JP4897713B2 - Sales information analyzer - Google Patents

Description

  The present invention relates to a data processing technique, and more particularly, to a technique for analyzing sales information of a product and creating information for supporting a user's sales activities.

  Along with the spread of computers, there are an increasing number of companies that store sales performance information indicating the results of selling products to customers in a database or the like. Some companies create information (hereinafter referred to as “sales activity support information”) for supporting a user's sales activities by using the sales performance information by a computer system. For example, in Patent Document 1, sales activity support information is created by classifying products from various viewpoints and associating the classification information with sales performance information.

As another method different from Patent Document 1, sales activity support information may be created by associating attributes of a customer who purchased a product with sales performance information. In this case, the customer attribute information registered at the time of card creation may be used for customers who purchased products using a point card or credit card.
JP 2001-167203 A

  However, the purchaser who actually purchases the product may be different from the consumer who actually consumes the product. For example, a consumer of goods purchased by a housewife in a household may be a husband in that household. Therefore, the sales activity support information created based on the purchaser's attributes tends to not reflect the consumer's attributes, and may not be useful information for the merchandise seller.

  In light of such a background, the present inventor relates to a region where the purchaser lives (hereinafter referred to as “area”), a group of regions where the attributes of the residents are similar (hereinafter referred to as “area cluster”). I paid attention to. In other words, sales information useful for merchandise sellers is analyzed by analyzing sales performance information from a macro perspective based on the resident attributes of the area cluster where the buyer resides, rather than from a micro perspective based on the attributes of the product buyer. I thought I could create activity support information.

  The present invention is an invention completed based on the above-mentioned attention of the inventor, and its main purpose is to analyze the sales performance information based on the area cluster where the purchaser of the product resides, thereby It is to provide a technique for creating sales activity support information that is useful to customers.

  In order to solve the above-described problem, a sales information analysis device according to an aspect of the present invention relates to an area cluster that is a group of areas having similar attributes related to residents and a plurality of geographically partitioned areas and correspondence between the areas. An area cluster information holding unit that holds area cluster information indicating a relationship, an area sales amount acquisition unit that acquires an area sales amount that is a sales amount of a product in the area for a predetermined period for each combination of the area and the product, and an area Cluster sales volume calculation unit that calculates area sales volume for each combination of clusters and products, and calculates the cluster sales volume, which is the sales volume of products in the area cluster for a specified period, stores, and store customers in each area Store information holding unit for holding store information in which the number of area customers indicating the number is associated, and store and area cluster The cluster customer number calculation unit that calculates the number of cluster customers, which is the number of customers in the area cluster, for each combination of stores, and the total number of customers in the store for each combination of store and area cluster Cluster customer ratio calculator that calculates the ratio of the number of cluster customers at stores in the area cluster as the cluster customer ratio, and detects the input of products subject to analysis to analyze customer support for products sold at stores Analysis request detection unit, cluster sales amount acquisition unit for acquiring the cluster sales amount of the product in each of the plurality of area clusters where the product was sold, and store clusters in each of the plurality of area clusters for the store to be analyzed The cluster customer ratio acquisition unit that acquires the customer ratio and the area group to be analyzed As an index value indicating the purchase performance of products by the customer of the store, the sales performance identification unit that identifies the cluster sales volume of the product in the area cluster, and the cluster for the store area cluster that indicates the ratio of the customer to all the customers of the store This is an index value indicating the level of customer support for products sold in stores by a customer ratio specifying unit that specifies a customer ratio and a predetermined evaluation function that is positively correlated with the specified cluster sales volume and the cluster customer ratio. A cluster index value calculation unit that calculates a cluster index value, and an index value that indicates how much all customers in the store support the products sold in the store by aggregating the cluster index values calculated for each area cluster A product index value calculation unit that calculates the product index value, and an analysis result output unit that outputs the calculated product index value to an external device.

  It should be noted that any combination of the above-described constituent elements and a representation of the present invention converted between an apparatus, method, system, program, recording medium storing the program, etc. are also effective as an aspect of the present invention.

  According to the present invention, sales activity support information useful for merchandise sellers can be created by analyzing sales performance information based on the area cluster in which the merchandise purchaser resides.

Before describing embodiments of the present invention, an outline of the present invention will be described first.
Now that competition between companies is intensifying, merchandise sellers are required to make appropriate decisions in sales activities. In order to support this decision making, it is important to use sales activity support information. Here, an example of sales activity support information is shown below.

1. Information on the effects of sales promotion measures. This information shows the effect of sales promotion measures including advertisements and discounts at stores, and supports the decision-making of sellers regarding the implementation of sales promotion measures.
2. Product share information. This is information for indicating the occupancy rate of competing products in the same product category, and supporting the decision-making of the seller for product purchase, shelf allocation, and the like.
3. Purchase factor information. This is information for showing the influence of the customer attribute on the purchase of the product, in other words, the correlation between the customer attribute and the sales amount of the product, and for supporting the decision making of the seller with respect to the product purchase, shelf allocation and the like.
4). Store customer demographic information. This is information for showing the configuration of the customer segment of the store and for supporting the decision-making of the seller in accordance with the configuration of the customer segment.

5). Sales condition impact information. This is information for showing the influence of the sales condition of the product on the sales of the product and for supporting the decision making of the seller regarding the setting of the sales condition of the product.
6). Repeat rate information. This information indicates the ratio of the same customer repeatedly purchasing the same product, and is information for supporting the seller's decision making regarding product purchase, shelf allocation, and the like.
7). Product appeal information. This is information for showing how much the product of a certain store is supported by the customer, and for supporting the decision making of the seller with respect to product purchase, shelf allocation, and the like.
8). Store sales volume forecast information. This is information for showing the prediction of how much the product of a certain store is purchased by the customer, and for supporting the decision-making of the seller with respect to product purchase, shelf allocation, and the like.

  Such sales activity support information is preferably created by associating attributes of consumers who actually consume the product with sales results of the product. In other words, the purchaser who actually purchased the product may simply be the person who performed the purchase intention of the consumer on behalf of the purchaser. Even if the purchaser's attributes and the sales performance of the product are matched, it is useful. In some cases, it may not be useful sales activity support information.

  For example, if the product is a male hair restorer, the purchaser is often a housewife in the household while the consumer is a husband in that household. In this case, even if the sales activity support information is created based on the attributes of the housewife who is the purchaser, the sales activity support information is targeted at the housewife. Therefore, sales activity support information that does not reflect consumer attributes may not be useful information for the seller.

  There are typically two methods as conventional means for obtaining customer attributes. The first method is a method of using customer attribute information registered at the time of card creation for a customer who purchased a product using a point card or a credit card. This first method is hereinafter referred to as “FSP (Frequent Shoppers Program) method”. The second method is a method of using attribute information of a small number of monitor customers and customer attribute information entered in a questionnaire (hereinafter referred to as “monitor customer method”).

  However, in the FSP method, the attribute information that can be acquired is purchaser attribute information, and consumer attribute information cannot be acquired. The customer attribute information registered at the time of card creation is only past information, and may be different from the attribute information at the time when the product is purchased. In the case of the monitor customer method, consumer attributes can be acquired by selecting monitor customers and respondents to a questionnaire. However, the number of monitor customers and surveys is generally small, and when customer attributes are determined from a small number of monitor customers and survey results, attributes that correspond to only a small number of people are attributed to the entire consumer, In some cases, the accuracy of sales activity support information was lowered. In addition, the monitor customer method has a problem that it generally takes time because of manual intervention.

  The present invention proposes a technique for creating sales activity support information by associating resident attributes and sales results for each area cluster in which a purchaser of goods resides. An area cluster is a group of multiple areas with similar inhabitant attributes, typically areas on the street level map, and each area cluster is associated with an inhabitant attribute common to these multiple areas. It has been. Therefore, it is possible to associate the consumer image with the sales performance in the area cluster, and realize automatic analysis without human intervention. Thereby, creation of sales activity support information useful for merchandise sellers is realized in a short time.

  Note that the sales activity support information described above is useful information for product development by the product manufacturer, measurement of advertising effectiveness, and the like. For example, product manufacturer information that indicates which consumers are purchasing products, and purchase factor information, the product manufacturer matches the target customer assumed during product development with the actual consumer of the product. You can check whether or not. In addition, the information on the effectiveness of sales promotion measures enables product manufacturers to confirm whether product advertisements and corporate advertisements on TV etc. had an impact on consumers who are expected to respond, and the huge amount of advertising expenses was reduced. It can be used as an index for verifying the effect. In this embodiment, it is described mainly from the viewpoint of creating support information for merchandise sellers, but the information created by the present invention is for various companies that appear in a series of supply chains that manufacture and sell merchandise. Useful information.

  Hereinafter, embodiments will be described. In the embodiment, a sales information analysis system for creating the above-described eight sales activity support information will be described.

  FIG. 1 shows a configuration of a sales information analysis system in the present embodiment. In the sales information analysis system 1000, the sales information analysis apparatus 1 includes a first user terminal 500a, a second user terminal 500b, and a third user terminal collectively referred to as a user terminal 500 via a LAN (Local Area Network) 400. It is connected to the user terminal 500c. The user terminal 500 is a general web terminal equipped with a web browser. The sales information analysis apparatus 1 has a function as a web server, and transmits a web page, typically an HTML (HyperText Markup Language) file, to the user terminal 500. By displaying this web page on the web browser of the user terminal 500, a user interface for the user to access the sales information analysis apparatus 1 is provided.

  The sales information analysis device 1 is also connected to the sales information holding device 300 via the LAN 400. The sales information analysis device 1 analyzes the sales information acquired from the sales information holding device 300 and creates the sales activity support information described above. The sales information holding device 300 is a device that holds various types of sales information, and is typically a database server equipped with DBMS (DataBase Management System) software. The sales information holding device 300 includes a sales promotion result information holding unit 302, a product sales information holding unit 304, a store customer information holding unit 306, and a store sales information holding unit 308.

  The sales promotion result information holding unit 302 holds sales promotion result information indicating a result of implementing a sales promotion measure for a product. FIG. 2 shows the data structure of sales promotion performance information. In the sales promotion column 310, identification information of the sales promotion measure is recorded. This sales promotion measure includes sales promotion measures in various modes from TV commercials and newspaper advertisements to over-the-counter sales at stores. In the target product column 312, a product targeted by the sales promotion measure (hereinafter referred to as “sales promotion target product”) is recorded. A plurality of products may be recorded in the sales promotion target product. For example, in the case of a company image CM, many products sold by the company are sales promotion target products. In the implementation date column 314, the implementation date of the sales promotion measure is recorded. Note that the sales promotion performance information may record sales promotion measures of various scales from the national scale to the regional scale. In this case, even if a target area column indicating the area targeted for the sales promotion measures is further provided. Good. Returning to FIG.

  The product sales information holding unit 304 holds product sales information indicating the sales amount per unit period in each area for each product. FIG. 3 shows the data structure of the merchandise sales information. In the figure, the sales volume for each area of each product is recorded by date and time. In the product column 320, product identification information is recorded. In the area column 322, an area where the product is sold is recorded. In the unit period sales volume column 324, the sales volume in a specific unit period such as a day unit or a week unit is recorded. Returning to FIG.

