Opportunity-considered purchase amount calculation method, opportunity-considered purchase amount calculation system, method for acquiring optimal opportunity-considered purchase amount, and client device

Abstract

To calculate the purchase amount per purchase opportunity of a target product from purchase data that collects purchase records of purchasers.SOLUTION: A method for calculating an opportunity-considered purchase amount, which is the purchase amount per purchase opportunity of a target product, from purchase data that collects purchase records, includes: calculating a total product purchase amount by summing up the purchase amounts of the target products; estimating stores that have in-store inventory of the target products; calculating the number of purchasers of a first product group included in a first product classification as a first estimated number of in-store inventory contact persons; calculating the number of purchasers of a second product group included in a second product classification as a second estimated number of in-store inventory contact persons; calculating the total product purchase amount per the first estimated number of in-store inventory contact persons as a first opportunity-considered purchase amount; calculating the total product purchase amount per the second estimated number of in-store inventory contact persons as a second opportunity-considered purchase amount; and selecting an optimal opportunity-considered purchase amount from the first opportunity-considered purchase amount and the second opportunity-considered purchase amount.SELECTED DRAWING: Figure 8

Description

[Technical field]

[0001] An embodiment of the present invention relates to an opportunity-considered purchase amount calculation method, an opportunity-considered purchase amount calculation system, an optimal opportunity-considered purchase amount acquisition method, and a client device. [Background technology]

[0002] A system has been proposed that uses purchasing data (hereinafter referred to as "collected purchasing data") collected from customer purchasing records such as receipts and household account books to provide statistical data such as compiling purchasing records and investigating purchasing trends (for example, Patent Document 1).

[0003] The collected purchase data is data that collects purchase records under various conditions in a wide variety of stores, and is uneven and non-uniform compared to purchase data recorded in stores. In addition, the collected purchase data is data collected not from all purchasers but from an unspecified number of purchasers. Therefore, the collected purchase data is not necessarily appropriate as a population for obtaining statistical data such as the compilation of purchase records or a survey of purchasing trends.

[0004] For example, two or more collected purchasing data sets that are collected from different subjects, such as collection area, collection period, collection age group, etc., often contain different numbers of purchasers, numbers of stores, etc. Therefore, two or more statistical data sets obtained from two or more collected purchasing data sets that are collected from different subjects are difficult to compare.

[0005] If the purchase amount per purchase opportunity for a target product can be obtained as statistical data from the collected purchase data, it becomes easier to compare two or more collected purchase data sets that are collected for different targets. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] JP 2022-150758 A Summary of the Invention [Problem to be solved by the invention]

[0007] The problem that the present invention aims to solve is to provide an opportunity-considered purchase amount calculation method, an opportunity-considered purchase amount calculation system, a method for obtaining an optimal opportunity-considered purchase amount, and a client device, which can calculate the purchase amount per purchasing opportunity of a target product from purchasing data collected from the purchase records of purchasers. [Means for solving the problem]

[0008] An embodiment of an opportunity-considered purchase amount calculation method is a method for calculating an opportunity-considered purchase amount, which is the purchase amount per purchasing opportunity of a target product, from purchasing data collected from purchasing records of purchasers, and calculates a total product purchase amount by adding up the purchase amounts of the target products in the purchasing data, estimates stores which have in-store stock of the target product based on the number of purchases of the target product in the purchasing data, calculates the number of purchasers of a first product group included in a first product classification to which the target product belongs as a first estimated number of in-store inventory contact persons in the purchasing data at the stores with in-store stock, calculates the number of purchasers of a second product group included in a second product classification to which the target product belongs and which is different from the first product classification in the purchasing data at the stores with in-store stock as a second estimated number of in-store inventory contact persons, calculates the total product purchase amount per first estimated number of in-store inventory contact persons as a first opportunity-considered purchase amount, calculates the total product purchase amount per second estimated number of in-store inventory contact persons as a second opportunity-considered purchase amount, and selects an optimal opportunity-considered purchase amount from the first opportunity-considered purchase amount and the second opportunity-considered purchase amount. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a configuration diagram of an opportunity-considered purchase amount calculation system according to an embodiment. [Diagram 2] FIG. 2 is a diagram showing a specific example of the opportunity consideration purchase amount calculation system. [Diagram 3] FIG. 2 is a functional block diagram of the opportunity-taking purchase amount calculation system. [Figure 4]FIG. 2 is a functional block diagram of a user interface section and a recording section of the opportunity consideration purchase amount calculation system. [Diagram 5] FIG. 4 is a diagram showing an example of collected purchase data. [Figure 6] FIG. 13 is a diagram showing an example of processed purchasing data. [Figure 7] FIG. 11 is a diagram showing an example of reference purchase data. [Figure 8] 13 is a flowchart of the opportunity consideration purchase amount calculation system. [Figure 9] FIG. 13 is a diagram showing an example of a plurality of opportunity-considered purchase amounts and a reference opportunity-considered purchase amount. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, an opportunity-considered purchase amount calculation method, an opportunity-considered purchase amount calculation system, an optimal opportunity-considered purchase amount acquisition method, and a client device according to the embodiments will be described with reference to the drawings.

