Product portrait self-adaptive construction method

Abstract

The invention relates to the technical field of data analysis, in particular to a product portrait adaptive construction method, which comprises the following steps of: 1, screening out a plurality of adjacent supermarkets meeting reference requirements of target supermarkets by taking analyzed supermarkets as target supermarkets; 2, analyzing, weighting and calculating a similarity evaluation index between the target supermarket and any adjacent supermarket based on the plurality of similarity values; taking the product portrait of the adjacent supermarket with the maximum similarity evaluation index as the initial portrait of the target supermarket; 3, constructing a commodity category set and a selling price set of the target supermarket based on the initial portrait, marking the commodities as flexible commodities or fixed commodities, and dividing the flexible commodities into low-order commodities, middle-order commodities and high-order commodities according to the selling prices; and step 4, adjusting the initial portrait according to the purchase order data of the customer, wherein the adjustment is divided into commodity category set adjustment and selling price set adjustment.

Description

Technical Field

[0001] The present invention relates to the technical field of data analysis, and in particular to a method for adaptively constructing a product portrait. Background Art

[0002] Portrait is a method that integrates information such as the characteristics, behaviors, needs and preferences of the target group. It forms one or more representative virtual representation features through data collection, analysis and integration. It aims to help companies understand the target audience of their services or products more deeply, so as to make more accurate decisions in product design, marketing strategy, user experience optimization, etc. Whether it is user portrait or product portrait, they are important tools for enterprises in digital transformation and market competition, aiming to improve business efficiency and effectiveness in a data-driven way.

[0003] Product portraits are usually used in areas such as product planning and product marketing, and mainly serve the early stages of product development. However, the existing technology lacks a product portrait construction method that can be applied to shopping malls and supermarkets. In addition, it is impossible to comprehensively consider users' consumption levels and habits when constructing product portraits, making it difficult to clearly define product category positioning and pricing strategies. As a result, the procurement and listing of goods are likely to deviate from the consumption levels and habits of users in the region. Summary of the invention

[0004] In view of the above-mentioned shortcomings of the prior art, the present invention provides a method for adaptively constructing product portraits, which can effectively solve the problem in the prior art that it is difficult to comprehensively consider user consumption levels and consumption habits in constructing product portraits of shopping malls and supermarkets.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions:

[0006] The present invention provides a method for adaptively constructing a product portrait, comprising the following steps:

[0007] Step 1: The analyzed supermarket is recorded as the target supermarket, and multiple adjacent supermarkets whose business hours meet the threshold requirements are selected based on the geographical area to which the target supermarket belongs and the per capita GDP of each statistical area;

[0008] Step 2: The target supermarket and its adjacent supermarkets are uniformly recorded as the analyzed supermarkets. The radiation area of ​​the analyzed supermarket is drawn based on the total business area of ​​the analyzed supermarket. The range impact value of the analyzed supermarket is calculated based on the population and per capita income of different circular areas in the radiation area.

[0009] Compare multiple adjacent supermarkets with the target supermarket one by one:

[0010] The area similarity value is calculated based on the total business area, the type similarity value is calculated based on the planned area of ​​different commodity categories, the impact similarity value is calculated based on the scope impact value, and the customer group similarity value is calculated based on the proportion of different types of ground buildings;

[0011] A similarity evaluation index between the target supermarket and any adjacent supermarket is calculated based on a weighted analysis of multiple similarity values;

[0012] The product portrait of the neighboring supermarket with the largest similarity evaluation index is used as the initial portrait of the target supermarket;

[0013] Step 3: Build a set of product categories and price sets for the target supermarket based on the initial profile, analyze the prices of different products in the target supermarket and adjacent supermarkets, mark the target products as flexible products or fixed products, and classify flexible products into low-end products, mid-end products, and high-end products based on their prices;

[0014] Step 4: Adjust the initial profile based on the customer's purchase order data. The adjustment is divided into product category set adjustment and price set adjustment;

[0015] Commodity category set adjustment adjusts commodity inventory based on the commodity profit share and value share in each commodity category;

[0016] The price set adjustment adjusts the flexible product prices based on the sales proportion of low-end products, mid-end products, and high-end products.