  The store customer information holding unit 306 holds store customer information indicating in which area and how many customers who purchase products in each store live. FIG. 4 shows the data structure of store customer information. In the store column 330, store identification information is recorded. The area where the customer resides is recorded in the area column 332. In the area customer number column 334, the number of customers residing in each area (hereinafter referred to as "area customer number") is recorded. The number of area customers may be the population of the area itself, or may be the number of customers who actually purchase products at the store in the population of the area. The number of customers of the store may be calculated by using a known analysis method such as an extended Hough model from the population of the area and the attribute information of the store. In the embodiment, the area population is used for the sake of simplicity. Returning to FIG.

  The store sales information holding unit 308 holds store sales information indicating the sales amount per unit period for products sold at each store. FIG. 5 shows the data structure of store sales information. In the store column 340, store identification information is recorded. In the product column 342, products sold at each store are recorded. In the unit period sales volume column 344, the sales volume in a specific unit period is recorded. Returning to FIG.

  Although FIG. 1 shows a sales information analysis system in which each device is connected via the LAN 400, each device may exist in a physically separated location, and each device may be a WAN (Wide Area). Network) or the Internet. Further, the analysis service by the sales information analysis apparatus 1 is not limited to provision within one company, but in the form of ASP (Application Service Provider) or SaaS (Software as a Service), user terminals 500 existing in different companies. May be provided.

  FIG. 6 is a block diagram showing a functional configuration of the sales information analysis apparatus 1 of FIG. Each block shown in the block diagram of the present specification can be realized in terms of hardware by an element such as a CPU of a computer or a mechanical device, and in terms of software, it can be realized by a computer program or the like. The functional block realized by those cooperation is drawn. Therefore, it is understood by those skilled in the art that these functional blocks can be realized in various forms by a combination of hardware and software, and is not limited to any of these.

  In addition, in the sales information analysis apparatus 1, an operation system (hereinafter referred to as “OS”) that provides functions and an environment for efficiently using the apparatus and controls the entire apparatus in an integrated manner is executed. Has been. A plurality of software is executed by operating each functional block in the block diagram by the OS.

  The sales information analysis apparatus 1 includes a user interface processing unit 10, a data holding unit 20, and a data processing unit 40. The user interface processing unit 10 is in charge of user interface processing with the user terminal 500. The data holding unit 20 is a storage area for holding various data. The data processing unit 40 executes various data processing based on data acquired from the user interface processing unit 10, the data holding unit 20, and the sales information holding device 300. Further, the data processing unit 40 also serves as an interface between the user interface processing unit 10 and the data holding unit 20.

User interface processing unit 10:
The user interface processing unit 10 includes an analysis request detection unit 12 and an analysis result output unit 14. The analysis request detection unit 12 receives data transmitted from the user terminal 500 via the LAN 400. For example, receiving analysis request data for requesting analysis of product share for a product category to which the product belongs and specifying the product, and requesting analysis for requesting analysis of the factor that the product was purchased . The analysis result output unit 14 transmits data to the user terminal 500 via the LAN 400. For example, analysis result data indicating a product share and analysis result data indicating a factor in which the product is purchased are transmitted.

Data holding unit 20:
The data holding unit 20 includes an area cluster information holding unit 22, a sales promotion effect information holding unit 24, a product category information holding unit 26, and a product index value information holding unit 28.

  The area cluster information holding unit 22 holds area cluster information indicating a correspondence relationship between an area cluster, each area, and a resident attribute. FIG. 7 shows the data structure of area cluster information. In the area cluster column 200, area cluster identification information is recorded. In the area column 202, a plurality of areas included in the area cluster are recorded. The population column 204 records the population of each area.

  In the resident attribute column 206, the resident attribute of the area cluster is recorded. In other words, the resident attribute of the area cluster is a resident attribute that applies to a plurality of areas in the area column 202 in common. This inhabitant attribute includes a demographic attribute indicating demographic attribute data for each area cluster, and a psychographic attribute indicating attribute data relating to human psychology such as values and lifestyle of residents of each area cluster. included.

  Examples of demographic attributes include “a nuclear family with a relatively small child in their 30s and 40s” and “income is slightly higher than average and there are many people who have graduated from university”. On the other hand, as an example of psychographic attributes, “For women in their 20s, brand, safety and economy are very important, but there is little environmental orientation”, “For women in their 30s, the brand is slightly emphasized, It is environmentally oriented. In other words, the area cluster information is information indicating a consumer's living environment and concept of each area cluster, in other words, a consumer image.

  In the resident index value column 208, a resident index value obtained by converting the resident attribute of the area cluster into an index value is recorded. The resident index value is a plurality of types of conforming values obtained by indexing the degree to which the resident attributes of the area cluster match a plurality of types of evaluation criteria using a predetermined evaluation function. Specific examples of the inhabitant index values include average annual income, average number of households, average number of children per household, 30s ratio, 40s ratio, and the like. In other words, the resident index value is a resident attribute converted into a plurality of numerical values for calculation by a computer.

  An example of a method for creating area cluster information will be described. The area cluster information is created using attribute information about each area, and various public statistical information, original estimation information, and questionnaire result information. The statistical information includes age ratio, gender ratio, occupation ratio, educational ratio, and the like. The estimation information includes average income, average asset, average land price, and the like. The questionnaire result information includes answer results such as lifestyle, values, and consumption preferences. The qualitative information is indexed by a predetermined evaluation function. An area cluster information creating unit (not shown) groups each area by cluster analysis using various attribute information converted into index values for each area as variables. The various index values used in the cluster analysis may be used as values in the resident index value column 208. Based on the values in the resident index value column 208, data to be set in the resident attribute column 206 is set by human judgment. Also good. Further, the sales results of various products in each area may be a variable of cluster analysis. Returning to FIG.

  The sales promotion effect information holding unit 24 holds sales promotion effect information indicating the influence of the sales promotion measure on the sales volume of the sales promotion target product. FIG. 8 shows the data structure of the sales promotion effect information. In the sales promotion column 210, identification information of the sales promotion measure is recorded. In the target product column 212, sales target products are recorded. In the area cluster column 214, area cluster identification information is recorded. In the sales volume increase rate column 216, a rate at which the sales volume of the sales promotion target product is increased by the sales promotion measure (hereinafter referred to as “sales volume increase rate”), in other words, the effect of the sales promotion is recorded.

  The merchandise category information holding unit 26 records merchandise category information indicating the correspondence between the merchandise and the merchandise category. FIG. 9 shows the data structure of product category information. In the product category column 220, product categories are recorded. In the product column 222, products associated with each product category are recorded.

  The merchandise index value information holding unit 28 is associated with a plurality of stores and index values (hereinafter referred to as “product index values”) indicating how much the merchandise is supported by the customers at each store. Holds index value information. FIG. 10 shows the data structure of product index value information. In the store column 230, store identification information is recorded. In the product column 232, products sold at each store are recorded. In the product index value column 234, the product index value of each product at each store is recorded. Returning to FIG.

Data processing unit 40:
The data processing unit 40 includes an area sales volume acquisition unit 42, a cluster sales volume calculation unit 44, a sales promotion effect analysis unit 50, a sales volume correction unit 60, a product share analysis unit 70, a purchase factor analysis unit 80, It has a store customer segment analysis unit 90, a sales condition influence analysis unit 100, a repeat rate analysis unit 110, a product appeal level analysis unit 120, a product appeal level correction unit 130, and a store sales volume prediction unit 140.

  The area sales amount acquisition unit 42 accesses the product sales information holding unit 304 and refers to the product sales information, and for each combination of area and product, the sales amount of the product in the area for a predetermined period (hereinafter referred to as “area sales amount”). ").). FIG. 11 shows the area sales volume acquired by the area sales volume acquisition unit 42. The area sales volume in the figure is the total sales volume from 10/30 to 11/3 of the product sales information shown in FIG. Returning to FIG.

  The cluster sales amount calculation unit 44 accesses the area cluster information holding unit 22 and refers to the area cluster information. Then, for each combination of the area cluster and the product, the area sales amount is totaled, and the sales amount of the product in the area cluster in a predetermined period (hereinafter referred to as “cluster sales amount”) is calculated. FIG. 12 shows the cluster sales volume calculated by the cluster sales volume calculation unit 44. The cluster sales volume in FIG. 11 is the total of the area sales volume shown in FIG. 11 for each area cluster for each product. Returning to FIG.

Promotion effect analysis unit 50:
The sales promotion effect analysis unit 50 creates sales promotion effect information that is one of sales activity support information. The effect information of the sales promotion measure in the present embodiment is information indicating the sales volume increase rate of the sales promotion target product by the sales promotion measure for each area cluster. FIG. 13 is a block diagram illustrating a functional configuration of the sales promotion effect analysis unit 50. The sales promotion effect analysis unit 50 includes a sales promotion sales amount acquisition unit 52, a sales promotion sales amount totaling unit 54, and a sales promotion effect calculation unit 56.

  The sales promotion sales amount acquisition unit 52 first accesses the sales promotion result information holding unit 302 and refers to the sales promotion result information, and specifies the sales promotion target product and the implementation date of the sales promotion measure to be analyzed. For example, when the sales promotion measure “TV advertisement α” in FIG. 2 is an analysis target, the sales promotion target product is specified as “XX coffee” and the implementation date is specified as “November 1”. Note that the sales promotion sales amount acquisition unit 52 may receive, from the user terminal 500, specified data of the sales promotion measure to be analyzed via the analysis request detection unit 12. Further, regardless of designation by the user terminal 500, the sales promotion measure recorded in the sales promotion performance information may be automatically acquired as the sales promotion measure to be analyzed.

  Next, the sales promotion sales amount acquisition unit 52 accesses the product sales information holding unit 304 to refer to the product sales information, and for the sales promotion products in each area, the sales amount in a predetermined period before the implementation of the sales promotion measures (hereinafter, This is called “sales volume before promotion”. Further, the sales amount for a predetermined period after the implementation of the sales promotion measure (hereinafter referred to as “sales amount after sales promotion”) is acquired. As for this period, a period considered to be appropriate for determining the effect of the sales promotion measure may be set based on the experience and experiment of the company. In the above example, the sales volume for two days before and after the implementation of the sales promotion measure “TV advertisement α” is acquired.

  FIG. 14A shows the sales volume before sales promotion and the sales volume after sales promotion acquired by the sales promotion sales volume acquisition unit 52. The figure shows the total sales volume of 10/30 to 10/31 for “XX Coffee” in FIG. 3 as the sales volume before promotion, and the sales volume of 11/1 to 11/2. It shows that it was acquired as sales volume after promotion. As described above, when the sales promotion measure is implemented only in a part of the area, the sales promotion sales amount acquisition unit 52 acquires the information of the area from the sales promotion performance information, and acquires the information from the product sales information. Only the sales amount before sales promotion and the sales volume after promotion may be acquired. Returning to FIG.

