Store item selection method and device, electronic equipment and storage medium
By analyzing store sales data, determining target product categories and category labels, and finding products from similar stores, the accuracy problem of offline store product selection is solved, and the rationality of product selection and sales results are improved.
Patent Information
- Application Number
- CN202410272559.3
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-03-08
- Publication Date
- 2025-09-09
AI Technical Summary
In the new retail field, offline stores target different user groups due to their different geographical locations and business districts, resulting in different types of goods suitable for sale. Moreover, due to limitations on area and shelf capacity, how to choose suitable goods becomes a problem.
By obtaining sales data from the first store and reference stores, we can determine the target product categories, aggregate them to form category labels, find similar stores, and determine product selection based on the products in similar stores. We can combine local and global data to improve product selection accuracy.
It achieves the accuracy and reliability of store product selection, increases the total transaction volume of goods, and reduces the shelf space occupied by unsuitable goods.
Smart Images
Figure CN120612110A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of big data technology, and in particular to a store product selection method, device, electronic device and storage medium. Background Art
[0002] In the new retail sector, there are tens of thousands of products of various types. Offline stores target different user groups due to their geographical locations and business districts, and thus different types of products are suitable for sale. At the same time, offline stores are restricted by factors such as store area, shelf capacity, and exhibition space, so the types of products that can be sold in stores are often limited. Therefore, how to select suitable products from a large number of products is a technical problem that needs to be solved urgently. Summary of the Invention
[0003] The present application aims to solve one of the technical problems in the related art at least to a certain extent.
[0004] To this end, this application proposes a store product selection method, device, electronic device and storage medium to achieve precise product selection for each store and improve the accuracy of product selection.
[0005] In one embodiment of the present application, a store product selection method is provided, including:
[0006] Obtaining sales data of the first store in at least one product category and sales data of the reference store in each of the product categories;
[0007] determining a target product category for the first store based on the sales data of the first store in at least one product category and the sales data of the reference store in each of the product categories;
[0008] Determining a first category target tag for the first store based on a result of aggregating target product categories of the first store;
[0009] determining, based on the first category label of the first store, at least one second store similar to the first store;
[0010] The commodities selected by the first store are determined based on the target commodities in the at least one second store.
[0011] Another embodiment of the present application provides a store product selection device, comprising:
[0012] an acquisition module, configured to acquire sales data of the first store in at least one product category and sales data of the reference store in each of the product categories;
[0013] a first determining module, configured to determine a target commodity category for the first store based on sales data of the first store in at least one commodity category and sales data of reference stores in each of the commodity categories;
[0014] a second determining module, configured to determine a first category target tag for the first store based on a result of aggregating target product categories of the first store;
[0015] a third determining module, configured to determine at least one second store similar to the first store based on the first category label of the first store;
[0016] The fourth determining module is used to determine the commodity selected by the first store based on the target commodity in the at least one second store.
[0017] Another embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method described in the above aspect is implemented.
[0018] Another aspect of the present application provides a non-transitory computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the method described in the aforementioned aspect is implemented.
[0019] Another embodiment of the present application provides a computer program product having a computer program stored thereon, which implements the method described in the above aspect when the program is executed by a processor.
[0020] The store product selection method, device, electronic device and storage medium proposed in the present application obtain sales data of a first store in at least one product category and sales data of a reference store in each product category, determine the target product category of the first store based on the sales data of the first store in at least one product category and the sales data of the reference store in each product category, determine the first category target label of the first store based on the result of aggregating the target product category of the first store, determine at least one second store similar to the first store based on the first category target label of the first store, determine the products selected by the first store based on the target products in the at least one second store, determine the target product category with sales advantage of the first store based on the local sales data and global sales data obtained of the first store, and then sequentially aggregate the target product categories under the first store as the category label of the first store, cluster according to the category label to determine the second store similar to the first store, and use the target products in the second store as the products selected by the first store, thereby improving the accuracy of product selection.
[0021] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS
[0022] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:
[0023] Figure 1 A flowchart of a store product selection method provided in an embodiment of the present application;
[0024] Figure 2 A flowchart of another store product selection method provided in an embodiment of the present application;
[0025] Figure 3 A flowchart of another store product selection method provided in an embodiment of the present application;
[0026] Figure 4 A schematic diagram showing the relationship between stores and target product categories provided in an embodiment of the present application;
[0027] Figure 5 A schematic diagram of the structure of a store product selection device provided in an embodiment of the present application;
[0028] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0029] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.