[0017] Furthermore, the screening process of adjacent supermarkets is as follows:

[0018] Divide the map into multiple geographical areas based on geographical location and natural conditions, divide the geographical areas into multiple statistical jurisdictions, and obtain the per capita GDP in each statistical jurisdiction;

[0019] Get the geographical area and statistical area to which the target supermarket belongs, record them as the target area and target area respectively. Filter out multiple statistical areas within the target area that are less than the preset distance threshold from the target area and record them as adjacent areas. Get the per capita GDP of any adjacent area and the target area, record them as GDP1 and GDP0 respectively, and substitute them into the formula When the calculation result is within the preset range, the adjacent jurisdiction is recorded as a similar jurisdiction;

[0020] The business hours of each supermarket are obtained, and multiple supermarkets with business hours greater than or equal to a preset time threshold in similar jurisdictions are selected and recorded as adjacent supermarkets.

[0021] Furthermore, the range impact value calculation process is as follows:

[0022] Record the supermarket with the calculated impact value as the analyzed supermarket, obtain the total business area of ​​the analyzed supermarket, and substitute it into the formula Calculate in and get the radiation index G of the analyzed supermarket, where:

[0023] S is the total business area of ​​the supermarket under analysis;

[0024] e is a natural constant;

[0025] S′ is the preset standard value of area;

[0026] k is a preset constant coefficient;

[0027] a is a preset constant index and the value range of a is;

[0028] Indicates that the value is not less than The smallest integer of ;

[0029] Draw a circle with a radius of Z*R with the location of the supermarket being analyzed as the center, and record it as the radiation area. R is the preset length value. The circle is divided into multiple circular areas with a width of R, which are recorded as AREA. j , where j = 1, 2, ..., G, to obtain the population number people in different circular areas j and per capita annual income j , substitute into the formula Calculate the range impact value DF of the analyzed supermarket area , where λ1 and λ2 are preset weight coefficients.

[0030] Furthermore, the customer group similarity value calculation process is as follows:

[0031] Get the radiation area of ​​the adjacent supermarket and the target supermarket, divide the surface buildings into commercial buildings, residential buildings and office buildings, and obtain the area ratio of commercial buildings, residential buildings and office buildings in the radiation area of ​​the adjacent supermarket, which are recorded as P A ′1, P A ′2, P A ′3, obtain the floor area ratio of commercial buildings, residential buildings and office buildings in the target supermarket radiation area, denoted as P B ′1, P B ′2, P B ′3, substitute into the formula Calculation is performed to obtain the customer group similarity value Similar4, where h = 1, 2, 3.

[0032] Furthermore, the target product labeling process is as follows:

[0033] Get the selling price of the target product in each adjacent supermarket and take the maximum and minimum values ​​to construct the selling price range [Sale0, Sale1];

[0034] Obtain the official suggested retail price of the target product and record it as the anchor price. If there is no official suggested retail price for the target product, obtain the midpoint of the price range and record it as the anchor price.

[0035] Substitute into the formula Calculate the price fluctuation index β sale , where Sale′ represents the anchored selling price, and a floating threshold is preset. When the price floating index is greater than or equal to the preset floating threshold, the target product is marked as a flexible product. When the price floating index is less than the preset floating threshold, the product is marked as a fixed product.

[0036] When the target product is a flexible product, split the target product's price range [Sale0, Sale1] into the first range Second interval and the third interval, When the price of the target product in the price set is in the first interval, the second interval or the third interval, it is recorded as a low-level product, a middle-level product or a high-level product.

[0037] Furthermore, different customers are marked as member customers and non-member customers. Each member customer has a unique member ID. An adjustment period is set. All customers' purchase orders are recorded during the adjustment period. The purchase orders include the purchased goods and the selling prices of the purchased goods. The profit per item of the purchased goods is calculated based on the selling prices of the purchased goods.

[0038] The total sales volume of each commodity during the adjustment period is calculated and multiplied by the single-unit profit to obtain the total period profit of each commodity. The total period profit of all commodities during the adjustment period is calculated to obtain the total profit.

[0039] Furthermore, the process of adjusting the commodity category set is as follows:

[0040] Get the inventory quantity of different commodities in the commodity category set and multiply it by the single-unit profit to obtain the inventory value of different commodities. Calculate the sum of the inventory values ​​of all commodities in the commodity category set to obtain the total value. Divide the commodities in the commodity category set into different commodity categories, and calculate the sum of the inventory values ​​of commodities in each commodity category as the category value. Calculate the category value of each commodity category and divide it by the total value to obtain the value proportion of each commodity category.

