JP6473194B2 - Sales estimation system - Google Patents

Description

  The present invention relates to a sales estimation system that estimates sales based on POS data.

  Conventionally, the Point Of Sale system (hereinafter referred to as POS system) has been used in retail stores. When a POS system is introduced, it is generally introduced for a franchise or the like that manages a plurality of stores. In such a franchise store, it is necessary to predict sales at a new store in the case of a new store opening. In addition, even after opening a store, it is necessary to predict market share in the region and advance competitive analysis by predicting sales at nearby stores.

There are various methods for forecasting sales. For example, in Patent Document 1, after predicting the number of customers in a specified area, calculating the number of products sold for each product category of products sold in a store, the product is multiplied by the product average unit price for each product category. The sales for each category are calculated, and the store sales of the sales prediction target store are predicted based on the sales for each product category.

JP 2003-167880 A

  As represented by Patent Document 1, since past sales forecasts are for candidate store openings, they are not based on actual sales data, but are geographical data such as local population and sales floor area. It was calculated based on. Therefore, the accuracy was not so high, but if it was not yet opened, it had to be calculated without using actual data. On the other hand, if you want to examine the sales of competing stores in the vicinity of stores that have already opened, you cannot know the sales of competing stores themselves, but sales at your own store are the most useful data. The use of this was not progressing. An object of the present invention is to improve the accuracy of sales prediction in a corresponding region by using actual data of store sales.

  The sales estimation system according to the present invention stores the market POS data indicating the sales by product for each participating store generated based on the POS system, and the market POS database that associates the division of each region for the participating stores, For each of the participating stores, a plurality of store characteristics each specifying one of a plurality of classification items regarding the properties of the store are set, the participating stores are grouped for each of the classification items, and sales by product for each group are aggregated The business area POS database storing the trade area POS data, the category calculation means for obtaining the index by classification, which is a relative value with respect to the overall sales, for each category and the sales by product for each group, and the target store is selected Target input means for inputting classification items for each store characteristic for the target store, For each classification item input by the get input means, an index by classification calculated by the classification calculation means is obtained, and an index by product of the target store is obtained based on the index by classification acquired for each of a plurality of store characteristics. For the target calculation means for calculating the area and the classification of the region to which the target store belongs, the sales by product of the market POS data are specified, and the sales by product are multiplied by the product index of the target store, An estimation means for calculating the estimated sales of is provided.

  According to the present invention, while reflecting the characteristics of the area to be analyzed and the store, it is possible to calculate the sales by product at the target store with high accuracy by using the POS data for each participating store obtained by the POS system. Can be realized.

The system block diagram of the sales estimation system which concerns on this Embodiment. The functional block diagram of the sales estimation apparatus which concerns on this Embodiment. A summary of sales trends by category for each product. Index list for each category. The flowchart which shows the POS data storage process which concerns on this Embodiment. Analysis target store selection screen. Input screen for competitor analysis and analysis conditions. The flowchart which shows the estimation process of the sales according to goods of a competitive store. The flowchart which shows the estimation process of the sales according to category of a competition store. The flowchart which shows the form creation process including a sales order and a share.

  FIG. 1 shows a system configuration diagram of a sales estimation system according to the present embodiment. The POS terminals 10, 20, 30 are connected to the POS management server 100. Although three POS terminals 10, 20, and 30 are shown as examples, more terminals may be connected. The POS management server 100 acquires the output of POS data from the POS terminals 10, 20, and 30 and transmits it to the sales estimation device 300. The POS data 150 sent from the POS terminals 10, 20, and 30 to the sales estimation device 300 via the POS management server 100 is created and exchanged as RDS (distributed POS database service) format data as an example. Other data formats may be used.

  The GIS server 200 is a server that manages geographic information and additional information (hereinafter referred to as GIS data) by a geographic information system (Geographic Information System (s): GIS). The sales estimation device 300 acquires the geographical information acquired by the GIS server 200.

  The sales estimation device 300 performs sales estimation processing based on the POS data 150 received from the POS management server 100 and the GIS data 250 received from the GIS server 200. The sales estimation data created by the sales estimation device 300 is sent by communication with the clients 410, 420, and 430.