[0030] The following describes the store product selection method, device, electronic device and storage medium of the embodiments of the present application with reference to the accompanying drawings.
[0031] Figure 1 A flowchart of a store product selection method provided in an embodiment of the present application.
[0032] The executor of the store product selection method in the embodiment of the present application is a store product selection device, which can be set in an electronic device. The electronic device can be a server or a terminal device, which is not limited in this embodiment.
[0033] like Figure 1 As shown, the method may include the following steps:
[0034] Step 101: Obtain sales data of a first store in at least one product category and sales data of reference stores in various product categories.
[0035] Among them, the commodity category indicates the classification of the commodities sold in the store, for example, power banks, bags, clothing, and selfie sticks are all commodity categories of the store. Among them, the commodity categories of stores in different areas or types are also different. In the embodiment of the present application, the first store is one of the multiple stores that need to select products. Since the method of selecting products for each store is the same, the first store is used for illustration. At least one commodity category is a commodity category that has an associated relationship with the first store. For example, if the first store is a grocery store, the commodity categories of grocery stores in various geographical locations and business districts can be counted as at least one commodity category related to the first store. The applicable scenarios of this application include: the first store is a normal sales store, and in order to expand the commodity categories, commodity selection is carried out; or the currently sold commodity categories are generally not selling well, so commodity selection is needed to increase the store's sales.
[0036] The reference store refers to a store with normal sales. The reference store may include the first store or exclude the first store. The reference store and the first store have similarities, and the similarities include at least one of similar categories, similar business districts, and similar geographical locations.
[0037] The sales data refers to at least one of sales amount and sales quantity. The sales amount can be determined by gross merchandise volume (GMV).
[0038] Step 102 : Determine a target product category for the first store based on the sales data of the first store in at least one product category and the sales data of the reference stores in each product category.
[0039] In an embodiment of the present application, based on the sales data of each commodity category of the first store and the reference store, the commodity category in which the sales data of the first store has a sales advantage over the reference store is calculated, that is, the target commodity category. The target commodity category of the first store is the commodity category that the first store is good at selling, and there is at least one target commodity category.
[0040] It should be noted that the target product categories for different primary stores may vary, depending on the customer base surrounding the primary store. For example, a primary store near a scenic spot may have strong sales in categories such as selfie sticks and power banks, so its target product categories would include selfie sticks and power banks. Meanwhile, a retail store near a community might target categories such as rice cookers and kitchen condiments.
[0041] Step 103 : Determine the first category target tag of the first store based on the result of aggregating the target product categories of the first store.
[0042] As an implementation method, the target product categories of the first store are aggregated to obtain an aggregated result, and the first category target tag of the first store is determined based on the aggregated result. This aggregated result is a collection of multiple target product categories, and there is no order between the multiple target product categories.
[0043] As another implementation method, the target product categories of the first store are aggregated in a set order to obtain an aggregated result. Multiple target product categories in the aggregated result are sorted in the set order, and the first category target label of the first store is determined based on the aggregated result.
[0044] The set order is a predefined order. When determining the corresponding category tags, each store is aggregated based on the set order to achieve unification, so as to increase the accuracy of subsequent clustering based on the category tags.
[0045] For example, the target product categories for the first store include selfie sticks, power banks, bags, and suitcases. The aggregation result, obtained by aggregating them in the set order, is: selfie sticks - power banks - bags - suitcases, meaning selfie sticks are ranked first, power banks second, bags third, and suitcases fourth. This order is fixed. If any store specifies that its target product categories include selfie sticks, power banks, bags, and suitcases, the aggregation result remains unchanged. Furthermore, the aggregation result for the target product categories corresponding to the first store is used as the first category tag for the first store, which includes multiple target product categories arranged in the set order.
[0046] Step 104: Determine at least one second store similar to the first store based on the first category label of the first store.
[0047] In one implementation of the embodiment of the present application, the first store corresponds to the first category target label, and the reference store can also use the same method as the first store to determine the corresponding category target label, which is called the second category target label for easy distinction. The second category target labels of multiple reference stores and the first category target label of the first store are clustered to obtain multiple clusters, each of which includes at least one store with a similar category target label. Then, for the target cluster including the first store, at least one target reference store in the target cluster is used as at least one second store similar to the first store, thereby achieving clustering-based aggregation of stores with similar category targets to determine at least one second store similar to the first store.