[0041] Obtain the total period profit of each commodity in the same commodity category and sum them up to get the category profit of the commodity category. Divide the category profit by the total profit in the adjustment period to get the profit share of each commodity category. Adjust the commodity inventory in each commodity category according to the profit share and value share. Specifically:

[0042] When the profit share minus the value share is lower than the preset share threshold, the commodity category is recorded as a reduced category, and the total period profit profit1 of each commodity in the reduced category and the total period profit profit2 of similar commodities are obtained, and substituted into the formula Calculation is performed to obtain the reduction reference value Re1 of each commodity, and the inventory of the commodity is reduced in descending order according to the reduction reference value, where φ is a preset value, which is 1 when the commodity is a flexible commodity and 2 when the commodity is a fixed commodity.

[0043] Furthermore, the price set adjustment process is as follows:

[0044] Get all purchase orders within the adjustment period and record them as order Oeder n , n is the order number;

[0045] When ordering Oeder n When the corresponding customer is a member customer, get the order Oeder n The quantity of low-end goods, mid-end goods, and high-end goods is multiplied by a preset expansion coefficient to obtain the order demand of low-end goods, mid-end goods, and high-end goods, respectively, recorded as Oeder n x Oeder n y Oeder n z , where x, y, and z represent low-order goods, mid-order goods, and high-order goods, respectively;

[0046] When ordering Oeder n When the corresponding customer is a non-member customer, get the order Oeder n The quantity of low-end goods, mid-end goods, and high-end goods is recorded as the order demand of low-end goods, mid-end goods, and high-end goods as Oeder n x Oeder n y Oeder n z ;

[0047] Substitute into the formula Calculate in and get the demand NUM of low-level goods, medium-level goods and high-level goods in the adjustment period respectively. X 、NUM Y 、NUM Z ;

[0048] When the demand for low-level goods reaches its maximum, the selling prices of mid-level and high-level goods are reduced;

[0049] When the demand for mid-level products reaches its maximum, increase the selling price of low-level products and reduce the selling price of high-level products;

[0050] When the demand for high-level products reaches its maximum value, the selling prices of low-level and high-level products are increased.

[0051] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0052] A computer-readable storage medium stores a computer program, which implements the steps of the above method when executed by a processor.

[0053] Compared with the known prior art, the technical solution provided by the present invention has the following beneficial effects:

[0054] 1. The adjacent supermarkets screened out by the duration threshold and the distance threshold in the present invention can be used as a source of horizontal comparison data for the target supermarket. The similar supermarkets obtained through multiple comparisons and screening have similar scales, operating environments and regional characteristics as the target supermarkets, and their product portraits are highly overlapped. By screening out similar supermarkets and citing their product portraits, the target supermarket can determine the positioning and pricing of its own products at the time of initial business operation to suit its own scale and local consumption environment.

[0055] 2. The commodity category set adjustment process of the present invention can compare the value ratio and the profit ratio to screen out the major commodity categories with low profits. By reducing the reference value to adjust the inventory of commodities in the major commodity categories, the inventory quantity of low-profit commodities on sale in supermarkets can be reduced, and the product portrait can be optimized to improve inventory turnover efficiency. The price set adjustment process can adjust the prices of different flexible commodities based on the demand for low-end commodities, mid-end commodities, and high-end commodities within the adjustment period, which can improve the degree of consistency between product portraits and customer consumption levels on the premise that they are acceptable to customers, thereby further improving the effect of constructing product portraits. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0057] Figure 1 It is a schematic diagram of the overall method of the present invention. DETAILED DESCRIPTION

[0058] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0059] The present invention will be further described below in conjunction with the embodiments.

[0060] See also Figure 1 , the product portrait adaptive construction method at least includes:

[0061] Step 1: The analyzed supermarket is recorded as the target supermarket, and the surrounding adjacent supermarkets are selected based on the data characteristics of the target supermarket, where:

[0062] Divide the map into multiple geographical regions based on geographical location and natural conditions (taking China as an example, it is divided into Northeast China, East China, Southwest China, and South China), and then divide the geographical regions into multiple statistical jurisdictions to obtain the per capita GDP in each statistical jurisdiction;

[0063] The geographical area and statistical jurisdiction to which the target supermarket belongs are obtained and recorded as the target area and target jurisdiction respectively. In the target area, multiple statistical jurisdictions whose distance from the target jurisdiction is less than a preset distance threshold are screened out and recorded as adjacent jurisdictions (in a specific embodiment, the distance threshold is 100km). The per capita GDP of any adjacent jurisdiction and the target jurisdiction is obtained and recorded as GDP1 and GDP0 respectively. Substitute them into the formula When the calculation result is within the preset range, the adjacent jurisdiction is recorded as a similar jurisdiction;

