Similar store extraction device, similar store extraction method, and computer program

Abstract

The present invention provides a similar store extraction device that can extract appropriate similar stores from among multiple candidate similar stores based on clearly explainable evidence. [Solution] The similar store extraction device includes: a location acquisition unit that acquires a location of interest and the locations of multiple similar store candidates; a statistical value acquisition unit that acquires predetermined statistical values ​​in the trade area including the location and statistical values ​​in an analysis area that includes the location and is larger than the trade area for each location of interest and store location; an index value calculation unit that calculates an index value based on the statistical values ​​in the trade area for each location of interest and store location; a reference value calculation unit that calculates a reference value based on the statistical values ​​in the analysis area for each location of interest and store location; an identification unit that identifies the location type by comparing the reference value and the index value for each location of interest and store location; and an extraction unit that extracts stores from among multiple similar store candidates in which the location type of the store location matches the location type of the location of interest.

Description

[Technical Field]

[0001] This invention relates to a similar store extraction device, a similar store extraction method, and a computer program. [Background technology]

[0002] Traditionally, chain store operators operating franchise businesses have used the financial performance of similar stores with similar location conditions to those of the target store in their financial forecasts, in order to prevent reliance on individual employees for sales forecasting or to improve the accuracy of sales forecasts. Furthermore, prospective franchisees are required to explain their projected financial performance to chain store operators based on the financial performance of similar stores with similar location conditions to the planned store location. In order to perform financial forecasts or projections with high accuracy, chain store operators are required to appropriately select similar stores from within their chain that are similar to the target store for financial forecasting or the target store to be opened.

[0003] The Enforcement Regulations of the Small and Medium-Sized Retail Business Promotion Act stipulate that when a person operating a specific chain store business issues a document to someone who wishes to join the business, it must include information on the income and expenditure for the most recent three fiscal years of the member's stores that have similar location conditions, such as population and traffic volume in the surrounding area. Traditionally, chain store operators have had experienced analysts subjectively select similar stores based on such location conditions.

[0004] Non-patent document 1 provides examples of indicators for identifying stores with similar location conditions, such as the population and traffic volume of the surrounding area. Traditionally, experienced analysts have used such indicators to subjectively classify location types and extract similar stores. [Prior art documents] [Non-patent literature]

[0005] [Non-Patent Document 1] "Part of the Enforcement Regulations of the Small and Medium-Sized Retail Business Promotion Act will be amended." (Ministry of Economy, Trade and Industry website, internet)<URL: https: / / www.meti.go.jp / press / 2021 / 04 / 20210401006 / 20210401006.html> [Overview of the project] [Problems that the invention aims to solve]

[0006] Traditionally, the location criteria used as the basis for identifying similar stores often included subjective opinions from the analyst, making it difficult to explain the basis for similarity in location. Therefore, it was necessary to appropriately select similar stores from among multiple potential similar stores based on clear and justifiable evidence that the location was similar to the planned store location or the existing store being analyzed.

[0007] The present invention aims to provide a similar store extraction device, a similar store extraction method, and a computer program that can extract appropriate similar stores from among multiple similar store candidates based on clearly explainable grounds. [Means for solving the problem]

[0008] The similar store extraction device according to the present invention includes: a location acquisition unit that acquires a location of interest and the store locations of a plurality of similar store candidates; a statistical value acquisition unit that acquires predetermined statistical values ​​in the trade area including the location and statistical values ​​in an analysis area that includes the location and is larger than the trade area for each location of interest and store location; an index value calculation unit that calculates an index value based on the statistical values ​​in the trade area for each location of interest and store location; a reference value calculation unit that calculates a reference value based on the statistical values ​​in the analysis area for each location of interest and store location; an identification unit that identifies the location type by comparing the reference value and the index value for each location of interest and store location; and an extraction unit that extracts stores from a plurality of similar store candidates in which the location type of the store location matches the location type of the location of the location of interest.

[0009] Furthermore, in the similar store extraction apparatus according to the present invention, the statistic acquisition unit acquires, as statistics, a store size statistic related to store size and an employee count statistic related to the number of employees in industries other than retail, the index value calculation unit calculates a store size index value based on the store size statistic as an index value, and calculates an employee count index value based on the employee count statistic, and it is preferable that the reference value calculation unit calculates a store size reference value based on the store size statistic as a reference value, and calculates an employee count reference value based on the employee count statistic.

[0010] Furthermore, in the similar store extraction apparatus according to the present invention, it is preferable that the specifying unit specifies the location type of a target position or a store position where the store size index value is lower than the store size reference value and the employee count index value is lower than the employee count reference value as a residential location.

[0011] Furthermore, in the similar store extraction apparatus according to the present invention, it is preferable that the specifying unit specifies the location type of a target position or a store position where the employee count index value is higher than the employee count reference value and a value obtained by normalizing the employee count index value with the employee count reference value is lower than a value obtained by normalizing the store size index value with the store size reference value as a business location.

[0012] Furthermore, in the similar store extraction apparatus according to the present invention, it is preferable that the specifying unit specifies the location type of a target position or a store position where the store size index value is higher than the store size reference value and a value obtained by normalizing the employee count index value with the employee count reference value is higher than a value obtained by normalizing the store size index value with the store size reference value as a shopping location.

[0013] Furthermore, in the similar store extraction apparatus according to the present invention, the statistic acquisition unit further acquires a population statistic related to population as a statistic, the index value calculation unit calculates the store size index value based on a ratio of the store size statistic to the population statistic, and calculates the employee count index value based on a ratio of the employee count statistic to the population statistic, and it is preferable that the reference value calculation unit calculates the store size reference value based on a ratio of the store size statistic to the population statistic, and calculates the employee count reference value based on a ratio of the employee count statistic to the population statistic.

[0014] Furthermore, the similar store extraction method according to the present invention involves a computer acquiring a location of interest and the locations of multiple candidate similar stores; for each location of interest and each store, acquiring predetermined statistical values ​​in the trade area including that location, and statistical values ​​in an analysis area that includes that location and is larger than the trade area; for each location of interest and each store, calculating an index value based on the statistical values ​​in the trade area; for each location of interest and each store, calculating a baseline value based on the statistical values ​​in the analysis area; for each location of interest and each store, identifying the location type by comparing the baseline value and the index value; and extracting stores from among multiple candidate similar stores whose location type matches that of the location of interest as similar stores.