[0048] In another implementation method of the embodiment of the present application, a first correspondence is obtained, wherein the first correspondence includes the correspondence between each reference store and the second category target tag, the first category target tag is matched with each second category target tag in the first correspondence, the second category target tag matching the first category target tag is determined, and at least one second store corresponding to the second category target tag matching the first category target tag is used as at least one second store similar to the first store.
[0049] Step 105: Determine the commodities selected by the first store based on the target commodities in at least one second store.
[0050] For each second store, the target products in the second store are determined in the following way:
[0051] As an implementation method, for each product sold in the second store, the sales volume of the product in each second store is obtained. Based on the sales volume of the product in each second store, a first average sales volume of the product in the second store is determined. Furthermore, the sales volume of the product in multiple first reference stores that stock the product is obtained to determine a second average sales volume of the product globally, i.e., in the first reference stores. If the first average is greater than the second average, the product is determined to be a high-selling product in the second store, i.e., the target product in the second store.
[0052] Furthermore, a collection of target commodities in at least one second store is used as the commodity selection result of the first store, thereby improving the reliability of the selection and thus increasing the total commodity transaction amount.
[0053] It should be noted that a commodity in the embodiment of the present application may be a stock keeping unit (SKU), which is a unit for measuring inventory in and out, and may be in units of pieces, boxes, pallets, etc. For example, a stainless steel box has two sizes, large and small. Thus, a commodity in the present application may be a stainless steel box, a large stainless steel box, or a small stainless steel box, which is not limited in the present embodiment.
[0054] In the store product selection method of the embodiment of the present application, sales data of the first store in at least one product category and sales data of the reference store in each product category are obtained, and the target product category of the first store is determined based on the sales data of the first store in at least one product category and the sales data of the reference store in each product category. According to the results of aggregation of the target product category of the first store, a first category target label of the first store is obtained, and at least one second store similar to the first store is determined based on the first category target label of the first store. According to the target product in the at least one second store, the product selected by the first store is determined, and based on the local sales data and global sales data obtained for the first store, the target product category with a sales advantage of the first store is determined, and then the target product categories under the first store are sequentially aggregated as the category label of the first store, and clustering is performed according to the category label to determine the second store similar to the first store, and the target product in the second store is used as the product selected by the first store, thereby improving the accuracy of product selection.
[0055] Based on the above embodiments, Figure 2 A flow chart of another store product selection method provided in an embodiment of the present application is shown as follows: Figure 2 As shown, the method comprises the following steps:
[0056] Step 201: Obtain sales data of a first store in at least one product category and sales data of reference stores in various product categories.
[0057] Among them, step 201 can refer to the explanation in the above embodiment, the principle is the same, and it will not be repeated here.
[0058] It should be understood that the sales data may be historical sales data obtained by statistics at the current moment, or historical sales data obtained by statistics at any statistical moment, which is not limited in this embodiment.
[0059] Step 202: Determine first total sales data of the first store in at least one product category based on the sales data of the first store in each product category.
[0060] In an embodiment of the present application, the sales data of the first store in each commodity category are added up, and the result of the addition is used as the first total sales data of the first store in at least one commodity category.
[0061] For example, the goods sold in the store correspond to three product categories, and the sales data is the total transaction amount of the goods, namely power banks, selfie sticks and bags. Among them, the total transaction amount of the current power bank category is X1, the total transaction amount of the selfie stick category is X2, and the total transaction amount of the bag category is X3. Then the first total sales data = X1+X2+X3.
[0062] Step 203 , based on the sales data of each reference store in each commodity category, determine the second total sales data of multiple reference stores under each commodity category, and the third total sales data of multiple reference stores in at least one commodity category.
[0063] In this embodiment of the present application, for each product category, the sales data of multiple reference stores in that product category are summed to obtain the second total sales data corresponding to that product category. For each reference store, the sales data of the reference store in at least one product category are summed to obtain sub-sales data, and the sub-sales data of each reference store are summed to obtain the third total sales data.
[0064] Step 204, for each product category, determine the sales coefficient of the product category in the first store based on the sales data of the first store in the product category, the first total sales data of the first store in at least one product category, the second total sales data of multiple reference stores in the product category, and the third total sales data of multiple reference stores.