[0011] [Opportunity-Consideration Purchase Amount Calculation System 100] FIG. 1 is a configuration diagram of an opportunity-considered purchase amount calculation system 100 according to an embodiment. The opportunity-considered purchase amount calculation system 100 is a system that calculates an opportunity-considered purchase amount, which is the purchase amount per purchase opportunity of a target product, as statistical data from purchase data (hereinafter also referred to as "collected purchase data") that is collected from the purchase records of individual users (purchasers) U. The "collected purchase data" may be purchase records (POS data) based on the consent of individual users (purchasers) U, or purchase records managed by individuals or companies, etc.

[0012] The opportunity-considered purchase amount calculation system 100 is composed of one or more servers. The basic infrastructure of the opportunity-considered purchase amount calculation system 100 is constructed using a known data distributed processing system. The functions of the opportunity-considered purchase amount calculation system 100 are realized by a program executed in the processor of the server.

[0013] FIG. 2 is a diagram showing a specific example of the opportunity-considered purchase amount calculation system 100. As shown in FIG. 2 includes servers SV1, SV2, SV3, and SV4. The servers SV1, SV2, SV3, and SV4 include hardware such as a processor such as a CPU, a memory, a recording medium such as a hard disk or a flash memory, and a communication device.

[0014] The servers SV1, SV2, SV3, and SV4 may be servers with different types of OS, software, etc. The servers SV1, SV2, SV3, and SV4 may be operated by different operating entities. The servers SV1, SV2, SV3, and SV4 may be installed in different countries or different regions.

[0015] In the servers SV1 and SV2, all of the hardware and software resources are occupied by the opportunity-considered purchase amount calculation system 100. On the other hand, in the servers SV3 and SV4, only a portion of the hardware and software resources are provided to the opportunity-considered purchase amount calculation system 100. In other words, the opportunity-considered purchase amount calculation system 100 may be a system that combines an on-site server such as the servers SV1 and SV2 with a cloud server such as the servers SV3 and SV4 that provides IaaS (Infrastructure as a Service), PaaS (Platform as a Service), or SaaS (Software as a Service). In the following description, "server" includes any of the above servers.

[0016] The functional blocks of opportunity-considered purchase amount calculation system 100 described below (such as data acquisition unit 1, user interface unit 2, recording unit 3, calculation unit 4, and data output unit 5) are realized by the operation of servers (for example, servers SV1, SV2, SV3, and SV4 shown in FIG. 2) that are distributed over network NW. Note that the functional blocks of opportunity-considered purchase amount calculation system 100 may be realized by the operation of a single server.

[0017] 1, the opportunity-considered purchase amount calculation system 100 is connected to a data device 200 managed by a data provider DP, a terminal 300 (hereinafter referred to as an individual terminal 300) such as a smartphone, tablet terminal, or personal computer owned by an individual user U, a management device 400 owned by an administrator A of the opportunity-considered purchase amount calculation system 100, and a service providing device (client device) 500 owned by a service provider SP via a network NW. The data device 200, the management device 400, and the service providing device 500 may be a system composed of one or more servers.

[0018] The network NW may be a wide area network (WAN) such as the Internet, a private network (LAN) in a building where the opportunity-based purchase amount calculation system 100 is installed, or a combination of these. The network NW may be either wired or wireless. Other devices such as access points may be involved in the communication between the devices.

[0019] FIG. 3 is a functional block diagram of the opportunity-considered purchase amount calculation system 100. As shown in FIG. The opportunity-considered purchase amount calculation system 100 includes a data acquisition unit 1, a user interface unit 2, a recording unit 3, a calculation unit 4, and a data output unit 5.

[0020] [Data Acquisition Section 1] The data acquisition unit 1 acquires collected purchase data 33 including purchase records of individual user U from the data device 200 of the data provider DP and records it in the recording unit 3. The data acquisition unit 1 can acquire collected purchase data 33 including purchase records of individual user U from a first data provider DB1 and a second data provider DB2, which are different data providers DP.

[0021] Specifically, the data acquisition unit 1 acquires collected purchase data 33 including purchase records of individual users U from the data device 200 of the data provider DP based on a contract with the data provider DP, and records the data in the recording unit 3. The data acquisition unit 1 may, for example, periodically acquire data in bulk from the data device 200 of the data provider DP (bulk type). The data acquisition unit 1 may also, for example, acquire data in real time from a device connected via an API (Application Programming Interface) (streaming type).

[0022] The data device 200 of the data provider DP acquires purchase records such as electronic receipts and household account books of the individual user U from the personal terminal 300 based on a contract with the individual user U or the like.

[0023] [User Interface Part 2] FIG. 4 is a functional block diagram of the user interface unit 2 and the recording unit 3. The user interface unit 2 is an interface connected to a management device 400 owned by an administrator A of the opportunity-considered purchase amount calculation system 100. Specifically, the user interface unit 2 includes an API that connects the management device 400 and the recording unit 3. The user interface unit 2 has an interface 21 for administrator.

[0024] The administrator interface 21 includes an API that connects the management device 400 owned by the administrator A and the recording unit 3. The management device 400 can be connected to the system setting information recording unit 31 of the recording unit 3 via the administrator interface 21.