  The sales promotion sales amount totaling unit 54 accesses the area cluster information holding unit 22 and refers to the area cluster information, and totals the sales amount before sales promotion and the sales amount after sales promotion for each area cluster. FIG. 14B shows the pre-promotion sales volume and the post-sales sales volume aggregated by the promotional sales volume aggregation unit 54. That is, the pre-sales sales volume and the post-sales sales volume for area a, area b, and area c are totaled to calculate the pre-sales sales volume and post-sales sales volume for cluster A. The same applies to the areas d and e and the cluster B. Return to FIG.

  The sales promotion effect calculation unit 56 compares the aggregate value of the sales volume before sales promotion with the aggregate value of the sales volume after sales promotion for each area cluster, and calculates the sales volume increase rate of the sales promotion target product by the sales promotion measure to be analyzed. Calculate for each cluster. Here, the sales promotion effect calculation unit 56 calculates the sales volume increase rate by calculating the ratio of the aggregate values. For example, when the aggregate value of the sales volume before sales promotion and the aggregate value of the sales volume after sales promotion are in the state of FIG. 14B, the sales promotion effect calculation unit 56 sets the sales volume increase rate for cluster A to “1.13. “, The sales volume increase rate for cluster B is calculated as“ 1.64 ”.

  The sales promotion effect calculation part 56 accesses the sales promotion effect information holding part 24, and records the sales volume increase rate of the sales promotion target product in each area cluster in the sales promotion effect information for the sales promotion measure to be analyzed. When the specification of the sales promotion measure to be analyzed is received from the user terminal 500, the sales amount increase rate in each area cluster calculated for the sales promotion measure is sent to the user terminal 500 via the analysis result output unit 14. You may send it.

  With the conventional method of analyzing the effects of sales promotion measures, we can see that the sales volume of products targeted for promotion has increased in total, but we have not been able to know what characteristics the sales volume to consumers has increased. Therefore, it was unclear whether the sales promotion measure appealed to the target customer group. According to the sales promotion effect analysis unit 50, the effect of the sales promotion measure can be calculated for each area cluster associated with the customer image from various viewpoints such as demographic attributes and psychographic attributes.

  FIG. 14C shows the sales volume increase rate in each area cluster of the sales promotion target product by the sales promotion measure. The content of the figure may be displayed on the user terminal 500 as the effect information of the sales promotion measure. The merchandise seller can confirm the effect of the sales promotion measure in detail by confirming the sales volume increase rate in each area cluster. For example, from the result of FIG. 14C, it can be confirmed that the customer of cluster B is strongly appealed and that when cluster C is targeted, different sales promotion measures are necessary.

  As a modification, the sales promotion effect analysis unit 50 may include a resident attribute influence analysis unit (not shown). The Resident Attribute Impact Analysis Department uses the regression analysis with the resident attributes of each area cluster as explanatory variables and the sales volume increase rate in each area cluster by specific sales promotion measures as the objective variable. Is calculated as a regression coefficient. By comparing the regression coefficients calculated for each resident attribute, it is possible to identify a resident attribute that has a relatively large influence on the sales volume increase rate. A merchandise seller can implement an effective sales promotion measure at a low cost by implementing the above-mentioned specific sales promotion measure limited to the area cluster having the resident attribute. Returning to FIG.

Sales volume correction unit 60:
The sales volume correction unit 60 removes the increase in sales volume due to the sales promotion measures not analyzed from the sales volume after sales promotion calculated by the sales promotion sales volume totaling unit 54. FIG. 15 is a block diagram illustrating a functional configuration of the sales amount correction unit 60. The sales amount correction unit 60 includes a sales promotion effect acquisition unit 62, a sales promotion sales amount correction unit 64, a sales promotion sales amount identification unit 66, and a cluster sales amount correction unit 68.

  First, the sales promotion effect acquisition unit 62 accesses the sales promotion result information holding unit 302 and refers to the sales promotion result information. Then, the sales promotion policy is the same as the sales promotion policy to be analyzed, and the non-analysis sales promotion policy implemented within the specified period after the sales promotion policy to be analyzed is removed. Identify the target sales promotion measures. This predetermined period may be a period for obtaining the post-sales sales volume for the sales promotion measure to be analyzed, and may be appropriately determined based on company experience, experiments, and the like. FIG. 16 shows the data structure of sales promotion performance information. This figure shows that in addition to the sales promotion performance information of FIG. 2, the non-analytical sales promotion measure “newspaper advertisement θ” was implemented the day after the sales promotion measure “TV advertisement α” was analyzed. Yes. In this case, the sales promotion effect acquisition unit 62 identifies “newspaper advertisement θ” as a sales promotion measure to be removed in order to remove the increase in the sales amount of “XX coffee” by “newspaper advertisement θ”. Returning to FIG.

  Next, the sales promotion effect acquisition unit 62 accesses the sales promotion effect information holding unit 24, refers to the sales promotion effect information, and acquires the sales volume increase rate of the sales promotion target product by the sales promotion measure to be removed. When the sales promotion effect information is in the state shown in FIG. 8, the sales promotion effect acquisition unit 62 determines the sales volume increase rate “1.4” of “XX coffee” in cluster A by “newspaper advertisement θ” and “ Acquire the sales increase rate of “1.1” for XX coffee. Returning to FIG.

  The sales promotion volume correction unit 64 sets the sales volume after sales promotion for each area cluster so that the sales volume increase rate for the sales promotion policy to be removed increases, and the sales volume for the sales promotion policy to be analyzed decreases. To correct. Specifically, the sales promotion sales amount correction unit 64 is a period in which the period for acquiring the sales promotion amount for the sales promotion measure to be analyzed overlaps with the predetermined period from the implementation date for the sales promotion measure to be removed. Is identified. This predetermined period is a period that is strongly influenced by the sales promotion measure to be removed, and may be appropriately determined based on company experience, experiments, and the like. The sales promotion sales volume correction unit 64 removes the sales volume increase rate due to the sales promotion measures to be removed from the sales volume after sales promotion in the overlapping period.

  FIG. 17 shows the data structure of product sales information. The unit period sales volume for “XX Coffee” in the figure is 11/2 and 11/3, unlike FIG. 3, not only the sales promotion measure “TV advertisement α”, but also sales promotions that are not analyzed The increase in sales volume by the measure “newspaper advertisement θ” is added.

  FIG. 18A shows the sales volume before sales promotion and the sales volume after sales promotion acquired by the sales promotion sales volume acquisition unit 52. The data in the figure is obtained from the product sales information of FIG. 17 on the two days before and after the implementation of the sales promotion measure “TV advertisement α”, that is, the sales volume of “XX coffee” from 10/30 to 11/2. It has been done. As described above, since the sales volume increase by the sales promotion measure “newspaper advertisement θ” that is not the object of analysis is added, the sales volume after the sales promotion is larger than that in FIG. Therefore, in this state, the sales volume increase rate due to “TV advertisement α” is largely calculated.

  FIG. 18B shows the post-sales sales volume corrected by the sales promotion sales volume correction unit 64. The sales volume after the sales promotion in the figure is obtained by removing the sales volume increase amount due to the “newspaper advertisement θ” from the sales volume of XX coffee in each area of 11/2. Specifically, the sales promotion sales amount correction unit 64 divides the sales volume of 11/2 in the areas a, b, and c belonging to the cluster A by the sales volume increase rate “1.4”. The sales volume of 11/2 in the areas d and e belonging to is divided by the sales volume increase rate “1.1”.

  The sales promotion sales volume totaling unit 54 described above totals the sales volume before sales promotion acquired by the sales promotion sales volume acquisition unit 52 and the sales volume after sales promotion corrected by the sales promotion sales volume correction unit 64 for each area cluster. The sales promotion effect calculation unit 56 compares these total values and calculates the sales volume increase rate of the sales promotion target product by the sales promotion measure to be analyzed.

  If the sales promotion measures that are subject to analysis and those that are not subject to analysis are implemented at the same time, the increase in sales volume due to the sales promotion measures that are not subject to analysis Will be added. As a result, the effect of the sales promotion measure to be analyzed is calculated to be greater than actual. The sales amount correction unit 60 removes the extra amount due to the sales promotion measure that is not the analysis target from the post-sales sales amount regarding the sales promotion measure that is the analysis target. Thereby, the sales promotion effect calculation part 56 can calculate correctly the sales volume increase rate of the sales promotion target product by the sales promotion measure to be analyzed.

  The sales promotion sales amount correction unit 64, when the area where the sales promotion measure to be removed is limited, is limited to the area where the sales promotion measure to be removed is applied, and the sales volume after sales promotion for the sales promotion measure to be analyzed is limited. May be corrected. For example, if the sales promotion measure to be analyzed is a nationwide TV advertisement and the sales promotion measure to be removed is an area-wide storefront discount sale, it will be limited to the area where the storefront discount sale was implemented, Correct sales volume. Accordingly, the sales volume increase rate of the sales promotion target product by the sales promotion measure to be analyzed can be correctly calculated in consideration of the scope of the sales promotion measure.

  Further, in the example shown in FIGS. 18A and 18B, the sales promotion sales amount correction unit 64 corrects the sales promotion amount for each area. It may be corrected. The sales promotion sales amount correcting unit 64 corrects the value of the period strongly influenced by the sales promotion measure to be removed among the aggregated values of the sales amount after the sales promotion according to the sales volume increase rate by the sales promotion measure to be removed. . Specifically, the larger the sales volume increase rate due to sales promotion measures targeted for removal, the smaller the value of the aggregate sales value after sales promotion for the period strongly affected by the sales promotion measures targeted for removal. Reduce the aggregate sales volume after sales promotion. The sales promotion effect calculation unit 56 includes a total value of the sales amount before sales totaled by the sales promotion sales amount totaling unit 54, a total value of the sales amount after sales promotion, and the total value corrected by the sales promotion sales amount correction unit 64. Are compared, and the sales volume increase rate of the sales promotion product by the sales promotion measure to be analyzed is calculated. Returning to FIG.

  Further, when the cluster sales amount of the sales promotion target product is calculated by the cluster sales amount calculation unit 44, the sales amount correction unit 60 removes the increase in the sales amount due to the sales promotion measure from the cluster sales amount. FIG. 19 shows the cluster sales volume after correction by the sales volume correction unit 60. The cluster sales amount in FIG. 12 is the cluster sales amount in FIG. 12, which is a value including an increase in sales amount due to the sales promotion measure “TV advertisement α”. The following specific example will be described with reference to FIG.

  In order to remove the increase in sales volume due to the sales promotion measure, the sales promotion sales volume acquisition unit 52 first acquires the pre-sales purchase amount of the sales promotion target product. Here, since the promotion target product is “XX coffee” in FIG. 3 and the implementation date of the sales promotion measure is 11/1, the sales promotion sales amount acquisition unit 52 performs “XX coffee” in each area of 10/31. ”Will be acquired. Next, the sales promotion sales amount totaling unit 54 totals the purchase amount before sales promotion of the sales promotion target product for each area cluster. In the case of “XX coffee” in FIG. 3, the aggregate value of 10/31 is 265 for cluster A including areas a, b, and c, and the aggregate value of 10/31 is 150 for cluster B including areas d and e. Become. This is shown in the sales volume before sales promotion in FIG.

