JP7244707B1 - Information processing system, computer program, and information processing method - Google Patents

Abstract

A technique capable of outputting the degree of contribution of multiple factors related to sales results is provided. A value of at least one index related to a product sales activity plan in a store is obtained as a planned value of a first index (S120). At least one index value related to the sales activity performance of the product in the store is acquired as the second index performance value (S140). The degree of contribution of the first index and the second index to the sales result is calculated based on the planned value and the performance value (S170). The calculated contributions of the first index and the second index are output (S180). [Selection drawing] Fig. 2

Description

The present disclosure relates to an information processing system, a computer program, and an information processing method.

Conventionally, there is known a system for finding a method that contributes to an increase in sales of merchandise in retail stores (see Patent Document 1). In the conventional system, the degree of influence of each item on sales, specifically, the standardized partial regression coefficient is calculated.

Japanese Patent Application Laid-Open No. 2003-337885

The display state of merchandise in a store is affected by the sales activity plan of the merchandise. Therefore, there is room for improvement in the conventional method of evaluating the degree of influence of multiple factors on sales simply based on the relationship between the display state and sales that is actually measured.

Therefore, according to one aspect of the present disclosure, it is desirable to provide a new technique capable of outputting the degree of contribution to sales performance of multiple factors related to sales performance as information useful for improving sales activities.

According to one aspect of the present disclosure, an information processing system is provided. The information processing system includes a first acquisition section, a second acquisition section, a calculation section, and an output section. The first acquisition unit is configured to acquire, as a planned value of the first index, the value of at least one index related to the sales activity plan for the product in the store.

The second acquisition unit is configured to acquire the value of at least one index related to the sales activity performance of the product in the store as the performance value of the second index. The calculation unit is configured to calculate the degree of contribution of the first index and the second index to the sales result of the product based on the planned value and the performance value. The output unit is configured to output the calculated contributions of the first index and the second index.

According to one aspect of the present disclosure, the information processing system can output the degree of contribution of a plurality of indices corresponding to plans and performance regarding sales activities of merchandise to sales results. Based on this contribution information, the user can consider improving store management, including improving the plan itself.

Therefore, according to one aspect of the present disclosure, it is possible to provide an information processing system capable of outputting the degree of contribution of multiple factors related to sales results as information useful for improving sales activities.

According to one aspect of the present disclosure, a merchandising plan may include a plan for product display. The first obtaining unit can obtain the value of at least one index including the index relating to the display of merchandise in the sales activity plan as the planned value of the first index. The second acquisition unit may acquire the value of at least one index related to the display performance of the product as the performance value of the second index.

According to one aspect of the present disclosure, the calculation unit can calculate the degree of contribution through constructing a regression model that has the first index and the second index as explanatory variables and the sales performance as the objective variable. According to one aspect of the present disclosure, the calculator may use a regression model to calculate the Shapley values of the first index and the second index.

By treating the first indicator related to sales activity planning and the second indicator related to performance as variables that explain sales results and constructing a regression model, the contribution of these indicators to sales results can be calculated appropriately. can do.

According to one aspect of the present disclosure, the information processing system may further include a third acquisition unit. The third acquisition unit may be configured to acquire the value of at least one index related to the sales environment of the product as the environmental value of the third index.

The calculation unit can calculate the degree of contribution of each of the first index, the second index, and the third index to the sales result based on the planned value, the performance value, and the environmental value. The output unit can output the calculated contributions of the first index, the second index, and the third index.

The sales environment can also affect sales performance. Therefore, an information processing system that outputs the degree of contribution including an index related to the sales environment is very useful for improving store management.

According to one aspect of the present disclosure, the third indicator may include at least one of an indicator related to weather around the store, an indicator related to traffic volume around the store, and an indicator related to spatial characteristics within the store.

According to one aspect of the present disclosure, the calculation unit has the first index, the second index, and the third index as explanatory variables, and calculates the degree of contribution through construction of a regression model having sales results as the objective variable. obtain. According to one aspect of the present disclosure, the calculator may calculate the contribution by calculating the Shapley values of the first index, the second index, and the third index using a regression model.