[0065] In one implementation of the embodiment of the present application, for each commodity category, the first sales share of the commodity category in the first store is determined based on the ratio between the sales data of the commodity category in the first store and the first total sales data, the second sales share of the commodity category in a set number of reference stores is determined based on the ratio between the second total sales data and the third total sales data, and the sales coefficient of the commodity category in the first store is determined based on the ratio between the first sales share and the second sales share.
[0066] As an example, the product category is Y, and the sales data includes the total merchandise volume (GMV).
[0067] For product category Y, the first sales percentage of product category Y = the GMV of the first store in product category Y / the first store's total GMV;
[0068] Secondary sales percentage of product category Y = Secondary total GMV / Thirdary total GMV under product category Y;
[0069] Sales coefficient of product category Y = the first sales share of product category Y / the second sales share of product category Y.
[0070] Step 205 : Compare the sales coefficient with the set coefficient. In response to the sales coefficient being greater than the set coefficient, determine that the product category is the target product category of the first store.
[0071] In the embodiment of the present application, the sales coefficient of the first store in the product category is compared with the set coefficient. In response to the sales coefficient being greater than the set coefficient, the product category is determined to be the target product category of the first store.
[0072] Similarly, the target product categories of other stores can be determined. The principles are the same and will not be repeated here.
[0073] It should be noted that in the embodiment of the present application, the determination of whether a certain commodity category is the target commodity category of the first store is not determined by sorting according to sales volume or sales revenue, because some commodity categories have high sales volume in the first store that currently needs to select products, but for multiple reference stores globally, the sales volume of this commodity category is also high, so it is not suitable to be the sales advantage category of the current first store. Some commodity categories with average sales volume globally but high sales volume in the current first store are suitable to be the sales advantage category of the current first store, that is, the target commodity category. In this application, the sales coefficient is used to indicate the sales preference of the first store, which is used to select the target commodity category whose sales share of the current first store is higher than the global sales share, thereby improving the accuracy of determining the target commodity category corresponding to the first store.
[0074] Step 206 : Determine the first category target tag of the first store based on the result of aggregating the target product categories of the first store.
[0075] In this embodiment of the present application, to improve the accuracy of determining the first category tag for the first store, the target product categories of the first store are aggregated in a set order to obtain an aggregated result. The aggregated result is not directly used as the first category tag for the first store, but is used as a candidate category tag, referred to as the first candidate category tag. Furthermore, based on the first candidate category tag and the number of stores corresponding to the first candidate category tag, the first category tag for the first store is determined to improve the accuracy of the determination.
[0076] The explanations and descriptions of the aforementioned steps are also applicable to this embodiment and will not be repeated here.
[0077] Step 207: Determine at least one second store similar to the first store based on the first category label of the first store.
[0078] Step 208: Determine the merchandise selected by the first store based on the target merchandise in at least one second store.
[0079] Among them, steps 207 and 208 can refer to the explanations in the aforementioned embodiment, and the principles are the same, so they will not be repeated here.
[0080] In the store product selection method of the embodiment of the present application, sales data of the first store in at least one product category and sales data of the reference store in each product category are obtained, and the target product category of the first store is determined based on the sales data of the first store in at least one product category and the sales data of the reference store in each product category. According to the result of aggregating the target product category of the first store, the first category target label of the first store is determined, and at least one second store similar to the first store is determined based on the first category target label of the first store. According to the target products in the at least one second store, the products selected by the first store are determined. Based on the local sales data and global sales data obtained for the first store, the target product category with a sales advantage for the first store is determined, and then the target product categories under the first store are sequentially aggregated as the category labels of the first store. Clustering is performed according to the category labels to determine a second store similar to the first store, and the target products in the second store are used as the products selected by the first store, thereby improving the accuracy of product selection.
[0081] Based on the above embodiment, the present application embodiment provides another store product selection method. Figure 3 A flow chart of another store product selection method provided in an embodiment of the present application is shown as follows: Figure 3 As shown, the method comprises the following steps:
[0082] Step 301: Obtain sales data of a first store in at least one product category and sales data of reference stores in each product category.
[0083] Step 302 : Determine a target product category for the first store based on the sales data of the first store in at least one product category and the sales data of the reference stores in each product category.
