JP6693900B2 - Content providing device, content providing method, and content providing program - Google Patents

Description

  The present invention relates to a content providing device, a content providing method, and a content providing program.

  2. Description of the Related Art In recent years, with the rapid spread of the Internet, sales via the Internet have become popular, and, for example, a technology for selling a product on an EC (Electronic Commerce) site and selling the product to a user is provided. For example, a technique has been proposed in which the selling price of a product is changed based on the sales target number set for the exhibited product and the current sales number (for example, see Patent Document 1).

JP, 2013-120562, A

  However, the above-mentioned conventional technology merely changes the selling price of the product, and does not predict whether or not the sales target number can be achieved. Therefore, for example, when the seller tries to place an advertisement in order to achieve the target number of sales, there is a possibility that an excessive advertisement is placed or an insufficient advertisement is placed. That is, conventionally, there is room for improvement in providing appropriate advertising content to the seller.

  The present application has been made in view of the above, and an object thereof is to provide a content providing device, a content providing method, and a content providing program capable of providing appropriate advertising content to a seller.

  The content providing apparatus according to the present application is an index classification unit that classifies the store into an index group having similar indexes based on actual values of indexes related to advertisement of products handled by the store, and the indexes that are classified by the index classification unit. A prediction unit that predicts a sales index related to sales of products handled by the store based on the actual values of other products handled by other stores belonging to the group, and the store based on the sales index predicted by the prediction unit. And a providing unit that provides content related to the advertisement of the product.

  According to the aspect of the embodiment, it is possible to provide appropriate advertising content to the seller.

FIG. 1 is a diagram illustrating an example of a content providing process according to the embodiment. FIG. 2 is a diagram showing the configuration of the content providing system according to the embodiment. FIG. 3 is a block diagram showing the configuration of the content providing device according to the embodiment. FIG. 4 is a diagram showing an example of product information according to the embodiment. FIG. 5 is a diagram showing an example of store information according to the embodiment. FIG. 6 is a diagram showing an example of advertisement information according to the embodiment. FIG. 7 is a diagram illustrating an example of a prediction process of the prediction unit according to the embodiment. FIG. 8 is a diagram showing an example of a sales forecast screen according to the embodiment. FIG. 9 is a flowchart showing a processing procedure of a providing process executed by the content providing device according to the embodiment. FIG. 10 is a hardware configuration diagram illustrating an example of a computer that realizes the function of the content providing device.

  Hereinafter, modes (hereinafter, referred to as “embodiments”) for carrying out a content providing apparatus, a content providing method, and a content providing program according to the present application will be described in detail with reference to the drawings. Note that the content providing apparatus, the content providing method, and the content providing program according to the present application are not limited by this embodiment. Also, in each of the following embodiments, the same parts are designated by the same reference numerals, and duplicate description will be omitted.

(Embodiment)
[1. Content provision processing]
First, an example of the content providing process according to the embodiment will be described with reference to FIG. FIG. 1 is a diagram illustrating an example of a content providing process according to the embodiment. In FIG. 1, it is assumed that the content providing device 1 according to the embodiment stores the actual value of the index regarding the advertisement of the product handled by each store.

  The index related to advertisement is an index related to the behavior of a user who searches for products or purchases products on the EC site. In the example shown in FIG. 1, as indexes related to advertisement, “imps”, “CTR”, “PV”, “UU”, “CVR”, “sales (10,000 yen)”, “ROAS”, “advertising performance”, etc. There is something. Details of these indexes will be described later with reference to FIG.

  The content providing device 1 according to the embodiment predicts a sales index related to sales of a product based on the actual value of an index related to an advertisement, and provides content related to an advertisement of a product based on the predicted sales index.

  The sales index mentioned here is an index related to sales of products handled by the store. For example, the sales target number of products, the time when the sales target number is achieved (hereinafter referred to as the sales achievement time), the sales target number, and the sales Includes advertising budget, etc., required according to the time of achievement.

  Here, a conventional content providing device will be described. The conventional content providing device changes the selling price of the product based on, for example, the target sales number of the products and the current sales number of the products.

  However, the conventional content providing apparatus cannot predict whether or not the sales target number of products can be achieved, for example, after changing the sales price of the products. Therefore, for example, when the seller tries to place an advertisement in order to achieve the target number of sales, there is a risk that an excessive advertisement is placed or an insufficient advertisement is placed. That is, conventionally, there is room for improvement in providing appropriate advertising content to the seller.

  Therefore, the content providing device 1 according to the embodiment is capable of predicting the sales of a product and providing appropriate advertising content to a store that handles the product based on the predicted sales.