  In the present embodiment, a case where communication processing is performed between the POS management server 100 and the sales estimation device 300 and between the GIS server 200 and the sales estimation device 300 will be described. However, it is necessary to exchange data by communication. However, the POS system connected to the POS management server 100 and the GIS system connected by the GIS server 200 may be independent systems that are not connected to the sales estimation device 300.

(About the hardware configuration of this system)
Each of the POS management server 100, the GIS server 200, and the sales estimation device 300 includes a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM (Random Access Memory), an image processing unit, and a memory. . The CPU, ROM, RAM, image processing unit, and memory are connected to each other via a bus.

  The CPU executes various processes according to a program recorded in the ROM or a program loaded from the memory to the RAM. The RAM appropriately stores data necessary for the CPU to execute various processes.

  The image processing unit is configured by a DSP (Digital Signal Processor), a VRAM (Video Random Access Memory), and the like, and performs various image processing on image data in cooperation with the CPU.

  The memory may be any storage medium such as DRAM, cache memory, magnetic disk, optical disk, magneto-optical disk, or semiconductor memory. The memory includes not only those connected by a bus but also those read and written via a drive. The data stored in this embodiment will be described as being temporarily stored in this memory, both in the case of temporary storage and in the case of long-term storage using a nonvolatile memory.

  An input / output interface is connected to each of the POS management server 100, the GIS server 200, and the sales estimation device 300. A display unit, an imaging unit, an input unit, and a communication unit are connected via an input / output interface. The input unit includes various buttons and accepts user instruction operations. The communication unit controls communication performed with other devices via a network including the Internet.

  The display unit has a display screen, and has a display device that displays and reproduces the image or video formed by the image processing unit. As the display device, various display devices such as a monitor and a liquid crystal display are assumed. In the present embodiment, image data to be displayed by a CPU or the like is generated, and image display processing is performed on the display screen via the image processing unit. Henceforth, when only “display” is described, it includes executing display processing including the above functions.

  The POS management server 100, the GIS server 200, and the sales estimation device 300 each have the above-described configurations, and functional configurations will be described later. Each functional configuration is functionally realized by the cooperative operation of the CPU, ROM, RAM, image processing unit, and memory. The function of each of these units is a module configuration provided by an electronic circuit or a program. The program is stored in the ROM, and is executed by cooperating with each unit while being appropriately read out by the CPU.

(Functional configuration of sales estimation device)
FIG. 2 shows a functional configuration of the sales estimation device according to the present embodiment. The sales estimation unit 300 generates sales estimation data 390 based on the POS data 150 and the GIS data 250 received from the POS management server 100. For this purpose, the sales estimation unit 300 includes a POS data processing unit 310, a market POS database 320, a trade area POS database 330, a classification calculation unit 340, a target input unit 350, a target calculation unit 360, and an estimation unit 370.

  The POS data processing unit 310 accumulates the POS data 150 received from the POS management server 100 in the market POS database 320 as market POS data. The market POS data stored in the market POS database 320 is data obtained by counting the sales of stores in a specific region and business category and quantifying which products are sold in that region. The market POS data will also be described assuming an example in which RDS data having the same data format as the POS data 150 is used, but other data formats may be used.

  Like the POS data 150, the market POS database 320 accumulates data describing sales by product for each store one by one, while totaling sales by business category and region. For example, sales data for the supermarket business category is accumulated by region such as Kanto, Hokuriku, Tokai, Kinki, Chu-Shikoku, and Kyushu other than Hokkaido, Tohoku, Keihin, and Keihin.

  In addition, business forms with a small number of participating stores are gathered to some extent, and for example, sales data for nations is accumulated for a business form called a drug store. In addition to the above, the business types of various stores such as convenience stores and 100-yen stores are aggregated, and the region may have a certain degree of regionality, so it may be further subdivided. As described above, the market POS database 320 is a database that stores the market POS data indicating the sales by product for each participating store generated based on the POS system, and associates the regional divisions with each of the participating stores.

  Thus, the market POS data is used as sales data as a calculation basis. That is, the market POS data is data indicating the sales value for each product based on the actual data on the regional sales for which the competitive research is conducted. For example, when the Keihin area is selected, the sales figures of each product of the store corresponding to the Keihin area are tabulated as market POS data.

