Market analysis guiding method and device for new products and computer equipment

Abstract

The invention relates to a market analysis guiding method and device for a new product and computer equipment. The method comprises the following steps: acquiring regional information, new product sales information and user feedback information of each regional range, and identifying regional feature information of each regional range; generating a regional sales distribution map, and identifying user feature data and new product feedback feature data of each regional range; on the basis of the regional sales distribution diagram, the user feature data of each regional range and the new product feedback feature data of each regional range, through a feature analysis network, sales adaptation information of the new product for each regional range and a product optimization strategy of the new product are identified; and on the basis of the regional feature information of each regional range, the sales adaptation information of the new product for each regional range, and the product optimization strategy of the new product, new product sales guidance information of each regional range and product optimization guidance information of the new product are generated. By adopting the method, the accuracy of store assessment can be improved.

Description

Technical Field

[0001] The present application relates to the technical field of big data analysis and product analysis guidance, and in particular to a market analysis guidance method, device and computer equipment for a new product. Background Art

[0002] In the modern business environment, new product launches and market performance analysis are critical factors for business success. Real-time data analysis and precise market positioning are becoming increasingly important, particularly in the field of intelligent liquid dispensers and liquid dispensing equipment. Traditional market analysis methods often rely on questionnaires and sales data, which are not only time-consuming and labor-intensive but also susceptible to subjective factors and data lags. Therefore, how to utilize advanced data analysis technologies and intelligent control systems to achieve comprehensive and accurate analysis of new products has become a pressing issue in this field.

[0003] Existing product market analysis methods involve manually collecting product information and online customer feedback to generate market research and analysis data for new products. However, this method is inefficient, is subject to subjective factors, and often has high limitations on information analysis due to geographical restrictions, resulting in poor market analysis guidance for new products. Summary of the Invention

[0004] Based on this, it is necessary to provide a new product market analysis guidance method, device, computer equipment, computer-readable storage medium and computer program product to address the above technical issues.

[0005] In a first aspect, the present application provides a method for guiding market analysis of a new product, comprising:

[0006] Acquire regional information of each area, new product sales information of each area, and user feedback information of each area, and identify regional feature information of each area based on the regional information of each area;

[0007] Generate a regional sales distribution map based on the new product sales information of each of the regional areas, and identify user feature data of each of the regional areas and new product feedback feature data of each of the regional areas based on user feedback information of each of the regional areas;

[0008] Based on the regional sales distribution map, the user characteristic data of each of the regional ranges, and the new product feedback characteristic data of each of the regional ranges, the sales adaptation information of the new product for each regional range and the product optimization strategy of the new product are identified through the feature analysis network, and based on the geographical characteristic information of each regional range, the sales adaptation information of the new product for each regional range, and the product optimization strategy of the new product, the new product sales guidance information for each of the regional ranges and the product optimization guidance information of the new product are generated.

[0009] Optionally, the identifying the regional feature information of each regional range based on the regional information of each regional range includes:

[0010] For each area range, the regional information of the area range is split into regional association data of each regional feature type;

[0011] Based on the regional association data of each of the regional feature types, extracting the regional feature data of each map feature type through a regional feature extraction network;

[0012] Among the regional feature data of each regional feature type, regional feature data associated with product sales are screened as target regional feature data of each regional feature type, and the target regional feature data of all regional feature types are used as the regional feature information of the area range.

[0013] Optionally, generating a regional sales distribution map based on the new product sales information in each of the regional ranges includes:

[0014] Normalize the sales data of new products in each region to obtain the standard sales data of new products in each region;

[0015] The standard sales data of new products in each regional range are distributed and identified in the spatial position range corresponding to each regional range to obtain the initial sales distribution map corresponding to each regional range, and the initial sales distribution map corresponding to each regional range is normalized and identified to obtain the regional sales distribution map.

[0016] Optionally, identifying user characteristic data of each area range and new product feedback characteristic data of each area range based on user feedback information of each area range includes:

[0017] For each regional range, user feedback information in the regional range is divided into feedback content of each feedback angle, and user information of each feedback user is collected;

[0018] Based on the user information of each feedback user, identifying the user groups corresponding to each feedback population type corresponding to the regional range and the population characteristic data of each feedback population type through a user portrait analysis model, and using the population characteristic data of each feedback population type as the user characteristic data of the regional range;

[0019] For each feedback group type, based on the feedback content of each feedback user in the user group corresponding to the feedback group type, the semantic feature extraction network is used to extract the feedback feature data of each feedback user at each feedback angle. Based on the feedback feature data of each feedback user at each feedback angle, a clustering algorithm is used to screen the target feedback feature data of each feedback angle.