  Specifically, the content providing apparatus 1 according to the embodiment first receives registration of a product from the manager M1 of the store A via the seller terminal 10 (step S11). In the example illustrated in FIG. 1, the administrator M1 of the store A registers the “set product name”, the “set product unit price”, the “sales achievement time”, and the “sales target number” in the content providing device 1.

  For example, FIG. 1 shows that the administrator M1 of the store A registers the product name “natural water 2L (6 bottles set)” in the content providing device 1 at a product unit price of 900 yen. In addition, the administrator M1 of the store A registers in the content providing device 1 that he / she wants to achieve the sale of 2000 pieces of the product name "natural water 2L (6 bottles set)" by April 1, 2017, which is the sales achievement time To do.

  The product to be registered may be a product already registered in the content providing device 1, or may be a new product, that is, a product not registered in the content providing device 1. In the case of the product already registered in the content providing device 1, the content providing device 1 accepts registration of “sales achievement time” and “sales target number”.

  Subsequently, the content providing apparatus 1 according to the embodiment classifies the stores that handle the registered products (step S12). For example, the content providing device 1 classifies the stores into index groups having similar indexes based on the actual values of the indexes related to the advertisement of the product.

  In the example illustrated in FIG. 1, the content providing device 1 classifies the store A into an index group that is similar to the actual value of the index related to the advertisement that the store A has. It should be noted that the content providing device 1 may be classified into a type group for each type of products handled by the store in advance, before being classified into the index group.

  For example, the content providing device 1 classifies the store A that handles natural water into drinking water type groups. Details of the classification of the classification group will be described later.

  Subsequently, the content providing apparatus 1 according to the embodiment predicts a sales index regarding sales of the product handled by the store based on the actual value of the other product handled by the other store belonging to the classified index group (step S13).

  In the example shown in FIG. 1, the content providing apparatus 1 according to the embodiment advertises when the registration of the “sales achievement time” and the “sales target number” of the product name “natural water 2L (set of 6)” is accepted. Budget 300,000 yen as a sales index.

  Subsequently, the content providing apparatus 1 according to the embodiment provides the content related to the advertisement of the product to the store A based on the predicted sales index (step S14). For example, as shown in FIG. 1, the required advertisement budget is notified, or the advertisement type in the advertisement budget is notified.

  For example, in the example shown in FIG. 1, the content providing apparatus 1 notifies the advertising budget of 300,000 yen, which is the predicted sales index of the product name "natural water 2L (6 bottles set)", and at the advertising budget of 300,000 yen. Provide recommended ad type A.

  That is, it is not necessary for the administrator M1 of the store A to calculate the advertisement budget for the product name “natural water 2L (set of 6)”, and it is not necessary to select the advertisement type that matches the advertisement budget. As described above, according to the content providing device 1 according to the embodiment, it is possible to prevent wasteful advertisement placement by predicting the sales of the product from the past results of other stores and providing an appropriate advertisement budget. You can Therefore, appropriate advertising content can be provided to the store that is the seller.

  The content providing device 1 according to the embodiment can generate and provide a user interface such as the sales forecast screen W1 (see FIG. 8) when providing the predicted sales index and the like to the store. The points will be described later with reference to FIG.

[2. Content provision system]
Next, the content providing system S including the content providing apparatus 1 will be described with reference to FIG. FIG. 2 is a diagram showing a configuration of the content providing system S according to the embodiment. As shown in FIG. 2, the content providing system S according to the embodiment includes a content providing device 1, a plurality of seller terminals 10-1 and 10-2, and a plurality of user terminals 100-1 and 100-. 2 and are included.

  These various devices are communicably connected to each other via a network N in a wired or wireless manner. In the following description, the plurality of seller terminals 10-1 and 10-2 and the plurality of user terminals 100-1 and 100-2 will be referred to as the seller terminal 10 and the user terminal 100, unless otherwise described. To do.

  The content providing device 1 is a server device that provides product information related to products to users who are consumers and that accepts the purchase of products from such users. For example, the content providing device 1 provides product information, accepts product purchases, and the like via a predetermined website (EC site). That is, the content providing device 1 is a device corresponding to the shopping server.

  In addition, the content providing device 1 is a device that predicts a sales index of a product handled by the store and provides the store with the content related to the advertisement of the product based on the predicted sales index.

  The seller terminal 10 is an information processing device used by the administrator M1 of the store A, which is the seller. The seller terminal 10 is realized by, for example, a smartphone, a tablet terminal, a notebook PC, a desktop PC, a mobile phone, a PDA, or the like.

