to a user terminal 200, an online store server 300, a physical store system 400, and a business operator system 500 via a network 1. Therefore, these may be collectively referred to as a purchasing support system 10.

The purchasing support device 100 of this embodiment can be said to be a service providing device that acquires behavior information of each customer at an online store or a physical store, which is observed and managed by the online store server 300 or the physical store system 400, and distributes information to customers and specified businesses based on this information.

Of course, at least one of the online store server 300, the physical store system 400, and the business operator system 500 may operate as the purchasing support device 100.

The user terminal 200 mentioned above is a terminal of a customer who visits an online store or a physical store to purchase a product. Specifically, it can be a smartphone, a tablet terminal, a personal computer, etc.

The online store server 300 is a server device that publishes an online store on the Internet and performs information distribution and payment processing in response to various requests from the user terminal 200 (e.g., a request to view product information or a request to purchase the product).

This online store server 300 observes the behavior of each customer in the online store and stores the observation results in an appropriate storage device. In this case, the behavior observation is performed by extracting data on events such as the number of accesses to each product page, the viewing time, and the purchase frequency from the access log of each customer, and is assumed to be performed using general web marketing techniques. The online store server 300 can distribute the above-mentioned observation results, that is, data on each customer's behavior, to the purchasing support device 100 via the network 1 at regular intervals or in response to a request from the purchasing support device 100.

The real store system 400 is a store management system installed in a real store, and includes a POS (Point Of Sales) unit 430 and an observation sensor 450. The POS unit 430 is a unit that executes the processes of registering and settling products brought to the cash register by customers, and information on the products that are the subject of the settling here is managed as purchase information of the products by the customer and distributed to the purchase support device 100.

The observation sensor 450 is a sensor unit that detects, for example, when a customer who has moved up to a specific product shelf in a physical store reaches for or actually picks up the desired product, i.e., which product the customer has shown interest in. This observation sensor 450 may be any suitable unit already used for the same purpose, such as an infrared laser that observes the position of the hand or arm, a camera for image recognition of the product that is the target of the action, or a weight sensor that detects the action of lifting a specific product on a specific shelf. Information on the customer's actions and the target product that are detected here is managed by the physical store system 400 as information on the customer's interest in the product, and is distributed to the purchasing support device 100.

The business operator system 500 is a system operated by the operator of an online store or a physical store, or by the manufacturer of the products sold at the stores. This business operator system 500 acquires information on the sales of products at the online store or the physical store, and the interest and purchasing behavior of each customer from the purchase support device 100, and utilizes the information for operations such as store operation, product development, and marketing strategy.
<Hardware Configuration>
The hardware configuration of the purchasing support device 100 of this embodiment is as shown in FIG.

That is, the purchasing support device 100 includes a storage device 101, a memory 103, a computing device 104, and a communication device 105.

Of these, the storage device 101 is composed of an appropriate non-volatile storage element such as an SSD (Solid State Drive) or a hard disk drive.

The memory 103 is also composed of a volatile memory element such as a RAM (Random Access Memory).

The calculation device 104 is a CPU that executes the program 102 stored in the storage device 101 by reading it into the memory 103, etc., and performs overall control of the device itself as well as various judgments, calculations, and control processes.

The communication device 105 is assumed to be a network interface card or the like that is connected to the network 1 and handles communication processing with the user terminal 200, the online store server 300, the physical store system 400, and the business operator system 500, etc.

In addition to the program 102 for implementing the functions required for the purchasing support device 100 of this embodiment, the storage device 101 also stores at least an individual behavior history DB 125, a product interest and purchase change DB 126, a product comment DB 127, and a product DB 128. However, details of these databases will be described later.

Next, FIG. 3 shows an example of the hardware configuration of the online store server 300 in this embodiment. The online store server 300 in this embodiment includes a storage device 301, a memory 303, a computing device 304, and a communication device 305.

Of these, the storage device 301 is composed of an appropriate non-volatile storage element such as an SSD (Solid State Drive) or a hard disk drive.

