JP2023077278A - Information processing device, analysis data output method, and program - Google Patents

Abstract

To provide an information processing device capable of providing information useful for a retail shop, an analysis data output method, and a program.SOLUTION: An information processing device according to an embodiment includes: acquisition means which acquires purchase data being narrowed-down by designated enterprises and commodities from a purchase information storage unit where purchase data in which sold commodities, sales prices of the commodities, stores in which the commodities are sold, enterprises operating the stores, and selling dates on which the commodities are sold are associated with each other is accumulated; setting means which sets a price representative value of the designated commodity for each of the stores on the basis of purchase data by stores obtained by classifying the purchase data acquired by the acquisition means for each of the stores; selection means which selects stores having the same price representative value set by the setting means; generation means which generates analysis data by integrating the purchase data by stores of the store selected by the selection means; and output means which outputs the analysis data generated by the generation means.SELECTED DRAWING: Figure 8

Description

TECHNICAL FIELD Embodiments of the present invention relate to an information processing device, an analysis data output method, and a program.

Retail stores are trying to improve sales by formulating and implementing sales strategies such as setting special sale days. Market information is essential for formulating sales strategies. For example, when setting a bargain sale date, a retail store sets the bargain sale period, target product, and price by researching changes in the selling prices of other companies for the product that the retailer sells.

Collecting information on other companies requires a lot of time and effort, as the actual situation is collecting information from in-store POPs (Point Of Purchase) and flyers. Electronic receipt systems capable of collecting receipt data from multiple companies are also known (for example, Patent Document 1), but simply obtaining receipt data does not provide sufficient information for formulating a sales strategy.

In addition, it is not limited to retailers that obtaining market information is useful. For example, even in a company that provides products to retail stores, market information is useful for formulating sales strategies such as developing new customers.

A problem to be solved by the present invention is to provide an information processing device, an analysis data output method, and a program capable of providing useful information to retail stores and the like.

The information processing device of the embodiment associates the product sold, the selling price of the product, the store where the product was sold, the company operating the store, and the date of sale when the product was sold. Acquisition means for acquiring purchase data narrowed down by a specified company and product from a purchase information storage unit storing attached purchase data, and purchase by store that classifies the purchase data acquired by the acquisition means for each store setting means for setting the representative price of the designated product for each store based on the data; selection means for selecting stores having the same representative price set by the setting means; A generating means for generating analysis data by integrating store-by-store purchase data, and an output means for outputting the analysis data generated by the generating means.

FIG. 1 is a diagram showing an overview of an entire system including an analysis data provision system including an analysis data provision device (information processing device) of an embodiment. FIG. 2 is a diagram showing the data structure of the purchase information storage unit of the electronic receipt server of the embodiment. FIG. 3 is a block diagram showing the hardware configuration of the analysis data providing device of the embodiment. FIG. 4 is a diagram showing the data configuration of an analysis data management file stored in the memory unit of the analysis data providing device of the embodiment. FIG. 5 is a block diagram showing the functional configuration of the control unit of the analysis data providing device according to the embodiment; FIG. 6 is a flowchart showing the flow of analysis data output processing by the control unit of the analysis data providing device of the embodiment. FIG. 7 is a diagram showing purchase data classified by store by the control unit of the analysis data providing device according to the embodiment. FIG. 8 is a diagram illustrating a method of generating display data generated by the control unit of the analysis data providing device according to the embodiment. FIG. 9 is a diagram illustrating an example of display data generated by the control unit of the analysis data providing device according to the embodiment; FIG. 10 is a diagram illustrating another example of display data generated by the control unit of the analysis data providing device according to the embodiment; FIG. 11 is a diagram illustrating another example of display data generated by the control unit of the analysis data providing device according to the embodiment;

Hereinafter, an information processing apparatus, an analysis data output method, and a program according to embodiments will be described with reference to the drawings. In addition, this invention is not limited by embodiment described below. For example, in the embodiments described below, an example will be described in which the business operator managing the electronic receipt server manages the analysis data providing device. may be managed by

FIG. 1 is a diagram showing an overview of the entire system including the analysis data providing system 1 of the embodiment. An analysis data providing system 1 includes an electronic receipt server 2 and an analysis data providing device 3 . The electronic receipt server 2 and the analysis data providing device 3 are managed by the same business operator and are communicably connected to each other via a network such as a LAN (Local Area Network).