According to one aspect of the present disclosure, the first indicators may include indicators relating to the placement plan of the product on the shelf. The second metric may include a metric relating to the shelf placement performance of the product. By adopting such an index, the user can consider which of product placement planning and realization should be improved with priority, based on the contribution that is output.

According to one aspect of the present disclosure, the output unit may output the contribution by display through a display. The contribution of each of the first index and the second index can be displayed side by side on the display. According to the parallel display of the first and second indicators, it is possible to display information that is visually easy for the user to understand and useful for improving the store.

According to one aspect of the present disclosure, the output unit may output the degree of contribution to a simulator that simulates sales of products based on the degree of contribution. Using the simulator, the user can learn in detail how the plan and results of sales activities affect sales results.

According to one aspect of the disclosure, a computer program product may be provided. The computer program may be a computer program for causing a computer to implement functions as at least part of the first acquisition unit, the second acquisition unit, the calculation unit, and the output unit in the information processing system described above.

According to one aspect of the present disclosure, the computer program may be recorded on a computer-readable recording medium and provided. According to one aspect of the present disclosure, an information processing method corresponding to the information processing system described above may be provided.

According to one aspect of the present disclosure, a computer-implemented information processing method may be provided. The information processing method may include acquiring the value of at least one index related to the sales activity plan for the product in the store as the planned value of the first index.

The information processing method may further include obtaining the value of at least one index relating to the sales activity performance of the product in the store as the performance value of the second index. The information processing method may further include calculating the contribution of the first index and the second index to sales performance based on the planned value and the performance value.

The information processing method may further include outputting the calculated contribution of the first index and the second index. According to this information processing method, it is possible to obtain the same effect as the information processing system described above.

1 is a block diagram showing the configuration of an information processing system; FIG. 4 is a flowchart representing analysis processing executed by a processor; It is a figure which shows the example of a contribution explanation screen. FIG. 4A is a diagram showing an example of a first index table, and FIG. 4B is a diagram showing an example of a second index table. FIG. 4 is an explanatory diagram regarding a simulator;

Exemplary embodiments of the present disclosure are described below with reference to the drawings.
The information processing system 1 of this embodiment is configured by installing a computer program specific to this embodiment in a general-purpose computer system. The information processing system 1 shown in FIG. 1 includes a processor 11, memory 12, storage 13, display 15, input device 17, media reader/writer 18, and communication device 19.

Processor 11 is configured to execute processing according to a computer program stored in storage 13 . Memory 12 includes RAM. Memory 12 is used as a work area when processor 11 executes processing according to a computer program. The memory 12 temporarily stores computer programs and data read from the storage 13 .

The storage 13 stores computer programs and various data. One of the computer programs stored in the storage 13 includes a plan for product sales activities in the store and results for product sales activities in the store, particularly based on the degree of realization of the plan and the sales results. includes a computer program for calculating the planned and performance contribution of sales activities to Examples of storage 13 include hard disk drives and solid state drives.

The display 15 is configured to display various information to the user. The display 15 is, for example, a liquid crystal display. The input device 17 is configured to input operation signals from the user to the processor 11 . The input device 17 includes a user-operable keyboard and pointing device.

The media reader/writer 18 is configured to be able to read information recorded on a recording medium such as a memory card and write new information to the recording medium. Communication device 19 is controlled by processor 11 and configured to communicate with external devices within a local area network and/or within a wide area network.

The processor 11 executes the analysis process shown in FIG. 2 according to a computer program when a user inputs an analysis process execution command through the input device 17 . The analysis processing is processing for calculating the degree of contribution to the sales performance of multiple factors that affect the sales performance.

Examples of sales performance include sales volume. Examples of sales volume include sales volume calculated in terms of quantity or money, ie, quantity sold or sales. The analytical process described below calculates the contribution of multiple factors to sales.

When starting the analysis process, the processor 11 acquires plan data (S110). The processor 11 can acquire the plan data by reading the plan data recorded in the storage 13 .

According to this embodiment, a sales activity plan, which is a plan relating to product sales activities, is formulated for a plurality of stores (hereinafter referred to as target stores). Below, the sales activity plan is simply expressed as a sales plan. Plan data describing the formulated sales plan may be generated by the user and recorded in the storage 13 .