[0064] Obtain the business hours of each supermarket (i.e., the length of time the supermarket has been in operation since its opening), and select multiple supermarkets in similar jurisdictions whose business hours are greater than or equal to a preset time threshold as adjacent supermarkets (in a specific embodiment, the distance threshold is 1 year);

[0065] It should be noted that, on the one hand, the distance threshold screening determines that the adjacent supermarkets and the target supermarkets have similar regional characteristics (affecting regional consumption habits), and the GDP comparison determines that the customer groups served have similar consumption habits. On the other hand, the duration threshold determines that the adjacent supermarkets have stable operations and can be used as a comparison reference. Therefore, the adjacent supermarkets screened by the duration threshold and the distance threshold can be used as a source of horizontal comparison data for the target supermarket, which is convenient for the target supermarket to determine the initial product portrait.

[0066] Step 2: Further analyze the adjacent supermarkets and compare them with the target supermarket to identify similar supermarkets with similar business conditions as the target supermarket. Based on the similar supermarkets, determine the initial product portrait of the target supermarket.

[0067] Multiple adjacent supermarkets are compared with the target supermarket one by one. The comparison process is as follows (for the data before the target supermarket opens):

[0068] Obtain the total business area of ​​the adjacent supermarket and the target supermarket, record them as S1 and S0 respectively, and substitute them into the formula Calculation is performed to obtain the area similarity value Similar1, which reflects to a certain extent the similarity of the overall business scale between the adjacent supermarkets and the target supermarket;

[0069] There are multiple categories of goods in advance. The planned area ratio of different categories of goods in adjacent supermarkets is obtained and recorded as P 1i , obtain the planned area proportion of different commodity categories in the target supermarket and record it as P 0i , where i represents different types of serial numbers (i.e. P 1i represents the ratio of the planned sales area of ​​the i-th commodity category in the adjacent supermarket to the total area of ​​the supermarket), substitute it into the formula The type similarity value Similar2 is calculated. The planned area ratio of different commodity categories reflects the category structure in the supermarket product portrait. The type similarity value is used to compare the similarity of the categories sold between adjacent supermarkets and the target supermarket.

[0070] It should be noted that the major categories of goods are determined based on the division of goods within the supermarket, such as the fresh food area, dry goods area, daily necessities area, wine and beverage area, and home appliance area.

[0071] Calculate the range impact value of the target supermarket and each adjacent supermarket separately. The calculation process is as follows:

[0072] Record the supermarket with the calculated impact value as the analyzed supermarket, obtain the total business area of ​​the analyzed supermarket, and substitute it into the formula Calculate in and get the radiation index G of the analyzed supermarket, where:

[0073] S is the total business area of ​​the supermarket under analysis;

[0074] e is a natural constant;

[0075] S′ is a preset standard area value (in a specific embodiment, the value is 100 square meters);

[0076] k is a preset constant coefficient (in a specific embodiment, the value is 2);

[0077] a is a preset constant exponent and the value range of a is (0.5, 1);

[0078] Indicates that the value is not less than The smallest integer (that is, the radiation index G is a positive integer greater than 0);

[0079] Draw a circle with a radius of Z*R with the location of the supermarket being analyzed as the center, and record it as the radiation area. R is a preset length value (in a specific embodiment, the value is 500 meters). The circle is divided into multiple ring areas with a width of R (the difference between the inner and outer radii is equal to R) (a circle with a radius of R is regarded as a ring with an inner radius of 0 and an outer radius of R), and are respectively recorded as AREA j , where j = 1, 2, ..., G, to obtain the population number people in different circular areas j and per capita annual income j , substitute into the formula Calculate the range impact value DF of the analyzed supermarket area , where λ1 and λ2 are preset weight coefficients (in a specific embodiment, the values ​​are 0.5 and 0.5);

[0080] It should be noted that, in a specific embodiment, the value of the population size adopts the number of permanent residents, and the per capita annual income can be obtained based on statistical data or questionnaire surveys.