The electronic receipt server 2 is communicably connected to company servers Sa to Sn owned by a plurality of companies Ca to Cn via a network. The network is a VPN (Virtual Private Network), the Internet, or the like. These companies Ca to Cn are companies that sell commodities or services (hereinafter collectively referred to as "commodities"), and in this embodiment, they are business operators that operate retail stores such as supermarkets. Each company Ca to Cn is a company that participates in an electronic receipt service that provides electronic receipts to customers.

Each company Ca-Cn has a plurality of stores. Each store is provided with a store server (not shown). The store server is connected to a plurality of POS (Point Of Sales) terminals (not shown) installed in the store, and acquires receipt data related to transactions from the POS terminals. The company server acquires receipt data from the store server.

For example, the receipt data of each store of the company Ca is transmitted to the company server Sa via the company network. Similarly, the receipt data of each store of the company Cn is transmitted to the company server Sn via the company network. Receipt data is data indicating transaction information for one transaction.

The electronic receipt server 2 has a purchase information storage unit 21 . The purchase information storage unit 21 stores the receipt data received from each company server Sa-Sn. The purchase information storage unit 21 stores receipt data for each electronic receipt member who can receive the electronic receipt service. Details of the purchase information storage unit 21 will be described later.

The electronic receipt server 2 is connected to an information terminal 4 such as a consumer's smart phone via a network such as the Internet. The consumer's information terminal 4 is an electronic receipt member's information terminal. The consumer can view the electronic receipt received from the electronic receipt server 2 on his/her own information terminal 4 .

The analysis data providing device 3 is connected to the member terminal 5 via a network such as the Internet. The member terminal 5 is an information terminal managed by a member company (hereinafter also referred to as "user") who can receive the analysis data providing service. A user can receive analysis data from the analysis data providing device 3 . The user may be a company that participates in the electronic receipt service, that is, a company that provides receipt data to the electronic receipt server 2, or a company that does not participate in the electronic receipt service.

The analytical data providing device 3 analyzes the receipt data (purchase data) managed by the electronic receipt server 2 and provides useful analytical data to the user. For example, the analysis data providing device 3 provides a representative price (reference price) P of a specific company for a specific product and analysis data indicating price transitions with respect to the representative price P. The analysis data providing device 3 is an example of an information processing device.

By recognizing the sales price transition of the above-mentioned specific company for the above-mentioned specific product, the user who has received the analysis data can grasp the discount status of the above-mentioned specific company, and can refer to the analysis data. It can be used to formulate a sales strategy. Formulation of a sales strategy based on analytical data, in other words, a method of utilizing analytical data is arbitrarily executed by each user. Therefore, even if the provided analysis data is the same, it is conceivable that different sales strategies may be developed depending on the user.

FIG. 2 is a diagram showing the data configuration of the purchase information storage unit 21 of the electronic receipt server. The purchase information storage unit 21 manages the receipt data received from each company server Sa-Sn. The purchase information storage unit 21 is rewritten each time receipt data is received from each company server Sa-Sn. Receipt data stored in the purchase information storage unit 21 is associated with each piece of information indicating the member, receipt number, date, company, store, product, and price.

Information identifying a customer, such as a member code, is registered in the member item. The member code is the member code of the electronic receipt service. Information for specifying receipt data, such as a receipt number, is registered in the receipt No. item. The receipt number can be said to be information specifying a transaction.

Information indicating the date on which the transaction was made is registered in the date field. In other words, in the date field, information indicating the date on which the product was sold is registered. Information for identifying a company that operates a store that sells a product, such as a company code, is registered in the company item. In the store item, information specifying the store where the product was sold, such as a store code, is registered.

Information specifying the sold product, for example, a product code is registered in the product item. The product code to be registered is commonly used by each company, and is, for example, a JAN (Japanese Article Number) code. Information indicating the price of the product sold in the transaction, that is, the selling price is registered in the item of price. In addition, when multiple items of one product are purchased in one transaction, multiple same product codes are registered in the product item. Therefore, it can be said that the purchase information storage unit 21 also stores the number of products purchased in one transaction.