The memory 303 is also composed of a volatile memory element such as a RAM (Random Access Memory).

The calculation device 304 is a CPU that executes the program 302 stored in the storage device 301 by reading it into the memory 303, etc., and performs overall control of the device itself as well as various judgments, calculations, and control processes.

The communication device 305 is assumed to be a network interface card or the like that is connected to the network 1 and handles communication processing with the purchasing support device 100, etc.

In addition to the program 302 for implementing the functions required for the online store server 300 of this embodiment, the storage device 301 also stores at least online behavior information 325. This online behavior information 325 is the observation result of the behavior of each customer at the online store, and corresponds to data such as the number of accesses to each product page, viewing time, and purchase history.

The online store server 300 performs customer authentication upon receiving a log-on action from the customer via the user terminal 200. The online store server 300 manages information related to various behaviors of the customer, such as interest behavior and purchasing behavior, by linking it to the customer's identification information (e.g., member ID, etc.).

FIG. 4 shows an example of the hardware configuration of the physical store system 400 in this embodiment. The physical store system 400 in this embodiment includes a storage device 401, a memory 403, a computing device 404, a communication device 405, a POS unit 430, and an observation sensor 450.

Of these, the storage device 401 is composed of an appropriate non-volatile storage element such as an SSD (Solid State Drive) or a hard disk drive.

In addition, memory 403 is composed of a volatile memory element such as RAM (Random Access Memory).

The calculation device 404 is a CPU that executes the program 402 stored in the storage device 401 by reading it into the memory 403, etc., to perform overall control of the device itself, as well as various judgments, calculations, and control processes.

The communication device 405 is assumed to be a network interface card or the like that is connected to the network 1 and handles communication processing with the purchasing support device 100, etc.

The POS unit 430 is a unit that executes the processes of registering and settling the items brought to the register by the customer, and the information on the items that are the subject of the settling here is managed as the purchase information of the item by the customer, i.e., as part of the physical store behavior information 425.

The observation sensor 450 is a sensor unit that detects, for example, when a customer moves up to a specific product shelf in a physical store and reaches for or actually picks up the desired product, i.e., detects which product the customer is interested in.

This observation sensor 450 may be an appropriate unit that is already in use for the same purpose, such as an infrared laser that observes the position of hands and arms, a camera for image recognition of the product that is the target of the action, or a weight sensor that detects the lifting action of a specific product on a designated shelf.

The information on the customer's behavior and the target products sensed here is managed as information on the customer's behavior of interest, i.e., as part of the physical store behavior information 425.

The physical store system 400 performs customer authentication in response to a log-on action from a customer via a specified in-store terminal or the user terminal 200. Therefore, the physical store system 400 manages information related to various behaviors of the customer, such as interest behavior and purchasing behavior, by linking it to the customer's identification information (e.g., member ID, etc.).

In addition to the program 402 for implementing the functions required for the physical store system 400 of this embodiment, at least physical store behavior information 425 is stored in the storage device 401. This physical store behavior information 425 is the observation result of the behavior of each customer in the physical store, and is composed of information obtained from the POS unit 430 and the observation sensor 450 described above.
<Data structure example>
Next, a description will be given of various types of information used by the purchase support device 100 of this embodiment. Fig. 5 shows an example of the individual behavior history DB 125 in this embodiment.

The individual behavior history DB125 in this embodiment is a database that stores behavior information acquired, for example, at regular intervals, from the online store server 300 and the physical store system 400 described above.

This individual behavior history DB125 is a collection of records that link data such as the identification information (such as a pre-assigned store number) of the store (online or brick-and-mortar) visited by the customer, the date and time, the ID of the product that was the subject of interest or purchase behavior, and the type of behavior (purchase or no purchase (interest only)), using a personal ID that uniquely identifies the customer as a key.

Figure 6 also shows an example of the configuration of the product interest/purchase change DB 126 in this embodiment. The product interest/purchase change DB 126 in this embodiment is a database in which each record of the individual behavior history DB 125 is identified, processed, and stored according to a predetermined condition (temporal transition, i.e., the presence or absence of change, in interest behavior or purchasing behavior).