[0020] The target feedback feature data of each feedback angle for each feedback group type corresponding to the regional range is used as the new product feedback feature data for the regional range.

[0021] Optionally, the identifying, through a feature analysis network, sales adaptation information of a new product for each regional range and a product optimization strategy for the new product based on the regional sales distribution map, user feature data for each regional range, and feature data of new product feedback for each regional range includes:

[0022] Based on the regional sales distribution map, identifying the degree of preference for the new product in each region, and based on the demographic data of each feedback group type in each region and the degree of preference for the new product in each region, identifying the demographic data of each target group for the new product through a feature adaptation network;

[0023] Based on the characteristic data of each target population adapted by the new product and the characteristic data of each feedback population type in each regional range, the characteristic data of the target population included in each regional range is filtered as sales adaptation information for each regional range;

[0024] For each regional range, based on the target feedback feature data of each feedback angle of each feedback population type corresponding to the regional range, the product demand features of new products adapted to the regional range are identified through the feature analysis network, and the product demand features of new products adapted to all regional ranges are used as the product optimization strategy for the new products.

[0025] Optionally, generating new product sales guidance information for each regional range and product optimization guidance information for the new product based on the regional feature information of each regional range, the sales adaptation information of the new product for each regional range, and the product optimization strategy of the new product includes:

[0026] For each regional range, based on the characteristic data of each target population in the regional range, the characteristic analysis network is used to identify each sales-related characteristic of the regional range, and based on each sales-related characteristic of the regional range and the target regional characteristic data of each regional characteristic type in the regional range, new product sales guidance information for the regional range is generated using a sales guidance template;

[0027] Obtaining a product feature adjustment range of the new product, and identifying target product feature data adapted to the regional range based on the product demand features of the new product adapted to the regional range and the product feature adjustment range of the new product;

[0028] The characteristic data of each target product adapted to the regional range is used as product optimization guidance information for the new product.

[0029] In a second aspect, the present application also provides a market analysis guidance device for a new product, comprising:

[0030] an acquisition module, configured to acquire geographical information of each area, new product sales information of each area, and user feedback information of each area, and identify geographical feature information of each area based on the geographical information of each area;

[0031] an identification module, configured to generate a regional sales distribution map based on the new product sales information of each of the regional areas, and to identify user characteristic data of each of the regional areas and new product feedback characteristic data of each of the regional areas based on user feedback information of each of the regional areas;

[0032] A generation module is used to identify the sales adaptation information of the new product for each regional range and the product optimization strategy of the new product based on the regional sales distribution map, the user characteristic data of each regional range, and the new product feedback characteristic data of each regional range through a feature analysis network, and generate new product sales guidance information for each regional range and product optimization guidance information for the new product based on the geographical characteristic information of each regional range, the sales adaptation information of the new product for each regional range, and the product optimization strategy of the new product.

[0033] Optionally, the acquisition module is specifically configured to:

[0034] For each area range, the regional information of the area range is split into regional association data of each regional feature type;

[0035] Based on the regional association data of each of the regional feature types, extracting the regional feature data of each map feature type through a regional feature extraction network;

[0036] Among the regional feature data of each regional feature type, regional feature data associated with product sales are screened as target regional feature data of each regional feature type, and the target regional feature data of all regional feature types are used as the regional feature information of the area range.

[0037] Optionally, the identification module is specifically configured to:

[0038] Normalize the sales data of new products in each region to obtain the standard sales data of new products in each region;

[0039] The standard sales data of new products in each regional range are distributed and identified in the spatial position range corresponding to each regional range to obtain the initial sales distribution map corresponding to each regional range, and the initial sales distribution map corresponding to each regional range is normalized and identified to obtain the regional sales distribution map.