[0025] [Record 3] The recording unit 3 records purchase information such as purchase data, system setting information, etc. The recording unit 3 may be distributed to the servers SV1, SV2, SV3, and SV4. The recording unit 3 has a system setting information recording unit 31 and a purchase information recording unit 32.

[0026] The system setting information recording unit 31 records system setting information 311 which is the system setting of the opportunity consideration purchase amount calculation system 100. The system setting information 311 is updated by the management device 400 connected via the manager interface 21.

[0027] The purchase information recording unit 32 records collected purchase data 33, processed purchase data 34, and reference purchase data 35.

[0028] FIG. 5 is a diagram showing an example of the collected purchase data 33. As shown in FIG. The collected purchase data 33 is information in which date and time information 331, user information 332, store information 333, product information 334, product classification information 335, and purchase information 336 are associated with each purchased product.

[0029] The date and time information 331 is information for identifying the date and time when the product was purchased, such as a date.

[0030] The user information 332 is information for identifying an individual user U who has purchased a product, such as a user ID.

[0031] The store information 333 is information for identifying the store where the product was purchased, such as a store ID.

[0032] Product information 334 is information that identifies the purchased product, such as a product ID and a product name. Product information 334 for a product that does not have a product ID assigned, such as fresh food such as Chinese cabbage, does not include a product ID and includes only the product name.

[0033] Product classification information 335 indicates the classification of purchased products. Product classification in product classification information 335 is hierarchical, and in the example shown in FIG. 5, it is hierarchically divided into three categories (major product category, medium product category, and minor product category). The major product category is the upper category with the greatest granularity among the three categories. The minor product category is the lower category with the smallest granularity among the three categories. Product classification information 335 for products whose product classification is not common between stores (non-common products) does not include the product classification.

[0034] The purchase information 336 is information related to the purchase of goods, such as the number of goods purchased and the unit price.

[0035] FIG. 6 is a diagram showing an example of the processed purchasing data 34. The processed purchasing data 34 is data that has been processed to make the collected purchasing data 33 easier to process. For example, the product classification of the product classification information 335 of the collected purchasing data 33 in the processed purchasing data 34 is processed to a more general classification. Specifically, the product medium classification "fresh food" and product small classification "agricultural products" of the product whose product surface is "Chinese cabbage 1 / 2" in FIG. 5 are changed to a more general product classification, and are changed to the product medium classification "fresh vegetables" and product small classification "Chinese cabbage" as shown in FIG. 6. In addition, the product classification information 335 of a product (non-common product) whose product classification is not common between stores, such as the product whose product name is "green onion" in FIG. 6, is supplemented with an appropriate product classification determined from the product name. By processing the product classification of the collected purchasing data 33 by changing and supplementing it as described above, the multiple opportunity consideration purchase amount described later can be more appropriately calculated.

[0036] FIG. 7 is a diagram showing an example of the reference purchase data 35. As shown in FIG. The reference purchase data 35 is purchase data different from the collected purchase data 33, and includes data equivalent to the purchase amount per purchase opportunity for each product. The reference purchase data 35 is, for example, data including the purchase amount per purchaser for each specific region. For example, the household survey (household income and expenditure edition) provided on the Web by the Statistics Bureau of the Ministry of Internal Affairs and Communications is a statistical value of the survey results on household income and expenditure conducted monthly for approximately 9,000 households nationwide selected based on a certain statistical sampling method. Hereinafter, data on expenditure among the data of the household survey (household income and expenditure edition) is referred to as "household expenditure data". For example, the survey results of households with two or more people in the household expenditure data are mainly data summarized into the income and expenditure amount per household for one month (including the purchase quantity by item) for each region, household, and income category. The reference purchase data 35 may be provided by other survey organizations as long as it includes equivalent data. The reference purchase data 35 is information in which date and time information 351, area information 352, product information 353, household information 354, and expenditure information 355 are associated with each purchased product.

[0037] The date and time information 351 is information for identifying the date and time when the product was purchased, such as a date.

[0038] The area information 352 is information for identifying the area where the product was purchased, and is, for example, a name indicating the area, such as "nationwide," "Kanto," or "Kansai."

[0039] The product information 353 is information for identifying the purchased product, such as the product name.

[0040] Household information 354 is information about the number of people in the household that made the purchase, for example, "single" or "two or more people."

[0041] Expenditure information 355 is expenditure on goods per household.

[0042] The calculation unit 4 calculates the opportunity-considered purchase amount based on the collected purchase data 33 (or processed purchase data 34) and the reference purchase data 35. The "opportunity-considered purchase amount" is the purchase amount per purchase opportunity, specifically the purchase amount per contact who had the opportunity to purchase in-store stock. In the following explanation, a contact who had the opportunity to purchase in-store stock is also referred to as a "in-store stock contactor."

[0043] The data output unit 5 outputs the opportunity-considered purchase amount calculated by the calculation unit 4 in response to a data access request from the service providing device (client device) 500 of the service provider SP.