[0015] Furthermore, the computer program according to the present invention acquires a location of interest and the locations of multiple candidate similar stores, acquires predetermined statistical values ​​for the trade area including that location and statistical values ​​for an analysis area that includes that location and is larger than the trade area, calculates an index value for each location of interest and store based on the statistical values ​​in the trade area, calculates a baseline value for each location of interest and store based on the statistical values ​​in the analysis area, identifies the location type for each location of interest and store by comparing the baseline value and the index value, and causes the computer to extract stores from among multiple candidate similar stores in which the location type of the store location matches the location type of the location of interest. [Effects of the Invention]

[0016] The similar store extraction device, similar store extraction method, and computer program according to the present invention are capable of extracting appropriate similar stores from among a plurality of similar store candidates based on clearly explainable grounds. [Brief explanation of the drawing]

[0017] [Figure 1] This is a diagram illustrating an example of the configuration of the similar store extraction system 1. [Figure 2] (A) is a diagram showing an example of the data structure of statistical table 111, and (B) is a diagram showing an example of the data structure of candidate table 112. [Figure 3]This flowchart shows an example of the pre-processing flow. [Figure 4] This is a schematic diagram illustrating the calculation of the correction value. [Figure 5] This is a schematic diagram to explain the identification of location types. [Figure 6] This is a flowchart illustrating an example of the extraction process flow. [Modes for carrying out the invention]

[0018] Various embodiments of the present invention will be described below with reference to the drawings. Please note that the technical scope of the present invention is not limited to these embodiments, but extends to the invention described in the claims and its equivalents.

[0019] Figure 1 is a diagram illustrating an example of the configuration of the similar store extraction system 1 according to the present invention.

[0020] The Similar Store Extraction System 1 is used by businesses that operate franchise businesses, such as chain store operators. The Similar Store Extraction System 1 extracts similar stores to the target store from among multiple chain stores owned by the user that are located within the analysis area. The multiple stores owned by the user are examples of multiple similar store candidates. The target store is a store located at a location specified by the operator, or a store planned to open at a location specified by the operator. The location specified by the operator is an example of a location of interest. The Similar Store Extraction System 1 comprises a Similar Store Extraction Device 100 and a Terminal Device T. The Similar Store Extraction Device 100 and the Terminal Device T are interconnected via a network N. The network N is an intranet or the internet, etc. The Terminal Device T is a device used by the user or operator, and is a personal computer, notebook computer, tablet PC, multifunction mobile phone (so-called smartphone), etc.

[0021] The similar store extraction device 100 is a personal computer, a notebook personal computer, a server, etc. The similar store extraction device 100 includes an operating device 101, a display device 102, a communication device 103, a storage device 110, and a processing circuit 120, etc.

[0022] The operating device 101 has input devices such as a keyboard and a mouse, and an interface circuit that acquires signals from the input devices. It accepts operations from the user and outputs a signal corresponding to the user's input to the processing circuit 120.

[0023] The display device 102 is an example of an output unit. The display device 102 has a display made of liquid crystal, organic EL, etc., and an interface circuit that outputs image data to the display, and displays the image data on the display according to instructions from the processing circuit 120.

[0024] The communication device 103 is an example of an output unit. The communication device 103 is equipped with a wired or wireless communication interface circuit and connects the similar store extraction device 100 to a communication network. The communication device 103 performs wired communication according to a communication protocol such as TCP / IP (Transmission Control Protocol / Internet Protocol). Alternatively, the communication device 103 may perform wireless communication according to the IEEE (Institute of Electrical and Electronics Engineers) 802.11 standard. The communication device 103 transmits information supplied from the processing circuit 120 to an external device. The communication device 103 also supplies information received from the external device to the processing circuit 120.

[0025] The storage device 110 includes, for example, semiconductor memory such as RAM (Random Access Memory) or ROM (Read Only Memory), a fixed disk device such as a hard disk, or a portable storage device such as an optical disc. The storage device 110 stores computer programs, data, etc., used for processing by the processing circuit 120. The computer program is installed in the storage device 110 from a server (not shown) via a communication device 103. Alternatively, the computer program may be installed in the storage device 110 from a computer-readable portable recording medium using a known setup program, etc. The portable recording medium is, for example, a CD-ROM or DVD-ROM. The computer program may also be distributed from a server, etc., and installed in the storage device 110. Furthermore, the storage device 110 stores statistical tables 111 and candidate tables 112, etc. Details of statistical tables 111 and candidate tables 112 will be described later.

[0026] The processing circuit 120 is, for example, a CPU (Central Processing Unit). The processing circuit 120 may also be an LSI (Large Scale Integration), ASIC (Application Specific Integrated Circuit), DSP (Digital Signal Processor), FPGA (Field Programmable Gate Array), etc. The processing circuit 120 is connected to the operating device 101, display device 102, communication device 103, and storage device 110, etc., and controls each of these parts. The processing circuit 120 reads the program stored in the storage device 110 and operates according to the read program, thereby functioning as a position acquisition unit 121, a statistical value acquisition unit 122, an index value calculation unit 123, a reference value calculation unit 124, a specific unit 125, an extraction unit 126, and an output control unit 127. The processing circuit 120 extracts similar stores from a plurality of similar store candidates that are similar to a predetermined store.

[0027] Figure 2(A) shows an example of the data structure of the statistical table 111. As shown in Figure 2(A), the statistical table 111 stores, for each of the multiple sub-regions, the identification information of each sub-region (sub-region ID), range information, identification information of the corresponding analysis region (analysis region ID), store size information, employee number information, and total population information, all of which are interrelated and stored.

[0028] A sub-region is an area obtained by dividing the entire area supported by the similar store extraction system 1 into a mesh of a predetermined size (for example, 500m on each side). The shape of a sub-region can be any shape, such as a rectangle, circle, or triangle, and the size of a sub-region can also be any size. The sub-region ID is information that identifies each sub-region. The range information indicates the geographical extent of each sub-region. The extent indicated in the range information is defined by the XY coordinates in a plane rectangular coordinate system on which the map containing the area supported by the similar store extraction system 1 is mapped, or by predetermined geographical coordinates such as latitude and longitude. The analysis region ID is information that identifies the analysis region to which each sub-region belongs. An analysis region is an area in which regional characteristics are similar within that region, for example, an administrative division such as a prefecture or city / town / village, or an area divided by a region such as Kanto, Chubu, or Kinki.

