JP2007272722A - Method and system for analyzing set-up of branch office - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To efficiently and accurately determine as to the set-up of a new branch office of a financial institution or capitals to be invested on existing offices. <P>SOLUTION: An arithmetic processing section 21 of an analysis processing server 20 specifies a market area based on selection of a candidate site for the new office. The arithmetic processing section 21 acquires the number of households in the market area. Amounts of savings and loan are obtained to calculate the amounts of savings and loan of the residents in this market area. The arithmetic processing section 21 specifies another existing office in the market area. A competing office in the market area is specified to calculate a predicted share. An expected amount of acquired capitals is calculated using the amounts of savings and loan, and the predicted share, then processing for calculating model-based revenue and a model-based cost are executed to calculate a predicted revenue and expenditure. The arithmetic processing section 21 executes processing for determining a model/determining whether to open the office. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a store opening analysis method and a store opening analysis system for analyzing a balance concerning a store when a new store of a financial institution or a determination of resource investment in an existing store is performed.

  When establishing a new store in the retail industry, the location conditions, business type, and scale are important factors in deciding investment. In this store development business, it is important to quantitatively evaluate sales and store profits. In calculating the sales, location factors such as the amount of traffic on the road that contacts the store, competing stores, and the future of the region are often considered. For this sales forecast, for example, we collect statistical data such as census data on the area where new stores are opened, data of existing competitors regardless of our company or other companies, and build a sales forecast model. Predict the sales of stores that open in stores.

However, the sales forecast model is generally difficult to construct because it requires data on existing stores, and the contents of the constructed sales forecast model are also difficult to understand. In view of this, for example, a new store evaluation support apparatus that allows a store opening person to evaluate an appropriate new store opening plan has been studied (see, for example, Patent Document 1). In the technology described in this document, a statistical database storing statistical data such as population, age group, traffic volume, and income distribution, a store database storing store data of competing stores, and sales performance data of competing stores are stored. Uses a performance database. According to the input search conditions, the competitor store data and store attributes for classifying the competitor stores are acquired from each database, and the competitor stores are classified hierarchically by the attribute. Then, an optimal sales prediction model is estimated for the classified competitor store group. Further, the promotion and stoppage of the classification of competing stores is controlled according to the classification control condition. Then, the hierarchical classification of the competitor stores whose classification is stopped and the optimal sales prediction model of the classified competitor store group are output.
Japanese Patent Laid-Open No. 10-240799 (first page)

However, business forms differ between retailers and financial institutions, and it is difficult to apply the techniques described in the above-mentioned literature to the decision to open a store.
The present invention has been made to solve the above-mentioned problems, and its purpose is to open a store for performing an analysis for efficiently and accurately judging a new store of a financial institution or a resource investment in an existing store. An analysis method and a store opening analysis system are provided.

  In order to solve the above problems, the invention described in claim 1 is connected to a statistical data storage unit that records the number of households and the amount of resources for each region, and a share data storage unit that records a share with a competing store. Using the store analysis system provided with the calculation means, and analyzing the store opening of a financial institution store, and when the calculation means selects a candidate store opening, a trade area based on the candidate store opening Identifying a competing store included in the trade area, calculating an expected share using the share data storage unit, and obtaining the number of households and the amount of resources related to the trade area from the statistical data storage unit Performing a step of calculating a regional resource amount by multiplying the acquired number of households and a resource amount, and a step of calculating an assumed acquired resource amount based on the calculated regional resource amount and expected share The gist of the door.

The invention according to claim 2 is the store opening analysis method according to claim 1, wherein the store opening analysis system further includes conversion processing means for calculating an expected revenue and an expected cost in correspondence with an assumed amount of acquired resources, The calculation means uses the conversion processing means to calculate the expected revenue and the expected cost based on the calculated assumed resource acquisition amount, and to determine whether or not to open a store based on the calculated expected revenue and expected cost. And further executing the step of outputting the evaluation result.

  According to a third aspect of the present invention, in the store opening analysis method according to the second aspect, the conversion processing means stores data for each store model in which a table for each store model for calculating an expected profit and an estimated cost for each store model is recorded. The estimated revenue and the estimated cost are calculated for each store model using the table for each store model.