[0044] [Operation of opportunity-considered purchase amount calculation system 100] Next, a description will be given of the operation of the opportunity-considered purchase amount calculation system 100. Fig. 8 is a flowchart of the opportunity-considered purchase amount calculation system 100. Note that multiple steps in the flowchart that have no dependency relationship may be executed in a different order.

[0045] <Step S100> In step S100, the calculation unit 4 extracts purchase data with limited collection targets such as region, period, and purchaser attributes from the collected purchase data 33 (or processed purchase data 34) in the recording unit 3. In the following explanation, the purchase data extracted by limiting the collection targets from the collected purchase data 33 (or processed purchase data 34) is also referred to as "extracted purchase data."

[0046] If the collected purchase data 33 (or processed purchase data 34) is not collected daily, the calculation unit 4 generates "extracted purchase data" collected daily.

[0047] <Step S110> In step S110, the calculation unit 4 calculates a total product purchase amount by adding up the purchase amounts of target products in the extracted purchase data. The target products are products for which the opportunity-considered purchase amount is calculated.

[0048] For example, if Figure 5 shows the extracted purchasing data for February 1, 2023, and the target product is a product with the product name "Milk Chocolate," the total product purchase amount is 800 yen, which is the sum of 110 yen x 1 item, 110 yen x 1 item, 105 yen x 2 items, 100 yen x 3 items, and 70 yen x 1 item.

[0049] <Step S120> In step S120, the calculation unit 4 estimates "stores with in-store stock" that are stores that have in-store stock of the target product based on the purchase quantity of the target product in the extracted purchase data. Specifically, a store with one or more purchases of the target product in the extracted purchase data is estimated to be a store with in-store stock.

[0050] For example, if Figure 5 shows extracted purchase data for February 1, 2023, and the target product is a product with the product name "Milk Chocolate", the store with the store ID "1101" and the store with the store ID "1102" are presumed to have in-store stock because the number of purchases of the product with the product name "Milk Chocolate" is 1 or more. On the other hand, the store with the store ID "1103" is not presumed to have in-store stock because the number of purchases of the product with the product name "Milk Chocolate" is zero.

[0051] The calculation unit 4 may estimate that a store where a purchase was made in the most recent period (e.g., one week) prior to the current day is a store with in-store inventory, regardless of the number of purchases of the target product in the extracted purchase data on that day. This is because, when the target product is a product with a relatively low purchase frequency, such as the product "rice," a store where the number of purchases of the target product on that day was zero is also likely to be a store with in-store inventory.

[0052] <Step S130> In step S130, the calculation unit 4 calculates a plurality of estimated numbers of in-store stock contacts. In the following description, four estimated numbers of in-store stock contacts (first estimated number of in-store stock contacts, second estimated number of in-store stock contacts, third estimated number of in-store stock contacts, and fourth estimated number of in-store stock contacts) are calculated. Note that the calculation unit 4 only needs to calculate at least two estimated numbers of in-store stock contacts, for example, the first estimated number of in-store stock contacts and the second estimated number of in-store stock contacts. Note that the plurality of estimated numbers of in-store stock contacts calculated by the calculation unit 4 is not limited to the above four estimated numbers of in-store stock contacts. Estimated numbers of in-store stock contacts obtained by recalculating the above four estimated numbers of in-store stock contacts based on different conditions or periods (days, weeks, months) may also be included.

[0053] The calculation unit 4 calculates the number of purchasers of a first product group included in a first product classification to which the target product belongs as a first estimated number of in-store inventory contacts in the purchase data from a store with in-store inventory. The first product classification is one of the three classifications (major product classification, medium product classification, minor product classification) or all classifications in the product classification information 335.

[0054] For example, if FIG. 5 shows the extracted purchase data for February 1, 2023, the product with the product name "Milk Chocolate" is the target product, and the first product classification is "All Classifications," the first product group is all products. In this case, the first estimated number of in-store inventory contacts is "10 people," which is the sum of the number of purchasers of the first product group "7 people" (user IDs "1001," "1002," "1003," "1004," "1005," "1006," and "1007") at the store with in-store inventory and store ID "1102," and the number of purchasers of the first product group "3 people" (user IDs "1008," "1009," and "1010") at the store with in-store inventory and store ID "1102."

[0055] The calculation unit 4 calculates the number of purchasers of a second product group included in a second product classification to which the target product belongs as the second estimated number of in-store inventory contacts in the purchase data from the store with in-store inventory. The second product classification is one of the three classifications (major product classification, medium product classification, minor product classification) or all classifications in the product classification information 335 that is different from the first product classification.

[0056] For example, if FIG. 5 shows the extracted purchase data for February 1, 2023, the product with the product name "Milk Chocolate" is the target product, and the second product classification is "Product Major Classification," the second product group is the product included in the product classification "Food" to which the target product "Milk Chocolate" belongs. In this case, the second estimated number of people who came into contact with the in-store inventory is "8 people," which is the sum of the number of people who purchased the second product group "6 people" (user IDs "1001," "1002," "1003," "1004," "1005," and "1006") at the store with in-store inventory and store ID "1102," and the number of people who purchased the second product group "2 people" (user IDs "1008," "1009") at the store with in-store inventory and store ID "1102."