[0040] Optionally, the identification module is specifically configured to:

[0041] For each regional range, user feedback information in the regional range is divided into feedback content of each feedback angle, and user information of each feedback user is collected;

[0042] Based on the user information of each feedback user, identifying the user groups corresponding to each feedback population type corresponding to the regional range and the population characteristic data of each feedback population type through a user portrait analysis model, and using the population characteristic data of each feedback population type as the user characteristic data of the regional range;

[0043] For each feedback group type, based on the feedback content of each feedback user in the user group corresponding to the feedback group type, the semantic feature extraction network is used to extract the feedback feature data of each feedback user at each feedback angle. Based on the feedback feature data of each feedback user at each feedback angle, a clustering algorithm is used to screen the target feedback feature data of each feedback angle.

[0044] The target feedback feature data of each feedback angle for each feedback group type corresponding to the regional range is used as the new product feedback feature data for the regional range.

[0045] Optionally, the generating module is specifically configured to:

[0046] Based on the regional sales distribution map, identifying the degree of preference for the new product in each region, and based on the demographic data of each feedback group type in each region and the degree of preference for the new product in each region, identifying the demographic data of each target group for the new product through a feature adaptation network;

[0047] Based on the characteristic data of each target population adapted by the new product and the characteristic data of each feedback population type in each regional range, the characteristic data of the target population included in each regional range is filtered as sales adaptation information for each regional range;

[0048] For each regional range, based on the target feedback feature data of each feedback angle of each feedback population type corresponding to the regional range, the product demand features of new products adapted to the regional range are identified through the feature analysis network, and the product demand features of new products adapted to all regional ranges are used as the product optimization strategy for the new products.

[0049] Optionally, the generating module is specifically configured to:

[0050] For each regional range, based on the characteristic data of each target population in the regional range, the characteristic analysis network is used to identify each sales-related characteristic of the regional range, and based on each sales-related characteristic of the regional range and the target regional characteristic data of each regional characteristic type in the regional range, new product sales guidance information for the regional range is generated using a sales guidance template;

[0051] Obtaining a product feature adjustment range of the new product, and identifying target product feature data adapted to the regional range based on the product demand features of the new product adapted to the regional range and the product feature adjustment range of the new product;

[0052] The characteristic data of each target product adapted to the regional range is used as product optimization guidance information for the new product.

[0053] In a third aspect, the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any one of the methods described in the first aspect when executing the computer program.

[0054] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any one of the methods in the first aspect.

[0055] In a fifth aspect, the present application provides a computer program product, wherein the computer program product comprises a computer program, and when the computer program is executed by a processor, the steps of any one of the methods in the first aspect are implemented.

[0056] The above-mentioned new product market analysis guidance method, device and computer equipment obtain the geographical information of each regional range, the new product sales information of each said regional range, and the user feedback information of each said regional range, and identify the geographical characteristic information of each regional range based on the geographical information of each said regional range; generate a regional sales distribution map based on the new product sales information of each said regional range, and identify the user characteristic data of each said regional range and the new product feedback characteristic data of each said regional range based on the user feedback information of each said regional range; based on the regional sales distribution map, the user characteristic data of each said regional range, and the new product feedback characteristic data of each said regional range, identify the sales adaptation information of the new product for each regional range and the product optimization strategy of the new product through a feature analysis network, and generate new product sales guidance information for each said regional range and the product optimization guidance information of the new product based on the sales adaptation information of the new product for each regional range and the product optimization strategy of the new product. This solution conducts a comprehensive analysis of regional information, new product sales information, user feedback information and other content in different regions, thereby identifying the sales adaptation information and product optimization strategies of new products for each region, and generating new product sales guidance information and product optimization guidance information for each region, thereby realizing product applicability analysis, user feedback analysis, and market demand analysis of new products in different regions, avoiding the inefficiency and deviation rate of manual analysis, and in actual analysis, combining the regional sales distribution maps of all regions to comprehensively analyze the sales adaptation information of each region, which can effectively conduct a comprehensive and targeted comprehensive analysis of the user groups applicable to the new products, thereby improving the analysis accuracy of the sales adaptation of new products, and this solution avoids the process of manual participation in the analysis, eliminates the influence of subjective factors, and the analysis limitations of regional restrictions, thereby improving the market analysis guidance effect of new products. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0058] Figure 1 A flowchart of a method for guiding market analysis of a new product in one embodiment is shown;