This product interest/purchase change DB126 is a collection of records that use the product ID of the product whose behavior information has been stored after the above-mentioned identification and processing as a key to link data such as the date of storage of the behavior information related to the product, the "type of change" in interest behavior or purchasing behavior, and, if the type is a "switch," i.e., a product change, the ID of the product after the change (the product to which the switch is made).

The conditions for identifying the above-mentioned records are as follows:
<Condition 1: Add01 (love at first sight)>
"A product that has not been purchased at a physical store or online store for a certain period of time (e.g., the past year)" and "a purchase history has occurred at a physical store after the certain period of time." The concept of this condition 1 is illustrated in FIG.
<Condition 2: Add02 (I was undecided but bought it immediately after seeing it in person)>
"A product that has not been purchased for a certain period of time (e.g., the past year)" and "has a browsing history at an online store during the certain period of time" and "has a purchase history at a brick-and-mortar store after the certain period of time has passed." The concept of this condition 2 is illustrated in FIG. 8A.
<Condition 3: Add03 (I was undecided but bought it after seeing it in person)>
"A product that has not been purchased for a certain period of time (e.g., the past year)", "there is a browsing history at an online store during that period = interest behavior but no purchase", and "after that period, a purchase history occurs at a brick-and-mortar store or online store". The concept of this condition 3 is illustrated in FIG. 8B.
<Condition 4: Switch01 (love at first sight)>
In a situation where there exists "product A with purchase history periodically (e.g. once every three months)" and "product C with no purchase history or browsing history for a certain period of time (e.g. past one year)", "after the certain period, a purchase history of product C occurs at a physical store" and "after that purchase history, no purchase history of product A occurs for a certain period of time or more." The concept of this condition 4 is illustrated in FIG. 9.
<Condition 5: Switch02 (I was undecided but bought it immediately after seeing it in person)>
In a situation where there exists "product A that is purchased periodically (e.g. once every three months)" and "product C that has no purchase history for a certain period of time (e.g. the past year) but has browsing history at an online store,""a purchase history of product C at a physical store occurs," and "after that purchase history, there is no purchase history of product A for a certain period of time or more." The concept of this condition 5 is illustrated in FIG. 10.
<Condition 6: Switch03 (I was undecided but bought it after seeing it in person)>
In a situation where there exists "product A that is purchased periodically (e.g. once every three months)" and "product C that has no purchase history for a certain period of time (e.g. the past year) but has a browsing history at an online store,""after the browsing history of product BC at the above-mentioned online store is generated, a browsing history is also generated at a physical store, and after a certain period of time has passed since the browsing, a purchase history of product B is generated at either the online store or the physical store," and "after the purchase history is generated, no purchase history of product A is generated for a certain period of time or more." The concept of this condition 6 is illustrated in FIG. 11.
<Condition 7: Loss01 (I was undecided, but lost interest after seeing the actual product)>
"A product that has no purchase history for a certain period of time (e.g., the past year) but has a browsing history at an online store,""after the browsing history at the online store was generated, a browsing history was generated at a brick-and-mortar store," and "after the browsing, no purchase history of the product has been generated at either the brick-and-mortar store or the online store for a certain period of time or more." The concept of this condition 7 is illustrated in FIG. 12.

Figure 13 also shows an example of the configuration of the product comment DB 127 in this embodiment. The product comment DB 127 in this embodiment is a database that stores comments generated by the purchasing support device 100 based on the contents of the records in the above-mentioned product interest purchase change DB 126.

Specifically, it is a collection of records that link together data such as the storage date and time of the comment on the product, and the comment itself, using a product ID that uniquely identifies the product that the comment is about as a key.

The method for generating comments is based on the values of "Product ID", "Type of change" in interest behavior or purchasing behavior, and "Switch" contained in a record in the product interest/purchase change DB126. For example, if the "Type of change" is "Switch", the purchasing support device 100 extracts the values of the "Product ID" and "Switch" items in the record, and sets the values of each item in a template text prepared in advance to complete the comment.