The purchase information storage unit 21 associates the product sold, the selling price of the product, the store where the product was sold, the company operating the store, and the date of sale of the product. purchased data is accumulated and stored. Moreover, it can be said that the receipt data includes purchase data. For example, when a plurality of products are purchased in one transaction, the receipt data includes a plurality of purchase data.

Next, the analysis data providing device 3 will be described in detail. FIG. 3 is a block diagram showing the main hardware configuration of the analysis data providing device 3. As shown in FIG. The analysis data providing device 3 includes a control section 30 , a memory section 31 , a display section 32 , an operation section 33 and a communication section 34 . The control unit 30, memory unit 31, display unit 32, operation unit 33, and communication unit 34 are connected to each other via a bus 35 or the like.

The control unit 30 is configured by a computer including a CPU (Central Processing Unit) 301 , a ROM (Read Only Memory) 302 and a RAM (Random Access Memory) 303 . CPU 301 , ROM 302 and RAM 303 are connected to each other via bus 35 .

A CPU 301 controls the overall operation of the analysis data providing device 3 . The ROM 302 stores various programs such as programs used to drive the CPU 301 and various data. The RAM 303 has an extraction data storage unit 304 . The extracted data storage unit 304 stores purchase data acquired from the electronic receipt server 2 during analysis data output processing by the control unit 30, which will be described later. Specifically, the extracted data storage unit 304 stores purchase data (hereinafter also referred to as “extracted data”) narrowed down by the specified company, product, and sales period from the purchase information storage unit 21 of the electronic receipt server 2. Remember. A RAM 303 is used as a work area for the CPU 301 and develops various programs and data stored in the ROM 302 and the memory section 31 . The control unit 30 executes various control processes of the analysis data providing device 3 by the CPU 301 operating according to the ROM 302 or the control program stored in the memory unit 31 and developed in the RAM 303 .

The memory unit 31 is composed of a storage medium such as an HDD (Hard Disk Drive) or flash memory, and maintains stored contents even when the power is turned off. The memory section 31 stores a control program 311 and an analysis data management file 312 .

The control program 311 is, for example, a control program for executing analysis data output processing, which will be described later.

The analysis data management file 312 is a file for managing analysis data output by the analysis data providing device 3 . Analytical data is data related to purchase information of a specific product in a specific company. The analysis data management file 312 is updated each time the analysis data providing device 3 outputs analysis data. FIG. 4 is a diagram showing the data configuration of the analysis data management file 312. As shown in FIG. Each piece of data registered in the analysis data management file 312 is associated with each piece of information indicating No, company, product, and analysis data.

A data number specifying analysis data is registered in the No item. A company code that identifies a target company for analysis data is registered in the company item. A product code that identifies the target product of the analysis data is registered in the item of product. In the analytical data item, analytical data relating to purchasing information of a specific product in a specific company is registered. The analysis data providing device 3 can appropriately output the analysis data stored in the analysis data management file 312 to the member terminal 5 in response to a request from the member terminal 5 . Alternatively, the analysis data providing device 3 can display the analysis data stored in the analysis data management file 312 on the display unit 32 as appropriate.

Returning to FIG. 3, the hardware configuration of the analysis data providing device 3 will be described.

The display unit 32 is composed of, for example, a liquid crystal panel, and displays various information. The display unit 32 displays analysis data stored in the analysis data management file 312, for example. The display unit 32 also displays an input screen for inputting conditions for narrowing down the purchase data in the purchase information storage unit 21 of the electronic receipt server 2 (hereinafter also referred to as “extraction conditions”).

The operation unit 33 is for inputting information to the control unit 30, and includes a keyboard, a touch panel, a mouse, and the like. The operation unit 33 inputs extraction conditions for purchase data in the purchase information storage unit 21, for example.

The communication unit 34 is an interface for communicating with external devices such as the electronic receipt server 2 and the user's member terminal 5 . The control unit 30 can transmit and receive information (data) to and from the external device by being connected to the external device via the communication unit 34 .

Next, the functional configuration of the analysis data providing device 3 will be described. FIG. 5 is a block diagram showing an example of the main functional configuration of the analysis data providing device 3. As shown in FIG. The control unit 30 operates according to the control program 311 stored in the ROM 302 and the memory unit 31 by the CPU 301 to perform input means 3001 , acquisition means 3002 , setting means 3003 , selection means 3004 , generation means 3005 and output means 3006 . function as Note that each of these functions may be configured by hardware such as a dedicated circuit.