Plan data describes one or more sales plans. Each sales plan applies to one or more of the plurality of target stores. One of the one or more sales plans is applied to each of the target stores. Each of the sales plans includes a plan regarding product display, which is one of the sales activities. At each target store, product sales activities, including product display, are carried out based on the applied sales plan.

The product display plan includes a display plan for a specific product group on a specific category product shelf (hereinafter referred to as “category shelf”). The categories here are those of products such as cosmetics, hair wash, cold medicine, and the like.

A specific product group is a product group of a specific category sold by a company that desires analysis results (hereinafter referred to as "target company"). The display plan includes a plan for product placement on category shelves, in other words, product layout.

Based on the acquired plan data, the processor 11 calculates a plurality of first index values for each sales plan with respect to one or more sales plans (S120). Through the calculation, the processor 11 obtains values of a plurality of first indices. A plurality of first indicators represent characteristics of the sales plan. Below, the value of the first index is also expressed as a planned value.

For example, in the final stage of the analysis process, the processor 11 sets the SKU for the specific product group in the corresponding sales plan as the planned value for each sales plan in order to display the contribution explanation screen shown in FIG. 3 on the display 15. Calculate the values of share, SKU composition, face share, display position score, and product appeal.

The SKU share is an index that expresses the percentage of the target company's products that are displayed on the category shelves in the sales plan. That is, the SKU share corresponds to the "number of products of the target company on the category shelf" divided by the "total number of products on the category shelf". The number of products referred to here is represented by SKU (Stock Keeping Unit). Specifically, the number of products is the number of product types.

The SKU composition is an index that expresses, in percentage, the ratio of products belonging to the group of products with the highest sales in the group of similar stores to the group of products planned to be displayed in the store. The similar store group here refers to a group of stores with similar display plans and/or sales performances. The SKU configuration corresponds to a value obtained by dividing "the number of products in the category shelf display product group based on the display plan" by "the number of products in the category shelf display product group with the highest sales included in the display plan".

The similar store group refers to a store group to which one sales plan is applied, or a store group in which the similarity based on the products on display in the display plan exceeds a certain threshold. The product group of the top-selling product group is the product group with the highest sales determined by totaling sales of similar store groups. The product group of the top-selling product group corresponds to a group of products ranked in a predetermined order from the product with the highest sales.

The face share is the ratio of the number of faces of the specific product group on the planned category shelf to the total number of faces on the category shelf, that is, the value obtained by dividing the "number of faces of the specific product group" by the "total number of faces". This is an index expressed as a percentage.

The display position score corresponds to the sum of the scores of the sections in which the specific product group is displayed when the category shelf is divided into a plurality of sections and a score is defined for each section. Scores are designed to give relatively higher scores than other sections to sections where products are likely to attract the attention of consumers, or sections that relatively contribute to sales according to sales performance on category shelves. defined as

Product appealing power is an index that expresses, in percentage, the ratio of the target company's product group to the product group for which sales promotion activities are performed on the category shelf. In order to calculate the product appeal power, it is possible to prepare not only the necessity or non-necessity of sales promotion activities for each specific product of the target company, but also the schedule of sales promotion activities of companies other than the target company. Planning data may have information describing this schedule.

In the above, SKU share, SKU composition, face share, display position score, and product appeal were given as examples of the first indicators, but other indicators representing the characteristics of the sales plan were adopted as the first indicators. may

Subsequently, the processor 11 acquires transaction data on purchasing activities for each target store with respect to a plurality of target stores, and further acquires status data for each survey store with respect to a plurality of survey stores (S130). The transaction data may be, for example, POS (Point Of Sales) data.

Transaction data for each target store is acquired from, for example, a point-of-sale management system and stored in the storage 13 . The transaction data of each target store includes sales records of products at the corresponding store, and is data capable of summarizing sales of specific product groups at the corresponding store. The processor 11 can acquire transaction data for each target store from the storage 13 .

A plurality of survey stores are stores to be visited and surveyed by surveyors among the plurality of target stores. When the all-store survey is conducted, the plurality of target stores and the plurality of survey stores are the same set of stores. Below, among the plurality of target stores, each target store other than the surveyed store is expressed as an unsurveyed store.