However, rather than generating a comment based on the existence of only one record, it is more appropriate to generate a comment when a certain number of records, i.e., a predetermined number of customers, exhibit similar behavior.

More specifically, when the template sentence described above is "When many people see the real thing, they switch from 'from' to 'to'!", the purchasing support device 100 sets the product name corresponding to the extracted "product ID" in "from" and the product name corresponding to the extracted "switch" product ID in "to", and generates a comment such as "When many people see the real thing, they switch from product A to product B!".

For example, if the "Change Type" is "Add," the purchasing support device 100 extracts the value of the "Product ID" field in the record, and sets the values of each field in a template text prepared in advance to complete the comment.

More specifically, when the template text above is "Many people buy 'Add' when they see the real thing!", the purchasing support device 100 sets the product name corresponding to the extracted "product ID" to "Add" and generates a comment such as "Many people buy product C when they see the real thing!".

Figure 14 also shows an example of the configuration of product DB 128 in this embodiment. Product DB 128 in this embodiment is a database that is generated and managed by receiving information about the product from a business operator system 500 operated by a manufacturer of the product sold in an online store or a physical store.

This product DB 128 is a collection of records that link data such as the product's major product classification, minor product classification, product category, and product name, using a product ID that uniquely identifies the product as a key.
<Flow example>
The actual procedure of the purchasing support method in this embodiment will be described below with reference to the drawings. The various operations corresponding to the purchasing support method described below are realized by a program that is read into memory or the like and executed by purchasing support device 100. This program is composed of codes for performing the various operations described below.

Figure 15 is a diagram showing an example of the flow of the purchasing support method in this embodiment. In this case, the purchasing support device 100 extracts records that match the conditions 1 to 7 already mentioned (illustrated in Figures 7 to 12) from the individual behavior history DB 125 of the storage device 101 (s10).

The purchasing support device 100 also identifies differences between physical stores and online stores in customer behavior regarding specific products, as indicated by the records extracted in s10 (s11). With regard to identifying these differences, the preliminary preparations have been completed by extracting records that match conditions 1 to 7 in s10, and the process here involves selecting records related to customers of "specific products" from among the records extracted in s10.

A "specific" product means that a certain number or more of records (e.g., a certain percentage or more of the total number of records) have been extracted, i.e., the product has a certain degree of universality in terms of behavior information. Therefore, even if a record is extracted in S10 as matching any of conditions 1 to 7, if, for example, only one record is extracted for that "product," it can be determined that there is no universality.

Therefore, when the number of records relating to the same product (e.g. product A) or products in the same genre (e.g. cosmetics, lipstick) that meet the same condition (e.g. condition 1) exceeds a certain threshold, and these records are stored for a certain number of customers (meaning that the records are not for only one customer), a "difference" in customers relating to a "specific product" is identified.

The above-mentioned "differences" are as already shown in Conditions 1 to 7, and examples of such differences include changes in purchasing behavior in accordance with the order of visits between the online store and the physical store (e.g., online store → physical store) (e.g., stopping purchases of a product that was continued at the online store or switching to another product after seeing it at the visited physical store), changes from a state where there was no interest or purchasing behavior at the online store to an interest or purchasing behavior occurring when visiting the physical store, and changes in interest behavior in accordance with the order of visits between the online store and the physical store (e.g., online store → physical store) (e.g., stopping interest in a product that was continued at the online store or switching to another product after seeing it at the visited physical store).

The purchasing support device 100 then applies the difference identified in s11, i.e., the value contained in the record that matches any one of conditions 1 to 7, to a specified template text to generate a comment (s12). As already mentioned, the method of generating such a comment involves selecting a template text according to the "type of change" in the record, and setting the product name (identified in product DB 128 based on the product ID) corresponding to the value of the "product ID" field in the record in the value setting field of the template text to complete the comment.