Various information based on the operation of the operation unit 33 is input to the input means 3001 . For example, the input means 3001 receives an extraction condition for purchase data in the purchase information storage unit 21 of the electronic receipt server 2 . The extraction conditions include a specified company (hereinafter also referred to as "designated company") and a specified product (hereinafter also referred to as "designated product"). In this embodiment, the period of the designated sale date (hereinafter also referred to as "designated period") is also an extraction condition.

Therefore, the input means 3001 receives the company code of the specified company, the product code of the specified product, and information specifying the specified period as extraction conditions. Note that the extraction conditions received from the member terminal 5 may be input to the input means 3001 .

Acquisition means 3002 stores a purchased product in which the sold product, the selling price of the product, the store where the product was sold, the company that operates the store, and the date of sale of the product are associated with each other. Purchasing data narrowed down by the specified company and the specified product are acquired from the purchase information storage unit 21 in which the data is accumulated. In this embodiment, the acquisition means 3002 acquires purchase data further narrowed down in a specified period.

Specifically, the acquisition unit 3002 acquires purchase data that matches the extraction conditions input to the input unit 3001 from among the purchase data accumulated in the purchase information storage unit 21 . More specifically, the acquisition unit 3002 acquires purchase data related to stores operated by the specified company specified by the company code input to the input unit 3001 and the product code specified by the input unit 3001. The purchase data related to the product and the purchase data related to the specified period specified by the information indicating the period input to the input means 3001 are obtained from the purchase information storage unit 21 as extracted data. The extraction data acquired by the acquisition means 3002 is stored in the extraction data storage unit 304 .

In this embodiment, the acquisition unit 3002 acquires the extracted data from the electronic receipt server 2 that manages the receipt information, but it is not limited to this. For example, the extracted data may be acquired from a device having a storage unit that stores purchase data separately from receipt information. Alternatively, the purchase data may be stored in the memory section 31 of the analysis data providing device 3 and the acquisition means 3002 may acquire the extraction data from the memory section 31 .

The setting unit 3003 sets the representative price value P of the specified product for each store based on the store-by-store purchase data obtained by classifying the purchase data acquired by the acquisition unit 3002 for each store. Specifically, the setting unit 3003 classifies the extraction data acquired by the acquisition unit 3002 for each store code to generate purchase data for each store. Then, the setting means 3003 sets the representative price value P of the designated product in the purchase data for each store.

The representative price value P can be said to be the standard price of the designated product in each store, and in this embodiment, it is the price of the designated product that has been sold the most in the store-by-store purchase data. In other words, the representative price value P is the price at which the largest number of items are sold among the sales prices of the specified product. Note that the representative price value P can also be the price of the designated product that has been sold for the most number of days in the store-by-store purchase data. The price representative value P can be arbitrarily set by an operator who manages the analysis data providing device 3 and provides the analysis data providing service, or a user of the analysis data providing service.

The selection means 3004 selects stores having the same representative price value P set by the setting means 3003 . In other words, the selection unit 3004 classifies the store-by-store purchase data generated by the setting unit 3003 for each set representative price value P.

The generating means 3005 integrates the store-by-store purchase data of the stores selected by the selecting means 3004 to generate analysis data. Specifically, the generating means 3005 integrates the store-by-store purchase data of stores with the same representative price value P to generate analysis data.

In addition, the generation unit 3005 visualizes the analysis data, such as graphing, and generates display data. For example, the generating means 3005 processes the analysis data obtained by integrating the store-by-store purchase data of the stores with the same price representative value P, and generates display data in the form of a graph showing the daily price transition of the specified product. The analysis data can be processed, in other words, how to display the analysis data can be arbitrarily performed, and the data suitable for examining the contents of the analysis data can be used as the display data. The generated display data is also an example of analysis data.

In general, product prices often differ between regions, between companies, and between stores. Simply looking at the entire purchase data reveals which price in the market is the standard price (normal selling price) and which price is It is difficult to judge whether the price is discounted. Even if you focus on specific companies and look at the purchase data, there are many cases where product pricing differs between regions and between stores. is difficult.