[0057] The calculation unit 4 calculates the number of purchasers of a third product group included in a third product classification to which the target product belongs as a third estimated number of in-store inventory contacts in the purchase data from a store with in-store inventory. The third product classification is one of the three classifications (major product classification, medium product classification, minor product classification) or all classifications in the product classification information 335 that is different from the first product classification and the second product classification.

[0058] For example, if FIG. 5 shows the extracted purchase data for February 1, 2023, the product with the product name "Milk Chocolate" is the target product, and the third product classification is "sub-category product," the third product group is the product included in the sub-category "confectionery" to which the target product "Milk Chocolate" belongs. In this case, the third estimated number of in-store inventory contacts is "7 people," which is the sum of the number of purchasers of the third product group "5 people" (user IDs "1001," "1002," "1003," "1004," and "1005") at the store with in-store inventory and store ID "1102," and the number of purchasers of the third product group "2 people" (user IDs "1008," "1009") at the store with in-store inventory and store ID "1102."

[0059] The calculation unit 4 calculates the number of purchasers of a fourth product group included in the fourth product classification to which the target product belongs as the fourth estimated number of in-store inventory contacts in the purchase data from the store with in-store inventory. The fourth product classification is one of the three classifications (major product classification, medium product classification, minor product classification) or all classifications in the product classification information 335 that is different from the first product classification, the second product classification, and the third product classification.

[0060] For example, if FIG. 5 shows the extracted purchase data for February 1, 2023, the product with the product name "Milk Chocolate" is the target product, and the fourth product category is "Product Subcategory", the fourth product group is the product included in the product subcategory "Chocolate" to which the target product "Milk Chocolate" belongs. In this case, the fourth estimated number of in-store inventory contacts is "6 people", which is the sum of the number of purchasers of the fourth product group "4 people" (user IDs "1001", "1002", "1003", "1004") at the store with in-store inventory and store ID "1102" and the number of purchasers of the fourth product group "2 people" (user IDs "1008", "1009") at the store with in-store inventory and store ID "1102".

[0061] <Step S140> In step S140, the calculation unit 4 calculates a plurality of opportunity-considered purchase amounts. In the following description, four opportunity-considered purchase amounts (first opportunity-considered purchase amount, second opportunity-considered purchase amount, third opportunity-considered purchase amount, fourth opportunity-considered purchase amount) corresponding to the four calculated estimated numbers of in-store stock contacts (first estimated number of in-store stock contacts, second estimated number of in-store stock contacts, third estimated number of in-store stock contacts, fourth estimated number of in-store stock contacts) are calculated.

[0062] The calculation unit 4 calculates the total product purchase amount per first estimated number of people in contact with in-store inventory as the first opportunity consideration purchase amount.

[0063] For example, if Figure 5 is the extracted purchasing data for February 1, 2023, and the target product is a product with the product name "Milk Chocolate," the first opportunity consideration purchasing amount on February 1, 2023 is approximately 80.0 yen, calculated by dividing the total product purchasing amount (800 yen) calculated in step S110 by the first estimated number of people in contact with the in-store inventory (10 people) calculated in step S130.

[0064] The calculation unit 4 calculates the total product purchase amount per second estimated number of people in contact with in-store inventory as the second opportunity consideration purchase amount.

[0065] For example, if Figure 5 is the extracted purchasing data for February 1, 2023, and the target product is a product with the product name "Milk Chocolate," the second opportunity consideration purchasing amount on February 1, 2023 is approximately 100.0 yen, calculated by dividing the total product purchasing amount (800 yen) calculated in step S110 by the second estimated number of people in contact with the in-store inventory (8 people) calculated in step S130.

[0066] The calculation unit 4 calculates the total product purchase amount per third estimated number of people in contact with in-store inventory as the third opportunity-considered purchase amount.

[0067] For example, if Figure 5 is the extracted purchasing data for February 1, 2023, and the target product is a product with the product name "Milk Chocolate," the third opportunity consideration purchasing amount on February 1, 2023 is approximately 114.3 yen, calculated by dividing the total product purchasing amount (800 yen) calculated in step S110 by the third estimated number of people in contact with in-store inventory (7 people) calculated in step S130.

[0068] The calculation unit 4 calculates the total product purchase amount per fourth estimated number of in-store inventory contacts as the fourth opportunity-considered purchase amount.

[0069] For example, if Figure 5 is the extracted purchasing data for February 1, 2023, and the target product is a product with the product name "Milk Chocolate," the fourth opportunity consideration purchasing amount on February 1, 2023 is approximately 133.3 yen, calculated by dividing the total product purchasing amount (800 yen) calculated in step S110 by the fourth estimated number of people in contact with in-store inventory (6 people) calculated in step S130.

[0070] <Step S150> In step S150, the calculation unit 4 extracts purchasing data of a corresponding product corresponding to the target product from the reference purchasing data 35, the purchasing data being limited to a collection target such as a region or a household. In the following description, the purchasing data extracted by limiting the collection target from the reference purchasing data 35 is also referred to as "extracted reference purchasing data."

[0071] If the reference purchase data 35 is not collected daily, the calculation unit 4 generates "extracted reference purchase data" collected daily.