[0029] Store size information shows the geographical distribution of retail sales area, for example, the sum of retail sales area within each sub-area. Store size information is pre-set using, for example, publicly available commercial statistics. Employee number information shows the geographical distribution of the number of employees in non-retail sectors, for example, the sum of the number of employees in non-retail sectors within each sub-area. Employee number information is pre-set using, for example, publicly available economic censuses. Total population information shows the geographical distribution of the total population, for example, the total population of each sub-area. Total population information is pre-set using, for example, publicly available census results.

[0030] Store size information, employee number information, and total population information are used to calculate statistics for the trade area and statistics for the analysis area. The statistics include store size statistics regarding the size of stores included in each area, employee number statistics regarding the number of employees in non-retail industries located within each area, and / or demographic statistics regarding the population within each area. Store size statistics for each area are calculated based on store size information, employee number statistics for each area are calculated based on employee number information, and demographic statistics for each area are calculated based on total population information.

[0031] Figure 2(B) shows an example of the data structure of candidate table 112. As shown in Figure 2(B), candidate table 112 stores, for each of the multiple stores owned by the user, the following information is interrelated and stored: store identification information (store ID), text information, location information, location type, indicator value, baseline value, census age and gender composition ratio within the primary trade area, and the number of competing stores within the primary trade area.

[0032] The store ID is information that identifies each store. The text information shows the name, address, etc. of each store. The location information shows the location of each store. The location shown in the location information is defined by the same geographic coordinates as the range shown in the range information.

[0033] Location type refers to the type of location for each store, including residential areas, business locations, shopping locations, etc. Indicator values ​​are values ​​that represent the characteristics of each store's trading area and are calculated based on statistical values ​​corresponding to each store's trading area. Each store's trading area is predetermined, for example, to include the store's location and within a predetermined distance from that location. Indicator values ​​include store size indicator values ​​calculated based on store size statistics, and / or employee number indicator values ​​calculated based on employee number statistics. Reference values ​​are values ​​that represent the regional characteristics of the analysis area to which each store belongs and are calculated based on statistical values ​​corresponding to each store's analysis area. Reference values ​​include store size reference values ​​calculated based on store size statistics, and / or employee number reference values ​​calculated based on employee number statistics.

[0034] The census-based gender and age distribution within the primary trade area represents the gender and age distribution within each store's primary trade area as determined by the census results. The primary trade area is, for example, the area within a predetermined distance from each store (e.g., within a 15-minute walk). The number of competing stores within the primary trade area is the number of competing stores located within each store's primary trade area.

[0035] The identification information (store ID), text information, location information, census-based age and gender breakdown within the primary trade area, and the number of competing stores within the primary trade area are pre-set by the operator using the operating device 101 or terminal device T. This information may also be set by inputting a pre-created list file. The location type, indicator values, and reference values ​​are identified or calculated by the processing circuit 120 during the pre-processing and extraction processes described later and stored in the candidate table 112.

[0036] Figure 3 is a flowchart showing an example of the pre-processing flow performed by the similar store extraction device 100.

[0037] The following describes an example of the pre-processing operation of the similar store extraction device 100, referring to the flowchart shown in Figure 3. The operation flow described below is primarily executed by the processing circuit 120 in cooperation with each element of the similar store extraction device 100, based on a program pre-stored in the memory device 110.

[0038] First, the location acquisition unit 121 acquires the store locations of multiple similar store candidates (step S101). The location acquisition unit 121 refers to the candidate table 112, extracts one store from the stores registered in the candidate table 112, and acquires the location indicated in the location information of the extracted store as the store location of the similar store candidate. The location acquisition unit 121 sets a trade area and an analysis area for the acquired store location. The location acquisition unit 121 sets the trade area to be an area that includes the acquired store location and is within a predetermined distance (e.g., 500m) from that store location (circular trade area). The location acquisition unit 121 may also set the trade area to be an area where the travel time from each store location is within a predetermined time (time trade area) or an area where the travel distance along the road from each store location is within a predetermined distance (road distance trade area), etc. In addition, the location acquisition unit 121 sets the analysis area to be an area corresponding to one or more administrative divisions or regions that include the acquired store location. The analysis area is set to be larger than the trade area. The analysis area is preferably set to encompass the trade area, but it does not have to overlap with any part of the trade area. For example, when setting the analysis area on a prefecture-by-prefecture basis, the location acquisition unit 121 sets the analysis area to the entire Tokyo metropolitan area for stores within Tokyo. In this case, for stores near the border between Tokyo and other prefectures, a part of their trade area does not overlap with the analysis area, but the analysis area may still be the entire Tokyo metropolitan area.

[0039] Next, the statistical value acquisition unit 122 acquires statistical values ​​in the set trade area and statistical values ​​in the set analysis area (step S102).

[0040] The statistical value acquisition unit 122 extracts sub-regions from the sub-regions stored in the statistical table 111 whose range information overlaps with the target trade area. For each extracted sub-region, the statistical value acquisition unit 122 calculates the ratio (first ratio) of the area of ​​the overlapping area with the trade area to the area of ​​that sub-region, and calculates a correction value by multiplying the sales area shown in the store size information by this first ratio. The statistical value acquisition unit 122 calculates the sum of the correction values ​​calculated for all extracted sub-regions as the store size statistics for that trade area.

[0041] Figure 4 is a schematic diagram illustrating the calculation of the correction value. Figure 4 shows a map M representing the area surrounding a store located at store location C. The area within map M is divided into multiple sub-regions B. As shown in Figure 4, the outer edge of the trade area A defined for the store located at store location C does not necessarily coincide with the outer edges of each sub-region B. Therefore, if the statistical values ​​for sub-region B1, which only partially overlaps with trade area A, are used as is, the index values ​​described later cannot be calculated with high accuracy. The statistical value acquisition unit 122 can calculate the index values ​​with high accuracy by correcting the statistical values ​​based on the proportion of the area of ​​the sub-region B that overlaps with trade area A.