  Based on the GIS data 250 received from the GIS server 200, the POS data processing unit 310 accumulates the POS data 150 in the commercial area POS database 330 as the commercial area POS data. The commercial area POS data stored in the commercial area POS database 330 is obtained by grouping stores according to the characteristics of the stores and quantifying the sales for each classification of the store characteristics.

  Store characteristics are regional data relating to sales in a region where stores are located. Since the POS data processing unit 310 can acquire geographical data related to the area from the GIS data 250, the POS data processing unit 310 also acquires the location and the store area from the GIS data 250 for the target stores to be tabulated as POS data. The location is classified according to the respective classification items such as in front of the station (within 200 m from the station) and other than in front of the station as data on store characteristics. Similarly, the store size is classified for each classification item as store characteristic data. For example, each store is classified according to the size of the store area, such as 0-150 square meters, 150-500 square meters, 500-1500 square meters, 1500-3000 square meters, and 3000 square meters.

The GIS data 250 further accumulates data relating to household conditions based on the national census. Based on the GIS data 250, the POS data processing unit 310 aggregates data such as the annual income of the local household and the age of each household, which is known from the situation of the household, and calculates store characteristic data.
Regarding the annual income of a local household, the classification that produces a prominent distribution with respect to the prefecture to which the store belongs is set as the store characteristic of the region. In other words, when the number of households in a specific annual income category is the most biased than the average region, the biased category is set as the annual income of the regional household to which the store belongs. For example,
0 million yen households | 3 million yen households | 5 million yen households | 7 million yen households | 10 million yen classified as households in Tokyo, respectively in Tokyo | 1000 | 2000 | 3000 | 2500 | 500
Around store A | 20 | 30 | 50 | 20 | 30
Suppose that the number of households is distributed. Converting to household composition,
Tokyo | 11.11111 | 22.22222 | 33.33333 | 27.77778 | 5.55556
Around store A | 13.33333 | 20 | 33.33333 | 13.33333 | 20
It becomes. Taking the difference in the household composition ratio between the two,
Difference | 2.22222 | -2.22222 | 0 | -14.44444 | 14.44444
As required. In this area, around A store, the biggest difference in the positive direction is the number of 10 million yen households at 14.44444, so the annual income characteristic of A store is 10 million yen.

  As another method for specifying the annual income of a regional household, there is a method of obtaining a total value of annual income for each household and obtaining it from an average value of annual income divided by the number of households in the corresponding region. Here, for each average value obtained, the target stores are classified by average annual income, and used as store characteristic data. For example, the annual income of the corresponding area is classified into clusters of less than 3 million yen, 3 million yen to less than 5 million yen, 5 million yen to less than 7 million yen, 7 million yen to less than 10 million yen, 10 million yen or more. Item.

  As for the age of each household, the characteristics of each household can be easily divided, and for example, it can be divided into a single household of young people, a silver household of elderly people only, and a family household. Count the number of households in each category and determine which category has more households than the average in that region. For example, the region is a single household type, a silver household type, or a family household type. Classification items such as regions are provided, and store characteristics are assigned.

  In this way, each store is classified by a plurality of store characteristics. For example, for a certain store, the store characteristic “annual income” belongs to the classification item “5 million yen to 7 million yen”, the store characteristic “age household” belongs to the classification item “family household”, and the store characteristic “ “Location” belongs to the classification item “other than the station”, and the store characteristic “store size” belongs to the classification item “500-1500 square meters”.

  After sorting as described above, the POS data processing unit 310 counts the sales for each classification item. For example, for a product ABC, the average daily sales per store is 15000 yen for a single young household store, 20000 yen for a family household store, 25000 yen for a silver household store, and so on. The sales trends of products are tabulated for each store characteristic, particularly for each category. FIG. 3 shows the result of counting sales trends for each category item for each product. This sales trend is accumulated in the trade area POS database 330 as trade area POS data.

  The commercial area POS database 330 thus sets a plurality of store characteristics each specifying one of a plurality of classification items regarding the properties of the store for each participating store, and groups the participating stores for each classification item. Stores trade area POS data that totals sales by product.