The condition data of each research store describes the condition of the product display at the corresponding store. For example, for each surveyed store, the state of product display on category shelves in the corresponding store is visually determined by an investigator visiting the store, and survey record data describing the product display state is created by the investigator operating an information terminal. be. Alternatively, based on the image data of the category shelf photographed by a photographing device such as a camera possessed by the researcher, research record data describing the product display state is created by the researcher's operation of the information terminal.

The created survey record data is taken into the information processing system 1 through, for example, the communication device 19 from the surveyor's information terminal, and recorded in the storage 13 as status data. The processor 11 can acquire the status data for each survey store from the storage 13 .

If the plurality of target stores includes unsurveyed stores, the processor 11 uses a deficit compensation technique to extract the state data of each unsurveyed store based on the transaction data of the plurality of target stores and the state data of the plurality of surveyed stores. is generated (S135).

For example, the processor 11 generates a model for estimating the product display state from the transaction data based on the transaction data and state data of a plurality of surveyed stores, and by inputting the transaction data of each unsurveyed store into the model, Condition data for each uninvestigated store can be generated.

The model can be generated by machine learning using transaction data and state data of multiple research stores as teacher data. Characteristic data such as the size and location of each target store may be acquired together with the transaction data and used for model generation.

In subsequent S140, the processor 11 calculates the values of the plurality of second indexes for each target store based on the state data of the corresponding store and the plan data of the corresponding store. Through the calculation, the processor 11 obtains values of a plurality of second indices.

The plurality of second indices are indices relating to sales activity results, and more specifically, indices relating to execution results and execution results of sales plans applied to corresponding stores. The plurality of second indices include indices relating to the degree of realization of the sales plan during the period in which sales activities based on the sales plan were performed. The second index includes an index representing the degree of realization of the plan regarding product display as the display result. Below, the value of the second index is also expressed as a performance value. Also, the period during which the sales activity based on the sales plan is performed is expressed as the implementation period.

Specifically, at the final stage of the analysis process, the processor 11 sets the product lineup realization rate, face realization rate , the display position realization rate, the sales promotion realization rate, and the out-of-stock rate.

The product assortment realization rate is an index that expresses, in percentage, the rate at which the product group planned to be displayed in the sales plan was displayed on the category shelf as planned. For example, the product assortment realization rate corresponds to a value obtained by dividing the number of products in the product group that was actually displayed by the product group that was planned to be displayed in relation to the product group that was planned to be displayed in the specific product group. The number of products is counted by SKU as described above.

The face realization rate is an index representing the realization rate of the number of faces of a specific product group planned in the sales plan. Specifically, the face realization rate corresponds to a value obtained by dividing the number of faces of the specific product group realized on the category shelf by the planned number of faces.

The display position realization rate is an index representing the realization rate of the display position score. Specifically, the display position realization rate is the number of products planned to be displayed on the category shelf, and the actual product placement was or was estimated to be appropriate with respect to the planned product placement. Refers to the percentage of the number of products.

The sales promotion realization rate is an indicator that expresses the percentage of product groups for which sales promotion activities were actually carried out in stores among the product groups for which sales promotion activities were planned in the sales plan for the specific product groups of the target company. is. For example, the rate at which point-of-purchase advertising (POP) advertisements that were planned to be placed on category shelves were actually installed corresponds to the sales promotion realization rate.

The out-of-stock rate is an index that expresses the percentage of the product group that is out of stock among the product groups that are planned to be displayed in the sales plan and displayed on the category shelves for the specific product group of the target company. .

In subsequent S150, the processor 11 acquires sales environment data. The sales environment data includes weather data and traffic data of the area where the target store group exists.

Weather data may include future weather information in addition to weather information observed during the implementation of the sales plan. The future here means later than the implementation period, and does not mean future with respect to the execution timing of the analysis processing. However, the future weather information may include future weather forecast information prior to the execution timing of the analysis processing. The weather data may include past weather information from the implementation period.

The traffic data describes the traffic volume observed in the region where the target store group is located during the implementation period. The traffic volume referred to here should be interpreted in a broad sense, and includes, for example, the traffic volume of vehicles traveling on roads as well as the traffic volume and the flow of people. The traffic data can be data that describes not only the traffic volume observed during the implementation period, but also the future traffic volume after the implementation period ends, and the past traffic volume before the implementation period starts.