[0042] Furthermore, the statistical value acquisition unit 122 extracts sub-regions from the sub-regions stored in the statistical table 111 that are associated with the analysis region ID of the target analysis region. For each extracted sub-region, the statistical value acquisition unit 122 calculates the ratio (second ratio) of the area of ​​the area overlapping with the target analysis region to the area of ​​that sub-region, and calculates a correction value by multiplying the sales area shown in the store size information by this second ratio. The statistical value acquisition unit 122 calculates the sum of the correction values ​​calculated for all extracted sub-regions as the store size statistics for that analysis region.

[0043] Similarly, the statistical value acquisition unit 122 calculates a correction value for each sub-region whose range indicated in the range information overlaps with the target trade area by multiplying the number of employees indicated in the number of employees information by the first ratio. The statistical value acquisition unit 122 calculates the sum of the correction values ​​calculated for all sub-regions overlapping with the trade area as the employee number statistics for that trade area. In addition, the statistical value acquisition unit 122 calculates a correction value for each sub-region associated with the analysis area ID of the target analysis area by multiplying the number of employees indicated in the number of employees information by the second ratio. The statistical value acquisition unit 122 calculates the sum of the correction values ​​calculated for all sub-regions associated with the analysis area as the employee number statistics for that analysis area.

[0044] Similarly, the statistical value acquisition unit 122 calculates a correction value for each sub-region whose range indicated in the range information overlaps with the target trade area by multiplying the total population indicated in the total population information by the first proportion. The statistical value acquisition unit 122 calculates the total population statistics for that trade area by summing the correction values ​​calculated for all sub-regions overlapping with the trade area. In addition, the statistical value acquisition unit 122 calculates a correction value for each sub-region associated with the analysis area ID of the target analysis area by multiplying the total population indicated in the total population information by the second proportion. The statistical value acquisition unit 122 calculates the total population statistics for that analysis area by summing the correction values ​​calculated for all sub-regions associated with the analysis area.

[0045] Furthermore, if the size of the sub-region is sufficiently small compared to the size of the trade area or analysis area, the statistical value acquisition unit 122 may use the sum corresponding to each sub-region as is, without correcting it based on the ratio of the area overlapping with the trade area or analysis area, to calculate each statistical value. Alternatively, store size information, employee number information, and total population information for each analysis area may be stored in the statistical table 111, and the statistical value acquisition unit 122 may acquire the values ​​shown in the store size information, employee number information, and total population information for each analysis area as the store size statistics, employee number statistics, and total population statistics for each analysis area.

[0046] In this way, the statistical value acquisition unit 122 acquires store size statistics, employee number statistics, and total population statistics as statistical values.

[0047] Next, the indicator value calculation unit 123 calculates an indicator value based on the statistical values ​​in the set trade area (step S103). The indicator value calculation unit 123 calculates a store size indicator value as an indicator value based on the store size statistical values ​​in the trade area. For example, the indicator value calculation unit 123 calculates a store size indicator value based on the ratio of the store size statistical values ​​in the trade area to the population statistical values ​​in the trade area. Specifically, the indicator value calculation unit 123 calculates a store size indicator value by dividing the store size statistical values ​​in the trade area by the population statistical values ​​in the trade area. In addition, the indicator value calculation unit 123 calculates an employee number indicator value as an indicator value based on the employee number statistical values ​​in the trade area. For example, the indicator value calculation unit 123 calculates an employee number indicator value based on the ratio of the employee number statistical values ​​in the trade area to the population statistical values ​​in the trade area. Specifically, the indicator value calculation unit 123 calculates an employee number indicator value by dividing the employee number statistical values ​​in the trade area by the population statistical values ​​in the trade area. The indicator value calculation unit 123 stores each calculated indicator value in the candidate table 112, associating it with the store whose store location was acquired by the location acquisition unit 121.

[0048] Next, the reference value calculation unit 124 calculates a reference value based on the statistical values ​​in the set analysis area (step S104). The reference value calculation unit 124 calculates a store size reference value as a reference value based on the store size statistical values ​​in the analysis area. For example, the reference value calculation unit 124 calculates a store size reference value based on the ratio of the store size statistical values ​​in the analysis area to the demographic statistical values ​​in the analysis area. Specifically, the reference value calculation unit 124 calculates a store size reference value by dividing the store size reference value in the analysis area by the demographic statistical values ​​in the analysis area. The reference value calculation unit 124 also calculates an employee number reference value as a reference value based on the employee number statistical values ​​in the analysis area. For example, the reference value calculation unit 124 calculates an employee number reference value based on the ratio of the employee number statistical values ​​in the analysis area to the demographic statistical values ​​in the analysis area. Specifically, the reference value calculation unit 124 calculates an employee number reference value by dividing the employee number statistical values ​​in the analysis area by the demographic statistical values ​​in the analysis area. The reference value calculation unit 124 stores each reference value calculated in the candidate table 112 in association with the store whose store location was acquired by the location acquisition unit 121.

[0049] In steps S101 to S106, the loop processing of multiple stores includes stores that share a common analysis area. From the viewpoint of reducing processing load, it is preferable to calculate the statistical values ​​and reference values ​​for these common analysis areas only once within the loop processing. In that case, in step S104, the reference value calculation unit 124 stores the analysis area ID of the analysis area set for the store location being processed in the candidate table 112, associating it with the reference value. Then, in step S102, the statistical value acquisition unit 122 omits the calculation of store size statistics, employee number statistics, and total population statistics for the analysis area if the reference value of the analysis area set for the store location being processed is stored in the candidate table 112. Also, in step S104, the reference value calculation unit 124 reads each reference value of the analysis area set for the store location being processed if it is stored in the candidate table 112, and omits the calculation of the reference value. As a result, the similar store extraction device 100 can reduce the processing load and processing time of the preprocessing.

[0050] Next, the identification unit 125 identifies the location type of the store location acquired by the location acquisition unit 121 (step S105). The identification unit 125 identifies the location type by comparing the index value calculated by the index value calculation unit 123 with the reference value calculated by the reference value calculation unit 124.