  FIG. 4 shows a list of commodity indices for each classification item. As shown in FIG. 3, since sales data for each classification item is stored as the trade area POS data, this is indexed this time. The classification calculation unit 340 obtains, for each classification item, a classification-specific index that is a relative value with respect to the overall sales for the sales by product for each group of classification items. In the example of FIG. 3, since the average sales for each store is 20,000 yen, it is indexed by dividing by the number of 20,000. The indexed result is as shown in FIG. As described above, the database is advanced based on the POS data 150 and accumulated in the trade area POS database 330.

(flowchart)
FIG. 5 is a flowchart showing POS data storage processing according to the present embodiment. In FIG. 5, a case where the market POS data and the commercial area POS data are sequentially generated and stored from the acquired POS data 150 is described as an example. However, the processing is not limited to the processing shown in FIG. May be acquired by some means inside or outside the network, and market POS data and commercial area POS data may be generated and stored collectively based on this.

  First, a classification item for each store characteristic is input for each participating store by the input unit (step S100). Thereby, store information for each participating store is accumulated. Then, POS data 150 is acquired (step S110). The acquisition means may be either via the network or outside the network.

  Next, the POS data 150 is stored in the market POS database 320 as market POS data (step S120). Then, a totaling process is performed by adding up the sales for each group of the store characteristic classification items shown in FIG. 3 and calculating an average (step S130). Furthermore, as shown in FIG. 4, the classification calculation unit 340 calculates a classification index for each product (step S140). Next, the trade area POS data 250 is stored in the trade area POS database 330 (step S150), and a series of processing ends.

(Target store analysis processing)
FIG. 6 shows an analysis target store selection screen. Next, a specific store to be analyzed is selected by a user operation. The store to be analyzed is a participating store that has already acquired POS data. For the store to be analyzed, for example, the store name may be input by using a method other than the map, or one of a plurality of stores arranged in advance may be selected. Select the target store. In the example of FIG. 6, the store is specified by selecting the cursor 500. Alternatively, the position may be specified by operating the mouse, and the cursor 500 may be displayed as a confirmation display.

  FIG. 7 shows an input screen for inputting the target of the competitor store analysis and the analysis conditions. The selection of competing stores to be analyzed may be directly selected in the same manner as the member store selection shown in FIG. 6, but in actuality, since analysis is often performed for a plurality of stores in the business area, Select with. For example, although it may be specified in units of towns and villages, an example will be described in which all stores included therein are analyzed by inputting the range of the commercial area centered on the position of the member store specified in FIG. To do.

  In addition, regarding the analysis target, the business type is also input into the check box. In the example of FIG. 7, items are provided so that a supermarket, a discount store, and a drug store are selected from check boxes, and the corresponding store is set as an analysis target. The target input unit 350 selects the geographical location of the target store by inputting the analysis conditions such as the geographical location of the participating store and the trade area distance as described above.

  In the present embodiment, the case where the daily sales are displayed in monetary units has been described so far and the following. However, as shown in FIG. It may be a target of aggregation and may be displayed not only in monetary units but also in quantity units. This is because some stores often sell at a discount, which may require competitive evaluation on a quantity basis.

  Furthermore, although the aggregation target may be performed for the total sales of the store, since the POS data 150 is accumulated for each product, it may be selected for each product. Further, as shown in FIG. 6, it is selected for each category. It is also possible to do. For example, a large category is selected from foods, daily necessities, cultural supplies, etc., and subordinate items include options such as beverages among foods, and also beverages, as shown in the lower right of FIG. , Fruit drinks, soft drinks, etc., and an analysis target can be input for each category.

  When the analysis target is input from the display screen shown in FIGS. 6 and 7, the target input unit 350 executes an analysis process. Since the narrowing-down conditions by the target input unit 350 include a plurality of competing stores, analysis is performed for each of the competing stores, and then aggregation is performed. First, each classification item is specified about the competition store used as a total object. In the example of FIG. 4, each classification item is specified and aggregated for the participating stores, but similarly classified for each competing store.

  For example, for a competing store, the target input unit 350 belongs to the classification item “3 million to 5 million yen” for the store characteristic “annual income” and the classification item “single young household” for the store characteristic “age household”. The store characteristic “location” belongs to the classification item “station square”, and the store characteristic “store size” belongs to the classification item “150-500 square meters”.

  In this way, the target input unit 350 obtains commercial area information based on the geographical position of the target store, and determines and inputs classification items for the store characteristics of the target store based on the commercial area information. Here, the trade area information is an aggregate value of statistical values related to neighboring residents based on the geographical position.