  The sales promotion sales amount specifying unit 66 determines whether or not the cluster sales amount calculation period calculated by the cluster sales amount calculation unit 44 includes the implementation date of the sales promotion measure for the product associated with the cluster sales amount. judge. When it is determined that the sales promotion measure is included, the cluster sales amount for a predetermined period in which the influence of the sales promotion measure is assumed to be high from the implementation date of the sales promotion measure is specified. About this period, an appropriate period should just be set by a company's experience and experiment.

  For example, it is assumed that the sales promotion measure “TV advertisement α” implemented on 11/1 for “XX coffee” greatly affects the sales volume of “XX coffee” on 11/1 and 11/2. At this time, the sales promotion sales amount specifying unit 66 specifies the cluster sales amounts of 11/1 and 11/2 from the cluster sales amount as the cluster sales amount greatly influenced by the “TV advertisement α”. This is shown in the sales promotion influence part of FIG. The sales promotion influence amount in the figure is the sum of the sales volume of “XX coffee” in 11/1 and 11/2 in FIG. 3 for each area cluster. Returning to FIG.

  The cluster sales amount correction unit 68 corrects the cluster sales amount based on the pre-promotion sales amount that is not affected by the sales promotion measure. One method is to replace the cluster sales volume affected by the sales promotion measure with the sales volume before sales promotion. For example, the sales amount before sales promotion is set to 2 in order to set the value of the sales promotion influence amount in FIG. 19, that is, the total value of the sales volume of 11/1 and 11/2 to the value of the sales volume before sales promotion, here two days. Replace with the doubled value. Then, the cluster sales volume that is not affected by the sales promotion measure, that is, the sales volume of 10/30, 10/31, and 11/3 are tabulated. The corrected cluster sales volume is shown in FIG. 19 as the corrected cluster sales volume.

  In another method of correcting the cluster sales volume, the sales promotion effect analysis unit 50 calculates the sales volume increase rate of the sales promotion target product by the sales promotion measure to be removed. The cluster sales volume correction unit 68 corrects the cluster sales volume affected by the sales promotion measure according to the value of the sales volume increase rate. For example, the cluster sales volume affected by the sales promotion measure may be divided by the sales volume increase rate and totaled with the cluster sales volume not affected by the sales promotion measure.

  In still another method of correcting the cluster sales volume, the cluster sales volume correction unit 68 accesses the sales promotion effect information holding unit 24 and acquires the sales volume increase rate of the sales promotion target product by the sales promotion measure to be removed. The cluster sales volume correction unit 68 corrects the cluster sales volume affected by the sales promotion measure according to the value of the sales volume increase rate. For example, the cluster sales volume affected by the sales promotion measure may be divided by the sales volume increase rate and totaled with the cluster sales volume not affected by the sales promotion measure.

  According to the sales amount correction unit 60, the increase in the sales amount due to the sales promotion measure can be removed from the cluster sales amount of the product affected by the sales promotion measure. The increase in sales volume due to sales promotion measures is often temporary, and the sales volume including the increase is often not a reflection of the ability of the product itself. Since the influence of the sales promotion measure can be removed by the sales amount correction unit 60, the cluster sales amount reflecting the ability of the product itself can be calculated.

  In addition, by setting an appropriate period for eliminating the impact of sales promotion measures, the rapid and temporary increase in sales volume due to sales promotion measures can be eliminated, while the increase in sales volume due to the improvement in the ability of the product itself. Is left in the cluster sales volume. That is, the increase in the sales volume due to the increase in the popularity of the product due to the sales promotion measure is considered to be an increase in the sales volume due to the improvement of the ability of the product itself, and should not be removed. Since these effects continue after the period when the influence of the sales promotion measure should be removed, the increase in the sales volume due to the improvement of the ability of the product itself can be reflected in the cluster sales volume.

  The cluster sales volume corrected by the cluster sales volume correction unit 68 is used in a product share analysis unit 70, a purchase factor analysis unit 80, a sales condition influence analysis unit 100, a repeat rate analysis unit 110, and a product appeal analysis unit 120, which will be described later. it can. Thereby, it is possible to remove the influence of the increase in the sales volume due to the sales promotion measure from the sales activity support information created in each functional block, and to create the sales activity support information based on the ability of the product itself. A specific example will be described in detail in the product share analysis unit 70 described later. Returning to FIG.

Product share analysis unit 70:
The merchandise share analysis unit 70 creates merchandise share information which is one of sales activity support information. The product share information in the present embodiment is information indicating the proportion of the sales amount of a product for each area cluster in the product category. FIG. 20 is a block diagram illustrating a functional configuration of the product share analysis unit 70. The product share analysis unit 70 includes a category sales amount calculation unit 72 and an occupancy rate calculation unit 74.

  The category sales amount calculation unit 72 accesses the product category information holding unit 26 and refers to the product category information. Then, the cluster sales volume calculated by the cluster sales volume calculation unit 44 is aggregated for each combination of the area cluster and the product category, and the sales volume (hereinafter referred to as “category sales”) for a specific product category in each area cluster. Called "quantity"). FIG. 21A shows the category sales volume calculated by the category sales volume calculation unit 72. The category sales volume of FIG. 12 is obtained by summing up the cluster sales volume of FIG. 12 for each area cluster for “XX coffee”, “ΔΔ 珈 琲”, and “□□ blend” belonging to the product category “coffee”.

  Further, the category sales volume calculation unit 72 may calculate the category sales volume by totaling the cluster sales volume corrected by the cluster sales volume correction unit 68 described above. FIG. 21B shows the category sales volume calculated by the category sales volume calculation unit 72. The category sales volume of the figure shows the cluster sales volume shown in FIG. 12 for “△△ 珈 琲” and “□□ blend”, and the corrected cluster sales volume of FIG. It is the total. Returning to FIG.

  The occupation rate calculation unit 74 calculates a ratio between the category sales amount for each area cluster and the cluster sales amount of each product in each area cluster as an occupation rate. For example, the category sales amount of cluster A in FIG. 21A is “2795”, and the cluster sales amount of “XX coffee” in FIG. 12 is “1475”, so the occupation rate is calculated as 53%. FIG. 22A shows the occupation ratio of each product for the product category “coffee”. The content of FIG. 5 may be displayed on the user terminal 500.

  Further, the occupation rate calculation unit 74 may calculate the ratio between the cluster sales amount corrected by the cluster sales amount correction unit 68 and the category sales amount obtained by collecting the corrected cluster sales amount as the occupation rate. FIG. 22B shows the occupation rate of each product for the product category “coffee”. The figure shows the ratio between the cluster sales volume of “XX Coffee”, which has removed the increase in sales volume due to the sales promotion measure “TV advertisement α”, and the category sales volume that has been aggregated. In this figure, the increase in sales volume of “XX coffee” due to sales promotion measures has been eliminated, so the share of “△△ 珈 琲” and “□□ blend” is larger than in FIG. 22 (a). It has become.

  Note that the category sales amount calculation unit 72 may receive specification data of a product to be analyzed from the user terminal 500 via the analysis request detection unit 12. At this time, the occupancy rate calculation unit 74 calculates the occupancy rate of the analysis target product in the product category to which the analysis target product belongs, and transmits it as the product share information to the user terminal 500 via the analysis result output unit 14. Similarly, the category sales amount calculation unit 72 may receive specification data for a product category to be analyzed from the user terminal 500. At this time, the occupancy rate calculation unit 74 calculates the occupancy rate of each product in the product category to be analyzed and transmits it to the user terminal 500.

  According to the product share analysis unit 70, the product seller can confirm the share of each product in the product category for each area cluster. That is, a detailed competitive product analysis is realized. Since the area cluster is associated with the consumer image as described above, the merchandise seller can compare and examine which merchandise is supported by which consumer in the merchandise category. Thereby, for example, it is possible to make an appropriate decision regarding the purchase of goods, shelf allocation, and the like. Further, by calculating the occupation rate based on the cluster sales amount excluding the increase in the sales amount due to the sales promotion measure, the product seller can also confirm the share of each product based on the ability of the product itself. Returning to FIG.

Purchasing factor analysis unit 80:
The purchase factor analysis unit 80 creates purchase factor information which is one of sales activity support information. The purchase factor information in the present embodiment is information indicating a resident attribute that affects the purchase of a product by a customer. FIG. 23 is a block diagram illustrating a functional configuration of the purchase factor analysis unit 80. The purchase factor analysis unit 80 includes a resident attribute acquisition unit 82 and a resident attribute evaluation unit 84.

  The resident attribute acquisition unit 82 receives the specification data of the product to be analyzed from the user terminal 500 via the analysis request detection unit 12. The inhabitant attribute acquisition unit 82 first acquires the sales amount of the analysis target product in each area cluster and the cluster sales amount calculated by the cluster sales amount calculation unit 44. Next, the resident attribute acquisition unit 82 specifies an area cluster associated with each cluster sales amount. The resident attribute acquisition unit 82 finally accesses the area cluster information holding unit 22 to refer to the area cluster information, acquires the resident attribute of the identified area cluster, and associates it with each cluster sales amount.

  For example, it is assumed that the cluster sales amount for the analysis target product “XX coffee” is in the state shown in FIG. At this time, the resident attribute acquisition unit 82 acquires the cluster sales amount “1475” in the cluster A and the cluster sales amount “960” in the cluster B for the analysis target product “XX coffee”. The inhabitant attribute acquisition unit 82 refers to the area cluster information in FIG. 7 and sets the cluster sales amount “1475” and the inhabitant attribute “50 years old” “The ratio is high” is associated with the cluster sales amount “960”. Returning to FIG.

  The resident attribute evaluation unit 84 compares the cluster sales volume of each area cluster to identify an area cluster having a relatively large cluster sales volume. Then, the resident attribute of the area cluster is determined as a resident attribute having a high correlation with the sales amount of the analysis target product. For example, the cluster sales amount of “XX coffee” is larger in the cluster A than in the cluster B. Therefore, the resident attribute evaluation unit 84 determines the resident attribute of cluster A “having a high 30s ratio” or the like as a resident attribute having a high correlation with the sales amount of “XX coffee”. The resident attribute evaluation unit 84 transmits purchase factor information indicating the resident attribute determined to be highly correlated with the sales amount of the analysis target product to the user terminal 500 via the analysis result output unit 14.

  According to the purchase factor analysis unit 80, it is possible to specify a resident attribute that affects the purchase of a product by a customer. That is, by confirming the purchase factor information created by the purchase factor analysis unit 80, the merchandise seller makes a decision on the product assortment and shelf allocation according to the customer attributes of each store. Sales volume can be increased.

  Next, a modified example for determining a resident attribute having a high correlation with the analysis target product will be described. The resident attribute acquisition unit 82 refers to the area cluster information and acquires the resident index value together with the resident attribute. The resident attribute evaluation unit 84 uses the cluster sales volume in each area cluster as an objective variable and the regression analysis using the resident index value in each area cluster as an explanatory variable, and the effect of each resident index value on the cluster sales volume as a regression coefficient. calculate. The resident attribute evaluation unit 84 may select an explanatory variable by using a known test method such as a t test or an F test and a known selection method such as a variable reduction method or a variable increase method. The inhabitant attribute evaluation unit 84 determines the inhabitant attribute associated with the inhabitant index value having a relatively large absolute value of the regression coefficient as an inhabitant attribute having a high correlation with the sales amount of the analysis target product. Send.