[0051] Figure 5 is a schematic diagram illustrating the identification of location types. Figure 5 shows a distribution map of location types. In Figure 5, the horizontal axis represents the employee count index value, and the vertical axis represents the store size index value. The origin is set at the point where the employee count index value is equal to the employee count baseline value and the store size index value is equal to the store size baseline value. The employee count index value is higher to the right of Figure 5 and lower to the left of Figure 5. The store size index value is higher at the top of Figure 5 and lower at the bottom of Figure 5. That is, store locations where the employee count index value is higher than the employee count baseline value are located in the area to the right of the vertical axis in Figure 5, and store locations where the employee count index value is lower than the employee count baseline value are located in the area to the left of the vertical axis in Figure 5. Also, store locations where the store size index value is higher than the store size baseline value are located in the area above the horizontal axis in Figure 5, and store locations where the store size index value is lower than the store size baseline value are located in the area below the horizontal axis in Figure 5.

[0052] Furthermore, in Figure 5, line L represents the line where the employee count index value normalized by the employee count baseline (the employee count index value divided by the employee count baseline) and the store size index value normalized by the store size baseline (the store size index value divided by the store size baseline) are the same. In other words, stores where the employee count index value normalized by the employee count baseline is lower than the store size index value normalized by the store size baseline are located in the area to the upper left of line L. On the other hand, stores where the employee count index value normalized by the employee count baseline is higher than the store size index value normalized by the store size baseline are located in the area to the lower right of line L.

[0053] The identification unit 125 identifies a store location type as a residential area location if the store size index value is lower than the store size standard value and the number of employees index value is lower than the number of employees standard value. In other words, the store locations distributed in area R1 in Figure 5 are identified as residential area locations. When the sales floor area of ​​retail businesses in a trade area tends to be small within the region and the number of non-retail employees in the trade area tends to be small within the region, the location is likely to be a residential area rather than a shopping district or an office district. The similar store extraction device 100 can appropriately identify the location type of a store located in a residential area based on clear and justifiable grounds by identifying the location type as a residential area location in such cases.

[0054] On the other hand, the identification unit 125 identifies a store location type as a business location if the employee number index value is higher than the employee number baseline value, and the value obtained by normalizing the employee number index value by the employee number baseline value is lower than the value obtained by normalizing the store size index value by the store size baseline value. In other words, the store locations distributed in area R2 of Figure 5 are identified as business locations. If the number of employees in non-retail industries within the trading area tends to be large, and this tendency is greater than the tendency due to retail sales area, then the location is likely to be an office district rather than a residential or shopping district. In such cases, the similar store extraction device 100 can appropriately identify the location type of a store located in an office district based on clear and justifiable grounds by identifying the location type as a business location.

[0055] Furthermore, the identification unit 125 identifies a store location type as a shopping location if the store size index value is higher than the store size standard value, and the value obtained by normalizing the number of employees index value by the number of employees standard value is also higher than the value obtained by normalizing the store size index value by the store size standard value. In other words, the location type of store locations distributed in area R3 of Figure 5 is identified as a shopping location. When the sales floor area of ​​retail businesses in a trade area tends to be large within the region, and this tendency is greater than the tendency due to the number of employees of non-retail businesses, the location is likely to be a shopping district rather than a residential or office district. In such cases, the similar store extraction device 100 can appropriately identify the location type of a store located in a shopping district based on clear and justifiable grounds by identifying the location type as a shopping location.

[0056] The comparable store extraction device 100 can identify location types with high accuracy by using statistical data on store size for retail businesses and the number of employees for non-retail businesses. In particular, the comparable store extraction device 100 can identify location types with even higher accuracy by comparing indicator values ​​in a trade area with reference values ​​in an analysis area wider than the trade area, taking into account the regional characteristics of each location. Furthermore, the comparable store extraction device 100 can identify location types with even higher accuracy by calculating each indicator value and each reference value based on demographic data, taking into account trends based on population.

[0057] Furthermore, the specific unit 125 may further subdivide the location types, which are classified into three types—residential area location, business location, and shopping location—using other indicators. For example, the specific unit 125 subdivides the location types into station-front location and non-station-front location based on the distance between the target location and the nearest station. In this case, the specific unit 125 identifies the location type as station-front location if the distance between the target location and the nearest station is less than a predetermined distance threshold, and identifies the location type as non-station-front location if the distance between the target location and the nearest station is equal to or greater than the distance threshold.

[0058] The identification unit 125 may subdivide the location type into roadside location and non-roadside location based on whether the target location is adjacent to a road of a predetermined width or greater (or whether a road of a predetermined width or greater exists within a predetermined distance from the target location) and the population density within the trade area. In this case, the identification unit 125 identifies the location type as roadside location if the target location is adjacent to a road of a predetermined width or greater (or a road of a predetermined width or greater exists within a predetermined distance from the target location) and the population density within the trade area is less than a predetermined population density threshold. On the other hand, the identification unit 125 identifies the location type as non-roadside location if the target location is not adjacent to a road of a predetermined width or greater (or no road of a predetermined width or greater exists within a predetermined distance from the target location), or if the population density within the trade area is greater than or equal to the population density threshold.

[0059] The specific unit 125 may further subdivide the location type into station-front locations and non-station-front locations, and roadside locations and non-roadside locations. Furthermore, the specific unit 125 may further subdivide the location type into station-front locations and non-station-front locations, and / or roadside locations and non-roadside locations, but only when the location type is a residential area location, a business location, or a shopping location. The similar store extraction device 100 can extract more appropriate similar stores by further subdividing the location type.

[0060] The identification unit 125 stores the identified location type in the candidate table 112, associating it with the store whose location was acquired by the location acquisition unit 121.

[0061] Next, the identification unit 125 determines whether the processing in steps S101 to S105 has been completed for all stores registered in the candidate table 112 (step S106). If there are stores for which processing has not yet been completed, the identification unit 125 returns to step S101. As a result, the processing in steps S101 to S105 is executed for the other stores registered in the candidate table 112. That is, the location acquisition unit 121 acquires the store locations of multiple similar store candidates. Then, for each acquired store location, the statistical value acquisition unit 122 acquires each statistical value, the index value calculation unit 123 calculates the index value, the standard value calculation unit 124 calculates the standard value, and the identification unit 125 identifies the location type. On the other hand, if the processing in steps S101 to S105 has been completed for all stores registered in the candidate table 112, the preprocessing is completed.