On the other hand, by focusing only on the purchase data of one store, attempts have been made to understand the changes in the sales prices of the products at that store. Sufficient purchase data cannot be obtained. As a result, it is again difficult to determine which price is the base price and which price is the discounted price. Therefore, it is difficult to understand how other companies set bargain sale dates and bargain prices.

On the other hand, the analysis data providing device 3 of the present embodiment sets a representative price value P as a reference price for each store for purchase data related to a specific product of a specific company, and Generate analytical data by integrating purchase data by store. That is, the analysis data is generated by grouping the purchase data of the designated company by reference price. Since the purchase data grouped by reference price is obtained by integrating a plurality of store-specific data, a sufficient number of data can be secured.

Therefore, with the generated analysis data, it is possible to accurately grasp the price transition with respect to each reference price for the designated product of the designated company. Therefore, it is possible to grasp at what timing and to what extent the designated company discounts the designated product for each reference price.

Output means 3006 outputs the analysis data generated by generating means 3005 . For example, the output means 3006 outputs the display data generated by the generating means 3005 to the display section 32 . The display unit 32 can display display data. Also, the output means 3006 outputs the display data generated by the generating means 3005 to the member terminal 5 . The output unit 3006 may output the display data to a separately provided web server. In this case, the output unit 3006 transmits to the member terminal 5 the URL (Uniform Resource Locator) of the web page where the display data is disclosed. By designating the received URL, the member terminal 5 equipped with a web browser can download the display data published on the web and view it with the web browser.

Further, the output means 3006 outputs to the electronic receipt server 2 extraction conditions for the purchase data in the purchase information storage section 21 input to the input means 3001 . Specifically, the output means 3006 outputs to the electronic receipt server 2 the company code of the specified company, the product code of the specified product, and the information for specifying the period of the sale date, which are input to the input means 3001 .

Next, analysis data output processing executed by the analysis data providing device 3 configured as described above will be described. FIG. 6 is a flowchart showing the flow of analysis data output processing by the control unit 30 of the analysis data providing device 3. As shown in FIG. It can be said that the flowchart shown in FIG. 6 shows the flow of the analysis data output method executed by the analysis data providing device 3 .

The control unit 30 determines whether or not an extraction condition has been input to the input means 3001 (step S1), and if not, returns to the process of step S1 and waits. When the extraction conditions are input (Y in step S1), the output unit 3006 outputs the input extraction conditions to the electronic receipt server 2 (step S2).

The extraction conditions are a specified company, a specified product, and a specified period. Therefore, the output means 3006 outputs to the electronic receipt server 2 information indicating the company code of the specified company, the product code of the specified product, and the specified period.

The electronic receipt server 2 receives the extraction conditions output by the output means 3006 . Then, the electronic receipt server 2 narrows down the purchase data in the purchase information storage unit 21 based on the received extraction conditions, and extracts extraction data that matches the extraction conditions. The electronic receipt server 2 outputs the extracted extraction data to the analysis data providing device 3 .

Acquisition means 3002 acquires purchase data, which is extracted data output by electronic receipt server 2 (step S3). The purchase information storage unit 21 may be provided in a server device different from the memory unit 31 or the electronic receipt server 2 , and the acquisition unit 3002 may acquire the extracted data from the purchase information storage unit 21 .

Subsequently, the setting means 3003 generates purchase data for each store (step S4). Specifically, the setting unit 3003 classifies the extraction data acquired by the acquisition unit 3002 using the store code as a key to generate a plurality of store-by-store purchase data.

FIG. 7 is a graph of purchase data for each store. The purchase data for each store shown in FIG. 7 is extracted from the purchase information storage unit 21 using the specified product "A", the specified company "B", and the specified period "8/1 to 10/31" as the extraction conditions. It is the purchase data related to the store "C". In FIG. 7, the vertical axis indicates the price, the horizontal axis indicates the date, and the plotted points indicate the sales price on the corresponding date. The setting means 3003 generates purchase data for each store by the number of store codes included in the extracted data.