  FIG. 24 illustrates the relationship between the resident index value and the cluster sales amount. The content of the figure may be displayed on the user terminal 500 as purchase factor information. The numerical values in the figure indicate the regression coefficients calculated by the above-described regression analysis, and the resident index values with blank numerical values do not significantly affect the cluster sales volume. Product sellers, for example, “△△ 珈 琲” is purchased by low-income customers, “XX coffee” is not purchased much by households with many children, while “△△ 珈 琲” is the number of children It can be confirmed from this result that households with a lot of purchases often purchase.

  According to this modification, the correlation between the sales amount of each product and the resident attributes can be specified in detail. That is, the merchandise seller can grasp the reason why each merchandise is purchased by the customer. For example, it is possible to grasp the influence of the average income of residents on the sales volume of each product. As a result, merchandise sellers will increase the volume of merchandise purchased by purchasing and selling appropriate products according to the customer's attributes at each store, such as purchasing many products that match the average income of major customers. Can be made. Returning to FIG.

Store customer segment analysis unit 90:
The store customer segment analysis unit 90 creates store customer segment information that is one type of sales activity support information. Store customer group information in the present embodiment is information indicating the ratio of the number of customers residing in each area cluster to the total number of customers in the store for each combination of store and area cluster. FIG. 25 is a block diagram illustrating a functional configuration of the store customer segment analysis unit 90. The store customer segment analysis unit 90 includes a cluster customer number calculation unit 92 and a cluster customer ratio calculation unit 94.

  The cluster customer number calculation unit 92 receives, from the user terminal 500, the designation data of the store to be analyzed via the analysis request detection unit 12. The cluster customer number calculation unit 92 accesses the store customer information holding unit 306, refers to store customer information, totals the number of area customers for each combination of store and area cluster, and the number of customers residing in each area cluster (Hereinafter referred to as “number of cluster customers”) is calculated. For example, in the case of “Kiba store” in FIG. 4, the number of cluster customers in cluster A is “600” which is the number of customers in area a, and the number of cluster customers in cluster C is an aggregate value of the number of customers in area f and area i. 1150 ". Actually, the larger the store size, the larger the number of areas where the customers of the store reside, so the number of area customers in many areas is aggregated for each area cluster to calculate the number of cluster customers. The

  The cluster customer ratio calculation unit 94 calculates the ratio of the number of cluster customers to the total number of customers in the store (hereinafter referred to as “cluster customer ratio”) for each combination of store and area cluster. For example, in the case of the Kiba store shown in FIG. The ratio between the total number of customers and the number of cluster customers “600” in cluster A is “23%”, and the ratio between the total number of customers and the number of cluster customers “1150” in cluster C is “45%”.

  FIG. 26A shows the cluster customer ratio of “Kiba store” in FIG. FIG. 26B shows the cluster customer ratio of “Kasai store” in FIG. FIG. 26C shows the cluster customer ratio of “Funabashi store” in FIG. The cluster customer ratio calculation unit 94 transmits store customer group information indicating the cluster customer ratio of each area cluster to the user terminal 500 for the analysis target store via the analysis result output unit 14.

  According to the store customer segment analysis unit 90, the ratio of customers residing in each area cluster of the analysis target store can be calculated. For example, from FIG. 26A, the merchandise seller can confirm that the customers of “Kiba store” have many residents of cluster C, and store strategy that emphasizes the residents attribute of cluster C is “Kiba store”. Can be executed. Returning to FIG.

Sales condition impact analysis unit 100:
The sales condition influence analysis unit 100 creates sales condition influence information which is one piece of sales activity support information. The sales condition influence information in the present embodiment is information indicating a sales volume increase rate for each area cluster before and after setting a sales condition for a certain product. FIG. 27 is a block diagram illustrating a functional configuration of the sales condition influence analysis unit 100. The sales condition influence analysis unit 100 includes a comparative sales volume acquisition unit 102 and a sensitivity rate evaluation unit 104.

  The comparative sales amount acquisition unit 102 receives specification data of a product to be analyzed from the user terminal 500 via the analysis request detection unit 12. For the analysis target product, designation data for the period before the sales condition change and the period after the sales condition change may be received from the user terminal 500, and the date and time of the sales condition change for each product is held in a storage unit (not shown). May be. The comparative sales volume acquisition unit 102 refers to the cluster sales volume of the analysis target product calculated by the cluster sales volume calculation unit 44 and refers to the cluster sales volume (hereinafter “sales volume before change”) for a predetermined period before the sales condition is changed. And the cluster sales volume for a predetermined period after the sales condition is changed (hereinafter referred to as “sales volume after change”).

  FIG. 28A shows the pre-change sales volume and the post-change sales volume acquired by the comparative sales volume acquisition unit 102. The sales volume before change and sales volume after change in the figure are the cluster sales volume and sales conditions before changing the sales conditions of the analyzed product “□ Blend” among the cluster sales volume calculated by the cluster sales volume calculation unit 44. It shows the cluster sales volume after the change. The following example will be described with reference to FIG.

  Sensitivity rate evaluation unit 104 calculates the ratio of the sales amount after change to the sales amount before change (hereinafter referred to as “sales condition sensitivity rate”) for each area cluster. For example, in the case of FIG. 28A, the sales condition sensitivity rates from cluster A to cluster D are “1.41”, “1.13”, “1.4”, and “1.25”, respectively. . FIG. 28B shows the sales condition sensitivity calculated by the sensitivity evaluation unit 104. The sensitivity evaluation unit 104 transmits sales condition influence information indicating the sales condition sensitivity of the analysis target product in each area cluster to the user terminal 500 via the analysis result output unit 14.

  The sensitivity evaluation unit 104 also refers to the cluster customer ratio calculated by the cluster customer ratio calculation unit 94, and the cluster customer ratio for an area cluster whose sales condition sensitivity is equal to or greater than a predetermined threshold is greater than or equal to the predetermined threshold. Identify stores that are Then, the area cluster, the sales condition sensitivity rate, and the store are associated with each other and transmitted to the user terminal 500.

  For example, assume that the sales condition sensitivity ratio threshold is “1.4” and the cluster customer ratio threshold is “40%”. At this time, the sensitivity evaluation unit 104 identifies clusters A and C from FIG. Then, referring to the cluster customer ratios shown in FIGS. 26A to 26C, “Kasai store” is identified as a store having a high cluster customer ratio in cluster A, and a store having a high cluster customer ratio in cluster C. Specify "Kiba store". From this result, the merchandise seller can confirm a store where the change of the sales conditions is particularly effective.

  According to the sales condition influence analysis unit 100, it is possible to calculate the sensitivity rate for the sales conditions of the products for the customers living in each area cluster. The merchandise seller can easily set the sales conditions for the merchandise by checking the sensitivity rate. A typical example of sales condition setting is price setting of a product, and a product seller can easily make a decision about a price strategy of the product. In addition, by associating an area cluster with a high sensitivity rate with a store whose main customer is a resident of the area cluster, a merchandise seller can easily make a strategy for each store for setting the sales conditions of the product. For example, it is possible to determine that the price of a product is to be reduced only for stores whose main customers are residents of an area cluster that has a high sensitivity to the price of a product.

Repeat rate analysis unit 110:
The repeat rate analysis unit 110 creates repeat rate information that is one type of sales activity support information. The repeat rate information of the present embodiment is information indicating the ratio of the same product repeatedly sold to the same customer in each area cluster. FIG. 29 is a block diagram illustrating a functional configuration of the repeat rate analysis unit 110. Repeat rate analysis unit 110 includes an area repeat number acquisition unit 112, a cluster repeat number calculation unit 114, a repeat rate calculation unit 116, and a repeat rate evaluation unit 118.

  The product sales information shown in FIG. 3 further records the number of area repeats (not shown) for each product area, and the number of times the product was repeatedly sold to the same customer in a predetermined period (hereinafter referred to as “area repeat number”). ") Is recorded. For example, for “ΔΔ 珈 琲” in the period from 10/30 to 11/3 in FIG. 3, the number of area repeats in area a, the number of area repeats in area b, and so on are recorded. The number of area repeats may be counted twice, three times, etc. when purchased at different times, even for the same customer. Alternatively, it may be counted only once.

  The area repeat number acquisition unit 112 accesses the product sales information holding unit 304, refers to the product sales information, and acquires the area repeat number for a predetermined period for each combination of area and product.

  The cluster repeat number calculation unit 114 accesses the area cluster information holding unit 22, refers to the area cluster information, and totals the number of area repeats for each combination of area cluster and product. Hereinafter, this total value is referred to as “cluster repeat number”. For example, the number of area repeats for areas a, b, and c is totaled to calculate the number of cluster repeats for cluster A. FIG. 30A shows the number of cluster repeats calculated by the cluster repeat number calculation unit 114. The figure shows the number of cluster repeats for “ΔΔ 珈 琲”, and the cluster sales volume in the figure shows the cluster sales volume calculated by the cluster sales volume calculation unit 44.

  The repeat rate calculation unit 116 receives the specification data of the product to be analyzed from the user terminal 500 via the analysis request detection unit 12. The repeat rate calculation unit 116 calculates a ratio between the cluster sales amount and the number of cluster repeats for the analysis target product (hereinafter referred to as “repeat rate”). For example, when the number of cluster repeats of the analysis target product “ΔΔ 珈 琲” is in the state of FIG. 30A, the repeat rate of “ΔΔ 珈 琲” in cluster A is calculated to be “8%”. FIG. 30B shows the repeat rate in each area cluster calculated by the repeat rate calculator 116.

  The repeat rate evaluation unit 118 identifies an area cluster having a relatively high repeat rate for the repeat rate in each area cluster calculated by the repeat rate calculation unit 116. For example, the repeat rate evaluation unit 118 may identify an area cluster having the highest repeat rate, or may identify an area cluster for which a repeat rate equal to or greater than a predetermined threshold is calculated. When the latter threshold value is “20%”, the repeat rate evaluation unit 118 identifies the area clusters B and C from the repeat rate shown in FIG. The repeat rate evaluation unit 118 transmits the identified area cluster to the user terminal 500 via the analysis result output unit 14.

  The repeat rate analysis unit 110 can calculate a repeat rate for each area cluster. The merchandise seller can grasp what attributes the consumer has repeatedly purchased the merchandise. You can also make decisions about product sales strategies according to the attributes of your key customers. For example, the amount of merchandise sold can be effectively increased by sending direct mail or the like to residents residing in an area cluster with a high repeat rate.

  In the first modified example, the repeat rate evaluation unit 118 for each of a plurality of area clusters is a sales amount per person indicating the ratio of the cluster sales amount to the population of the area cluster (hereinafter referred to as “unit population sales amount”). And the repeat rate is associated with the unit population sales.

  Specifically, the repeat rate evaluation unit 118 accesses the area cluster information holding unit 22, refers to the area cluster information, and acquires the population of the area cluster for which the repeat rate is calculated. For example, when the area cluster information is in the state shown in FIG. 7, for the population of cluster A, the populations of areas a, b, and c are totaled to obtain “2150”. Similarly, “1150”, “2000”, and “900” are acquired for cluster B, cluster C, and cluster D.