  The seller terminal 10 adds the product to be sold in the electronic commerce service or submits an advertisement of the product by transmitting the product information regarding the product according to the instruction of the administrator M1 of the store A, for example.

  The user terminal 100 is a terminal device used by a user. The user terminal 100 is realized by, for example, a smartphone, a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, a PDA (Personal Digital Assistant), or the like.

  The user terminal 100 displays the search input screen, the search result screen, the product detail screen, and the like received from the content providing device 1 by accessing the content providing device 1 in accordance with a user's instruction, for example. In addition, when the user terminal 100 receives an operation indicating purchase of a product on the product detail screen, the user terminal 100 receives and displays a screen regarding a product delivery method or a screen regarding payment from the content providing device 1.

[3. Configuration of content providing device]
Next, the configuration of the content providing device 1 according to the embodiment will be described with reference to FIG. FIG. 3 is a diagram showing a configuration example of the content providing device 1 according to the embodiment. As shown in FIG. 3, the content providing device 1 includes a control unit 2, a storage unit 3, and a communication unit 4.

  The content providing device 1 includes an input unit (for example, a keyboard and a mouse) that receives various operations from an administrator of the content providing device 1 and a display unit (for example, a liquid crystal display) for displaying various information. You may have.

(3-1. Communication unit 4)
The communication unit 4 is realized by, for example, a NIC (Network Interface Card) or the like. The communication unit 4 is connected to the network N by wire or wirelessly, and transmits and receives information to and from the seller terminal 10 and the user terminal 100 included in the content providing system S, for example.

(3-2. Storage unit 3)
The storage unit 3 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory (Flash Memory), or a storage device such as a hard disk or an optical disk. As illustrated in FIG. 3, the storage unit 3 according to the embodiment stores product information 31, store information 32, and advertisement information 33.

(3-2-1. Product information 31)
The product information 31 according to the embodiment is information including various kinds of information about products. FIG. 4 is a diagram showing an example of the product information 31 according to the embodiment. As shown in FIG. 4, the product information 31 includes “store”, “product ID”, “product name”, “price (yen)”, “category”, “sales (10,000 yen)”, and “stock quantity ( Item) ”is included.

  The “store” indicates the store that is the seller of the corresponding product. The “store” may include identification information such as a store ID for identifying the store that is the selling source. “Product ID” indicates identification information for identifying a product.

  “Product name” indicates a specific name of the product. “Price (yen)” indicates the selling price (yen) of the corresponding product. “Category” indicates the type of product. The category “beverage” shown in FIG. 4 is an example, and may be subdivided. For example, the categories may be classified into a plurality of layers such as “food and drink → drinks → water”.

  “Sales (10,000 yen)” indicates sales of products in a predetermined period. The "sales (10,000 yen)" may be the sales of the products for the entire period or the sales of the products for a part of the period.

  “Inventory number (pieces)” indicates the current inventory number of products registered in the content providing device 1. The “inventory number (pieces)” may be the inventory number of the products registered in the content providing device 1 or the actual inventory number remaining in the actual store of the store.

  In other words, when the “stock quantity (pieces)” is the stock quantity of the product registered in the content providing device 1, it is preferable to make it smaller than the actual stock quantity remaining in the actual store of the store. This is to prevent the shortage of the product due to so-called double booking at the EC site and the actual store, for example, when the product is exhibited and sold at the actual store in addition to the shop at the EC site.

  For example, in FIG. 4, the seller identified by "Store A" is the product identified by the product ID "GD1", and the product with the product name "Natural water A" is sold at "900 yen". Here is an example of a listing. Further, the seller identified by “Store A” sets the category “beverage” for the product GD1. Further, the product name “natural water A” indicates that the sales for the past predetermined period is 2 million yen and the number of registered stocks at present is 3000.

  Note that the product information 31 illustrated in FIG. 4 is an example, and may include content data such as a product image and a detailed statement of the product, an index regarding an advertisement for each product, and the like.

(3-2-2. Store information 32)
The store information 32 according to the embodiment is information including the actual value of the index regarding the advertisement of the product. FIG. 5 is a diagram showing an example of the store information 32 according to the embodiment. As shown in FIG. 5, the store information 32 includes “store”, “product number”, “imps (impression)”, “CTR (Click Through Rate)”, “PV (Page View)”, and “UU (Unique. “User)”, “CVR (Conversion Rate)”, “Sales (10,000 Yen)”, “ROAS (Return On Advertising Spend)”, and “Advertisement Performance”.