[0081] Obtain the range influence values ​​of the target supermarket and any adjacent supermarket, record them as DF0 and DF1 respectively, and substitute them into the formula Calculation is performed to obtain the influence similarity value Similar3, which reflects to a certain extent the similarity in the overall business prospects of adjacent supermarkets and target supermarkets affected by the population distribution and per capita income in the surrounding area;

[0082] Obtain the radiation area of ​​the adjacent supermarkets and the target supermarket, divide the surface buildings into commercial buildings, residential buildings and office buildings, and obtain the area ratio of commercial buildings, residential buildings and office buildings in the radiation area of ​​the adjacent supermarkets based on the map, which are recorded as P A ′1, P A ′2, P A ′3, obtain the floor area ratio of commercial buildings, residential buildings and office buildings in the target supermarket radiation area, denoted as P B ′1, P B ′2, P B ′3, substitute into the formula Calculate in and get the customer group (customer group) similarity value Similar4, where h = 1, 2, 3;

[0083] Commercial buildings, residential buildings and office buildings correspond to ground buildings mainly used for consumption, residence and office purposes respectively. The distinction between different types of ground buildings can be based on the proportion of internal space usage. For example, if more than a certain proportion (60%) of the space in a building is used for living, the ground building can be recorded as a residential building, such as a common apartment building. Ground buildings of different types of uses will determine the consumption habits of the people. For example, people in residential buildings will buy daily necessities, while people in office buildings will tend to buy office supplies and food and beverages.

[0084] Get the area similarity value Similar1, type similarity value Similar2, influence similarity value Similar3, and customer group similarity value Similar4 between the target supermarket and any adjacent supermarket, and substitute them into the formula Similar all =η1*Similar1+η2*Similar2+η3*Similar3+η4*Similar4 to get the similarity evaluation index Similar all , where η1, η2, η3, and η4 are all preset weight coefficients, and satisfy η1+η2+η3+η4=1. The adjacent supermarket with the largest similarity evaluation index is selected as the similar supermarket of the target supermarket, and the product portrait of the similar supermarket is used as the initial portrait of the target supermarket.

[0085] Generally speaking, similar supermarkets obtained after multiple comparisons and screening have similar scale, operating environment and regional characteristics as the target supermarket (these factors dominate the consumption habits and consumption level of supermarket customers, and consumption habits and consumption levels are the key conditions for adaptation required to build product portraits). Therefore, the product portraits of the two are highly overlapping. It should be noted that the product portrait in the present invention refers to the category composition and pricing standards of products in the supermarket. When implemented specifically, it refers to the category list and price list of each commodity in the supermarket. By screening out similar supermarkets and citing their product portraits, the target supermarket can determine the positioning and pricing of its own products at the time of initial business operation to suit its own scale and local consumption environment.

[0086] Step 3: Based on the initial portrait, build the product category set and price set of the target supermarket. The product category set includes multiple different types of products, and the price set includes the price of each product. Perform price analysis on different products, including:

[0087] The analyzed product is recorded as the target product, the selling price of the target product in each adjacent supermarket is obtained, and the maximum and minimum values ​​are taken to construct the selling price range [Sale0, Sale1];

[0088] Obtain the official suggested retail price of the target product and record it as the anchor price. If there is no official suggested retail price for the target product, obtain the midpoint of the price range and record it as the anchor price.

[0089] Substitute into the formula Calculate the price fluctuation index β sale , where Sale′ represents the anchored selling price, and a floating threshold is preset (in a specific embodiment, the value is 0.1). When the price floating index is greater than or equal to the preset floating threshold, the target product is marked as a flexible product, and when the price floating index is less than the preset floating threshold, the product is marked as a fixed product;

[0090] Furthermore, when the target product is a flexible product, the price range of the target product [Sale0, Sale1] is split into the first range Second interval and the third interval, When the price of the target product in the price set is in the first range, it is recorded as a low-level product. When the price of the target product in the price set is in the second range, it is recorded as a mid-level product. When the price of the target product in the price set is in the third range, it is recorded as a high-level product.

[0091] It should be noted that the target commodity is specific to a certain brand, a certain model, or a certain batch, so that the target commodities of different supermarkets are comparable. When the target commodity is a flexible commodity, it means that the commodity has room for price adjustment, and customers are more accepting of price changes of this commodity. In other words, the selling price of the target commodity can be adjusted within a certain range according to the demand for the target commodity, so as to increase profits without losing too much sales. When the target commodity is a fixed commodity, it means that the price of the commodity in the market has no room for adjustment, and customers are more sensitive to the price of this commodity.