The gist of the invention described in claim 4 is the store opening analysis method according to any one of claims 1 to 3, wherein the resource amount includes at least either a saving amount or a loan amount.
The invention according to claim 5 is provided with a statistical data storage unit that records the number of households and the amount of resources for each region, and a calculation means connected to a share data storage unit that records a share with competing stores. A store analysis system for analyzing store openings in which, when the candidate store location is selected, the calculation means specifies a trade area based on the candidate store location, and the statistical data storage unit stores the trade area. Means for acquiring the number of households and the amount of resources, means for calculating the amount of regional resources by multiplying the number of households acquired and the amount of resources, and the assumed amount of resources acquired based on the calculated amount of regional resources and expected share The gist is to function as a means for calculating.

  The invention described in claim 6 is the store opening analysis system according to claim 5, wherein the store opening analysis system further includes conversion processing means for calculating an expected revenue and an expected cost in correspondence with an assumed amount of acquired resources, The calculating means uses the conversion processing means to calculate the expected revenue and the expected cost based on the calculated assumed resource acquisition amount, and to determine whether or not to open a store based on the calculated expected revenue and estimated cost. The gist is to further function as a means for outputting the evaluation result.

  According to a seventh aspect of the present invention, in the store opening analysis system according to the sixth aspect, the conversion processing means stores data for each store model in which a table for each store model for calculating the expected revenue and the estimated cost for each store model is recorded. The estimated revenue and the estimated cost are calculated for each store model using the table for each store model.

  The gist of the invention described in claim 8 is the store opening analysis system according to any one of claims 5 to 7, wherein the resource amount includes at least one of a saving amount and a loan amount.

(Function)
According to the first or fifth aspect of the invention, the calculation means of the store opening analysis system specifies a trade area based on the store opening candidate sites. When a competitor store included in the trade area is specified, an expected share is calculated using the share data storage unit. Furthermore, the number of households and the amount of resources related to the trade area are acquired from the statistical data storage unit, and the regional resource amount is calculated by multiplying the acquired number of households and the amount of resources. Then, an assumed acquired resource amount is calculated based on the calculated regional resource amount and expected share. This makes it possible to analyze the transaction volume (potential) that the financial institution may be able to acquire for the candidate site to be evaluated in consideration of existing stores and regional characteristics. In the case of financial products, since they relate to customer assets, the transaction purpose of the products is different from general distribution products. Therefore, it is possible to accurately grasp the potential of the trade area. Furthermore, it is possible to quantitatively evaluate the potential profitability of existing stores. This evaluation can be used to objectively understand the fertility of the market in the store trade area and make decisions on investing resources such as investing in existing stores and staffing.

  According to the invention described in claim 2 or 6, the conversion processing means is used to calculate the expected revenue and the expected cost based on the calculated assumed amount of acquired resources, and whether or not the store can be opened based on the calculated expected revenue and estimated cost. The evaluation result for judging is output. As a result, it is possible to more accurately determine the effectiveness of opening a financial institution while considering the potential of the trade area.

  According to the invention described in claim 3 or 7, the store opening analysis system includes a store model-specific data storage unit that records a store model-specific table for calculating expected revenue and cost for each store model. The expected revenue and the expected cost are calculated for each store model using the store model-specific table. This makes it possible to efficiently evaluate a store model suitable for the candidate site (a store business form according to location characteristics, assumed customer attributes, etc.).

  According to the invention described in claim 4 or 8, the amount of resources includes at least one of a saving amount and a loan amount. Thereby, the potential of the trade area concerning financial assets can be grasped by savings and loans.

  ADVANTAGE OF THE INVENTION According to this invention, the analysis for judging efficiently and accurately about the new store of a financial institution or the resource investment to the existing store can be performed.

  Hereinafter, an embodiment embodying the present invention will be described with reference to FIGS. In the present embodiment, when a financial institution establishes a new store, an evaluation result for determining whether or not a new store can be opened is output. Here, as shown in FIG. 1, an analysis processing server 20 and a client terminal 10 connected through a network are used.

  The client terminal 10 is a computer terminal that communicates with the analysis processing server 20. Using this client terminal 10, data is transmitted to the analysis processing server 20 via the network, and evaluation results are received.