  The “store” indicates the store that is the seller of the corresponding product. The “number of products” indicates the number of types of products handled by the store. “Imps” is an impression and indicates the total number of times of display on the search result screen for each product handled by the store. "CTR" is a click-through rate, and indicates the ratio of the number of clicks to the product on the search result screen with respect to "imps". “PV” indicates the total number of times the product detail screen is displayed. “UU” indicates the number of users who have visited the product detail screen. For example, even if one user browses the same detail screen 10 times, "UU" counts "1".

  “CVR” is a conversion rate and indicates the ratio of the purchase price (or the number of purchases) of the product to “PV” or “UU”. “Sales (10,000 yen)” indicates sales of products handled by the store. “ROAS” is the cost-effectiveness of advertisement, and is a value obtained by dividing “sales (10,000 yen)” by “advertisement performance”. The “advertising record” indicates the total amount of advertising expenses that the store has spent on products.

  For example, in the example shown in FIG. 5, the store A handles 300 types of products, the sales of all products are 8 million yen, and the advertising spending is 1 million yen. Indicates 8 (800%).

  In addition, the store A indicates that all the products are displayed on the search result screen 8,000 times and that one of the products is clicked on the search result screen 250 times. Further, in the store A, the detailed screen of any of the products is displayed 200 times, which indicates that 100 users have visited.

  Further, the store A indicates that, for example, the purchase rate of the product is 1% for 200 times (or 100 users who visited) on which the detailed screen is displayed. The store information 32 is not limited to the above, and may include various types of information according to the purpose.

(3-2-3. Advertisement information 33)
The advertisement information 33 according to the embodiment is information about advertisements sold by the content providing device 1 to the store. FIG. 6 is a diagram showing an example of the advertisement information 33 according to the embodiment. As shown in FIG. 6, the advertisement information 33 includes items such as “advertisement ID”, “type”, “placement period”, and “placement fee (total amount)”.

  The “advertisement ID” indicates identification information for identifying the type of advertisement. “Type” indicates the type of advertisement. The “posting period” is a period during which the advertisement is displayed and indicates a contract period with the store. The "placement fee (total amount)" indicates a fee paid by the store when placing an advertisement.

  For example, in the example shown in FIG. 6, the type of advertisement identified by “type A” is a moving image, the contract period for publishing is on a monthly basis, and the monthly publishing fee is at least 30,000. Is shown. The submission fee is determined by, for example, the position where the advertisement is displayed, the size of the displayed area, the number of characters, the reproduction time of the moving image, and the like. The advertisement information 33 is not limited to the above, and may include various types of information according to the purpose.

  Returning to the description of FIG. 3, the control unit 2 is a controller, and is stored in a storage device inside the content providing device 1 by, for example, a CPU (Central Processing Unit) or an MPU (Micro Processing Unit). It is realized by executing various programs (corresponding to an example of the content providing program) that are present in the RAM as a work area. The control unit 2 is a controller, and is realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

  As shown in FIG. 3, the control unit 2 includes a reception unit 21, a type classification unit 22, an index classification unit 23, a prediction unit 24, a screen generation unit 25, and a provision unit 26. It realizes or executes the function or action of the content providing process described.

(3-3. Reception unit 21)
The reception unit 21 receives various kinds of information. For example, the reception unit 21 receives registration of a product from the store via the seller terminal 10. For example, the reception unit 21 receives designation of the sales target quantity, the sales achievement time, and the advertisement budget (or advertisement type). Further, the reception unit 21 receives a user operation on the sales forecast screen W1 (see FIG. 8) generated by the screen generation unit 25 described later.

(3-4. Classification unit 22)
The type classification unit 22 classifies the store into a type group for each type based on the types of products registered from the store. For example, the type classification unit 22 classifies the products into classification groups based on the “category” of the product information 31. For example, the product name “natural water A” (see FIG. 4) is classified into beverage type groups.

  The product name “natural water A” has been classified into the beverage type group, but the product name is not limited to this, and may be further classified into the drinking water type group. For example, the drinking water type group may further include a soft drink type group and an alcoholic beverage type group.

  Further, the type classification unit 22 is not limited to the product category, and may be classified into, for example, the “price (yen)” type group of the product information 31. Specifically, the type classification unit 22 may be classified for each price range of products.

(3-5. Index classification unit 23)
The index classification unit 23 classifies the stores into index groups having similar indexes based on the actual values of the indexes related to the advertisement of the products handled by the store. The index classification unit 23 calculates the average value of each index of the other stores belonging to the index group, and classifies the store based on the degree of coincidence between the calculated average value and the actual value of the product.

  Specifically, the index classification unit 23 determines the product related to the index group having the smallest difference between the average value of each index of the other stores belonging to the index group and the index of the product registered by the reception unit 21. Classify the stores to handle.