  The repeat rate evaluation unit 118 calculates a unit population sales volume for each area cluster. For example, for cluster A, since the population is “2150” and the cluster sales volume is “505”, the unit population sales volume “0.23” is calculated. Similarly, “0.27”, “0.09”, and “0.09” are calculated for cluster B, cluster C, and cluster D. The repeat rate evaluation unit 118 associates the repeat rate with the unit customer sales amount, and transmits it to the user terminal 500 via the analysis result output unit 14.

  FIG. 31 illustrates the association between the repeat rate and the unit customer sales volume. The figure shows that the customer's purchase tendency for “△△ 珈 琲” is that the customer of cluster A has a large unit customer sales volume, but the repeat rate is small, while the customer of cluster C has a small unit customer sales volume but a large repeat rate. Is shown.

  According to the first modification, the merchandise seller can make a detailed sales strategy decision from the viewpoint of the sales volume and the repeat rate. For example, from the results shown in FIG. 31, for stores whose main customers are the customers of cluster A, “△△ 珈 琲” should be purchased a lot at the beginning of the sale, and the number of purchases should be greatly reduced over time. Can make decisions. In addition, for stores whose main customers are cluster C customers, the purchase amount of “△△△” may be small, but since there are many repeat customers, “△△ 珈 琲” is continuously purchased so that it will not run out of stock. It can be decided that it should be.

  In the second modified example, the repeat rate evaluation unit 118 refers to the cluster customer rate calculated by the cluster customer rate calculation unit 94, and the cluster customer rate for an area cluster whose repeat rate is equal to or greater than a predetermined threshold is predetermined. Stores that are equal to or greater than the threshold. Then, an area cluster having a repeat rate equal to or higher than a predetermined threshold is associated with a store whose main customer is a resident of the area cluster, and is transmitted to the user terminal 500.

  According to the second modification, the merchandise seller can make a decision on a sales strategy for each store in accordance with the repeat rate of a certain merchandise. For example, a store whose main customer is an resident of an area cluster with a high repeat rate of a product will start selling immediately if the product is not currently sold, and will continue to keep the product from running out. Making decisions such as checking. Returning to FIG.

Product appeal analysis unit 120:
The product appeal level analysis unit 120 creates product appeal level information that is one of sales activity information. The product appeal level information in the present embodiment is referred to as a product sold at a certain store and an index value (hereinafter referred to as “product index value”) indicating how much the product is supported by all customers of the store. ) Is associated information. FIG. 32 is a block diagram illustrating a functional configuration of the product appeal degree analysis unit 120. The product appeal level analysis unit 120 includes an analysis information acquisition unit 122, an analysis target specifying unit 124, a cluster index value calculation unit 126, and a product index value calculation unit 128.

  The analysis information acquisition unit 122 receives, from the user terminal 500, the product to be analyzed and the designation data of the store via the analysis request detection unit 12. The analysis information acquisition unit 122 acquires the cluster sales amount of the analysis target product calculated by the cluster sales amount calculation unit 44 and the cluster customer ratio of the analysis target store calculated by the cluster customer ratio calculation unit 94.

  FIG. 33 shows the cluster sales volume of the analysis target product acquired by the analysis information acquisition unit 122. This figure shows a state in which the cluster sales volume of the latest December is acquired for the analysis target products “XX coffee”, “ΔΔ 珈 琲”, and “□□ blend”. The cluster customer ratios of the analysis target stores acquired by the analysis information acquisition unit 122, in this example, “Kiba store”, “Kasai store”, and “Funabashi store” are shown in FIGS. 26 (a), 26 (b), The contents shown in FIG. 26C will be used in the following description.

  The analysis target specifying unit 124 uses the cluster sales volume of the analysis target product in the analysis target cluster as an index value indicating the purchase performance of the product by the customers in the area cluster to be analyzed (hereinafter referred to as “analysis target cluster”). Identify. For example, in FIG. 33, the cluster sales amount “100” of the analysis target product “XX coffee” in the analysis target cluster “cluster A” is specified.

  The analysis target specifying unit 124 also specifies the cluster customer ratio for the analysis target cluster of the analysis target store. For example, in FIG. 26A, the cluster customer ratio “23%” for the cluster A in the analysis target store “Kiba store” is specified. Subsequently, the analysis target specifying unit 124 associates the cluster sales amount with the cluster customer ratio for each combination of the analysis target product, the analysis target cluster, and the analysis target store. In the above example, the analysis target specifying unit 124 associates the specified cluster sales amount “100” with the cluster customer ratio “23%”. Returning to FIG.

  The cluster index value calculation unit 126 is an index indicating how much the customer residing in a specific area cluster in the analysis target store supports the analysis target product by a predetermined evaluation function that is positively correlated with the cluster sales amount and the cluster customer ratio. A value (hereinafter referred to as “cluster index value”) is calculated. Specifically, a calculation result obtained by inputting the cluster sales amount and the cluster customer ratio specified by the analysis target specifying unit 124 into the above-described evaluation function is set as a cluster index value. The above-described evaluation function may be a known calculation method. In the following example, it is assumed that the evaluation function is a function that outputs a result of the integration of the cluster sales volume and the cluster customer ratio.

  In the above example, the cluster sales amount “100” in “Cluster A” of “XX Coffee” and the cluster customer ratio “23%” for “Cluster A” in the analysis target store “Kiba store” are evaluated using the evaluation function. The cluster index value “23” is calculated by integration. Similarly, the cluster index value calculation unit 126 calculates a cluster index value for each combination of the analysis target product, the analysis target cluster, and the analysis target store. FIG. 34 shows the cluster index value calculated by the cluster index value calculation unit 126. In the figure, the index value “−” indicates that the customer of the store does not have a resident in the area cluster. Returning to FIG.

  The product index value calculation unit 128 aggregates the cluster index values calculated for each area cluster by the cluster index value calculation unit 126 for each analysis target store, and obtains the product index value for the analysis target product sold at the analysis target store. calculate. For example, in FIG. 34, the cluster index values in the clusters A, B, and C of “XX coffee” at the Kiba store are “23”, “16”, “270”, and “48”, respectively. At this time, the merchandise index value calculation unit 128 aggregates these cluster index values and calculates the merchandise index value “357” of “XX coffee” in the Kiba store.

  The product index value calculation unit 128 transmits the product index value in the analysis target store to the user terminal 500 for the analysis target product via the analysis result output unit 14. The product index value calculation unit 128 also accesses the product index value information holding unit 28 and records the calculated product index value in the product index value information. In the product index value column 234 of FIG. 10, the product index value calculated in the above example is recorded.

  The merchandise appeal analysis unit 120 calculates the cluster customer ratio by the store customer segment analysis unit 90 for the products for which the cluster sales amount is calculated by the cluster sales amount calculation unit 44 regardless of the request from the user terminal 500. You may calculate the merchandise index value in a store. Also in this case, the product index value calculating unit 128 accesses the product index value information holding unit 28 and records the calculated product index value in the product index value information.

  The reason why the merchandise index value calculated by the merchandise appeal analysis unit 120 becomes an index value indicating how much all the customers of the analysis target store support the analysis target product will be described. As described above, the input data for calculating the product index value is the cluster sales volume of the analysis target product and the cluster customer ratio of the analysis target store. The cluster sales amount indicates a record of the sales of the analysis target product in each area cluster, and is an index value indicating the level of support of customers residing in each area cluster for the analysis target product. On the other hand, the cluster customer ratio indicates the ratio of customers residing in each area cluster to the total customers of the analysis target stores, and indicates the magnitude of the influence of the customer purchasing behavior of each area cluster on the analysis target stores. It is an index value.

  The cluster index value calculated by the evaluation function that is positively correlated with the cluster sales volume and the cluster customer ratio increases as the cluster sales volume increases for one area cluster, and increases as the cluster customer ratio increases. From this, it can be said that the cluster index value is an index value indicating the magnitude of the influence of the customer's support in one area cluster for the analysis target product on the analysis target store. Therefore, the product index value obtained by collecting the cluster index values is an index value indicating how much the analysis target store supports all the customers of the analysis target store.

  The merchandise index value calculated by the merchandise appeal analysis unit 120 enables the merchandise seller to compare the level of customer support between the analysis target products in the analysis target store, and the purchase amount and shelf allocation of each product are appropriate. Decision making is possible. For example, when the merchandise index value is in the state shown in FIG. 10, the merchandise seller has priority for “Kasai store” in terms of purchase amount and shelf allocation as “□□ blend”, “△△ 珈 琲”, “○ ○ Decision can be made in the order of “coffee”.

  Further, the cluster sales volume, which is an input of the product index value, can be obtained from the sales performance of stores other than the analysis target store because the sales volume at each store is aggregated for each area cluster. Therefore, even when the analysis target product is not sold at the analysis target store, the product index value can be calculated. For example, it is assumed that the merchandise index value is in the state of FIG. 10 and the Funabashi store does not sell “XX coffee”. At this time, merchandise sellers can understand that they are more likely to be supported by customers than “□□ Blend” by selling “XX Coffee” at Funabashi Store, and purchase “XX Coffee”. Make decisions.

  The conventional method for specifying how much the products sold at the store are instructed to the customer is to conduct a questionnaire for the customer. However, conducting surveys on a large number of customers increases costs, so the number of surveys is generally small, and the survey results tend to strongly reflect the subjectivity of a small number of respondents. there were. In addition, it took time because of human intervention. According to the product appeal analysis unit 120, a highly accurate product index value can be calculated in a short time based on objective data such as the cluster sales volume and the cluster customer ratio. Returning to FIG.

Product appeal correction unit 130:
The merchandise appeal correction unit 130 corrects the cluster index value so that a merchandise index value reflecting the future sales trend of the merchandise is calculated based on the transition of the cluster sales volume for the analysis target merchandise. FIG. 35 is a block diagram illustrating a functional configuration of the product appeal degree correction unit 130. The product appeal degree correction unit 130 includes a sales volume trend calculation unit 132 and an index value correction unit 134.

  The sales volume trend calculation unit 132 calculates, for each area cluster, the change rate of the cluster sales volume with the passage of time from the release date of the product based on the transition of the cluster sales volume for the analysis target product. Specifically, the sales volume trend calculation unit 132 acquires the cluster sales volume calculated by the cluster sales volume calculation unit 44 and for each predetermined period from the release date of the analysis target product to the time of analysis. To do. FIG. 36 shows the transition of the cluster sales volume acquired by the sales volume trend calculation unit 132. This figure shows the transition of the cluster sales volume from October to December calculated by the cluster sales volume calculation unit 44 for the analysis target products “ΔΔ 珈 琲” and “□□ blend”. It is assumed that both products to be analyzed have been released in October.