  The target calculation unit 360 acquires corresponding index information from the trade area POS database 330 for each classification item of the competing store to be counted. Regarding the area to which the competing stores belong, according to the index list shown in FIG. 4, the store characteristic “annual income” is “0.9”, the store characteristic “age household” is “0.75”, and the store characteristic “location” Corresponds to “1.25”, and the store characteristic “store size” corresponds to “0.7”.

  Next, the target calculation unit 360 acquires the calculated classification index for the classification item input by the target input unit 350. Then, the product-specific index of the target store is calculated based on the classification-specific indexes acquired for the plurality of store characteristics. For the calculation of the product-specific index, specifically, the total is simply obtained by an average value. In other words, the index by product = (annual income index + age household index + location index + store size index) / 4.

  Since the importance weight varies depending on each index, a weighted average value may be obtained. For example, the product index = (1.1 × annual income index + 0.7 × age household index + 1.8 × location index + 1.4 × store size index) / 5. Since the sum of the weight values is 5 in this case, the denominator is 5 instead of 4.

  Assuming that the above example is a simple average without weighting, the product index is obtained by (0.9 + 0.75 + 1.25 + 0.7) / 4, and the product index is 0.9. As described above, the target calculation unit 360 calculates the average value of the index by classification obtained for each of the plurality of store characteristics or the average value of the values weighted for the index by classification as the index by product of the target store.

  Next, the estimation unit 370 identifies the sales by product of the market POS data for the division of the region to which the target store belongs, and multiplies the sales by product by the product index of the target store, thereby calculating the estimated sales by product. calculate. Specifically, since the affiliated store has already been designated in FIG. 5, a reference value such as an average value of sales in the area to which the affiliated store belongs is acquired from the market POS database 320. Or you may use the sales numerical value of the designated participating store as it is.

  The estimation unit 370 multiplies the reference value acquired from the market POS database 320 by the product index of the target store calculated by the target calculation unit 360. For example, if the standard sales amount for a certain product is 33000 yen and the product index is 0.9 as described above, 33000 x 0.9 = 29700 yen is the sales at the competing store corresponding to the product ABC. This is the estimated value.

  The above explanation was calculated as an estimate of sales for a single product at a single competing store, but it is not a single product unit, Actually, the total sales of the store will be counted. Further, not only for one day, but also for example, weekly or monthly, or a specific past time may be selected. For example, for sales prediction at the event holding time, it is more effective to refer to the sales data at the time when the same event was held in the past.

  Then, by repeating the estimation for each competitor store included in the same business area, it is possible to obtain an estimated sales value for each competitor shop in the same business area. As a result, it is possible to obtain an estimated value of sales at each store for each product, so by calculating the rank within the same business area and the total value of sales of a predetermined product within the same business area, share from there Can be calculated.

  Thus, by calculating the sales estimate value of the competing store, it is possible to proceed with the competition analysis in the area based on the actual data. For example, it can be used for selection of products to be sold and judgment of store renovation. Alternatively, when multiple store development is predicted in the same region, it can be used as a material for determining a new store opening near a competitor store from the sales estimate of the competitor store.

  Moreover, since it is an estimated value obtained from geographical factors and actual data, it can also be used to estimate personal factors such as staff, campaign effects, etc., based on this estimated value. For example, when there is a campaign, when the sales figures are unexpectedly too low or the sales figures are quite high, the base figures can be obtained, so an accurate prediction can be realized.

(flowchart)
FIG. 8 is a flowchart showing an estimation process of sales by product of competing stores. First, as illustrated in FIG. 6, the target input unit 350 selects a participating store to be analyzed from, for example, a map (step S200). Next, as shown in FIG. 7, the analysis conditions for the competitor store are input by the user operation. (Step S210). The above is as already described with reference to FIGS.

  Next, the target input unit 350 extracts store characteristics for the competing store (step S220). Since the data regarding the trade area to be analyzed is input as the analysis condition of the entered competitive store, based on this, for example, a trade area range within a radius of 2 km of the affiliated store is specified, and the corresponding competitive store To extract. Then, store characteristics are extracted for each competing store based on the GIS data 250. Next, the target calculation unit 360 acquires an index by category corresponding to the extracted store characteristic (step S230), and calculates an index by product for the competitors based on a simple average or a weighted average of the acquired index by category. (Step S240).