[0084] Among them, steps 301 and 302 can refer to the explanations in the above-mentioned embodiment, and the principles are the same, so they will not be repeated here.
[0085] As an example, Figure 4 A schematic diagram of the store and target product category provided in the embodiment of this application, such as Figure 4 As shown in the figure, due to the different geographical locations and business districts, stores face different customer groups, and thus different stores correspond to different target product categories. For example, the target product categories corresponding to store 1 include product category 2, product category 3, and product category m, while the target product categories corresponding to store n include product category 1, product category 3, and product category m-1.
[0086] Step 303: Aggregate the first number of target product categories according to a set order to obtain a first candidate category label for the first store.
[0087] In order to improve the accuracy of determining the first category label of the first store, the result obtained by aggregation is not directly used as the first category label of the first store, but is used as a candidate category label, which is called the first candidate category label.
[0088] Step 304: Determine the number of stores corresponding to the first candidate category label.
[0089] In one implementation of the embodiment of the present application, the first store corresponds to the first candidate category target label. The reference store can also use the same method as the first store to determine the corresponding candidate category target label. For ease of distinction, it is called the second candidate category target label. Candidate category target labels are clustered for the second candidate category target labels of the multiple reference stores and the first candidate category target label of the first store to obtain multiple clusters, each of which includes at least one store with a similar candidate category target label. This achieves clustering-based aggregation of stores with similar candidate category target labels. Then, for the target cluster including the first store, the stores and the number of stores included in the target cluster are determined.
[0090] Step 305: In response to the quantity being greater than the set threshold, the first candidate category tag is used as the first category tag of the first store.
[0091] In an embodiment of the present application, in response to the number being greater than the set threshold, it indicates that the number of stores corresponding to the first candidate category tag is large, and the accuracy of determining the first candidate category tag is high, so the first candidate category tag is used as the first category tag for the first store.
[0092] Step 306 : In response to the quantity being less than or equal to the set threshold, a second number of product categories are deleted from the first candidate category tag to obtain a second candidate category tag.
[0093] In an embodiment of the present application, in response to the number being less than or equal to the set threshold, it indicates that the number of stores corresponding to the first candidate category tag is small, and the first candidate category tag includes a large number of target product categories, and a dimensionality reduction process of the number of target product categories is required. Then, the second number of product categories is deleted from the first candidate category tag to obtain the second candidate category tag. As an implementation method, since the multiple target product categories in the first candidate category tag are sorted according to a set order, the set order usually indicates the order of importance of the multiple target product categories. If the set order is sorted in descending order of importance, the second number of target product categories at the tail is deleted to obtain the second candidate category tag; if the set order is sorted in ascending order of importance, the second number of target product categories at the head is deleted to obtain the second candidate category tag. Among them, the second number can be set according to demand. In order to improve the accuracy of the determination, the second number can be 1, that is, it is sorted in descending order of 1 to improve the precision of the determination.
[0094] As an example, the first candidate category tags include: selfie sticks - power banks - bags - boxes - water heaters - umbrellas. At this time, the first candidate category tags have a finer granularity. If the number of stores under the finer-grained first candidate category tags is less than the set threshold, that is, the number of stores does not meet the standard, then the number of coarse-grained tags is reduced to a coarse-grained label, that is, the second candidate category tags are generated, such as selfie sticks - power banks - bags - boxes. The advantage of the fine-grained first candidate category tags is that they more accurately describe the sales preferences of stores, but the disadvantage is that they cover a smaller number of stores. The characteristics of the coarse-grained second candidate category tags are just the opposite. The advantage is that they cover a relatively large number of stores, but they describe the sales preferences of stores more generally. Therefore, through the above determination method, the first category tag of the first store can be accurately determined, thereby improving accuracy.
[0095] Step 307: Determine the first category tag of the first store based on the second candidate category tag.
[0096] In an embodiment of the present application, after determining the second candidate category target tag, the method of the aforementioned steps 304-306 is repeated for confirmation until it is determined through clustering that the number of stores under the second candidate category target tag is greater than the set threshold, and the second candidate category target tag is used as the first category target tag for the first store.