The sales environment data may further comprise, for each target store, spatial feature data describing the spatial features of the corresponding store. Spatial feature data includes information describing the arrangement of product shelves of multiple categories including a product shelf of a specific category in the corresponding store, and/or information describing product categories of product shelves adjacent to the product shelf of the specific category. can have

Sales environment data can be recorded in the storage 13 in advance. Processor 11 can acquire sales environment data recorded in storage 13 . Alternatively, processor 11 may acquire sales environment data from an external device by communication.

For example, the weather data of the area where the target store group exists may be acquired from an external server that provides weather data. Traffic data for the area where the target store group is located may be obtained from an external server that provides traffic data.

In subsequent S160, the processor 11 calculates a plurality of third index values for each target store with respect to the plurality of target stores based on the sales environment data. Through the calculation, the processor 11 acquires values of a plurality of third indices. Below, the value of the third index is also expressed as an environmental value. The plurality of third indicators are indicators relating to the sales environment of the corresponding store, including indicators relating to weather, indicators relating to traffic volume, and indicators relating to spatial features of the store.

Multiple third indicators are indicators related to the weather around the corresponding store, average temperature, maximum temperature, minimum temperature, average humidity, maximum humidity, minimum humidity, rate of fine weather, rate of rainy weather per unit time during and before the implementation period. , and average sunshine hours. A unit of time can be a day, a week, two weeks, a month, or any length of time. However, the weather index to be calculated is not limited to this.

The plurality of third indicators may include, as indicators relating to traffic, indicators representing the traffic volume of vehicles and people in the region where the corresponding store is located per unit time during and before and after the implementation period. The plurality of third indicators may include, as indicators relating to the spatial features of the store, indicators representing features relating to the arrangement of product shelves of multiple categories in the corresponding store.

In subsequent S170, the processor 11 sets the plurality of first indexes X1[1], X1[2], . , X2[M2], a plurality of third indices X3[1], X3[2], . X1[1], X1[2], ... X1[M1], X2[1], X2[2], ..., X2[M2], X3[1], X3[2], ..., X3[M3] , with the objective variable Y. Here, M1 is the number of first indices, M2 is the number of second indices, and M3 is the number of third indices.

The regression model is, for example, a linear regression model. [M1]+K2[1]・X2[1]+K2[1]・X2[2]+…+K2[M2]・X2[M2]+K3[1]・X3[1]+K3[2]・X3[2] + . . . +K3[M3]·X3[M3].

The vector X1 is the vector representation of the first index X1=(X1[1],X1[2],...,X1[M1]). Vector X2 is the vector representation of the second index X2=(X2[1], X2[2], . . . , X2[M2]). Vector X3 is the vector representation of the third index X3=(X3[1], X3[2], . . . , X3[M3]).

Vector K1 is a vector representation K1=(K1[1], K1[2], . . . , K1[M1]) of regression coefficients acting on vector X1. Vector K2 is a vector representation K2=(K2[1], K2[2], . . . , K2[M2]) of regression coefficients acting on vector X2. Vector K3 is a vector representation K3=(K3[1], K3[2], . . . , K3[M3]) of regression coefficients acting on vector X3.

For building a regression model, the processor 11 generates teacher data summarizing indexes and sales values corresponding to explanatory variables and objective variables for each target store. , X1[M1] according to the sales plan of the corresponding store, the second indexes X2[1], X2[2 ], ..., X2 [M2], the values of the third indices X3 [1], X3 [2], ..., X3 [M3] of the corresponding store, and the sales Y of the corresponding store. be. Sales Y are determined from the corresponding store transaction data. Sales Y is the sales result of the corresponding store, and may be, for example, the sales during the implementation period of the sales plan at the corresponding store.

The processor 11 performs regression analysis using a set of training data of multiple target stores. , K1[M1], K2[1], K2[2], . . . , K2[M2], K3[1 ], K3[2], . . . , K3[M3]). The optimum value is calculated, for example, using the least-squares method of searching for the regression coefficient that minimizes the sum of squared errors related to the error of the teacher data from the regression equation.

During regression analysis, explanatory and objective variables can be standardized. The index and sales values used as training data can be standardized. As a result, as the regression coefficient K, a standardized partial regression coefficient that takes values ranging from 0 to 1 can be calculated.