  The sales volume trend calculation unit 132 calculates the change rate of the cluster sales volume for each area cluster from the transition of the cluster sales volume for the analysis target product. Although the rate of change may be obtained by a known method, since the sales volume of the product generally decreases with the passage of time, typically, the value of 0 <change rate <1 is taken. Here, the sales volume trend calculation unit 132 calculates the change rate of the cluster sales volume shown in FIG. 36 by a recurrence formula. In the case of FIG. 36, since the recurrence formula in cluster A for “ΔΔ 珈 琲” is a geometric sequence with a common ratio of “0.9”, the sales volume trend calculation unit 132 determines that the cluster of “ΔΔ 珈 琲” The rate of change in A is calculated as “0.9”.

  FIG. 37A shows the cluster index value and the rate of change in the cluster sales volume. The cluster index value before correction in the figure indicates the cluster index value of the analysis target product in “Kiba store” calculated by the cluster index value calculation unit 126, and is the same as the value of the Kiba store cluster index value in FIG. is there. The change rate in FIG. 37A indicates the change rate of the cluster index value calculated by the sales volume trend calculation unit 132 based on the transition of the cluster sales volume shown in FIG. Returning to FIG.

  The index value correction unit 134 adjusts the cluster index value calculated by the cluster index value calculation unit 126 according to the change rate calculated by the sales volume trend calculation unit 132 for the analysis target product. Here, the index value correction unit 134 corrects the cluster index value by integrating the cluster index value and the change rate of the cluster sales amount. For example, the cluster index value “46” in the cluster A of “ΔΔ 珈 琲” and the change rate “0.9” of the cluster sales amount are integrated to correct the cluster index value to “41”.

  The post-correction cluster index value column in FIG. 37A shows the cluster index value corrected by the index value correction unit 134. This figure shows that the cluster index value is corrected so that the cluster index value of the area cluster is smaller in the area cluster where the decrease with time of the cluster sales volume is larger.

  The product index value calculation unit 128 of the product appeal degree analysis unit 120 calculates the product index value by totaling the cluster index values corrected by the index value correction unit 134. FIG. 37B shows the product index value before correction and the product index value after correction. The product index values before correction in the figure are the sums of the cluster index values before correction in FIG. 37A, and the product index values after the correction are the sums of the cluster index values after correction in FIG. 37B. It is.

  According to the merchandise appeal correction unit 130, the cluster index value can be corrected so that the merchandise index value calculation unit 128 calculates a merchandise index value that reflects the future sales trend of the analysis target product. Thereby, the merchandise seller can make a more appropriate decision regarding the purchase of the merchandise and the shelf allocation. For example, products that have a large decrease rate at the present time are considered to have a relatively large downward trend in the future. The merchandise appeal correction unit 130 makes the merchandise index value more useful for merchandise sellers by correcting the merchandise index value so that the merchandise index value becomes smaller for products with a decreasing tendency.

  When the pre-correction product index value shown in FIG. 37B is shown, the product seller can easily make a decision so as to give priority to “□□ blend” in purchasing and shelf allocation. However, by displaying the corrected product index value that reflects the future sales trend, the product seller can make a decision to give priority to “ΔΔ 珈 琲” based on the future sales trend. Returning to FIG.

Store sales volume prediction unit 140:
The store sales amount prediction unit 140 creates store sales amount prediction information that is one of sales activity support information. Store sales amount prediction information in the present embodiment is information indicating a sales amount predicted for a product in a certain store (hereinafter referred to as “expected sales amount”). FIG. 38 is a block diagram illustrating a functional configuration of the store sales volume prediction unit 140. The store sales amount prediction unit 140 includes a unit sales amount acquisition unit 142, a correlation analysis unit 144, and an estimated sales amount calculation unit 146.

  FIG. 39 shows the estimated sales volume at each store. The figure shows the estimated sales volume of “XX coffee” at each store and the data related to the calculation, and the following example will be described with reference to the figure.

  The unit sales volume acquisition unit 142 is an index value of the sales volume of products in each of a plurality of stores, and acquires the sales volume per customer of each store (hereinafter referred to as “unit customer sales volume”). Specifically, the unit sales amount acquisition unit 142 first accesses the store customer information holding unit 306, refers to the store customer information, and acquires the total number of customers for each store. When the store customer information is in the state shown in FIG. 4, the unit sales amount acquisition unit 142 adds up the area customer number column 334 for each store, and acquires the total number of customers in each store. Next, the unit sales amount acquisition unit 142 also accesses the store sales information holding unit 308, refers to the store sales information, and acquires the product sales amount in a predetermined period. Here, with regard to the store sales information in FIG. 5, the December sales amount is acquired for “XX coffee” at each store.

  Finally, the unit sales amount acquisition unit 142 calculates the unit customer sales amount by calculating the ratio between the total number of customers for each store and the product sales amount at each store. For example, since the total number of customers at “Kiba store” is “2550” and the sales volume of “XX coffee” in December is “255”, the unit customer sales volume in this case is “0.1”. Become. The unit customer sales amount in FIG. 39 indicates the unit customer sales amount of “XX coffee” at each store calculated by the unit sales amount acquisition unit 142. Note that “Makuhari store” and “Akita store”, which have no unit customer sales volume, indicate that “XX coffee” is not sold. Returning to FIG.

  The correlation analysis unit 144 accesses the product index value information holding unit 28, refers to the product index value information, acquires the product index value of the product at each store, and also obtains the unit sales amount acquisition unit for the product at each store The unit customer sales volume calculated by 142 is acquired. The product index value in FIG. 39 is the product index value of “XX coffee” at each store.

  Next, the correlation analysis unit 144 uses a regression analysis method based on the product index value and the unit customer sales amount at each of a plurality of stores, using the product index value and the unit customer sales amount of the product as explanatory variables and objective variables, respectively. The regression equation indicating the magnitude of the correlation between the product index value of the product and the unit customer sales amount is calculated.

For example, the correlation degree analysis unit 144 calculates the following regression equation by performing regression analysis on the product index value and the unit sales amount in “Kiba store”, “Kasai store”, and “Funabashi store” in FIG.
Unit sales volume = 0.0002 × commodity index value + 0.0231
The regression coefficient “0.0002” in this regression equation indicates the magnitude of the influence of the product index value on the unit sales volume. In this example, a single regression analysis using only the product index value as an explanatory variable is shown. However, the correlation analysis unit 144 adds a plurality of explanatory variables such as a resident index value and other statistical information to perform a multiple regression analysis. The above-described variable selection may be performed. Returning to FIG.

  The expected sales amount calculation unit 146 is a store (hereinafter referred to as “prediction target store”) that predicts the sales amount of a specific product (hereinafter referred to as “prediction target product”) via the analysis request detection unit 12. Designation data) is received from the user terminal 500. The estimated sales volume calculation unit 146 accesses the product index value information holding unit 28, refers to the product index value information, and acquires the product index value for the prediction target product in the prediction target store.

  Next, the expected sales amount calculation unit 146 inputs the product index value in the prediction target store into the regression equation calculated by the correlation analysis unit 144, and the unit customer sales amount of the prediction target product in the prediction target store (hereinafter, Called “expected unit sales volume”). The expected unit sales amount in FIG. 39 indicates the expected unit sales amount obtained by inputting the product index value of XX coffee at each store into the above regression equation.

  The expected sales amount calculation unit 146 calculates the sales amount of the prediction target product in the prediction target store (hereinafter referred to as “expected sales amount”) based on the expected unit sales amount. Here, the estimated sales amount is calculated by integrating the total number of customers of the prediction target store and the expected unit sales amount of the prediction target product. The expected sales amount in FIG. 39 shows the integration result of the expected unit sales amount of XX coffee in each store and the total number of customers in each store in FIG. The total number of customers of “Makuhari store” is “5000”, and the total number of customers of “Akita store” is “1000”.

  The estimated sales amount calculation unit 146 transmits the estimated sales amount of the prediction target product in the prediction target store to the user terminal 500 via the analysis result output unit 14. FIG. 40 shows the sales volume results and prospects in each store in association with each other. The solid sales volume in the figure indicates the sales volume of “XX coffee” at each store, and the diagonal sales volume indicates the estimated sales volume of “XX coffee” at each store.

  The conventional method for predicting the sales amount of a product is to actually sell a prediction target product at a sample store and predict the sales amount at another store from the sales trend. However, if the number of sample stores is small and the sales period is short, the accuracy of the analysis is low. To increase the accuracy, if the number of sample stores is long and the sales period is long, the time and research costs will rise. There was a problem. According to the store sales volume prediction unit 140, reasonable sales prediction is realized in a short time by calculating the expected sales volume based on the product index value.

  The reason why the store sales volume prediction unit 140 can calculate a reasonable expected sales volume will be described. As described above, the store sales volume prediction unit 140 first calculates a regression equation indicating the correlation between the product index value and the unit customer sales volume. The product index value is an index value indicating how much the customer of each store supports the prediction target product. On the other hand, the unit customer sales volume is the ratio of the sales volume of the prediction target product at each store to the total number of customers at each store. Since the pair of product index value and unit sales volume for each store that sells the forecasted product can be handled equally, the regression analysis correlates the product index value as the index value and the unit customer sales volume as the actual value. A regression equation can be calculated.

  Next, the store sales amount prediction unit 140 calculates the expected unit sales amount by inputting the product index value of the prediction target store into the regression equation calculated for the prediction target product. Since the estimated unit sales amount is a value per customer, the estimated sales amount can be calculated by adding up with the total number of customers of the prediction target store. This estimated sales volume reflects the cluster sales volume indicating the sales performance at other stores based on the cluster customer ratio of the target store. In addition, residents who have the same living environment and way of thinking reside in the same area cluster. Therefore, it can be said that the estimated sales volume is a sales volume that can be realized also in the target store based on the sales performance at other stores.

  Further, as described above, the product index value is an index value calculated from objective data based on results, and the unit customer sales volume is also objective data based on results. Therefore, since it is based on sales results at a plurality of stores, the accuracy is high, and calculation in a short time is realized.

  Further, according to the store sales volume prediction unit 140, the merchandise seller compares the actual sales performance with the estimated sales volume as shown in “Kiba store”, “Kasai store”, and “Funabashi store” in FIG. it can. As a result, the merchandise seller can grasp that the sales volume of “XX coffee” at the “Funabashi store” can be increased, and can make a decision to execute sales activities such as sales promotion measures on the “Funabashi store”.

  Furthermore, as shown in FIG. 40, the expected sales amount can be provided to the merchandise seller for “Makuhari store” and “Akita store” that do not sell “XX coffee”. Thereby, the merchandise seller can make a decision to sell “XX coffee” at the “Makuhari store”, for example. That is, the merchandise seller can specify an appropriate store where the merchandise should be sold and can purchase an appropriate amount of the merchandise at the store.

  The present invention has been described based on the embodiments. This embodiment is an exemplification, and it will be understood by those skilled in the art that various modifications can be made to combinations of the respective constituent elements and processing processes, and such modifications are also within the scope of the present invention. is there.

  It should also be understood by those skilled in the art that the functions to be fulfilled by the constituent elements described in the claims are realized by the individual functional blocks shown in the present embodiment or their linkage.