  On the other hand, the estimation unit 370 acquires, from the market POS database 320, the POS data 150 of the sales of the subscribing store itself or the area to which the subscribing store is input in step S200, so that the market POS The sales for each product of the data is specified (step S250). Next, the estimation unit 370 calculates product-specific estimated sales by multiplying the product-specific sales by the product-specific product index calculated for the target store (step S260), and ends the series of processing. Through the series of processes described above, it was possible to calculate the estimated sales data and estimated POS data for each product for each product.

(About calculation processing by category)
As described above, the estimated POS data for a single product is obtained, but this time, estimated POS data is obtained for each category. Estimated data for sales by product is also important, but in terms of product units, the number of points is enormous even for small stores, and it is often required to obtain a little rougher data. Actually, POS data is estimated by the category unit to which the product belongs. I often want to calculate

  On the other hand, if the sales of each product are simply summed, the numerical value of the sales may be too large than expected. In addition, since sales increase and decrease depending on the number of products handled by stores, a case will be described in which the number of target products is set based on the category and store size, and handled as products to be counted in descending order of sales.

  FIG. 9 is a flowchart showing a process for estimating sales by category of competing stores. First, a competitor store and its scale are input (step S300). The input screen is as described with reference to FIG. Next, the product category is input (step S310). The category input screen is also as described with reference to FIG.

  The categories are divided into multiple stages, and the largest category consists of items such as food, daily necessities, clothing, and so on. Under the major category, for example, in the case of food, a middle category comprising items such as fresh food, beverages, confectionery, bread, etc. is prepared and displayed as items corresponding to the major category shown in the lower left of FIG. When the portion is further expanded, although not shown, items corresponding to the middle category existing below are displayed.

  Here, when a beverage is selected from the items in the middle category, a smaller category is prepared from there. The items corresponding to the small category are as shown in the lower right of FIG. 7 and are listed as a preference drink, a fruit drink, a soft drink, and so on. In addition to the large, medium, and small items, a smaller category item “Fine” is also provided. For example, for fruit drinks, there are “orange” and “apple”. On the category input screen, any category item corresponding to one of the large, medium, small, and fine categories is selected and input.

  Next, the number of products to be tabulated for the selected category item is determined (step S320). The number of products by category uses a calculation method in which the number of handling points increases as the store size increases. For the subcategory, for example, if you choose cola, 2 items for stores with 0-150 square meters, 3 items for stores with 150-500 square meters, 6 items for stores with 500-1500 square meters, 10 items for stores with 1500-3000 square meters The number of products handled is determined for each store area size, for example, a 3000 square meter store is a full product.

  On the other hand, when the middle category is selected as the category selection, the number of products further increases. However, instead of determining the product score for each middle category, the total of the product scores given to the lowermost subcategories included in the middle category is used as the product score for the middle category. The middle category will be described as an example, but the same applies to the large category and the small category. When “Japanese tea / barley tea drink” is selected as the middle category, the product score based on the store size is obtained for the fine categories “Japanese tea”, “barley tea”, etc. included in “Japanese tea / barley tea drink”. For example, a store of 0-150 square meters is determined to have 3 products for Japanese tea and 3 products for barley tea. In the meantime, 0-150 square meters stores are 10 products, 150-500 square meters stores are 20 products, 500-1500 square meters stores are 40 products, 1500-3000 square meters stores are 50 products, 3000 square meters stores are all The number of products handled depends on the size of the store area.

  Next, the products for the number of products determined for the category are extracted (step S330). For example, in the above example, when the store area is 1000 square meters, if cola is selected, there are 6 products, so 6 are extracted from the top sales products of cola. In addition, when Japanese tea / barley tea drink is selected, 40 products are extracted, so 40 products are extracted from the top selling products of Japanese tea / barley tea drink.

  Finally, the estimated sales obtained by the processing shown in the flowchart of FIG. 8 is acquired for each extracted product and added together (step S340). The total value is the estimated sales for the selected category. When a plurality of categories are selected, the above process is executed for each category, and the series of processes is terminated.