Further, the setting means 3003 sets the representative price P of the specified product in each store from the store-by-store purchase data of each store (step S5). In this embodiment, the representative price P is set to the price of the designated product that has been sold the most in the store-by-store purchase data. In the store-by-store purchase data, the price at which the commodity A was sold most frequently is 160 yen, and the setting means 3003 sets the representative price value P to 160 yen. Note that FIG. 7 does not show the number of sales (number of sales) of product A, so it does not show that the price at which product A was sold the most was 160 yen. On the other hand, in the store-by-store data, when the representative price P is set to the price for which the product A was sold for the largest number of days, the representative price P is the price for which the number of plots is the largest in FIG. In this case, as is clear from FIG. 7, the representative price value P is also 160 yen.

Returning to the description of the flowchart in FIG. After the setting unit 3003 has set the representative price value P for each store-specific purchase data, the selection unit 3004 selects stores having the same representative price value P (step S6). In other words, the selection means 3004 classifies a plurality of store-by-store purchase data into groups having the same representative price value P. FIG.

Then, the generating means 3005 combines the store-by-store purchase data of the selected stores (step S7). In other words, the generating means 3005 generates analysis data by combining store-by-store purchase data classified into the same group. Subsequently, the generating means 3005 generates display data based on the analysis data (step S8).

FIG. 8 is a diagram showing a method of generating analysis data. As shown in FIG. 8, the generating means 3005 combines store-by-store data purchase data of a plurality of stores (44 in the example) such as store C, store D, . Then, the generating means 3005 generates analysis data, and generates display data graphed as shown in FIG. 8, for example.

Subsequently, the output means 3006 outputs the display data generated by the generation means 3005 (step S9). The output destination to which the output means 3006 outputs display data can be set arbitrarily. Also, the output means 3006 does not necessarily have to output the display data generated by the generation means 3005 . For example, the display data shown in FIG. 8 may be generated by the member terminal 5 that receives the analysis data.

When outputting the display data by the output unit 3006 is completed, the control unit 30 terminates the analysis data output process.

9 to 11 show examples of generated display data. The analysis data shown in FIG. 9 is purchase data of a plurality of stores of company "X" in which the representative price P of product "AA" is "170 yen" during the period "8/1-10/31". .

A user who has been provided with the analysis data shown in FIG. 9 can grasp the price transition of the store of the company X where the representative price of the product AA is 170 yen. The user can understand that the store of the company X discounts the product AA about once a week, and the discount rate is about 10% or 20%.

If the user is a competitor of company X, the user can formulate a sales strategy for increasing sales based on the analysis data. For example, the user can make a plan to sell the product AA at a discount on a day other than the day on which the store of company X is expected to sell the product AA at a discount. In addition, the user can make a plan to sell the product AA at a lower price than the price of the company X store on the day when the company X store is expected to sell the product AA at a discount.

The analysis data shown in FIG. 10 is purchase data of a plurality of stores of the company "Y" whose representative price P of the product "BB" is "75 yen" during the period "8/1 to 10/31". . In the present embodiment, as described above, the price at which the number of sales is the largest is set as the representative price value P. Therefore, the representative price value P of the product BB is not 98 yen, which has the largest number of plots in the graph of FIG. It is 75 yen.

A user who has been provided with the analysis data shown in FIG. 10 can understand that the store of the company Y periodically and repeatedly sells the product BB at a discount. Specifically, the user can understand that the store of company Y sells product BB for 98 yen for 5 days in a week and sells it for 75 yen for the remaining 2 days. From this, it can be inferred that the store of the company Y purchases the product BB once a week and sells it at a discount when the expiration date approaches, and moreover, the product BB sells a lot during the discount sale.

If the user is a manufacturer of product BB, the user can draft a proposal for acquiring new customers based on the analysis data. For example, the user can propose to Company Y to reduce the number of discount sales by reducing the number of product BB purchased per time and purchasing the product twice a week.

The analysis data shown in FIG. 11 is purchase data of a plurality of stores of company "Z" in which the representative price P of product "CC" is "98 yen" during the period "8/1 to 10/31". .

A user who has been provided with the analysis data shown in FIG. 11 can understand that the store of the company Z is intensively selling the product CC at a discount during a specific period.

If the user is a competitor of company Z, the user can formulate a sales strategy for attracting customers based on the analysis data. For example, the user can propose discount sales of products similar to the product CC on the day when the store of the company Z is expected to sell the product CC at a discount.