In subsequent S180, the processor 11 sets explanatory variables X1[1], X1[2], ..., X1[M1], X2[1], X2[2], ..., X2[M2], X3[1], X3 [2], . . . , the regression coefficients K1[1], K1[2], . , K3[1], K3[2], . Displayed on the display 15 .

As shown in FIG. 3, the contribution degree explanation screen displays the degree of contribution of each of the first indices, SKU share, SKU composition, face share, display position score, and product appealing power, in correspondence with the numerical value and the numerical value. Display by bar graph and length.

The contribution explanation screen further displays the contribution of each of the second indicators, namely, the product assortment realization rate, the face realization rate, the display position realization rate, the sales promotion realization rate, and the out-of-stock rate, and the corresponding numerical values. Display by bar graph and length. The contribution degree explanation screen further displays the degree of contribution of each of the weather, traffic volume, and spatial features of the store, which are the third indexes, by numerical values and bar graphs of lengths corresponding to the numerical values.

It should be noted here that on the contribution explanation screen, the contribution of sales plan indicators related to "merchandise," "face share," "display position," and "sales promotion activities" and the contribution of indicators of the degree of plan realization are displayed. , are the points displayed in parallel.

Specifically, on the contribution explanation screen, the contribution of the SKU share, which is an index related to the product lineup, and the contribution of the product lineup realization rate are displayed in parallel. Similarly, on the contribution degree explanation screen, the contribution degree of face share, which is an index related to the face, and the face realization rate are displayed side by side.

A display position score, which is an index related to the display position, and a display position realization rate are displayed in parallel. The product appeal power and the sales promotion realization rate, which are indices related to sales promotion activities, are displayed side by side.

This side-by-side display allows the user to easily compare the degree of contribution between the plan and the realization rate. Therefore, based on the degree of contribution displayed on the contribution degree explanation screen, the user can easily understand whether the improvement of the sales plan itself should be prioritized or the degree of plan realization should be prioritized. can do.

As described above, according to the present embodiment, as a result of displaying in parallel the contributions of the indicators relating to the plan and the realization rate on the contribution explanation screen, it is possible to improve product sales activities more efficiently. be.

In S180, the processor 11 may cause the display 15 to display, in addition to the contribution degree explanation screen shown in FIG.

For example, as shown in FIG. 4A, the display 15 may display a table in which the planned values of the first index in the corresponding sales plan are arranged for each sales plan. As shown in FIG. 4B, the display 15 may display, for each target store, a table in which the performance values of the second index relating to the degree of realization of the sales plan applied to the corresponding store are arranged.

, K1[M1], K2[1], K2[2], . . . , K2[M2], K3[1], K3[ 2], . . . , K3[M3] may be recorded in the storage 13 as contribution degree data. In S180, the processor 11 can perform the screen display and the data output in this way, and end the analysis process.

The information processing system 1 may also have a computer program in the storage 13 for causing the processor 11 to function as a simulator. The processor 11 may function as a simulator by executing the computer program based on user input through the input device 17 .

The simulator, as shown in FIG. It can be configured to predict the sales Y based on the input value X3 of the three indices. The simulator can simulate the sales of a specific product group in the form of sales forecast based on the regression model.

With this simulator, the user can obtain store sales forecast values based on the sales plan, the degree of realization of the target plan, and the expected sales environment. can be obtained from

[Other embodiments]
The present disclosure is not limited to the above embodiments, and can take various forms. For example, in the above embodiment, the regression model was constructed by executing linear regression analysis based on a set of teacher data, but the regression model may be constructed based on other algorithms.

For example, as a method of constructing a regression model using a machine learning algorithm, a method of constructing a regression model using gradient boosting and a method of constructing a regression model using Support Vector Regression may be employed.

In addition, in the above embodiment, the regression coefficient of the regression model is used as the degree of contribution, but the Shapley value, for example, may be used as the degree of contribution. That is, in S170, the processor 11 calculates the Shapley value of each of the first index, the second index, and the third index based on the constructed regression model, and adopts the Shapley value as the degree of contribution. good too. The Shapley value of the corresponding index may be displayed as the contribution of each index on the contribution degree explanation screen.