  The analysis processing server 20 functions as a store opening analysis system that analyzes new store openings of financial institution stores, and is a computer system that manages communication with the client terminal 10. The analysis processing server 20 communicates with the client terminal 10 and executes an analysis process regarding the suitability of a new store opening. When the analysis is completed, the result is provided via the network.

  The arithmetic processing unit 21 of the analysis processing server 20 includes control means such as a CPU (not shown) and memories such as RAM and ROM, and functions as arithmetic means. Specifically, the processing described later (trade area identification stage, expected share calculation stage, number of households / resource amount acquisition stage, regional resource amount calculation stage, assumed earned resource quantity calculation stage, expected revenue / expected cost calculation stage, evaluation result output Process including stages). By executing the program for this purpose, the arithmetic processing unit of the analysis processing server 20 is a trade area specifying unit, an expected share calculating unit, a household / resource amount acquiring unit, a regional resource amount calculating unit, an assumed acquired resource amount calculating unit, It functions as an expected revenue / expected cost calculation means, evaluation result output means, and the like. In the present embodiment, as will be described later, the candidate site processing unit 211 identifies a trade area based on the candidate store openings. The trade area evaluation processing means 212 acquires the number of households and the amount of resources related to the trade area, and multiplies the acquired number of households and the amount of resources to calculate the regional resource quantity. The share calculation processing unit 213 calculates an expected share. The balance calculation processing means 214 calculates an expected revenue based on the amount of resources, calculates an expected cost based on the store model and scale, and outputs an evaluation result for determining whether or not to open a store based on the predicted revenue and the expected cost. To do. Here, the “store model” means a store business form according to location characteristics, assumed customer attributes, and the like.

  As illustrated in FIG. 1, the analysis processing server 20 includes a statistical data storage unit 22, a regional data storage unit 23, a share determination data storage unit 24, a model-specific profit data storage unit 25, and a model-specific cost data storage unit 26. In this embodiment, the model-specific profit data storage unit 25 and the model-specific cost data storage unit 26 function as conversion processing means. In particular, in the present embodiment, it also functions as a store model-specific data storage unit for calculating expected revenue and expected cost for each store model.

  The statistical data storage unit 22 stores regional statistical data, national statistical data, and various data for calculating the resource amount. In the present embodiment, the statistical data storage unit 22 stores a household number table, a savings table, a house transaction number table, and a loan amount table.

  In the number of households table, data on the number of households is recorded for each area division (here, the street division). In this household number table, the number of resident households and the inflowing population are recorded for each town division. The number of resident households is the number of households living in this town-cho section, and the inflowing population is the number of office workers that flow into this town-cho section in the daytime. This inflowing population is converted into the number of households by multiplying by the household head ratio, and is added to the number of households in the household number table.

The savings table records data related to savings by multiplying the nationwide average savings (the amount of assets deposited per household) by the wealth (regional coefficient) for each town division.
In the housing transaction number table, data on the number of newly constructed properties and the number of second-hand property distributions for each town category are recorded.
In the loan amount table, data on new housing loan unit prices for newly built properties and used home loan unit prices for used properties are recorded for each town category.

  The regional data storage unit 23 stores a commercial area determination table for determining a commercial area. In the present embodiment, the regional data storage unit 23 stores a table in which the street divisions of the areas included in the trade area are associated with each store opening candidate site. Therefore, when the area of the candidate site (chosen section) is specified based on the address of the candidate place, the trade area is determined based on the surrounding choch section associated with this area (chosen area of the candidate place). be able to. In the present embodiment, the trade area determination table associates the street divisions of the areas included in the same trade area using the remaining area except for the trade area of the existing store.

  Data for determining a share of a new store is recorded in the share determination data storage unit 24 and functions as a share data storage unit. In the present embodiment, an existing store location table and a share table are recorded in the share determination data storage unit 24.

In the existing store location table, identification data (store identifier, financial institution name, store name, address, etc.) for specifying an existing store (competitor store) located in another bank is recorded.
In the share table, data related to the assumed share of the bank is recorded in association with the product category for each product provided by the bank and other competing banks.

  The model-specific profit data storage unit 25 stores data for calculating an expected profit for the acquired resource amount. In the present embodiment, a savings profit table and a loan profit table are recorded in the model-specific profit data storage unit 25.