  In other words, the index classification unit 23 first sets a multidimensional coordinate axis having each index as an axis. For example, "imps (impression)", "CTR (Click Through Rate)", "PV (Page View)", "UU (Unique User)", "CVR (Conversion Rate)", "Sales (10,000 Yen)", Eight-dimensional coordinate axes having "ROAS (Return On Advertising Spend)" and "advertising record" as axes are set.

  Then, the index classification unit 23 plots the average value of each index described above as a coordinate position and the index of the registered product as a coordinate position on the set multidimensional coordinate axes. Then, the index classification unit 23 calculates the distance between the coordinate point of the average value and the coordinate point of the registered product, and classifies the store into the index group having the shortest distance.

  Further, for example, the index classification unit 23 classifies other stores belonging to the type group classified by the type classification unit 22 into an index group. That is, the index classification unit 23 performs a process of classifying the index group for each type group.

(3-6. Prediction unit 24)
The prediction unit 24 predicts a sales index related to the sale of the product handled by the store based on the actual value of the other product handled by the other store belonging to the index group classified by the index classification unit 23. For example, the prediction unit 24 calculates an average value of the actual values of other products and performs machine learning based on the average value.

  For example, the prediction unit 24 predicts a sales index by multiple regression analysis using the average value of each index as an independent variable. The machine learning used by the prediction unit 24 is not limited to the multiple regression analysis, and machine learning to which various algorithms are applied can be used.

  In addition, the prediction unit 24 predicts at least one of the sales achievement time, the sales target number, and the advertisement budget. This point will be described with reference to FIG.

  FIG. 7 is a diagram illustrating an example of a prediction process of the prediction unit 24 according to the embodiment. FIG. 7 shows a process of accepting a product parameter from the manager M1 of the store A, predicting a sales index based on the accepted parameter, and notifying the sales index.

  As shown in FIG. 7, the prediction unit 24 receives parameters of “commodity name”, “commodity unit price”, and “sales index” as input items. “Product name” indicates the name of the product. In the “product name”, the category of the product may be further accepted as a parameter.

  “Product unit price” indicates the selling price of the product. The "sales index" includes "sales achievement time", "sales target number", and "advertising budget (ad type)", and any two of these three sales indexes are accepted from the manager M1 of the store A. .. That is, the prediction unit 24 predicts the remaining one sales index that is not accepted.

  As illustrated in FIG. 7, it is assumed that the prediction unit 24 receives “2000 pieces” as the sales target number A2 and “160,000 yen” as the advertising budget A3. In such a case, the prediction unit 24 calculates “April 1, 2017” as the sales achievement time A1 as a prediction result, and notifies the manager M1 of the store A of the result.

  Further, for example, it is assumed that the prediction unit 24 receives “April 1, 2017” as the sales achievement time A1 and “80,000 yen” as the advertising budget A3. In such a case, the prediction unit 24 calculates “750” as the sales target number A2 as a prediction result.

  In addition, for example, it is assumed that the prediction unit 24 receives "April 1, 2017" as the sales achievement time A1 and "2000" as the sales target number A2. In such a case, the prediction unit 24 calculates “300,000 yen” as the prediction result as the advertisement budget A3. The advertisement budget A3 is not limited to the amount of money, and may be the advertisement type (see FIG. 6).

(3-7. Screen generation unit 25)
The screen generator 25 generates various screens. For example, the screen generation unit 25 generates the sales prediction screen W1 in which the predicted sales number of the products from the start of sales of the products to the sales achievement time are displayed in time series. Here, the sales forecast screen W1 will be described with reference to FIG.

  FIG. 8 is a diagram showing an example of the sales forecast screen W1 according to the embodiment. FIG. 8 shows an example of the sales forecast screen W1 displayed on the seller terminal 10 owned by the manager M1 of the store A.

  Further, FIG. 8 shows a graph in which the vertical axis represents the sales target number and the horizontal axis represents the sales achievement time. The vertical axis and the horizontal axis have a so-called scroll bar shape, and the vertical axis knob P1 slides up and down, and the horizontal axis knob P2 slides left and right. The higher the vertical axis knob P1 is, the larger the sales target number is, and the further the horizontal axis knob P2 is, the later the sales achievement time is delayed.

  Further, in the sales forecast screen W1 shown in FIG. 8, a budget area P3 displaying an advertisement budget and an advertisement type and a type area P4 are arranged. When the positions of the vertical axis knob P1 and the horizontal axis knob P2 are determined on the sales forecast screen W1, the advertisement budget and advertisement type are automatically calculated and displayed in the budget area P3 and the type area P4.