It is a figure which shows the structure of the sales information analysis system in this Embodiment. It is a figure which shows the data structure of sales promotion performance information. It is a figure which shows the data structure of merchandise sales information. It is a figure which shows the data structure of shop customer information. It is a figure which shows the data structure of store sales information. It is a block diagram which shows the function structure of the sales information analysis apparatus of FIG. It is a figure which shows the data structure of area cluster information. It is a figure which shows the data structure of sales promotion effect information. It is a figure which shows the data structure of merchandise category information. It is a figure which shows the data structure of goods index value information. It is a figure which shows the area sales amount acquired by the area sales amount acquisition part. It is a figure which shows the cluster sales volume calculated by the cluster sales volume calculation part. It is a block diagram which shows the function structure of the sales promotion effect analysis part. It is a figure which shows the sales volume before sales promotion after the sales promotion volume acquisition part, and the sales volume after sales promotion. It is a figure which shows the sales volume before sales promotion after the sales promotion volume totaling part, and the sales volume after sales promotion. It is a figure which shows the sales volume increase rate in each area cluster of the sales promotion target product by a sales promotion measure. It is a block diagram which shows the function structure of a sales amount correction | amendment part. It is a figure which shows the data structure of sales promotion performance information. It is a figure which shows the data structure of merchandise sales information. It is a figure which shows the sales volume before sales promotion after the sales promotion volume acquisition part, and the sales volume after sales promotion. It is a figure which shows the sales amount after sales promotion correct | amended by the sales promotion sales amount correction | amendment part. It is a figure which shows the cluster sales amount after correction | amendment by a sales amount correction | amendment part. It is a block diagram which shows the function structure of a goods share analysis part. It is a figure which shows the category sales volume calculated by the category sales volume calculation part. It is a figure which shows the category sales volume calculated by the category sales volume calculation part. It is a figure which shows the occupation rate of each goods about goods category "coffee." It is a figure which shows the occupation rate of each goods about goods category "coffee." It is a block diagram which shows the function structure of a purchase factor analysis part. It is a figure which illustrates the relationship between a resident index value and cluster sales volume. It is a block diagram which shows the function structure of a store customer layer analysis part. It is a figure which shows the cluster customer ratio of "Kiba store" of FIG. It is a figure which shows the cluster customer ratio of "Kasai store" of FIG. It is a figure which shows the cluster customer ratio of the "Funabashi store" of FIG. It is a block diagram which shows the function structure of a sales condition influence analysis part. It is a figure which shows the sales amount before a change acquired by the comparative sales amount acquisition part, and the sales amount after a change. It is a figure which shows the sales condition sensitivity calculated by the sensitivity evaluation part. It is a block diagram which shows the function structure of a repeat rate analysis part. It is a figure which shows the number of cluster repeats calculated by the cluster repeat number calculation part. It is a figure which shows the repeat rate in each area cluster calculated by the repeat rate calculation part. It is a figure which illustrates matching with a repeat rate and a unit customer sales amount. It is a block diagram which shows the function structure of a goods appeal degree analysis part. The cluster sales volume of the analysis target product acquired by the analysis information acquisition unit is shown. It is a figure which shows the cluster index value calculated by the cluster index value calculation part. It is a block diagram which shows the function structure of a goods appeal degree correction | amendment part. It is a figure which shows transition of the cluster sales volume acquired by the sales volume tendency calculation part. It is a figure which shows a cluster index value and the change rate of cluster sales volume. It is a figure which shows the goods index value before correction | amendment, and the goods index value after correction | amendment. It is a block diagram which shows the function structure of a store sales volume estimation part. Shows the estimated sales volume at each store. It is a figure which matches and shows the result and expectation of the sales amount in each store.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1 Sales information analyzer, 10 User interface processing part, 12 Analysis request | requirement detection part, 14 Analysis result output part, 20 Data holding part, 22 Area cluster information holding part, 24 Sales promotion effect information holding part, 26 Merchandise category information holding part, 28 product index value information holding unit, 40 data processing unit, 42 area sales volume acquisition unit, 44 cluster sales volume calculation unit, 50 sales promotion effect analysis unit, 52 sales promotion sales volume acquisition unit, 54 sales sales volume totaling unit, 56 sales promotion effect Calculation section, 60 sales volume correction section, 62 sales promotion effect acquisition section, 64 sales promotion sales volume correction section, 66 sales promotion sales volume identification section, 68 cluster sales volume correction section, 70 product share analysis section, 72 category sales volume calculation section, 74 Occupancy rate calculation part, 80 Purchasing factor analysis part, 82 Resident attribute acquisition part, 84 Resident attribute Evaluation unit, 90 store customer segment analysis unit, 92 cluster customer number calculation unit, 94 cluster customer ratio calculation unit, 100 sales condition impact analysis unit, 102 comparative sales volume acquisition unit, 104 sensitivity rate evaluation unit, 110 repeat rate analysis unit, 112 area repeat number acquisition unit, 114 cluster repeat number calculation unit, 116 repeat rate calculation unit, 118 repeat rate evaluation unit, 120 product appeal level analysis unit, 122 analysis information acquisition unit, 124 analysis target identification unit, 126 cluster index value calculation , 128 product index value calculation unit, 130 product appeal level correction unit, 132 sales volume trend calculation unit, 134 index value correction unit, 140 store sales volume prediction unit, 142 unit sales volume acquisition unit, 144 correlation analysis unit, 146 Expected sales volume calculation section, 200 area cluster column, 202 Area column, 204 Population column, 206 Resident attribute column, 208 Resident index value column, 210 Promotion column, 212 Target product column, 214 Area cluster column, 216 Sales volume increase rate column, 220 Product category column, 222 Product column, 230 stores Column, 232 product column, 234 product index value column, 300 sales information holding device, 302 sales promotion result information holding unit, 304 product sales information holding unit, 306 store customer information holding unit, 308 store sales information holding unit, 310 sales promotion column, 312 Target product column, 314 Implementation date column, 320 Product column, 322 Area column, 324 Unit period sales volume column, 330 Store column, 332 Area column, 334 Area customer number column, 340 Store column, 342 Product column, 344 Unit period Sales volume column, 400 LAN, 500 users Terminal, 500a first user terminal, 500b second user terminal, 500c third user terminal, 1000 sales information analysis system.

Claims (4)

An area cluster information holding unit for holding area cluster information indicating a correspondence relationship between an area cluster and a group of areas having similar attributes regarding residents with respect to a plurality of geographically divided areas;
For each combination of area and product, an area sales amount acquisition unit that acquires an area sales amount that is the sales amount of the product in the area for a predetermined period;
A cluster sales amount calculation unit that calculates the cluster sales amount that is the sales amount of the product in a predetermined period in the area cluster by totalizing the area sales amount for each combination of area cluster and product;
A store information holding unit that holds store information that associates a store with the number of area customers indicating the number of customers of the store in each area;
A cluster customer number calculating unit that calculates the number of customer of the store in the area cluster by calculating the number of area customers for each combination of the store and the area cluster,
For each combination of store and area cluster, a cluster customer ratio calculating unit that calculates a ratio of the number of cluster customers of the store in the area cluster to the total number of customers of the store as a cluster customer ratio;
In order to analyze customer support for products sold in stores, an analysis request detection unit that detects input of products to be analyzed,
A cluster sales amount acquisition unit that acquires a cluster sales amount of the product in each of a plurality of area clusters in which the product is sold;
For a store to be analyzed, a cluster customer ratio acquisition unit that acquires a cluster customer ratio of the store in each of the plurality of area clusters,
As an index value indicating the purchase record of the product by the customer of the area cluster to be analyzed, a sales result specifying unit for specifying the cluster sales amount of the product in the area cluster,
A customer ratio specifying unit for specifying a cluster customer ratio for the area cluster of the store, indicating a ratio of the customer to all customers of the store;
A cluster that calculates a cluster index value that is an index value indicating the degree of support of the customer for the product sold at the store, by a predetermined evaluation function that is positively correlated with the specified cluster sales volume and cluster customer ratio An index value calculator,
A product index for calculating a product index value that is an index value indicating how much the products sold at the store support all the customers of the store by collecting the cluster index values calculated for each area cluster A value calculator,
An analysis result output unit for outputting the calculated product index value to an external device;
A sales information analyzing apparatus comprising: A sales promotion result information holding unit that holds sales promotion result information that associates the implementation date and time of the sales promotion measure with the product that is the target of the sales promotion measure;
The sales amount of the product per unit period before the implementation of the sales promotion measure as the sales amount before sales promotion, a sales promotion sales amount acquisition unit for acquiring the sales amount before sales promotion for each of a plurality of areas,
A sales promotion sales amount totaling unit for totaling the acquired sales amount before sales promotion for each of a plurality of area clusters;
Regarding the cluster sales volume calculated by the cluster sales volume calculation unit, when the implementation date of the sales promotion measure for the area cluster associated with the cluster sales volume is included in the measurement period of the cluster sales volume, A sales promotion sales amount specifying unit for specifying a cluster sales amount for a predetermined influence period in which the influence of the sales promotion measure is assumed to be high from the execution date and time among the cluster sales volume;
The effect of the sales promotion measure was removed from the cluster sales amount by correcting the cluster sales amount for the influence period based on the aggregate value of the sales amount before promotion for the area cluster associated with the cluster sales amount. A cluster sales volume correction unit for calculating a new cluster sales volume as a cluster sales volume,
Further comprising
The sales performance specifying unit specifies a new cluster sales amount for the product to be analyzed in the area cluster to be analyzed as an index value indicating the purchase performance,
The cluster index value calculation unit calculates a cluster index value by a predetermined evaluation function that is positively correlated with the new cluster sales volume and the cluster customer ratio,
The product index value calculation unit calculates a product index value from which an effect of the sales promotion measure is removed by aggregating cluster index values based on the new cluster sales amount. Sales information analysis equipment. Based on the transition of the cluster sales volume for the product to be analyzed, a sales volume trend calculation unit that calculates the change rate of the cluster sales volume over time from the release date of the product for each area cluster;
Index value correction for correcting the cluster index value calculated for each area cluster according to the rate of change in order to reflect the future sales trend predicted based on the past sales trend of the product in the product index value And
Further comprising
The sales information according to claim 1 or 2, wherein the product index value calculation unit calculates a product index value reflecting a future sales trend by aggregating the corrected cluster index values. Analysis equipment. A product index value information holding unit that holds product index value information in which a plurality of stores and a product index value of one product in each store are associated;
It is an index value of the sales amount of the product in each of the plurality of stores, a unit sales amount acquisition unit for acquiring the sales amount of the product per customer of each store as a unit sales amount,
The product index value and unit sales volume of the product are analyzed by regression analysis from the product index value and unit sales volume at each of the plurality of stores, with the product index value and unit sales volume of the product as explanatory variables and objective variables, respectively. A correlation analysis unit that calculates a regression equation indicating the magnitude of the correlation with
A sales volume prediction request detection unit that detects an input of a store to be analyzed in order to predict the sales volume of the product in one store;
By referring to the product index value information, obtaining the product index value of the product in the store, and calculating the unit sales volume of the product in the store by inputting the product index value in the regression equation, An estimated sales volume calculation unit that calculates the sales volume of the product in the store as an estimated sales volume based on the unit sales volume;
A sales volume prediction result output unit for outputting the calculated estimated sales volume to an external device;
The sales information analysis apparatus according to claim 1, further comprising:

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