In the savings revenue table, data on the rate of return calculated in consideration of the ratio of investment management products to savings (deposited assets) is recorded for each identifier of the store model considering the target segment and scale.
In the loan revenue table, data on the rate of return obtained from the provision of the mortgage is recorded for each identifier of the store model.

  In the cost data storage unit 26 for each model, data related to the cost required for opening a store is recorded for each store model. In the present embodiment, a cost table is recorded in the model-specific cost data storage unit 26. In the cost table, data relating to the total cost required for opening a new store or maintaining a store is associated with each identifier of the store model.

Using the system configured as described above, the processing procedure relating to the analysis of new store openings will be described with reference to FIG.
First, the arithmetic processing unit 21 of the analysis processing server 20 executes a store opening candidate site selection process (step S1). Specifically, a user of this system (for example, a planner of a new store opening) accesses the analysis processing server 20 using the client terminal 10. In this case, the candidate site processing means 211 of the arithmetic processing unit 21 provides candidate site input screen data to the client terminal 10 via the network. The user inputs the address of the desired candidate site on the candidate site input screen acquired from the analysis processing server 20. Based on the user's transmission instruction, the client terminal 10 transmits an analysis request to the analysis processing server 20 via the network. This analysis request includes data relating to the address of the candidate site entered on the candidate site input screen. Then, the candidate site processing unit 211 of the arithmetic processing unit 21 acquires address data of the candidate site designated on the client terminal 10. Here, processing related to the trade area will be described using the map 500 shown in FIG. For example, the case where the address of the candidate site 501 on the map 500 is designated is assumed.

  Next, the arithmetic processing unit 21 of the analysis processing server 20 executes a trade area specifying process based on the address of the candidate site (step S2). Specifically, the candidate site processing unit 211 of the arithmetic processing unit 21 specifies an area (town sorting) of the candidate site based on the received candidate site address. Then, by using the trade area determination table recorded in the regional data storage unit 23, the town-sector classification that constitutes the trade area for the candidate site is specified. In this case, the shaded area including the candidate site 501 on the map 500 is specified as the trade area 502.

  Next, the arithmetic processing part 21 of the analysis processing server 20 performs the acquisition process of the number of households (step S3). In the present embodiment, the trade area evaluation processing means 212 of the arithmetic processing unit 21 uses the number of households table of the statistical data storage unit 22 for the town segment classification that constitutes the trade area specified by the candidate place processing means 211. Acquire data on the number of households for each category.

  Next, the arithmetic processing unit 21 of the analysis processing server 20 executes saving amount and loan amount acquisition processing (step S4). In the present embodiment, the trade area evaluation processing means 212 of the arithmetic processing unit 21 stores the saving table recorded in the statistical data storage section 22, the housing transaction for the town district classification that constitutes the trade area 502 specified by the candidate place processing means 211. Get number table and loan amount table.

  Then, the arithmetic processing unit 21 of the analysis processing server 20 executes a resident saving amount / loan amount calculation process (step S5). In this embodiment, the trade area evaluation processing means 212 of the arithmetic processing unit 21 first multiplies the number of households acquired in step S3 by the saving amount of the saving table acquired in step S4 to save the residents in the trade area 502. Calculate the amount. This amount of saving is the potential for saving.

  Furthermore, the amount of loans related to the trade area 502 is calculated by multiplying the number of newly-built properties and the number of used properties distributed in the housing transaction number table acquired in step S4 by the newly-built housing loan unit price and the used home loan unit price in the loan amount table. This loan amount is the potential for mortgages.

  Further, the arithmetic processing unit 21 of the analysis processing server 20 executes a competition store specifying process (step S6). In the present embodiment, the share calculation processing unit 213 of the arithmetic processing unit 21 specifies the other existing store in the business area determined in step S <b> 2 using the existing store location table recorded in the share determination data storage unit 24. . In this case, the other bank existing store 504 existing in the trade area 502 is the object of evaluation.

  The arithmetic processing unit 21 of the analysis processing server 20 executes an expected share calculation process (step S7). In the present embodiment, the share calculation processing means 213 of the arithmetic processing unit 21 uses the share table recorded in the share determination data storage unit 24 for each competing product for the existing other bank store identified in step S6. , Identify your assumed share.