[0092] Furthermore, when it is impossible to construct a price range based on the prices of the target product in adjacent supermarkets, similar products of the target product are determined, and the price range of the similar products is used as the price range of the target product. The similar products and the target product must meet the following conditions:

[0093] Condition 1: The similar product and the target product are of the same type (the same type is determined based on the product name, international design classification and the classification position of the product on the shelf);

[0094] Condition 2: The price difference between similar products of the same type and the target product is minimal;

[0095] Condition 3: Similar products have a range of selling prices.

[0096] Step 4: Optimize and adjust the initial profile based on customer purchase data. The adjustments are divided into product category set adjustment and price set adjustment, including:

[0097] Different customers are marked as member customers and non-member customers. Each member customer has a unique member ID to distinguish them at checkout. The impact coefficients of different customers are adjusted by distinguishing between non-members and members, because member customers have stronger demand stickiness for supermarket products than non-member customers. An adjustment period is set, and all customer purchase orders are recorded during the adjustment period. The purchase orders include the purchased goods and the selling price of the purchased goods. The profit per item of the purchased goods is calculated based on the selling price of the purchased goods.

[0098] The total sales volume of each product in the adjustment period is calculated and multiplied by the single-unit profit to obtain the total period profit of each product. The total period profit of all products in the adjustment period is calculated to obtain the total profit.

[0099] The process of adjusting the product category set is as follows:

[0100] Get the inventory quantity of different commodities in the commodity category set and multiply it by the single-unit profit to obtain the inventory value of different commodities. Calculate the sum of the inventory values ​​of all commodities in the commodity category set to obtain the total value. Divide the commodities in the commodity category set into different commodity categories, and calculate the sum of the inventory values ​​of commodities in each commodity category as the category value. Calculate the category value of each commodity category and divide it by the total value to obtain the value proportion of each commodity category.

[0101] Obtain the total period profit of each commodity in the same commodity category and sum them up to get the category profit of the commodity category. Divide the category profit by the total profit in the adjustment period to get the profit share of each commodity category. Adjust the commodity inventory in each commodity category according to the profit share and value share. Specifically:

[0102] When the profit share minus the value share is lower than the preset share threshold, the commodity category is recorded as a reduced category, and the total period profit profit1 of each commodity in the reduced category and the total period profit profit2 of similar commodities are obtained, and substituted into the formula Calculation is performed to obtain the reduction reference value Re1 of each commodity, and the inventory of the commodity is reduced in descending order according to the reduction reference value, where φ is a preset value, which is 1 when the commodity is a flexible commodity and 2 when the commodity is a fixed commodity.

[0103] It should be noted that by comparing the value share and the profit share, it is possible to screen out the major categories of goods with low profits. By reducing the reference value to adjust the inventory of goods in the major categories, it is possible to reduce the inventory of low-profit goods on sale in supermarkets and optimize product portraits to improve inventory turnover efficiency.

[0104] The price collection adjustment process is as follows:

[0105] Calculate the demand for low-level products, mid-level products, and high-level products during the adjustment period. The demand calculation process is as follows:

[0106] Get all purchase orders within the adjustment period and record them as order Oeder n , n is the order number;

[0107] When ordering Oeder n When the corresponding customer is a member customer, get the order Oeder n The quantity of low-end and medium-end products, medium-end products, and high-end products is multiplied by a preset expansion coefficient (in a specific embodiment, the value is 1.5) to obtain the order demand of low-end products, medium-end products, and high-end products, respectively, which are recorded as Oeder n x Oeder n y Oeder n z , where x, y, and z represent low-order goods, mid-order goods, and high-order goods, respectively;

[0108] When ordering Oeder n When the corresponding customer is a non-member customer, get the order Oeder n The quantity of low-end goods, mid-end goods, and high-end goods is recorded as the order demand of low-end goods, mid-end goods, and high-end goods as Oeder n x Oeder n y Oeder n z ;

[0109] Substitute into the formula Calculate in and get the demand NUM of low-level goods, medium-level goods and high-level goods in the adjustment period respectively. X 、NUM Y 、NUM Z ;

[0110] When the demand for low-level goods reaches its maximum, the selling prices of mid-level and high-level goods are reduced;

[0111] When the demand for mid-level products reaches its maximum, increase the selling price of low-level products and reduce the selling price of high-level products;

[0112] When the demand for high-level products reaches its maximum value, the selling prices of low-level and high-level products are increased.

[0113] It should be noted that the proportion of low-end, mid-end and high-end goods in purchase orders to a certain extent illustrates the user's acceptance of high-priced goods. Adjusting the prices of different flexible goods based on the demand for low-end, mid-end and high-end goods within the adjustment period can improve the degree of consistency between product portraits and customer consumption levels under the premise of customer acceptance, thereby further improving the effect of constructing product portraits.