[0062] Figure 6 is a flowchart showing an example of the extraction process performed by the similar store extraction device 100 after the pre-processing shown in Figure 3.

[0063] The following describes an example of the operation of the extraction process of the similar store extraction device 100, referring to the flowchart shown in Figure 6.

[0064] First, the location acquisition unit 121 acquires a location of interest specified by the operator using the operating device 101 or terminal device T (step S201). The operator specifies, for example, the location of a store that is planned to open as the location of interest. The location acquisition unit 121 acquires the location of interest specified by the operator using the operating device 101 or terminal device T by receiving it from the operating device 101 or communication device 103. In the same manner as in step S101, the location acquisition unit 121 sets a trade area that includes the acquired location of interest and is within a predetermined distance from that location of interest, and sets an area corresponding to one or more administrative divisions or regions that include the acquired location of interest as the analysis area.

[0065] Next, the statistical value acquisition unit 122 acquires statistical values ​​in the set trade area and statistical values ​​in the set analysis area in the same manner as the process in step S102 (step S202). That is, for a location of interest, the statistical value acquisition unit 122 acquires statistical values ​​in the trade area that includes that location (store size statistics, employee number statistics, and population statistics) and statistical values ​​in the analysis area that includes that location (store size statistics, employee number statistics, and population statistics).

[0066] Next, the indicator value calculation unit 123 calculates indicator values ​​based on statistical values ​​in the set trade area, in the same manner as the process in step S103 (step S203). That is, for the location of interest, the indicator value calculation unit 123 calculates indicator values ​​(store size indicator value and employee number indicator value) based on statistical values ​​(store size statistics, employee number statistics, and population statistics) in the trade area that includes that location.

[0067] Next, the reference value calculation unit 124 calculates a reference value based on the statistical values ​​in the set analysis area, in the same manner as the process in step S104 (step S204). That is, for a location of interest, the reference value calculation unit 124 calculates a reference value (store size reference value and employee number reference value) based on the statistical values ​​(store size statistics, employee number statistics, and demographic statistics) in the analysis area that includes that location.

[0068] Next, the identification unit 125 identifies the location type of the location of interest acquired by the location acquisition unit 121 in the same manner as in step S105 (step S205). That is, the identification unit 125 identifies the location type of the location of interest by comparing a reference value (store size reference value and employee number reference value) with an index value (store size index value and employee number index value). The identification unit 125 identifies a location of interest where the store size index value is lower than the store size reference value and the employee number index value is lower than the employee number reference value as a residential area location. The identification unit 125 identifies a location of interest where the employee number index value is higher than the employee number reference value and the value of the employee number index value normalized by the employee number reference value is lower than the value of the store size index value normalized by the store size reference value as a business location. The specific unit 125 identifies a location type of a particular spot as a shopping location if the store size index value is higher than the store size standard value, and the value obtained by normalizing the number of employees index value by the number of employees standard value is higher than the value obtained by normalizing the store size index value by the store size standard value.

[0069] Next, the extraction unit 126 extracts stores from among multiple similar store candidates whose store location type matches the location type of the location of the location of interest (step S206). The extraction unit 126 refers to the candidate table 112 and extracts stores whose location type stored in the candidate table 112 matches the location type of the location of interest as similar stores. The extraction unit 126 extracts all stores whose store location type matches the location type of the location of interest as similar stores. The extraction unit 126 may also extract only stores whose store location is included in the analysis area that includes the location of interest as similar stores.

[0070] Furthermore, the extraction unit 126 may calculate the similarity between the location of interest and the store location, and sort the extracted similar stores in descending order of similarity. Alternatively, the extraction unit 126 may narrow down the similar stores to extract only a predetermined number of stores in descending order of similarity.

[0071] For example, the extraction unit 126 calculates the similarity using any one or more of a store size index value, an employee number index value, an age-based composition ratio from the national census in the primary commercial area, and the number of competing stores in the primary commercial area. For example, the extraction unit 126 obtains the normalized value A of the store size index value according to the following Equations 1 to 4 i , the normalized value B of the employee number index value i , the normalized value C of the age-based composition ratio from the national census in the primary commercial area i and the normalized value D of the number of competing stores in the primary commercial area i is calculated. A i ={a i -MIN(a0,a1,…,a N )} / {MAX(a0,a1,…,a N )-MIN(a0,a1,…,a N )} (Equation 1) B i ={b i -MIN(b0,b1,…,b N )} / {MAX(b0,b1,…,b N )-MIN(b0,b1,…,b N )} (Equation 2) C i ={c i -MIN(c0,c1,…,c N )} / {MAX(c0,c1,…,c N )-MIN(c0,c1,…,c N )} (Equation 3) D i ={d i -MIN(d0,d1,…,d N )} / {MAX(d0,d1,…,d N )-MIN(d0,d1,…,d N )} (Equation 4) Here, i is an integer satisfying 0≦i≦N. N is the number of store positions. a0 is the store size index value at the target position, and a1,…,a N are the store size index values at each store position. b0 is the employee number index value at the target position, and b1,…,b N are the employee number index values at each store position. c0 is the age-based composition ratio from the national census in the primary commercial area at the target position, and c1,…,c Nd0 represents the census-based age and gender distribution within the primary trade area for each store location. d0 is the number of competing stores within the primary trade area of ​​the location of interest, and d1, ..., d N is the number of competing stores within the primary trading area of ​​each store location. MIN() is the minimum value of each element in the parentheses, and MAX() is the maximum value of each element in the parentheses. If the minimum and maximum values ​​are equal (the denominator on the right side is 0), each normalized value is set to 0.5.

[0072] Next, the extraction unit 126 calculates the distance (Euclidean distance) δ between each parameter of each store location and each parameter of the location of interest using the following equation 5. j Calculate. δ j ={(A0-A j ) 2 +(B0-B j ) 2 +(C0-C j ) 2 +(D0-D j ) 2} 1 / 2 (Formula 5) Here, j is an integer between 1 and j. Terms within the curly braces {} (store size index value, employee number index value, census age and gender breakdown within the primary trade area, and number of competing stores within the primary trade area) that are not used are omitted.