  Next, the arithmetic processing unit 21 of the analysis processing server 20 executes an assumed acquired resource amount calculation process (step S8). In the present embodiment, the balance calculation processing means 214 of the arithmetic processing unit 21 multiplies the resident saving amount / loan amount (potential) calculated in step S5 by the expected share calculated in step S7, respectively. Calculate the assumed amount of resources acquired. In the present embodiment, the saving amount and the loan amount are calculated as the assumed acquired resource amount.

  The arithmetic processing unit 21 of the analysis processing server 20 executes a model-specific profit calculation process (step S9). In this embodiment, the balance calculation processing means 214 of the arithmetic processing unit 21 uses the saving amount calculated in step S5 and the profit rate of the savings revenue table recorded in the model-specific profit data storage unit 25 to return the profit related to saving. Is calculated for each store model. Further, the balance calculation processing means 214 of the arithmetic processing unit 21 uses the loan amount calculated in step S5 and the profit rate of the loan profit table recorded in the model-specific profit data storage unit 25 to generate the profit related to the loan in the store. Calculate for each model.

  Furthermore, the arithmetic processing unit 21 of the analysis processing server 20 executes a cost calculation process for each model (step S10). In the present embodiment, the balance calculation processing means 214 of the arithmetic processing unit 21 calculates a cost for each store model using the model-specific cost data storage unit 26. In this case, it calculates with reference to the store model specified based on the assumption acquisition resource amount calculated in step S8.

  The arithmetic processing unit 21 of the analysis processing server 20 executes an expected balance calculation process (step S11). In the present embodiment, the balance calculation processing means 214 of the arithmetic processing unit 21 compares the model-specific revenue calculated in step S9 with the model-specific cost calculated in step S10 for each store model.

  The arithmetic processing unit 21 of the analysis processing server 20 executes a model determination / opening store determination process (step S12). In the present embodiment, the balance calculation processing means 214 of the arithmetic processing unit 21 determines whether or not to open a store based on the comparison result in step S11. Specifically, it calculates profitability by subtracting costs from profits, calculates the time required to make a profitable return on investment, and compares the result with a predetermined reference value to output an evaluation result that analyzes whether stores can be opened for each store model. To do. The evaluation result can be used to determine whether or not a new store can be opened.

According to this embodiment, the following effects can be obtained.
In the present embodiment, the arithmetic processing unit 21 of the analysis processing server 20 performs a trade area specifying process (Step S2), a household number acquisition process (Step S3), a savings amount and a loan amount acquisition based on the address of the candidate site. Processing (step S4) and resident saving amount / loan amount calculation processing (step S5) are executed. Thereby, the potential of the trade area can be calculated. Since financial products are different from general distribution products, the decision to open a new store is different. As described above, it is possible to efficiently and accurately determine whether a financial institution opens a new store in consideration of regional potential.

  -In this embodiment, the arithmetic processing part 21 of the analysis processing server 20 performs a competition store specific process (step S7). Thereby, the share of a new store can be assumed, considering the influence of an existing store.

  In the present embodiment, the balance calculation processing means 214 of the arithmetic processing unit 21 acquires the resident saving amount / loan amount (potential) calculated in step S5 and the expected share calculated in step S7. This makes it possible to calculate the amount of resources that can be acquired while taking into account the potential and the influence of existing stores.

  -In this embodiment, the arithmetic processing part 21 of the analysis processing server 20 performs the profit calculation process classified by model (step S9), and the cost calculation process classified by model (step S10). In this case, revenue and cost are calculated for each store model. Thereby, it is possible to determine whether or not to open a store according to the store model. In addition, the savings revenue table recorded in the model-specific profit data storage unit 25 is calculated in consideration of the ratio of investment management products in savings (deposited assets) for each identifier of the store model considering the target segment and scale. Data on the rate of return made. Accordingly, it is possible to efficiently calculate the profit in consideration of the potential and the store model.

  Furthermore, in the cost table recorded in the model-specific cost data storage unit 26, data relating to the total cost required for new store openings and store maintenance is recorded in association with each store model identifier. Thereby, if a store model is decided, cost can be calculated efficiently.