Specifically, when the template text above is "Many people buy 'Add' when they see the real thing!", the purchasing support device 100 sets the product name (e.g. product C) corresponding to the extracted "product ID" to "Add" and generates a comment such as "Many people buy product C when they see the real thing!".

The purchasing support device 100 also identifies customers who exhibit a predetermined behavior (e.g., interest behavior or purchasing behavior) regarding the specific product based on the individual behavior history DB 125 (s13).

Then, the purchasing support device 100 notifies the user terminal 200 of the customer identified in s13 of the comment generated in s12 (see Figures 16 to 18) (s14), and ends the process.

Note that if a difference cannot be identified in step S11 above, i.e., if records matching any of conditions 1 to 7 cannot be identified, the purchasing support device 100 will notify the user terminal 200 of this fact.

The above describes in detail the best mode for carrying out the present invention, but the present invention is not limited to this, and various modifications are possible without departing from the spirit of the present invention.

This embodiment makes it possible to efficiently provide accurate purchasing support information based on customer purchasing behavior both online and in brick-and-mortar stores.

The description in this specification makes clear at least the following. That is, in the purchasing support device of this embodiment, when identifying the difference, the computing device may identify a change in purchasing behavior in accordance with the order of visits to the online store and the physical store, and notify the terminal of information on the change.

This makes it possible to identify changes, such as when a customer continues to purchase a product at an online store before visiting a physical store, but when they receive the actual product at the physical store, they stop purchasing the product, switch to another product, or end up purchasing an additional product. It also makes it possible to present information to customers who have purchased or viewed the product multiple times at online stores, informing them of the change in purchasing behavior when they actually check out the product at a physical store (e.g., stop purchasing the product, switch to another product, or purchase an additional product).

In other words, by visiting a brick-and-mortar store as well as an online store to check out the actual products, it becomes possible to efficiently output suggestions for products that the customer should avoid purchasing, change their purchase choices, or purchase in addition. Ultimately, it becomes possible to efficiently provide more accurate purchasing support information that takes into account the customer's purchasing behavior in both online and brick-and-mortar stores.

Furthermore, in the purchasing support device of this embodiment, when identifying the difference, the calculation device may identify a change from a state in which there was no interest behavior or purchasing behavior at the online store to an interest behavior or purchasing behavior occurring when visiting the physical store, and notify the terminal of information about the change.

This makes it possible to deal with situations where a customer who does not even check out a product in an online store actually checks the product in a brick-and-mortar store and immediately purchases it, and to propose information about the situation to other customers who have the same or similar attributes as the customer who ended up making the immediate purchase. Ultimately, it becomes possible to efficiently provide more accurate purchasing support information that takes into account the purchasing behavior of customers in both online and brick-and-mortar stores.

In addition, in the purchasing support device of this embodiment, when identifying the difference, the computing device may identify a change in interest behavior in accordance with the order of visits to the online store and the physical store, and notify the terminal of information on the change.

This makes it possible to identify changes such as whether a certain number of customers lose interest in a product that they only viewed in an online store when they get the actual product in a physical store, or whether they end up purchasing it at the physical store, or whether they end up purchasing it at the next online store they visit, and to provide information such as "in many cases, customers end up purchasing the product after actually checking it out at a physical store" to customers who have shown a certain level of interest in the product by viewing it multiple times in the online store. In other words, it is possible to efficiently output suggestions that may lead to a good purchasing experience for the customer if they visit a physical store as well as an online store to check out the actual product, or suggestions that they may lose interest. Ultimately, it becomes possible to efficiently provide more accurate purchasing support information based on the purchasing behavior of customers in both online and physical stores.

In addition, in the purchasing support device of this embodiment, if the computing device is unable to identify the difference, it may notify the terminal of that fact.

This makes it possible to inform customers of events in which there is no difference in interest or purchasing behavior regarding a product, whether the customer actually checks out the product in a brick-and-mortar store or views product information in an online store. This in turn makes it possible to efficiently provide more accurate purchasing support information that takes into account the customer's purchasing behavior in both online and brick-and-mortar stores.