[0073] Next, the extraction unit 126 calculates the similarity λ of each store location using the following equation 6. j Calculate. λ j = 1-δ j / M 1 / 2 (Formula 6) Here, M is the number of terms in {} of Equation 5, i.e., the number of items used from the store size index value, employee number index value, census gender and age distribution ratio within the primary trade area, and the number of competing stores within the primary trade area. In this way, even if there are multiple similar stores with the same location type, it becomes possible to provide similar store information sorted or filtered according to similarity priority, according to the user's request.

[0074] While an example of calculating similarity using four indicators has been shown, the method is not limited to this. The extraction unit 126 may calculate similarity using only two indicators: the store size indicator and the number of employees indicator, which were used to identify the location type. This allows for prioritization based on evidence that can be explained in common with the location type, making it possible to provide easily understandable sorted or filtered similar store information. Alternatively, the extraction unit 126 may calculate similarity using only two indicators that were not used to identify the location type: the census age and gender composition ratio within the primary trade area and the number of competing stores within the primary trade area. This allows for prioritization based on evidence different from that of the location type, making it possible to provide sorted or filtered similar store information from a more multifaceted perspective. Furthermore, by calculating similarity using indicators other than the four mentioned above, it is also possible to provide sorted or filtered similar store information from a more multifaceted perspective.

[0075] Next, the output control unit 127 generates display data for displaying information about similar stores extracted by the extraction unit 126. The output control unit 127 outputs the generated display data by displaying it on the display device 102 or by transmitting it to the terminal device T via the communication device 103 (step S207), thus ending the series of steps. When the terminal device T receives the display data from the similar store extraction device 100, it displays the received display data on a display unit (not shown).

[0076] For example, the output control unit 127 generates display data to display a map showing a point of interest marker indicating a point of interest specified by the operator, and a similar store location marker indicating the store location of the extracted similar store. The output control unit 127 may also generate display data such that the size, density, or saturation of the similar store location marker changes according to the similarity calculated for each similar store. In that case, the output control unit 127 may generate display data such that the similar store location marker is displayed larger, darker, or in a brighter color the higher the similarity. The output control unit 127 may also generate display data to display text information (name, address), location type, index value and / or reference value for each similar store by referring to the candidate table 112.

[0077] Furthermore, the output control unit 127 may generate display data to display a list showing the text information (name, address), location type, indicator value, reference value, and / or similarity for each extracted similar store. The output control unit 127 may also generate display data such that the density or saturation of the text or background in the similar store column changes according to the similarity calculated for each similar store. In that case, the output control unit 127 may generate display data such that the higher the similarity, the darker or more vivid the text or background in the similar store column. The output control unit 127 may also generate display data such that each similar store is arranged in descending order of its similarity.

[0078] Furthermore, the output control unit 127 may generate display data such that it displays the distribution map shown in Figure 5, and superimposes a predetermined marker representing the location of interest at a position where the horizontal axis coordinate is the "store size index value of the location of interest" and the vertical axis coordinate is the "number of employees index value of the location of interest," and superimposes a predetermined marker representing a similar store at a position where the horizontal axis coordinate is the "store size index value of a similar store" and the vertical axis coordinate is the "number of employees index value of a similar store."

[0079] This allows chain store operators engaged in franchise businesses to introduce similar stores to their planned locations to prospective franchisees and explain the profitability and other aspects of those similar stores. Therefore, the similar store extraction device 100 can improve user convenience.

[0080] In step S201, the operator may specify the location of the target store among the multiple stores owned by the user as the location of interest. In this case, if statistical values ​​have already been obtained for the location of the target store, indicator values ​​and reference values ​​have been calculated, and the location type has been identified in the preprocessing shown in Figure 3, steps S202 to S205 may be omitted. In this case, in step S205, the identification unit 125 identifies the location type stored in the candidate table 112 in association with the target store as the location type of the target store's location. In step S206, the extraction unit 126 extracts stores from among the multiple similar store candidates, excluding the target store corresponding to the location of interest, whose location type matches that of the location of the location of interest, as similar stores. This allows the similar store extraction device 100 to reduce the processing load and processing time of the extraction process.

[0081] In this case, a chain store operator operating a franchise business can identify similar stores to the store under analysis, compare the revenue and expenses of the store under analysis with those of the similar stores, and conduct a detailed analysis of the management status of each store. In particular, the chain store operator can improve the store under analysis by referring to the product assortment, layout, etc., of the high-selling stores among the extracted similar stores. Therefore, the similar store extraction device 100 can improve user convenience.

[0082] As explained above, the similar store extraction device 100 identifies the location type for each point of interest and store location by comparing an index value based on statistical values ​​in the trade area with a reference value based on statistical values ​​in an analysis area larger than the trade area. Then, from among multiple similar store candidates, the similar store extraction device 100 extracts stores as similar stores whose store location type matches the location type of the point of interest. Because the similar store extraction device 100 identifies location types using clear criteria and extracts stores as similar stores whose location type matches the location type of the point of interest, it can extract similar stores with high explanatory power. Therefore, the similar store extraction device 100 can extract appropriate similar stores from among multiple similar store candidates based on clearly explainable evidence.

[0083] In particular, the similar store extraction device 100 identifies location types using the same statistical values ​​as statistical values ​​for the trade area and statistical values ​​for the analysis area. Therefore, the similar store extraction device 100 can identify location types with high accuracy by considering what characteristics the trade area has in relation to the regional characteristics of the analysis area (where it is located).

[0084] Furthermore, the similar store extraction device 100 extracts similar stores by identifying location types in a way that does not involve the analyst's subjectivity, thus enabling the extraction of more objective similar stores regardless of the user's experience or ability. In addition, the similar store extraction device 100 extracts similar stores by identifying location types based on two indicators: store size for retail businesses and the number of employees in non-retail businesses. This suppresses the extraction of similar stores that are difficult to explain, such as those where some indicators are similar but others are not. Therefore, the similar store extraction device 100 can extract appropriate similar stores based on clearly explainable evidence.