In addition, you may change each said embodiment as follows.
In the above embodiment, when a financial institution newly establishes a store, an evaluation result for determining whether or not a new store can be opened is output. The use of the store opening analysis method and the store opening analysis system is not limited to the analysis of new stores, and can also be used for evaluation of existing stores. In this case, the location of the existing store is specified on the candidate site input screen. Thereby, it is possible to obtain a quantitative analysis result of potential profitability such as a profit / loss comparison obtained by subtracting cost from profit for an existing store. Therefore, this analysis result can be used to evaluate the store model and the scale of the existing store, and can be used for decision making of resource investment such as investment in the existing store and staffing.

  In the above embodiment, the arithmetic processing unit 21 of the analysis processing server 20 executes the model-specific profit calculation process (step S9). In this case, the profit related to saving is calculated for each store model using the saving amount and the profit rate of the saving profit table recorded in the profit data storage unit 25 by model. Further, the balance calculation processing means 214 of the arithmetic processing unit 21 calculates the profit related to the loan for each store model using the loan amount and the profit rate of the loan profit table recorded in the model-specific profit data storage part 25. . Instead of this, or in addition to this, an optimal store model may be specified and output based on the assumed amount of resources acquired such as savings and loan amount. In this case, a store model determination data storage unit that stores a store model determination table that associates the resource amount with a suitable store model identifier is prepared. Thereby, the store model suitable for the amount of resources can be identified efficiently.

In the above embodiment, the statistical data storage unit 22 records regional statistical data for each town, national statistical data, and various data for calculating the resource amount. In the present embodiment, the statistical data storage unit 22 stores a savings table and a loan amount table. In this savings table, data relating to the savings amount obtained by multiplying the national average savings amount (deposited assets in one household) by the wealth (regional coefficient) of each town division is recorded. In addition, the loan amount table records data on the new home loan unit price for newly built properties and the used home loan unit price for used properties for each town category. The parameters for calculating the resource amount are not limited to these. For example, it is possible to correct the resource amount of the specified town-cho using customer information of the existing store in or near the trade area. Thereby, it is possible to calculate a resource amount that is more realistic.

  In the above-described embodiment, the table in which the district divisions of the areas included in the trade area are associated with each store opening candidate site is recorded in the trade area determination table of the regional data storage unit 23. The method of determining the trade area is not limited to this, and is determined based on the area within a predetermined distance from a specific address or until reaching a predetermined numerical value (for example, a predetermined number of houses or the number of existing stores). May be. Further, based on the attributes of the address of the candidate site, the range of the trade area may be specified based on the attribute of the town division (for example, the type of use area such as a residential area, a commercial area, or a semi-industrial area).

  In the above embodiment, the estimated amount of acquired resources is calculated by multiplying the resident saving amount / loan amount (potential) by the expected share. The assumed amount of resources acquired is not limited to the amount of savings or loans, but it is also possible to use products and commissions that can be sold at financial institutions.

  In the above embodiment, in the trade area determination table, except for the trade area of the existing bank, the remaining areas are used to associate the town divisions of the areas included in the same trade area. Instead of this, it may be configured to correct the trade area in consideration of the influence of the existing store after identifying the range that becomes the trade area from the attribute of the area. Specifically, in the existing store location table, in addition to other existing banks (competitor stores) located in this area, identification data (store identifier, financial (Institution name, store name, address, etc.) are recorded. The arithmetic processing unit 21 of the analysis processing server 20 executes a trade area specifying process based on the address of the candidate site (step S2). In this case, first, the calculation is performed without considering the existing store. Next, the arithmetic processing unit 21 of the analysis processing server 20 executes an evaluation process for the existing store. Here, the candidate site processing means 211 of the arithmetic processing unit 21 specifies the own existing store in the business area first using the existing store location table. In the existing store location table, data related to the trade area of the existing store is recorded, and the trade area of the existing store is deleted from the trade area previously identified based on the address of the candidate site. As a result, it is possible to flexibly determine the trade area in accordance with the store opening situation while taking into account the influence of the existing store.