[0072] The reference purchasing data 35 does not necessarily have to be purchasing data including the target product. If the reference purchasing data 35 is not purchasing data including the target product, the calculation unit 4 identifies a corresponding product that corresponds to the target product. A corresponding product that corresponds to the target product is a product that is identical or similar to the target product, or a product that is related to the target product.

[0073] For example, when Fig. 7 is the reference purchasing data 35 and a product with the product name "milk chocolate" is the target product, the corresponding product in the reference purchasing data 35 is a product with the product name "chocolate" (a product similar to the target product). The calculation unit 4 extracts the purchasing data of the product with the product name "chocolate" from the reference purchasing data 35 as "extracted reference purchasing data". For example, when Fig. 7 is the reference purchasing data 35 and a product with the product name "Chinese Cabbage 1 / 2" is the target product, the corresponding product in the reference purchasing data 35 is a product with the product name "Chinese Cabbage" (the same product as the target product). The calculation unit 4 extracts the purchasing data of the product with the product name "Chinese Cabbage" from the reference purchasing data 35 as "extracted reference purchasing data".

[0074] <Step S160> In step S160, the calculation unit 4 acquires expenditure information 355 equivalent to the purchase amount per purchasing opportunity in the extracted reference purchasing data as the "reference opportunity considered purchase amount." If the extracted reference purchasing data does not contain information equivalent to the purchase amount per purchasing opportunity, the calculation unit 4 processes the extracted reference purchasing data to generate information equivalent to the purchase amount per purchasing opportunity and acquires it as the "reference opportunity considered purchase amount." For example, even if the extracted reference purchasing data does not contain information equivalent to the purchase amount per purchasing opportunity, if it contains the number of purchases or the number of purchasers, the calculation unit 4 may generate information equivalent to the purchase amount per purchasing opportunity based on the number of purchases or the number of purchasers.

[0075] <Step S170> In step S170, the calculation unit 4 selects, from among the multiple opportunity-considered purchase amounts (first opportunity-considered purchase amount, second opportunity-considered purchase amount, third opportunity-considered purchase amount, and fourth opportunity-considered purchase amount), the one that has a tendency similar to the reference opportunity-considered purchase amount as the "opportunity-considered purchase amount." The optimal opportunity-considered purchase amount selected by the calculation unit 4 from the multiple opportunity-considered purchases is called the "optimum opportunity-considered purchase amount." Here, whether the tendency is "similar" is determined by the calculation unit 4 based on indicators such as a strong positive correlation (a large correlation coefficient) or a small average value of the difference between the opportunity-considered purchase amount for each day and the reference opportunity-considered purchase amount.

[0076] In this embodiment, the calculation unit 4 selects, from among the multiple opportunity-considered purchase amounts, the one that has a strong positive correlation with the reference opportunity-considered purchase amount as the “optimum opportunity-considered purchase amount.” As described above, the calculation unit 4 may select the optimal opportunity-considered purchase amount based on another index, such as using the average value of the difference between the opportunity-considered purchase amount and the reference opportunity purchase amount for each day.

[0077] The calculation unit 4 may select the optimum opportunity-considered purchase amount from the multiple opportunity-considered purchase amounts without using the reference opportunity-considered purchase amount. For example, the calculation unit 4 may select the optimum opportunity-considered purchase amount from the multiple opportunity-considered purchase amounts using a model that learns the relationship between the multiple opportunity-considered purchase amounts by machine learning, a calculation algorithm for comparing the size of the multiple opportunity-considered purchase amounts, or the like.

[0078] FIG. 9 is a diagram showing an example of the "reference opportunity consideration purchase amount", the "multiple opportunity consideration purchase amount", and the "correlation coefficient between the reference opportunity purchase amount and the multiple machine consideration purchase amount". Specifically, the "reference opportunity consideration purchase amount" indicates the expenditure information 355 of the product name "chocolate" in FIG. 7, the "multiple opportunity consideration purchase amount" (first opportunity consideration purchase amount, second opportunity consideration purchase amount, third opportunity consideration purchase amount, fourth opportunity consideration purchase amount) is the opportunity consideration purchase amount when the product name "milk chocolate" in FIG. 5 is the target product on February 1, 2023, and from February 2, 2023 to February 7, 2023, it indicates the opportunity consideration purchase amount calculated in the same manner as on February 1, 2023, and the "correlation coefficient between the reference opportunity consideration purchase amount and the multiple machine consideration purchase amount" indicates the correlation coefficient between the "reference opportunity consideration purchase amount" and the "multiple machine consideration purchase amount" from February 1, 2023 to February 7, 2023. In this case, the calculation unit 4 selects the first opportunity-considered purchase amount that has the strongest positive correlation (the largest correlation coefficient) with the reference opportunity-considered purchase amount from among the multiple opportunity-considered purchase amounts as the optimal opportunity-considered purchase amount. The calculation unit 4 calculates the correlation coefficient using a known function.