[0097] It is important to understand that the essential relationships in store product selection are the relationships between stores and products, and between stores. These relationships can be characterized through store sales data. Store product sales data is also easily accessible. By using store product sales data and the product categories to which they belong, we can calculate the product categories that each store excels in selling, namely the target product categories. The target product categories are related to the customer base surrounding the store. For example, stores near scenic spots have good sales in product categories such as selfie sticks and power banks. Furthermore, after calculating the target product categories that each store excels in selling, a category tag can be formed by combining the target product categories. For example, the store belongs to the category tag of "selfie sticks - power banks - bags". Furthermore, stores that are determined to belong to the same category tag are considered similar stores. For a first store, the set of target products under the list of at least one second store similar to the first store is the product selection result for the first store. Compared with the solution based on the classification model of supervised training, the product selection method of the embodiment of the present application does not rely on the calibrated product labels, and does not rely on the accuracy of the product labels to affect the product selection effect, thereby improving the versatility and product selection effect.
[0098] In the store selection method of the embodiment of the present application, when determining the first category target tag for the first store, stores are first aggregated starting from fine-grained tags. If the number of stores at the fine-grained level does not meet the requirement, dimensionality reduction is performed on coarse-grained tags. The advantage of fine-grained tags is that they more accurately depict the sales preferences of stores, but the disadvantage is that they cover a smaller number of stores. Coarse-grained tags have the opposite characteristics. The advantage is that they cover a relatively large number of stores, but they depict the sales preferences of stores in a more general way, thus accurately determining the target category tag corresponding to the first store, i.e., the first category target tag.
[0099] In order to implement the above embodiment, the embodiment of the present application also proposes a store product selection device.
[0100] Figure 5 A schematic diagram of the structure of a store product selection device provided in an embodiment of the present application.
[0101] like Figure 5 As shown, the device may include:
[0102] An acquisition module 51 is configured to acquire sales data of the first store in at least one product category and sales data of the reference store in each of the product categories;
[0103] The first determining module 52 is configured to determine a target commodity category for the first store based on the sales data of the first store in at least one commodity category and the sales data of the reference store in each of the commodity categories.
[0104] The second determining module 53 is configured to determine the first category target tag of the first store according to a result of aggregating the target product categories of the first store.
[0105] The third determining module 54 is configured to determine at least one second store similar to the first store based on the first category label of the first store.
[0106] The fourth determining module 55 is configured to determine the commodity selected by the first store based on the target commodity in the at least one second store.
[0107] Furthermore, in an implementation of the embodiment of the present application, referring to the number of stores, the first determining module 52 is configured to:
[0108] determining first total sales data of the first store in the at least one product category based on the sales data of the first store in each of the product categories;
[0109] Determining, based on the sales data of each reference store in each product category, second total sales data of the plurality of reference stores in each product category, and third total sales data of the plurality of reference stores in the at least one product category;
[0110] For each product category, determining a sales coefficient for the product category at the first store based on the sales data of the first store in the product category, the first total sales data of the first store in the at least one product category, the second total sales data of the multiple reference stores, and the third total sales data of the multiple reference stores;
[0111] comparing the sales coefficient with a set coefficient;
[0112] In response to the sales coefficient being greater than the set coefficient, the product category is determined to be a target product category for the first store.
[0113] In one implementation of the embodiment of the present application, the first determining module 52 is further configured to:
[0114] determining a first sales share of the product category in the first store based on a ratio between the sales data of the first store and the first total sales data;
[0115] determining a second sales proportion of the product category in the set number of reference stores based on a ratio between the second total sales data and the third total sales data;
[0116] The sales coefficient of the commodity category in the first store is determined based on the ratio between the first sales share and the second sales share.
[0117] In one implementation of the embodiment of the present application, the second determining module 53 is configured to:
[0118] Aggregating the first number of target product categories in a set order to obtain a first candidate category label for the first store;
[0119] A first category label for the first store is determined based on the first candidate category label.
[0120] In one implementation of the embodiment of the present application, the second determining module 53 is further configured to:
[0121] Determine the number of stores corresponding to the first candidate category label;
[0122] In response to the number being greater than a set threshold, the first candidate category tag is used as the first category tag for the first store.
[0123] In one implementation of the embodiment of the present application, the second determining module 53 is further configured to:
[0124] In response to the number being less than or equal to the set threshold, deleting a second number of product categories from the first candidate category tag to obtain a second candidate category tag;
[0125] Determine a first category label for the first store based on the second candidate category label.