The Shapley value corresponds to a value obtained by calculating, on average, how much the value of the objective variable increases with respect to changes in each explanatory variable, using, for example, a regression model obtained by a machine learning algorithm as an explanatory model. Therefore, the Shapley value indicates the degree of contribution of the corresponding explanatory variable to the objective variable.

In S170, the processor 11 uses, for example, a machine learning algorithm to set a plurality of first indices X1[1], X1[2], ..., X1[M1], a plurality of second indices X2[1], X2[ 2], ..., X2 [M2], a plurality of third indexes X3 [1], X3 [2], ..., X3 [M3], and a regression model having store sales Y during the implementation period as the objective variable , learning using a set of teacher data of a plurality of target stores described above, thereby building a regression model (S171).

After that, the processor 11 can use the constructed regression model as an explanation model and use a set of teacher data to calculate the Shapley value of each index. The processor 11 can calculate each Shapley value as a contribution by rescaling each Shapley value so that the sum of the Shapley values for the entire multiple indices is 100 (S175).

In addition, other explanatory variables may be introduced into the regression model. For example, an index representing price advantage may be employed as an explanatory variable. Then, the degree of contribution of the price advantage to the sales Y may be displayed on the contribution degree explanation screen.

Price superiority is expressed as a value obtained by dividing the average selling price of a competitor's product group in a specific category by the target company's average selling price of a specific product group. In other words, the price advantage is the ratio of the competitor's average selling price to the target company's average selling price for a particular category of products. The higher the average selling price of a competitor than the average selling price of the target company, the higher the price advantage.

A function possessed by one component in the above embodiment may be distributed to a plurality of components. Functions possessed by multiple components may be integrated into one component. A part of the configuration of the above embodiment may be omitted. At least part of the configurations of the above embodiments may be added or replaced with respect to the configurations of other above embodiments. All aspects included in the technical ideas specified by the language in the claims are embodiments of the present disclosure.

[Technical concept disclosed in this specification]
It can be understood that the following technical ideas are disclosed in this specification.
[Item 1]
a first acquiring unit configured to acquire, as a planned value of the first indicator, a value of at least one indicator related to a product sales activity plan in the store;
a second acquiring unit configured to acquire the value of at least one index related to the sales activity performance of the product in the store as a second index performance value;
a calculation unit configured to calculate the degree of contribution of the first index and the second index to the sales result of the product based on the planned value and the performance value;
an output unit configured to output the calculated contributions of the first index and the second index;
An information processing system comprising
[Item 2]
The sales activity plan includes a plan for displaying the merchandise,
The first acquisition unit acquires the value of at least one index including an index related to the display of the product in the sales activity plan as a planned value of the first index,
The information processing system according to item 1, wherein the second acquisition unit acquires the value of at least one index related to the display performance of the product as the performance value of the second index.
[Item 3]
The calculation unit has the first index and the second index as explanatory variables, and the information processing according to item 1 or item 2 for calculating the degree of contribution through construction of a regression model having the sales result as an objective variable. system.
[Item 4]
4. The information processing system according to item 3, wherein the calculation unit calculates the degree of contribution by calculating Shapley values of the first index and the second index using the regression model.
[Item 5]
a third acquisition unit configured to acquire the value of at least one index related to the sales environment of the product as the environmental value of the third index;
The calculation unit calculates the degree of contribution of each of the first index, the second index, and the third index to the sales result based on the planned value, the performance value, and the environmental value,
3. The information processing system according to item 1 or 2, wherein the output unit outputs the calculated contributions of the first index, the second index, and the third index.
[Item 6]
6. The information processing system according to item 5, wherein the third indicator includes at least one of an indicator related to weather around the store, an indicator related to traffic volume around the store, and an indicator related to spatial features in the store.
[Item 7]
The calculation unit calculates the degree of contribution through construction of a regression model having the first index, the second index, and the third index as explanatory variables, and having the sales result as an objective variable, or The information processing system according to item 6.
[Item 8]
8. The information processing system according to item 7, wherein the calculation unit calculates the contribution by calculating Shapley values of the first index, the second index, and the third index using the regression model. .
[Item 9]
The first indicator includes an indicator related to an arrangement plan of the product on the product shelf,
9. The information processing system according to any one of items 1 to 8, wherein the second index includes an index relating to placement performance of the product on the product shelf.
[Item 10]
The output unit outputs the degree of contribution by display through a display, and any one of items 1 to 9 in which the degree of contribution of each of the first index and the second index is displayed side by side on the display. Information processing system according to paragraph.
[Item 11]
11. The information processing system according to any one of items 1 to 10, wherein the output unit outputs the degree of contribution to a simulator that simulates sales of the product based on the degree of contribution.
[Item 12]
A computer program for causing a computer to implement the functions of the first acquisition unit, the second acquisition unit, the calculation unit, and the output unit in the information processing system according to any one of items 1 to 11.
[Item 13]
A computer-implemented information processing method comprising:
Acquiring the value of at least one index related to the product sales activity plan in the store as the planned value of the first index;
Acquiring the value of at least one index related to the sales activity performance of the product in the store as a second index performance value;
calculating the degree of contribution of the first index and the second index to the sales result based on the planned value and the performance value;
outputting the calculated contributions of the first index and the second index;
Information processing method including.