[0079] The multiple opportunity-considered purchase amounts (first opportunity-considered purchase amount, second opportunity-considered purchase amount, third opportunity-considered purchase amount, fourth opportunity-considered purchase amount) are estimated values ​​calculated from the extracted purchase data by estimating "stores with in-store stock" and "estimated number of people who have contact with in-store stock." In addition, the "reference opportunity-considered purchase amount" is calculated from extracted reference purchase data whose period, region, and product are the same as or similar to those of the extracted purchase data, and is considered to have a similar tendency to the opportunity-considered purchase amount. Therefore, the calculation unit 4 can select the most likely optimal opportunity-considered purchase amount from the multiple opportunity-considered purchase amounts based on the "reference opportunity-considered purchase amount."

[0080] The data output unit 5 outputs the opportunity-considered purchase amount calculated by the calculation unit 4 in response to a data access request from the service providing device 500 of the service provider SP. The service provider SP can use the opportunity-considered purchase amount calculated from the collected purchase data in a service business that provides statistical data such as tallying up purchase records and investigating purchasing trends.

[0081] According to at least one of the embodiments described above, it is possible to calculate the "opportunity-considered purchase amount" based on reference collected data different from the purchase data (collected purchase data) that is a collection of purchase records of purchasers that are inconsistent and non-uniform compared to the purchase data collected from the store. This makes it easier to compare two or more statistical data obtained from two or more collected purchase data that are collected on different subjects.

[0082] For example, it becomes easier to compare two statistical data obtained from collected purchasing data from a past period and collected purchasing data from a current period, and increases or decreases in a group's purchasing volume and fluctuations in purchasing trends can be evaluated from statistical data sampled from this collected purchasing data.

[0083] For example, it becomes easier to compare multiple statistical data obtained from collected purchase data from multiple regions, and it becomes possible to compare the purchase volumes between multiple regions and to compare the magnitude of the purchase volumes between multiple regions.

[0084] For example, it becomes easier to compare multiple statistical data obtained from the collected purchasing data of multiple member clusters, and it becomes possible to compare purchasing volumes between multiple member clusters and to compare the magnitude of purchasing volumes between multiple member clusters.

[0085] For example, by setting the target product as a product group (higher-level product classification), it is possible to calculate the "opportunity-considered purchase amount" for the product group. This makes it possible to compare the purchase amounts between multiple product groups and to compare the magnitude of purchase amounts between multiple product groups.

[0086] For example, if the "opportunity-considered purchase amount" can be calculated from purchasing data collected from buyers' purchasing records, this can be used as one of the parameters for statistics such as multivariate analysis and machine learning.

[0087] Although some embodiments of the present invention have been described, 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, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents described in the claims, as well as in the scope and spirit of the invention. [Explanation of symbols]

[0088] 100...opportunity-considered purchase amount calculation system, 200...data device, 300...personal terminal, 1...data acquisition section, 2...user interface section, 3...recording section, 31...system setting information recording section, 32...purchase information recording section, 33...collected purchase data, 34...processed purchase data, 35...reference purchase data, 4...calculation section, 5...data output section

Claims

1. A method for calculating an opportunity-based purchase amount, which is a purchase amount per purchase opportunity of a target product, from purchase data obtained by collecting purchase records of purchasers, comprising: Calculating a total product purchase amount by adding up the purchase amounts of the target products in the purchase data; estimating, based on the purchase number of the target product in the purchase data, the stores that have the target product in stock; calculating the number of purchasers of a first product group included in a first product classification to which the target product belongs in the purchase data from the store with in-store inventory as a first estimated number of in-store inventory contact persons; calculating, in the purchase data from the store with in-store inventory, the number of purchasers of a second product group included in a second product classification to which the target product belongs and which is different from the first product classification, as a second estimated number of in-store inventory contact persons; The total product purchase amount per the first estimated number of in-store inventory contact persons is calculated as a first opportunity consideration purchase amount; The total product purchase amount per the second estimated number of in-store inventory contact persons is calculated as a second opportunity consideration purchase amount; selecting an optimal opportunity-considered purchase amount from the first opportunity-considered purchase amount and the second opportunity-considered purchase amount; Method for calculating opportunity-based purchase amount.

2. In reference purchase data different from the purchase data, a purchase amount per purchase opportunity of a corresponding product corresponding to the target product is obtained as a reference opportunity consideration purchase amount; selecting the optimal opportunity consideration purchase amount from the first opportunity consideration purchase amount and the second opportunity consideration purchase amount based on the reference opportunity consideration purchase amount; The opportunity-considered purchase amount calculation method according to claim 1 .

3. selecting, from the first opportunity consideration purchase amount and the second opportunity consideration purchase amount, the amount that has a tendency closest to the reference opportunity consideration purchase amount as the optimal opportunity consideration purchase amount; The opportunity-considered purchase amount calculation method according to claim 2 .

4. selecting, from among the first opportunity consideration purchase amount and the second opportunity consideration purchase amount, the amount having a stronger positive correlation with the reference opportunity consideration purchase amount as the optimal opportunity consideration purchase amount; The opportunity-considered purchase amount calculation method according to claim 2 .

5. The corresponding product is a product included in the reference purchase data and is identical or similar to the target product. The opportunity-considered purchase amount calculation method according to claim 2 .