[0126] In one implementation of the embodiment of the present application, the third determining module 54 is configured to:
[0127] performing category signature clustering on the second category signatures of the plurality of reference stores and the first category signatures of the first store to obtain a plurality of clusters;
[0128] For a target cluster including the first store, at least one target reference store in the target cluster is used as at least one second store similar to the first store.
[0129] It should be noted that the above explanation of the method embodiment is also applicable to the device of this embodiment and will not be repeated here.
[0130] In the store selection device of the embodiment of the present application, the sales data of the first store in at least one product category and the sales data of the reference store in each product category are obtained, and the target product category of the first store is determined based on the sales data of the first store in at least one product category and the sales data of the reference store in each product category. According to the result of aggregating the target product category of the first store, the first category target label of the first store is determined, and at least one second store similar to the first store is determined based on the first category target label of the first store. According to the target products in the at least one second store, the products selected by the first store are determined. Based on the local sales data and global sales data obtained for the first store, the target product category with a sales advantage of the first store is determined, and then the target product categories under the first store are sequentially aggregated as the category labels of the first store. Clustering is performed according to the category labels to determine the second store similar to the first store, and the target products in the second store are used as the products selected by the first store, thereby improving the accuracy of product selection.
[0131] In order to implement the above embodiments, the present application also proposes an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method described in the above method embodiments is implemented.
[0132] In order to implement the above embodiments, the present application also proposes a non-transitory computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method described in the above method embodiments is implemented.
[0133] In order to implement the above embodiments, the present application further proposes a computer program product on which a computer program is stored. When the computer program is executed by a processor, the method described in the above method embodiments is implemented.
[0134] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. For example, the electronic device 800 can be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.
[0135] Reference Figure 6 , the electronic device 800 may include one or more of the following components: a processing component 802 , a memory 804 , a power component 806 , a multimedia component 808 , an audio component 810 , an input / output (I / O) interface 812 , a sensor component 814 , and a communication component 816 .
[0136] The processing component 802 generally controls the overall operation of the electronic device 800, such as operations associated with display, phone calls, data communications, camera operation, and recording operations. The processing component 802 may include one or more processors 820 to execute instructions to perform all or part of the steps of the above-described method. In addition, the processing component 802 may include one or more modules to facilitate interaction between the processing component 802 and other components. For example, the processing component 802 may include a multimedia module to facilitate interaction between the multimedia component 808 and the processing component 802.
[0137] The memory 804 is configured to store various types of data to support operations on the electronic device 800. Examples of such data include instructions for any application or method operating on the electronic device 800, contact data, phone book data, messages, pictures, videos, etc. The memory 804 can be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.
[0138] The power component 806 provides power to the various components of the electronic device 800. The power component 806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the electronic device 800.
[0139] The multimedia component 808 includes a screen that provides an output interface between the electronic device 800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor can not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 808 includes a front camera and / or a rear camera. When the electronic device 800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera can receive external multimedia data. Each front camera and rear camera can be a fixed optical lens system or have a focal length and optical zoom capability.
[0140] The audio component 810 is configured to output and / or input audio signals. For example, the audio component 810 includes a microphone (MIC), which is configured to receive external audio signals when the electronic device 800 is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal can be further stored in the memory 804 or transmitted via the communication component 816. In some embodiments, the audio component 810 also includes a speaker for outputting audio signals.
[0141] I / O interface 812 provides an interface between processing component 802 and peripheral interface modules, such as a keyboard, click wheel, buttons, etc. These buttons may include but are not limited to: a home button, volume buttons, a start button, and a lock button.
[0142] The sensor assembly 814 includes one or more sensors for providing various aspects of status assessment for the electronic device 800. For example, the sensor assembly 814 can detect the open / closed state of the electronic device 800, the relative positioning of components, such as the display and keypad of the electronic device 800. The sensor assembly 814 can also detect changes in the position of the electronic device 800 or a component of the electronic device 800, the presence or absence of user contact with the electronic device 800, the orientation or acceleration / deceleration of the electronic device 800, and temperature changes of the electronic device 800. The sensor assembly 814 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 814 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 814 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.
[0143] The communication component 816 is configured to facilitate wired or wireless communication between the electronic device 800 and other devices. The electronic device 800 can access a wireless network based on a communication standard, such as WiFi, 4G or 5G, or a combination thereof. In an exemplary embodiment, the communication component 816 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.