[0085] While preferred embodiments have been described above, the embodiments are not limited to these. For example, the similar store extraction device 100 may calculate each indicator value and each baseline value using other factors, such as the area of ​​the trade area and the analysis area (small area), or the area of ​​offices in the trade area and the analysis area (small area), instead of the total population in the trade area and the analysis area (small area). However, it is preferable that the factor used in place of the total population is the same for the trade area and the analysis area, and for all of their indicator values ​​and baseline values. Alternatively, the similar store extraction device 100 may calculate each indicator value and each baseline value without using the total population in the trade area or the analysis area (small area). In that case, the similar store extraction device 100 uses the store size statistics and employee number statistics in the trade area as the store size indicator value and employee number indicator value, and the store size statistics and employee number statistics in the analysis area as the store size baseline value and employee number baseline value.

[0086] Furthermore, the similar store extraction device 100 may identify the location type using only one of the store size and the number of employees. When the number of employees is not used, the similar store extraction device 100 identifies the location type as a residential area if the store size index value is lower than the store size standard value, and identifies the location type as a shopping area if the store size index value is higher than the store size standard value. When store size is not used, the similar store extraction device 100 identifies the location type as a residential area if the number of employees index value is lower than the number of employees standard value, and identifies the location type as a business area if the number of employees index value is higher than the number of employees standard value. In addition, although the process in Figure 3 has been described as a preprocessing step, the process in Figure 3 may be executed before step S206 in the extraction process in Figure 5.

[0087] Those skilled in the art will understand that various changes, substitutions, and modifications can be made without departing from the spirit and scope of the present invention. For example, the embodiments and modifications described above may be combined as appropriate within the scope of the invention. [Explanation of Symbols]

[0088] 1 Similar store extraction system, 100 Similar store extraction device, 121 Location acquisition unit, 122 Statistical value acquisition unit, 123 Indicator value calculation unit, 124 Reference value calculation unit, 125 Identification unit, 126 Extraction unit, 127 Output control unit

Claims

1. A location acquisition unit that acquires the location of interest and the locations of multiple similar store candidates, A statistical value acquisition unit acquires predetermined statistical values ​​for each of the aforementioned locations of interest and the aforementioned store locations in the trade area including the said location, and in an analysis area that includes the said location and is larger than the said trade area. An index value calculation unit that calculates an index value for each of the aforementioned locations of interest and the aforementioned store locations based on the aforementioned statistical values ​​in the trade area, A reference value calculation unit calculates a reference value for each of the aforementioned locations of interest and the aforementioned store locations based on the statistical values ​​in the analysis area, A location identification unit identifies the location type by comparing the reference value and the indicator value for each of the aforementioned location of interest and the aforementioned store location, An extraction unit that extracts stores from among the plurality of similar store candidates in which the location type of the store's location matches the location type of the location of the point of interest, A similar store extraction device characterized by having the following features.

2. The statistical value acquisition unit acquires store size statistics and employee number statistics for industries other than retail as the statistical values. The indicator value calculation unit calculates a store size indicator value based on the store size statistics and calculates an employee number indicator value based on the employee number statistics as the indicator values. The similar store extraction device according to claim 1, wherein the reference value calculation unit calculates a store size reference value based on the store size statistics and calculates an employee number reference value based on the employee number statistics as the reference values.

3. The similar store extraction device according to claim 2, wherein the identifying unit identifies the location type of the location of the location of interest or the store location as a residential area location, where the store size index value is lower than the store size standard value and the number of employees index value is lower than the number of employees standard value.

4. The similar store extraction device according to claim 2, wherein the identifying unit identifies the location type of the location of the location of interest or the store location as a business location, where the employee number index value is higher than the employee number standard value, and the value obtained by normalizing the employee number index value by the employee number standard value is lower than the value obtained by normalizing the store size index value by the store size standard value.

5. The similar store extraction device according to claim 2, wherein the identifying unit identifies the location type of the location of the location of the location of interest or the store location as a shopping location, wherein the store size index value is higher than the store size standard value, and the value obtained by normalizing the number of employees index value by the number of employees standard value is higher than the value obtained by normalizing the store size index value by the store size standard value.

6. The statistical value acquisition unit further acquires demographic data relating to the population as the statistical value, The indicator value calculation unit calculates the store size indicator value based on the ratio of the store size statistics to the population statistics, and calculates the number of employees indicator value based on the ratio of the number of employees statistics to the population statistics, The similar store extraction device according to any one of claims 2 to 5, wherein the standard value calculation unit calculates the standard value of store size based on the ratio of the store size statistics to the population statistics, and calculates the standard value of the number of employees based on the ratio of the number of employees statistics to the population statistics.

7. Computers Obtain the location of the focus and the store locations of multiple similar candidate stores. For each of the aforementioned locations of interest and the aforementioned store locations, predetermined statistical values ​​are obtained for the trade area including the said location, and for the aforementioned statistical values ​​in an analysis area that includes the said location and is larger than the said trade area. For each of the aforementioned locations of interest and the aforementioned store locations, an index value is calculated based on the aforementioned statistical values ​​in the trade area. For each of the aforementioned locations of interest and store locations, a reference value is calculated based on the statistical values ​​in the analysis area. For each of the aforementioned locations of interest and the aforementioned store locations, the location type is identified by comparing the aforementioned standard value with the aforementioned indicator value. From among the multiple candidate similar stores, stores whose location type matches the location type of the location of the point of interest are extracted as similar stores. A method for extracting similar stores, characterized by the following features.

8. Obtain the location of the focus and the store locations of multiple similar candidate stores. For each of the aforementioned locations of interest and the aforementioned store locations, predetermined statistical values ​​are obtained for the trade area including the said location, and for the aforementioned statistical values ​​in an analysis area that includes the said location and is larger than the said trade area. For each of the aforementioned locations of interest and the aforementioned store locations, an index value is calculated based on the aforementioned statistical values ​​in the trade area. For each of the aforementioned locations of interest and store locations, a reference value is calculated based on the statistical values ​​in the analysis area. For each of the aforementioned locations of interest and the aforementioned store locations, the location type is identified by comparing the aforementioned standard value with the aforementioned indicator value. From among the multiple candidate similar stores, stores whose location type matches the location type of the location of the point of interest are extracted as similar stores. A computer program characterized by causing a computer to perform a certain action.

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