DESCRIPTION OF SYMBOLS 1... Information processing system, 11... Processor, 12... Memory, 13... Storage, 15... Display, 17... Input device, 18... Media reader/writer, 19... Communication device.

Claims (13)

a first acquiring unit configured to acquire, as a planned value of the first indicator, a value of at least one indicator related to a product sales activity plan in the store;
a second acquiring unit configured to acquire the value of at least one index related to the sales activity performance of the product in the store as a second index performance value;
a calculation unit configured to calculate the degree of contribution of the first index and the second index to the sales result of the product based on the planned value and the performance value;
an output unit configured to output the calculated contributions of the first index and the second index;
An information processing system comprising The sales activity plan includes a plan for displaying the merchandise,
The first acquisition unit acquires the value of at least one index including an index related to the display of the product in the sales activity plan as a planned value of the first index,
2. The information processing system according to claim 1, wherein the second acquisition unit acquires the value of at least one index related to the display performance of the product as the performance value of the second index. 2. The information processing system according to claim 1, wherein the calculation unit calculates the degree of contribution through construction of a regression model having the first index and the second index as explanatory variables and the sales result as an objective variable. 4. The information processing system according to claim 3, wherein the calculation unit calculates the contribution by calculating Shapley values of the first index and the second index using the regression model. a third acquisition unit configured to acquire the value of at least one index related to the sales environment of the product as the environmental value of the third index;
The calculation unit calculates the degree of contribution of each of the first index, the second index, and the third index to the sales result based on the planned value, the performance value, and the environmental value,
2. The information processing system according to claim 1, wherein the output unit outputs the calculated contributions of the first index, the second index, and the third index. 6. The information processing system according to claim 5, wherein said third index includes at least one of an index relating to weather around said store, an index relating to traffic volume around said store, and an index relating to spatial characteristics within said store. . 6. The calculation unit calculates the degree of contribution through construction of a regression model having the first index, the second index, and the third index as explanatory variables, and having the sales result as an objective variable. Information processing system as described. 8. The information processing according to claim 7, wherein the calculation unit calculates the degree of contribution by calculating Shapley values of the first index, the second index, and the third index using the regression model. system. The first indicator includes an indicator related to an arrangement plan of the product on the product shelf,
2. The information processing system according to claim 1, wherein said second index includes an index relating to placement performance of said product on said product shelf. 2. The information processing system according to claim 1, wherein the output unit outputs the contribution by display through a display, and the contribution of the first index and the contribution of the second index are displayed side by side on the display. 2. The information processing system according to claim 1, wherein said output unit outputs said contribution to a simulator that simulates sales of said product based on said contribution. A computer for realizing the functions of the first acquisition unit, the second acquisition unit, the calculation unit, and the output unit in the information processing system according to any one of claims 1 to 11. program. A computer-implemented information processing method comprising:
Acquiring the value of at least one index related to the product sales activity plan in the store as the planned value of the first index;
Acquiring the value of at least one index related to the sales activity performance of the product in the store as a second index performance value;
calculating the degree of contribution of the first index and the second index to the sales result based on the planned value and the performance value;
outputting the calculated contributions of the first index and the second index;
Information processing method including.

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