  At the same time, in the graph shown in FIG. 8, the predicted sales quantity when the advertisement type A is submitted is represented in a linear form (solid line). In addition, in the graph shown in FIG. 8, the display form in which the predicted sales quantity in the case of the advertisement type B is different from that of the advertisement type A (in FIG. 8, is displayed by a dashed line). This is shown for comparison.

  That is, in the example shown in FIG. 8, if the advertisement type A is posted, the predicted sales volume is 2000 as of April 1, 2017, and if the advertisement type B is posted, it is predicted. It indicates that the number of sales is 1000.

  In addition, in the example shown in FIG. 8, as of January 1, 2017, it can be seen that the advertisement with the advertisement type B is slightly more effective than the advertisement with the advertisement type A. That is, the administrator M1 can easily determine the optimum advertisement type by looking at the graph of the sales forecast screen W1 and comparing the advertisement type A and the advertisement type B.

  Further, the sales index can be reset on the sales forecast screen W1. Specifically, when the manager M1 slides the vertical axis knob P1 and the horizontal axis knob P2, the advertisement budget and advertisement type are recalculated according to the position after the slide. That is, the reception unit 21 receives a change in at least one of the sales achievement time and the sales target number by a user operation (operation by the administrator M1) on the sales forecast screen W1. Then, the prediction unit 24 re-estimates the sales index based on the content of the change received by the reception unit 21.

  It should be noted that the sales forecast screen W1 allows re-estimation of the sales achievement time and the sales target number. Specifically, the reception unit 21 receives an input from the manager M1 to at least one of the budget area P3 and the type area P4. The reception unit 21 also receives an operation from the manager M1 to fix either the sales achievement time or the sales target number.

  Then, the prediction unit 24 predicts a sales index that is not fixed among the sales achievement time and the sales target number, and moves the vertical axis knob P1 or the horizontal axis knob P2 to the predicted position.

(3-8. Providing section 26)
The providing unit 26 provides the content related to the advertisement of the product to the store based on the sales index predicted by the prediction unit 24. For example, the providing unit 26 provides the sales forecast screen W1 generated by the screen generating unit 25. Further, for example, the providing unit 26 provides the type of advertisement according to the advertisement budget predicted by the prediction unit 24.

  Specifically, the providing unit 26 selects the optimum advertisement type from the advertisement information 33 based on the advertisement budget, and provides the selected advertisement type. Note that the providing unit 26 may select only one type of advertisement or may select a combination of a plurality of types.

[4. Processing procedure]
Next, the procedure of the providing process executed by the content providing device 1 according to the embodiment will be described with reference to FIG. FIG. 9 is a flowchart showing a procedure of a providing process executed by the content providing device 1 according to the embodiment.

  As shown in FIG. 9, first, the type classification unit 22 of the content providing apparatus 1 classifies the store into a type group for each type based on the type of the product handled by the store (step S101). Subsequently, the index classification unit 23 classifies the stores into index groups having similar indexes based on the actual values of the indexes related to the advertisement of the product (step S102).

  Subsequently, the prediction unit 24 predicts the sales index regarding the sale of the product handled by the store, based on the actual value of the other product handled by the other store belonging to the index group classified by the index classification unit 23 (step S103).

  Subsequently, the providing unit 26 determines the advertisement type based on the advertisement budget in the sales index (step S104). Subsequently, the providing unit 26 provides the determined advertisement type to the store (step S105), and ends the process.

[5. effect〕
As described above, the content providing device 1 according to the embodiment includes the index classification unit 23, the prediction unit 24, and the providing unit 26. The index classification unit 23 classifies the stores into index groups having similar indexes based on the actual values of the indexes related to the advertisement of the products handled by the store. The prediction unit 24 predicts a sales index related to the sale of the product handled by the store based on the actual value of the other product handled by the other store belonging to the index group classified by the index classification unit 23. The providing unit 26 provides the content related to the advertisement of the product to the store based on the sales index predicted by the prediction unit 24.

  As a result, the content providing device 1 according to the embodiment can prevent wasteful advertisement submission by predicting the sales of products from past records of other stores and providing an appropriate advertising budget. , Appropriate advertising content can be provided to the store that is the seller.

  Further, the content providing device 1 according to the embodiment includes a type classification unit 22. The type classification unit 22 classifies the stores into type groups for each type based on the types of products. The index classification unit 23 classifies other stores belonging to the type group classified by the type classification unit 22 into an index group.

  As a result, the content providing apparatus 1 according to the embodiment can narrow down the registered products for each category, and thus can improve the prediction accuracy of the sales index.