  In the above embodiment, the trade area is specified based on the address of the candidate site using the trade area determination table in consideration of the trade area of the existing store. Instead of this, it may be configured to calculate the assumed amount of acquired resources in consideration of the influence of the existing store after specifying the range of the commercial area from the attributes of the area. Specifically, a customer data storage unit provided in the analysis processing server 20 is used. In the customer data storage unit, data relating to the customer's savings amount and loan amount associated with the store identifier of the existing bank is recorded. By using this customer data storage unit, it is possible to identify a customer of an existing store in the trade area and acquire the saving amount and loan amount of this customer.

  Therefore, the analysis processing server 20 first calculates a trade area using a trade area determination table created without taking into consideration the existing store. Next, the customer related to the calculated trade area is specified using the customer address recorded in the customer data storage unit. Furthermore, the customer's saving amount and loan amount of the customer of the specified own bank are specified using the customer data storage unit. Then, the assumed amount of acquired resources is corrected by subtracting the amount of saving or the loan from the amount of assumed resources acquired. Accordingly, it is possible to analyze the store opening while evaluating the existing store in consideration of the customers of the existing store.

The system schematic of embodiment of this invention. Explanatory drawing of the process sequence of this embodiment. Explanatory drawing of the relationship between the determined trade area and an existing store.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 10 ... Client terminal, 20 ... Analysis processing server, 21 ... Operation processing part, 22 ... Statistical data storage part, 23 ... Area data storage part, 24 ... Share determination data storage part, 25 ... Revenue data storage part according to model, 26 ... Cost data storage unit by model, 502...

Claims (8)

Store opening of a financial institution store using a statistical data storage unit that records the number of households and the amount of resources for each region, and a store opening analysis system that is connected to a share data storage unit that records a share with competing stores A method of analyzing
The computing means is
If a candidate store location is selected, a step of identifying a trade area based on the candidate store location;
When identifying a competitor store included in the trade area, calculating an expected share using the share data storage unit;
Obtaining the number of households and the amount of resources related to the trade area from the statistical data storage unit;
Multiplying the acquired number of households and the amount of resources to calculate a regional resource amount;
And a step of calculating an assumed amount of acquired resources based on the calculated amount of regional resources and expected share. The store opening analysis system further includes conversion processing means for calculating the expected revenue and the expected cost corresponding to the assumed amount of acquired resources,
The computing means is
Using the conversion processing means to calculate expected revenue and cost based on the calculated assumed resource acquisition amount;
The store opening analysis method according to claim 1, further comprising the step of outputting an evaluation result for determining whether or not to open a store based on the calculated expected revenue and expected cost. The conversion processing means includes a store model-specific data storage unit that records a store model-specific table for calculating expected revenue and cost for each store model,
The store opening analysis method according to claim 2, wherein the expected revenue and the expected cost are calculated for each store model using the table for each store model.   The store opening analysis method according to any one of claims 1 to 3, wherein the resource amount includes at least one of a saving amount and a loan amount. A store analysis system that includes a statistical data storage unit that records the number of households and the amount of resources for each region, and a calculation means connected to a share data storage unit that records the share with competing stores. There,
The computing means is
Means for specifying a trade area based on the candidate store openings when the candidate store openings are selected;
Means for obtaining the number of households and the amount of resources related to the trade area from the statistical data storage unit;
Means for calculating the regional resource amount by multiplying the acquired number of households and the resource amount;
A store opening analysis system, which functions as a means for calculating an assumed acquired resource amount based on the calculated regional resource amount and predicted share. The store opening analysis system further includes conversion processing means for calculating the expected revenue and the expected cost corresponding to the assumed amount of acquired resources,
The computing means is
Means for calculating an expected revenue and an expected cost based on the calculated assumed resource acquisition amount using the conversion processing means;
6. The store opening analysis system according to claim 5, further functioning as means for outputting an evaluation result for determining whether or not to open a store based on the calculated expected profit and expected cost. The conversion processing means includes a store model-specific data storage unit that records a store model-specific table for calculating expected revenue and cost for each store model,
The store opening analysis system according to claim 6, wherein the expected revenue and the expected cost are calculated for each store model using the table for each store model.   The store opening analysis system according to any one of claims 5 to 7, wherein the resource amount includes at least one of a saving amount and a loan amount.

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