Note that the method of utilizing the analytical data described above is merely an example, and the user can utilize the analytical data provided by the analytical data providing device in any manner.

As described above, the analysis data providing device 3 of the present embodiment includes the product sold, the selling price of the product, the store where the product was sold, the company operating the store, and the sales price of the product. Acquisition means 3002 for acquiring purchase data narrowed down by a specified company and product from the purchase information storage unit 21 storing purchase data associated with the specified sales date; A setting means 3003 for setting the representative price value P of the designated product for each store based on purchase data classified by store, which is obtained by classifying the data for each store, and the store where the representative price value P set by the setting means 3003 is the same. selection means 3004 for selecting, generation means 3005 for generating analysis data by integrating the store-by-store purchase data of the stores selected by the selection means 3004, output means 3006 for outputting the analysis data generated by the generation means 3005, Prepare.

As a result, the analysis data providing device 3 can use the purchase data to set the sales reference value of the product, and generate analysis data indicating the price transition with respect to the set sales reference value. Therefore, the analysis data providing device 3 can provide the user with useful information useful for formulating a sales strategy.

In addition, the generating means 3005 of the analysis data providing device 3 of this embodiment can generate display data showing daily price transitions.

As a result, the analysis data providing device 3 can provide the user with more useful information by making the price transition easier to see.

Although embodiments of the present invention have been described above, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, and modifications can be made without departing from the scope of the invention.

In the above embodiment, the control program executed by the analysis data providing device 3 and the member terminal 5 may be configured to be provided by being recorded in a computer-readable recording medium such as a CD-ROM. Further, the control program executed by the analysis data providing device 3 and the member terminal 5 may be stored on a computer connected to a network such as the Internet, and may be provided by being downloaded via the network. Furthermore, it may be configured to be provided via a network such as the Internet.

3 Analysis data providing device (information processing device)
21 purchase information storage unit 3002 acquisition means 3003 setting means 3004 selection means 3005 generation means 3006 output means

JP 2020-191127 A

Claims (6)

Purchasing data that associates the product sold, the selling price of the product, the store where the product was sold, the company that operates the store, and the date on which the product was sold is accumulated. Acquisition means for acquiring purchase data narrowed down by specified companies and products from the purchase information storage unit;
setting means for setting a representative price value of the designated product for each store based on store-by-store purchase data obtained by classifying the purchase data acquired by the acquisition means for each store;
a selecting means for selecting stores having the same representative price value set by the setting means;
a generation means for generating analysis data by integrating the store-by-store purchase data of the stores selected by the selection means;
an output means for outputting analysis data generated by the generating means;
Information processing device with The representative price value is the price at which the number of times of sales is the highest in the purchase data by store,
The information processing device according to claim 1 . The purchase data is data associated with the sale date of the product,
The representative price value is the price with the largest number of days sold in the purchase data by store,
The information processing device according to claim 1 . The generating means generates display data showing daily price transitions.
The information processing apparatus according to any one of claims 1 to 3. An analysis data output method executed by an information processing device,
Purchasing data that associates the product sold, the selling price of the product, the store where the product was sold, the company that operates the store, and the date on which the product was sold is accumulated. an acquisition step of acquiring purchase data narrowed down by specified companies and products from the purchase information storage unit;
a setting step of setting a representative price value of the specified product for each store based on store-by-store purchase data obtained by classifying the purchase data acquired in the acquisition step for each store;
a selection step of selecting stores having the same representative price value set in the setting step;
a generation step of generating analysis data by integrating the store-by-store purchase data of the stores selected in the selection step;
an output step of outputting the analysis data generated in the generating step;
Analysis data output method including. A program for controlling an information processing device by a computer,
the computer,
Purchasing data that associates the product sold, the selling price of the product, the store where the product was sold, the company that operates the store, and the date on which the product was sold is accumulated. Acquisition means for acquiring purchase data narrowed down by specified companies and products from the purchase information storage unit;
setting means for setting a representative price value of the designated product for each store based on store-by-store purchase data obtained by classifying the purchase data acquired by the acquisition means for each store;
a selecting means for selecting stores having the same representative price value set by the setting means;
a generation means for generating analysis data by integrating the store-by-store purchase data of the stores selected by the selection means;
an output means for outputting analysis data generated by the generating means;
A program that works as

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