  Further, in the content providing device 1 according to the embodiment, the prediction unit 24 predicts at least one of the sales achievement time of the product, the sales target number, and the advertisement budget as the sales index.

  As a result, the content providing device 1 according to the embodiment can provide the sales index prediction result necessary for the store that is the seller. That is, it is possible to provide appropriate advertising content to the seller.

  Further, in the content providing device 1 according to the embodiment, the prediction unit 24 predicts the advertisement budget. The providing unit 26 provides the type of advertisement according to the advertisement budget predicted by the prediction unit 24.

  As a result, in the content providing device 1 according to the embodiment, the store, which is the seller, does not have to manually select the advertisement type from the advertisement budget, so that the number of operations can be reduced and the wrong advertisement type is selected. Can be prevented.

  Further, in the content providing device 1 according to the embodiment, the index classification unit 23 calculates an average value of each index of the other stores belonging to the index group, and based on the degree of coincidence between the average value and the actual value of the product. Classify stores.

  As a result, the content providing device 1 according to the embodiment can classify the stores into index groups having high similarity, and thus can improve the prediction accuracy of the sales index.

  Further, in the content providing device 1 according to the embodiment, the prediction unit 24 predicts a sales index by calculating an average value of the actual values of other products and performing machine learning based on the average value.

  As a result, the content providing device 1 according to the embodiment can more accurately predict the sales index based on the actual values of other products.

  Further, the content providing device 1 according to the embodiment includes the screen generation unit 25. The screen generation unit 25 generates a sales forecast screen in which the predicted number of sales of products from the start of sales of the products to the sales achievement time is displayed in time series. The providing unit 26 provides the sales forecast screen generated by the screen generating unit 25.

  As a result, in the content providing device 1 according to the embodiment, the store administrator who is the seller can easily determine the sales index and can confirm the predicted sales quantity in time series. It is possible to improve the satisfaction of the seller.

  In addition, the content providing device 1 according to the embodiment includes a reception unit 21. The reception unit 21 receives a change in at least one of the sales achievement time and the sales target number by a user operation on the sales forecast screen. The prediction unit 24 re-estimates the sales index based on the content of the change received by the reception unit 21.

  As a result, in the content providing device 1 according to the embodiment, the store administrator who is the seller can easily recalculate the sales index.

[6. program〕
Further, the content providing device 1 according to the above-described embodiment is realized by, for example, a computer 1000 having a configuration shown in FIG. FIG. 10 is a hardware configuration diagram showing an example of a computer 1000 that realizes the functions of the content providing device 1. The computer 1000 has a CPU 1100, a RAM 1200, a ROM 1300, an HDD 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F) 1700.

  The CPU 1100 operates based on a program stored in the ROM 1300 or the HDD 1400, and controls each unit. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 starts up, a program dependent on the hardware of the computer 1000, and the like.

  The HDD 1400 stores programs executed by the CPU 1100, data used by the programs, and the like. The communication interface 1500 receives data from another device via the network N, sends the data to the CPU 1100, and transmits the data generated by the CPU 1100 to the other device via the network N.

  The CPU 1100 controls output devices such as a display and a printer and input devices such as a keyboard and a mouse via the input / output interface 1600. The CPU 1100 acquires data from the input device via the input / output interface 1600. The CPU 1100 also outputs the generated data to the output device via the input / output interface 1600.

  The media interface 1700 reads a program or data stored in the recording medium 1800 and provides the program or data to the CPU 1100 via the RAM 1200. The CPU 1100 loads the program from the recording medium 1800 onto the RAM 1200 via the media interface 1700, and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or PD (Phase change rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. Etc.

  For example, when the computer 1000 functions as the content providing device 1 according to the embodiment, the CPU 1100 of the computer 1000 realizes the function of the control unit 2 by executing the program loaded on the RAM 1200. Further, the HDD 1400 stores the data in the storage unit 3. The CPU 1100 of the computer 1000 reads these programs from the recording medium 1800 and executes them, but as another example, these programs may be acquired from another device via the network N.

  As described above, some of the embodiments of the present application have been described in detail based on the drawings, but these are examples, and various modifications based on the knowledge of those skilled in the art, including the modes described in the section of the disclosure of the invention, It is possible to implement the present invention in other forms with improvements.

[7. Other]
Further, of the processes described in the above embodiment, all or part of the processes described as being automatically performed may be manually performed, or the processes described as manually performed may be performed. All or part of the process can be automatically performed by a known method. In addition, the processing procedures, specific names, information including various data and parameters shown in the above-mentioned documents and drawings can be arbitrarily changed unless otherwise specified. For example, the various kinds of information shown in each drawing are not limited to the illustrated information.