[0144] In an exemplary embodiment, the electronic device 800 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above methods.
[0145] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 804 including instructions, and the instructions can be executed by the processor 820 of the electronic device 800 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.
[0146] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.
[0147] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of the technical features being referred to. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of such features. Throughout the description of this application, "plurality" means at least two, for example, two, three, etc., unless otherwise specifically defined.
[0148] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code comprising one or more executable instructions for implementing the steps of a custom logical function or process, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed out of the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application belong.
[0149] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). Furthermore, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing it in another suitable manner if necessary, and then storing it in a computer memory.
[0150] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used to implement: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.
[0151] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiment.
[0152] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.
[0153] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.
Claims
1. A store product selection method, characterized in that: include: Obtaining sales data of the first store in at least one product category and sales data of the reference store in each of the product categories; determining a target product category for the first store based on the sales data of the first store in at least one product category and the sales data of the reference store in each of the product categories; Determining a first category target tag for the first store based on a result of aggregating target product categories of the first store; determining, based on the first category label of the first store, at least one second store similar to the first store; The commodities selected by the first store are determined based on the target commodities in the at least one second store.
2. The method according to claim 1, wherein There are multiple reference stores, and determining a target product category for the first store based on the sales data of the first store in at least one product category and the sales data of the reference stores in each of the product categories includes: determining first total sales data of the first store in the at least one product category based on the sales data of the first store in each of the product categories; Determining, based on the sales data of each reference store in each product category, second total sales data of the plurality of reference stores in each product category, and third total sales data of the plurality of reference stores in the at least one product category; For each product category, determining a sales coefficient for the product category at the first store based on the sales data of the first store in the product category, the first total sales data of the first store in the at least one product category, the second total sales data of the multiple reference stores, and the third total sales data of the multiple reference stores; comparing the sales coefficient with a set coefficient; In response to the sales coefficient being greater than the set coefficient, the product category is determined to be a target product category for the first store.
3. The method according to claim 2, wherein The determining, based on the sales data of the first store in the product category, the first total sales data of the first store in the at least one product category, the second total sales data of the plurality of reference stores, and the third total sales data of the plurality of reference stores, of the sales coefficient of the product category in the first store includes: determining a first sales share of the product category in the first store based on a ratio between the sales data of the first store and the first total sales data; determining a second sales proportion of the product category in the set number of reference stores based on a ratio between the second total sales data and the third total sales data; The sales coefficient of the commodity category in the first store is determined based on the ratio between the first sales share and the second sales share.
4. The method according to claim 1, wherein The target product category is a first quantity, and determining the first category target tag of the first store according to a result of aggregating the target product categories of the first store includes: Aggregating the first number of target product categories in a set order to obtain a first candidate category label for the first store; A first category label for the first store is determined based on the first candidate category label.
5. The method according to claim 4, wherein Determining the first category tag of the first store based on the first candidate category tag includes: Determine the number of stores corresponding to the first candidate category label; In response to the number being greater than a set threshold, the first candidate category tag is used as the first category tag for the first store.
6. The method according to claim 5, wherein The method further comprises: In response to the number being less than or equal to the set threshold, deleting a second number of product categories from the first candidate category tag to obtain a second candidate category tag; Determine a first category label for the first store based on the second candidate category label.
7. The method according to claim 2, wherein The determining, based on the first category label of the first store, at least one second store similar to the first store includes: performing category signature clustering on the second category signatures of the plurality of reference stores and the first category signatures of the first store to obtain a plurality of clusters; For a target cluster including the first store, at least one target reference store in the target cluster is used as at least one second store similar to the first store.
8. A store product selection device, characterized in that: include: an acquisition module, configured to acquire sales data of the first store in at least one product category and sales data of the reference store in each of the product categories; a first determining module, configured to determine a target commodity category for the first store based on sales data of the first store in at least one commodity category and sales data of reference stores in each of the commodity categories; a second determining module, configured to determine a first category target tag for the first store based on a result of aggregating target product categories of the first store; a third determining module, configured to determine at least one second store similar to the first store based on the first category label of the first store; The fourth determining module is used to determine the commodity selected by the first store based on the target commodity in the at least one second store.
9. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method according to any one of claims 1 to 7 is implemented.
10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.