[0114] A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps in the above method when executing the computer program.

[0115] A computer-readable storage medium stores a computer program, which implements the steps in the above method when executed by a processor.

[0116] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements will not cause the essence of the corresponding technical solutions to deviate from the protection scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for adaptively constructing a product portrait, characterized in that: The following steps are involved: Step 1: The analyzed supermarket is recorded as the target supermarket, and multiple adjacent supermarkets whose business hours meet the threshold requirements are selected based on the geographical area to which the target supermarket belongs and the per capita GDP of each statistical area; Step 2: The target supermarket and its adjacent supermarkets are uniformly recorded as the analyzed supermarkets. The radiation area of ​​the analyzed supermarket is drawn based on the total business area of ​​the analyzed supermarket. The range impact value of the analyzed supermarket is calculated based on the population and per capita income of different circular areas in the radiation area. Compare multiple adjacent supermarkets with the target supermarket one by one: The area similarity value is calculated based on the total business area, the type similarity value is calculated based on the planned area of ​​different commodity categories, the impact similarity value is calculated based on the scope impact value, and the customer group similarity value is calculated based on the proportion of different types of ground buildings; A similarity evaluation index between the target supermarket and any adjacent supermarket is calculated based on a weighted analysis of multiple similarity values; The product portrait of the neighboring supermarket with the largest similarity evaluation index is used as the initial portrait of the target supermarket; Step 3: Build a set of product categories and price sets for the target supermarket based on the initial profile, analyze the prices of different products in the target supermarket and adjacent supermarkets, mark the target products as flexible products or fixed products, and classify flexible products into low-end products, mid-end products, and high-end products based on their prices; Step 4: Adjust the initial profile based on the customer's purchase order data. The adjustment is divided into product category set adjustment and price set adjustment; Commodity category set adjustment adjusts commodity inventory based on the commodity profit share and value share in each commodity category; The price set adjustment adjusts the flexible product prices based on the sales proportion of low-end products, mid-end products, and high-end products.

2. The method for adaptively constructing a product portrait according to claim 1, characterized in that: The screening process of adjacent supermarkets is as follows: Divide the map into multiple geographical areas based on geographical location and natural conditions, divide the geographical areas into multiple statistical jurisdictions, and obtain the per capita GDP in each statistical jurisdiction; Get the geographical area and statistical area to which the target supermarket belongs, record them as the target area and target area respectively. Filter out multiple statistical areas within the target area that are less than the preset distance threshold from the target area and record them as adjacent areas. Get the per capita GDP of any adjacent area and the target area, record them as GDP1 and GDP0 respectively, and substitute them into the formula When the calculation result is within the preset range, the adjacent jurisdiction is recorded as a similar jurisdiction; The business hours of each supermarket are obtained, and multiple supermarkets with business hours greater than or equal to a preset time threshold in similar jurisdictions are selected and recorded as adjacent supermarkets.

3. The method for adaptively constructing a product portrait according to claim 1, characterized in that: The range impact value calculation process is as follows: Record the supermarket with the calculated impact value as the analyzed supermarket, obtain the total business area of ​​the analyzed supermarket, and substitute it into the formula Calculate in and get the radiation index G of the analyzed supermarket, where: S is the total business area of ​​the supermarket under analysis; e is a natural constant; S′ is the preset standard value of area; k is a preset constant coefficient; a is a preset constant index and the value range of a is; Indicates that the value is not less than The smallest integer of ; Draw a circle with a radius of Z*R with the location of the supermarket being analyzed as the center, and record it as the radiation area. R is the preset length value. The circle is divided into multiple circular areas with a width of R, which are recorded as AREA. j , where j = 1, 2, ..., G, to obtain the population number people in different circular areas j and per capita annual income j , substitute into the formula Calculate the range impact value DF of the analyzed supermarket area , where λ1 and λ2 are preset weight coefficients.

4. The method for adaptively constructing a product portrait according to claim 1, characterized in that: The customer group similarity value calculation process is as follows: Get the radiation area of ​​the adjacent supermarket and the target supermarket, divide the surface buildings into commercial buildings, residential buildings and office buildings, and obtain the floor area ratio of commercial buildings, residential buildings and office buildings in the radiation area of ​​the adjacent supermarket, which are recorded as P A ′1, P A ′2, P A ′3, obtain the floor area ratio of commercial buildings, residential buildings and office buildings in the target supermarket radiation area, denoted as P B ′1, P B ′2, P B ′3, substitute into the formula Calculation is performed to obtain the customer group similarity value Similar4, where h = 1, 2, 3.