6. A store where the purchase number of the target product in the purchase data is one or more is estimated to be a store having the target product in stock. The opportunity-considered purchase amount calculation method according to claim 1 .

7. Regardless of the number of purchases of the target product on that day in the purchase data, a store where a purchase was made in a period prior to the day is assumed to be a store with in-store stock. The opportunity-considered purchase amount calculation method according to claim 1 .

8. The second product category is a product category higher than the first product category. The opportunity-considered purchase amount calculation method according to claim 1 .

9. The second product category is all categories included in the purchase data. The opportunity-considered purchase amount calculation method according to claim 1 .

10. The reference purchase data does not include data of the target product. The opportunity-considered purchase amount calculation method according to claim 2 .

11. The reference purchase data includes data corresponding to a purchase amount per purchase opportunity for each product. The opportunity-considered purchase amount calculation method according to claim 2 .

12. The reference purchase data is data from a household budget survey. The opportunity-considered purchase amount calculation method according to claim 2 .

13. A recording unit for recording purchase data collected from purchase records of purchasers and reference purchase data different from the purchase data; A calculation unit that calculates an opportunity-considered purchase amount, which is a purchase amount per purchase opportunity of the target product, based on the purchase data and the reference purchase data; Equipped with The calculation unit is Calculating a total product purchase amount by adding up the purchase amounts of the target products in the purchase data; estimating, based on the purchase number of the target product in the purchase data, the stores that have the target product in stock; calculating the number of purchasers of a first product group included in a first product classification to which the target product belongs in the purchase data from the store with in-store inventory as a first estimated number of in-store inventory contact persons; calculating, in the purchase data from the store with in-store inventory, the number of purchasers of a second product group included in a second product classification to which the target product belongs and which is different from the first product classification, as a second estimated number of in-store inventory contact persons; The total product purchase amount per the first estimated number of in-store inventory contact persons is calculated as a first opportunity consideration purchase amount; The total product purchase amount per the second estimated number of in-store inventory contact persons is calculated as a second opportunity consideration purchase amount; selecting an optimal opportunity-considered purchase amount from the first opportunity-considered purchase amount and the second opportunity-considered purchase amount; Opportunity-based purchase amount calculation system.

14. The calculation unit is In the reference purchase data, a purchase amount per purchase opportunity of a corresponding product corresponding to the target product is obtained as a reference opportunity consideration purchase amount; selecting the optimal opportunity consideration purchase amount from the first opportunity consideration purchase amount and the second opportunity consideration purchase amount based on the reference opportunity consideration purchase amount; The opportunity-considered purchase amount calculation system according to claim 13 .

15. A method for obtaining an optimal opportunity-considered purchase amount from a calculation result of an opportunity-considered purchase amount, which is a purchase amount per purchase opportunity of a target product, from purchase data collected from purchase records of purchasers, comprising: The optimal opportunity consideration purchase amount is: Calculating a total product purchase amount by adding up the purchase amounts of the target products in the purchase data; estimating, based on the purchase number of the target product in the purchase data, the stores that have the target product in stock; calculating the number of purchasers of a first product group included in a first product classification to which the target product belongs in the purchase data from the store with in-store inventory as a first estimated number of in-store inventory contact persons; calculating, in the purchase data from the store with in-store inventory, the number of purchasers of a second product group included in a second product classification to which the target product belongs and which is different from the first product classification, as a second estimated number of in-store inventory contact persons; The total product purchase amount per the first estimated number of in-store inventory contact persons is calculated as a first opportunity consideration purchase amount; The total product purchase amount per the second estimated number of in-store inventory contact persons is calculated as a second opportunity consideration purchase amount; The purchase amount is obtained by selecting from the first opportunity consideration purchase amount and the second opportunity consideration purchase amount. How to obtain optimal opportunity-based purchasing amount.

16. A client device that acquires an opportunity-considered purchase amount, which is a purchase amount per purchase opportunity of a target product, from purchase data that is collected from purchase records of purchasers, from a server device, The server device includes: A recording unit for recording purchase data collected from purchase records of purchasers and reference purchase data different from the purchase data; A calculation unit that calculates an opportunity-considered purchase amount, which is a purchase amount per purchase opportunity of the target product, based on the purchase data and the reference purchase data; Equipped with The calculation unit is Calculating a total product purchase amount by adding up the purchase amounts of the target products in the purchase data; estimating, based on the purchase number of the target product in the purchase data, the stores that have the target product in stock; calculating the number of purchasers of a first product group included in a first product classification to which the target product belongs in the purchase data from the store with in-store inventory as a first estimated number of in-store inventory contact persons; calculating, in the purchase data from the store with in-store inventory, the number of purchasers of a second product group included in a second product classification to which the target product belongs and which is different from the first product classification, as a second estimated number of in-store inventory contact persons; The total product purchase amount per the first estimated number of in-store inventory contact persons is calculated as a first opportunity consideration purchase amount; The total product purchase amount per the second estimated number of in-store inventory contact persons is calculated as a second opportunity consideration purchase amount; selecting an optimal opportunity-considered purchase amount from the first opportunity-considered purchase amount and the second opportunity-considered purchase amount; Client device.

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