In addition, in the purchasing support method of this embodiment, when identifying the difference, the information processing device may identify a change in purchasing behavior in accordance with the order of visits to the online store and the physical store, and notify the terminal of information on the change.

In addition, in the purchasing support method of this embodiment, when the information processing device identifies the difference, it may identify a change from a state in which there was no interest or purchase behavior at the online store to an interest or purchase behavior occurring when visiting the physical store, and notify the terminal of information on the change.

In addition, in the purchasing support method of this embodiment, when identifying the difference, the information processing device may identify a change in interest behavior in accordance with the order of visits to the online store and the physical store, and notify the terminal of information on the change.

In addition, in the purchasing support method of this embodiment, if the information processing device is unable to identify the difference, it may notify the terminal of that fact.

1 Network 10 Purchasing support system 100 Purchasing support device 101 Storage device 102 Program 103 Memory 104 Arithmetic device 105 Communication device 125 Personal behavior history DB
126 Product Interest and Purchase Change DB
127 Product Comment DB
128 Product DB
200 User terminal 300 Online store server 301 Storage device 302 Program 303 Memory 304 Arithmetic device 305 Communication device 325 Online behavior information 400 Physical store system 401 Storage device 402 Program 403 Memory 404 Arithmetic device 405 Communication device 425 Physical store behavior information 430 POS unit 450 Observation sensor 500 Business operator system

Claims (10)

A storage device that stores customer behavior information in physical stores and online stores;
a computing device that executes a process of extracting behavior information of a specific customer observed with respect to a specific product at both the physical store and the online store from the behavior information, a process of identifying a difference between the behavior of the specific customer with respect to the specific product at the physical store and the online store, which is indicated by the behavior information, and a process of identifying a customer who exhibits a predetermined behavior with respect to the specific product based on the behavior information, and notifying a terminal of the customer of information of the difference;
A purchasing support device comprising: The computing device includes:
When identifying the difference, a change in purchasing behavior according to the order of visits to the online store and the physical store is identified, and information on the change is notified to the terminal.
2. The purchasing support device according to claim 1. The computing device includes:
When identifying the difference, a change from a state in which there was no interest behavior or purchase behavior at the online store to a state in which there was interest behavior or purchase behavior when visiting the physical store is identified, and information on the change is notified to the terminal.
3. The purchasing support device according to claim 2. The computing device includes:
When identifying the difference, a change in the interest behavior according to the order of visits to the online store and the physical store is identified, and information on the change is notified to the terminal.
2. The purchasing support device according to claim 1. The computing device includes:
If the difference cannot be identified, a notification to that effect is sent to the terminal.
2. The purchasing support device according to claim 1. An information processing device,
A storage device is provided for storing customer behavior information in physical stores and online stores,
a process of extracting behavior information of a specific customer observed with respect to a specific product at both the physical store and the online store from the behavior information, a process of identifying a difference between the physical store and the online store in the behavior of the specific customer with respect to the specific product indicated by the behavior information, and a process of identifying a customer who exhibits a predetermined behavior with respect to the specific product based on the behavior information and notifying a terminal of the customer of the difference,
A purchasing support method comprising: The information processing device,
When identifying the difference, a change in purchasing behavior according to the order of visits to the online store and the physical store is identified, and information on the change is notified to the terminal.
7. The purchasing support method according to claim 6. The information processing device,
When identifying the difference, a change is identified in which an interest behavior or a purchase behavior occurs when visiting the physical store from a state in which there is no interest behavior or purchase behavior at the online store, and information on the change is notified to the terminal.
8. The purchasing support method according to claim 7. The information processing device,
When identifying the difference, a change in the interest behavior according to the order of visits to the online store and the physical store is identified, and information on the change is notified to the terminal.
7. The purchasing support method according to claim 6. The information processing device,
If the difference cannot be identified, a notification to that effect is sent to the terminal.
7. The purchasing support method according to claim 6.

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