5. The method for adaptively constructing a product portrait according to claim 1, characterized in that: The target product tagging process is as follows: Get the selling price of the target product in each adjacent supermarket and take the maximum and minimum values ​​to construct the selling price range [Sale0, Sale1]; Obtain the official suggested retail price of the target product and record it as the anchor price. If there is no official suggested retail price for the target product, obtain the midpoint of the price range and record it as the anchor price. Substitute into the formula Calculate the price fluctuation index β sale , where Sale′ represents the anchored selling price, and a floating threshold is preset. When the price floating index is greater than or equal to the preset floating threshold, the target product is marked as a flexible product. When the price floating index is less than the preset floating threshold, the product is marked as a fixed product. When the target product is a flexible product, split the target product's price range [Sale0, Sale1] into the first range Second interval and the third interval, When the price of the target product in the price set is in the first interval, the second interval or the third interval, it is recorded as a low-level product, a middle-level product or a high-level product.

6. The method for adaptively constructing a product portrait according to claim 1, characterized in that: Different customers are marked as member customers and non-member customers. Each member customer has a unique member ID. An adjustment period is set. All customers' purchase orders are recorded during the adjustment period. The purchase orders include the purchased goods and the selling price of the purchased goods. The profit per item of the purchased goods is calculated based on the selling price of the purchased goods. The total sales volume of each commodity during the adjustment period is calculated and multiplied by the single-unit profit to obtain the total period profit of each commodity. The total period profit of all commodities during the adjustment period is calculated to obtain the total profit.

7. The method for adaptively constructing a product portrait according to claim 6, characterized in that: The process of adjusting the product category set is as follows: Get the inventory quantity of different commodities in the commodity category set and multiply it by the single-unit profit to obtain the inventory value of different commodities. Calculate the sum of the inventory values ​​of all commodities in the commodity category set to obtain the total value. Divide the commodities in the commodity category set into different commodity categories, and calculate the sum of the inventory values ​​of commodities in each commodity category as the category value. Calculate the category value of each commodity category and divide it by the total value to obtain the value proportion of each commodity category. Obtain the total period profit of each commodity in the same commodity category and sum them up to get the category profit of the commodity category. Divide the category profit by the total profit in the adjustment period to get the profit share of each commodity category. Adjust the commodity inventory in each commodity category according to the profit share and value share. Specifically: When the profit share minus the value share is lower than the preset share threshold, the commodity category is recorded as a reduced category, and the total period profit profit1 of each commodity in the reduced category and the total period profit profit2 of similar commodities are obtained, and substituted into the formula Calculation is performed to obtain the reduction reference value Re1 of each commodity, and the inventory of the commodity is reduced in descending order according to the reduction reference value, where φ is a preset value, which is 1 when the commodity is a flexible commodity and 2 when the commodity is a fixed commodity.

8. The method for adaptively constructing a product portrait according to claim 7, characterized in that: The price collection adjustment process is as follows: Get all purchase orders within the adjustment period and record them as order Oeder n , n is the order number; When ordering Oeder n When the corresponding customer is a member customer, get the order Oeder n The quantity of low-end goods, mid-end goods, and high-end goods is multiplied by a preset expansion coefficient to obtain the order demand of low-end goods, mid-end goods, and high-end goods, respectively, recorded as Oeder n x Oeder n y Oeder n z , where x, y, and z represent low-order goods, mid-order goods, and high-order goods, respectively; When ordering Oeder n When the corresponding customer is a non-member customer, get the order Oeder n The quantity of low-end goods, mid-end goods, and high-end goods is recorded as the order demand of low-end goods, mid-end goods, and high-end goods as Oeder n x Oeder n y Oeder n z ; Substitute into the formula Calculate in and get the demand NUM of low-level goods, medium-level goods and high-level goods in the adjustment period respectively. X 、NUM Y 、NUM Z ; When the demand for low-level goods reaches its maximum, the selling prices of mid-level and high-level goods are reduced; When the demand for mid-level products reaches its maximum, increase the selling price of low-level products and reduce the selling price of high-level products; When the demand for high-level products reaches its maximum value, the selling prices of low-level and high-level products are increased.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 8 are implemented.

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