  FIG. 10 is a flowchart showing a form creation process including a sales order and a share. First, the target input unit 350 selects a participating store to be analyzed from, for example, a map as in steps S200 and S210 illustrated in FIG. 8 (step S400). Next, as shown in FIG. 7, the analysis conditions for the competitor store are input by the user operation. (Step S410).

  Next, sales are calculated for the selected competitor store (step S420). Since the sales calculated here are sales values for rank and share calculation described later, for example, when performing rank / share analysis for each product, the sales value of the corresponding product calculated in FIG. 8 is acquired. . In the case of each category, the sales value of the corresponding category calculated in FIG. 9 is acquired. When comparing the overall sales, the category-specific sales calculated in FIG. 9 are acquired for the large categories, and the sales figures for each large category are added together.

  The above is the extraction of the comparison data for one competing store, so the processing is executed for all competing stores that match the analysis conditions, and if not finished, step S420 is looped (step S430). If finished, continue.

  As a result of the above processing, sales data for all competing stores to be analyzed is extracted, and sales ranks and shares in the analysis target area including the participating stores are calculated for the corresponding sales items (step S440). For example, for sales items, if the subcategory “Cola” is targeted, the sales figures for each store in the subcategory “Cola” are obtained, so the sales order is for each store and for each store. Are counted. On the other hand, the total sales figure of “Coke” in the area is obtained simply by adding up. By dividing by the total sales figure, the “Cola” share of each store in the region can be calculated. .

  Finally, a form is created and output (step S450). For a specific product or category, sales numbers, sales ranks, and shares are calculated for each of the participating stores and competing stores, and these are tabulated. A series of processing is completed by creating and outputting a form.

  As mentioned above, although this invention was demonstrated using the Example, the technical scope of this invention is not limited to the range as described in the said Example. It will be apparent to those skilled in the art that various modifications or improvements can be made to the above-described embodiments. It is apparent from the scope of the claims that the embodiments added with such changes or improvements can be included in the technical scope of the present invention.

POS terminal 10, 20, 30, POS management server 100, POS data 150, GIS server 200, GIS data 250, sales estimation device 300, POS data processing unit 310, market POS database 320, trade area POS database 330, classification calculation unit 340 , Target input unit 350, target calculation unit 360, estimation unit 370, sales estimation data 390, clients 410, 420, 430,

Claims (4)

Stores the market POS data indicating the sales by product for each participating store generated based on the POS system, and a market POS database that associates each of the participating stores with a region classification,
For each of the participating stores, a plurality of store characteristics each specifying one of a plurality of classification items regarding the properties of the store are set, the participating stores are grouped for each of the classification items, and sales by product for each group are aggregated A trade area POS database storing stored trade area POS data;
For each of the classification items, for the sales by product for each group, classification calculation means for obtaining an index by classification that is a relative value to the overall sales;
A target input means for selecting a target store and inputting a classification item for each store characteristic for the target store;
For each classification item input by the target input unit, the index for each category calculated by the category calculation unit is acquired, and based on the index for each category acquired for each of a plurality of store characteristics, A target calculation means for calculating an index;
Estimating means for determining the sales by product of the market POS data for the division of the area to which the target store belongs, and calculating the estimated sales by product by multiplying the sales by product with the product index of the target store equipped with a,
The target calculation means, the average value of the classification Indices obtained respectively for the plurality of stores characteristics, or the average value of the weighted values for the classification Indices, sales estimation system that calculates the trade Indices of the target store .   The target input means selects a geographical location of the target store, obtains commercial area information based on the geographical location, and determines and inputs classification items for store characteristics of the target store based on the commercial area information. Item 2. The sales estimation system according to item 1.   The sales estimation system according to claim 2, wherein the trade area information is an aggregate value of statistical values related to neighboring residents based on the geographical position. When the estimation of sales by category divided by a plurality of products is designated, it further comprises a category calculating means for calculating the sales by category,
The category calculating means includes
Based on the scale of the target store input by the target input means,
Determine the number of products to be aggregated for the category,
From the products that belong to the category, from the data ranked by sales situation, determine the products for aggregation by the number of the determined products,
The sales estimation system according to claim 1, wherein the sales for each category are calculated by adding the estimated sales calculated by the estimating means for each product for aggregation.