  Further, each constituent element of each illustrated device is functionally conceptual, and does not necessarily have to be physically configured as illustrated. That is, the specific form of distribution / integration of each device is not limited to that shown in the figure, and all or a part of the device may be functionally or physically distributed / arranged in arbitrary units according to various loads or usage conditions. It can be integrated and configured.

  Further, the respective processes described in the above-described embodiments can be appropriately combined within a range in which the processing contents do not contradict each other.

  Further, the above-mentioned "section (module, unit)" can be read as "means" or "circuit". For example, the reception unit can be read as a reception unit or a reception circuit.

DESCRIPTION OF SYMBOLS 1 Content providing device 2 Control part 3 Storage part 4 Communication part 10 Seller terminal 21 Reception part 22 Classification part 23 Index classification part 24 Prediction part 25 Screen generation part 26 Providing part 31 Product information 32 Store information 33 Advertising information 100 User terminal

Claims (10)

A storage unit that stores information on the actual value of the index related to the advertisement of the product handled by the store,
Based on the performance value stored in the storage unit, the degree of coincidence between the performance value of one of the stores and the performance value of another store included in an index group to which the index is similar is calculated, and the match is calculated. An index classification unit that classifies the one store into the index group based on a degree ,
By performing machine learning with the actual value of other products handled by other stores belonging to the index group classified by the index classification unit as an explanatory variable, and using a sales index related to sales of products handled by the store as a target variable, and a prediction unit to predict the sales index,
Based on the sales index predicted by the prediction unit, advertising budget of the product achievable the sales index to the store or, and a providing unit for providing contents including at least one of the advertisement type A content providing device characterized by the above. A type classification unit that classifies the store into a type group for each type based on the type of the product,
The index classification unit,
The content providing device according to claim 1, wherein the other stores belonging to the type group classified by the type classification unit are classified into the index group. The prediction unit is
The content providing device according to claim 1, wherein at least one of a sales achievement time of the product, a sales target number, and an advertisement budget is predicted as the sales index. The prediction unit is
Predict the advertising budget,
The providing unit is
The content providing apparatus according to claim 3, wherein the advertisement type is provided according to the advertisement budget predicted by the prediction unit. The index classification unit,
The average value of each of the indexes of the other stores belonging to the index group is calculated, and the stores are classified based on the degree of coincidence between the average value and the actual value. The content providing device according to any one of the above. The prediction unit is
The average value of the actual values of the other products is calculated, and the sales index is predicted by performing machine learning based on the average value, The sales index is predicted. Content providing device. A screen generation unit that generates a sales forecast screen in which the expected sales number of the product from the start of the sale of the product to the sales achievement time is displayed in time series,
The providing unit is
The content providing device according to claim 3 or 4, wherein the sales forecast screen generated by the screen generation unit is provided. The sales forecast screen is provided with a reception unit that receives a change in at least one of the sales achievement time and the sales target number by a user operation.
The prediction unit is
The content providing device according to claim 7, wherein the sales index is re-estimated based on the content of the change received by the reception unit. A content providing method executed by a content providing device, comprising:
A storage step of storing the information of the actual value of the index regarding the advertisement of the product handled by the store,
Based on the actual value stored in the storing step, the degree of coincidence between the actual value of one of the stores and the actual value of another store included in an index group to which the index is similar is calculated, and the match is calculated. An index classification step of classifying the one store into the index group based on degree ,
By performing machine learning with the actual value of other products handled by other stores belonging to the index group classified by the index classification step as an explanatory variable, and using a sales index related to sales of products handled by the store as an objective variable, a prediction step of predicting the sales index,
Based on the sales index predicted by the prediction step, advertising budget of the product achievable the sales index to the store or, comprises a providing step of providing a content including at least one of the advertisement type A content providing method characterized by the above. A storage procedure for storing the information of the actual value of the index regarding the advertisement of the product handled by the store,
Based on the performance value stored by the storage procedure, the degree of coincidence between the performance value of one of the stores and the performance value of another store included in an index group to which the index is similar is calculated, and the match is calculated. An index classification procedure for classifying the one store into the index group based on degree ,
By performing machine learning with the actual value of other products handled by other stores belonging to the index group classified by the index classification procedure as an explanatory variable, and using a sales index related to sales of products handled by the store as a target variable, a prediction procedure to predict the sales index,
Wherein, based on the sales index predicted by the prediction procedure, advertising budget of the product achievable the sales index to the store or computer and providing instructions for providing contents including at least one of advertisement type, A content providing program characterized by being executed by.

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