[0059] Figure 2 A flowchart illustrating an example of a market analysis guide for a new product in one embodiment;

[0060] Figure 3 A structural block diagram of a market analysis and guidance device for new products in one embodiment;

[0061] Figure 4 FIG. 1 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0062] In order to make the purpose, technical solutions and advantages of this application more clearly understood, the present application is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0063] The market analysis guidance method for new products provided in the embodiment of the present application can be applied to the intelligent control system for market analysis guidance of new products. The system can be applied to a terminal, which can be, but not limited to, various personal computers, laptops, mid-range computers, etc. Among them, the terminal comprehensively analyzes the regional information, new product sales information, user feedback information, etc. of different regional ranges, thereby identifying the sales adaptation information of new products and product optimization strategies for each regional range, thereby generating new product sales guidance information and product optimization guidance information for each regional range, thereby realizing product applicability analysis, user feedback analysis, and market demand analysis of new products in different regional ranges, avoiding the inefficiency and deviation rate of manual analysis, and in actual analysis, combining the regional sales distribution map of all regional ranges, thereby comprehensively analyzing the sales adaptation information of each regional range, and effectively conducting a comprehensive and targeted comprehensive analysis of the user groups to which the new product is applicable, thereby improving the analysis accuracy of the sales adaptation of the new product, and this solution avoids the process of manual participation in the analysis, eliminates the influence of subjective factors, and the analysis limitations of regional restrictions, thereby improving the market analysis guidance effect of new products.

[0064] In an exemplary embodiment, Figure 1 As shown, a market analysis guidance method for a new product is provided, which is described by taking the application of the method to a terminal as an example, and includes the following steps S101 to S103.

[0065] in:

[0066] Step S101 : obtaining the regional information of each area, the new product sales information of each area, and the user feedback information of each area, and identifying the regional feature information of each area based on the regional information of each area.

[0067] In this embodiment, in response to a staff member's information upload operation, the terminal obtains regional information for each region. This regional information includes, but is not limited to, topographic information, population distribution information, climate information, hydrological information, economic level information, and transportation network information. The terminal then uses intelligent liquid dispensers installed in each store within the region to collect product sales information from each store. This product sales information includes, among other things, sales volume at each point in time. Finally, the terminal obtains user feedback from different regions through online and offline channels. This user feedback includes feedback from various perspectives, including, but not limited to, taste, ingredients, packaging, material ratios, and new product promotions. Based on the regional information for each region, the terminal identifies regional characteristic information for each region. The regional characteristic information for each region includes regional characteristic data for various regional characteristic types that influence product sales. These regional characteristic types include, but are not limited to, climate, topographic, hydrological, economic, and transportation network types. The specific identification process will be described in detail later.

[0068] Step S102 : generating a regional sales distribution map based on the new product sales information of each region, and identifying user feature data of each region and new product feedback feature data of each region based on user feedback information of each region.

[0069] In this embodiment, the terminal generates a regional sales distribution map based on new product sales information for each region, and identifies user characteristic data and new product feedback characteristic data for each region based on user feedback information for each region. The regional sales distribution map includes the sales volume distribution ranges of the new product in different regions. The user characteristic data includes demographic characteristic data for different feedback population types, while the new product feedback characteristic data includes target feedback characteristic data for each feedback perspective for each feedback population type. The specific identification process will be described in detail later.

[0070] Step S103, based on the regional sales distribution map, user feature data of each regional range, and new product feedback feature data of each regional range, through the feature analysis network, identify the sales adaptation information of the new product for each regional range, and the product optimization strategy of the new product, and based on the sales adaptation information of the new product for each regional range, and the product optimization strategy of the new product, generate new product sales guidance information for each regional range, and product optimization guidance information for the new product.

[0071] In this embodiment, the terminal uses a feature analysis network to identify the sales adaptation information of new products for each region and the product optimization strategy for the new products based on the regional sales distribution map, user feature data for each region, and feature data for new product feedback for each region. Based on the sales adaptation information and product optimization strategy for the new products for each region, the terminal generates new product sales guidance information and product optimization guidance information for each region. The feature analysis network is a deep learning-based SENS (Social Epistemic Network Signature) feature analysis network. The new product sales guidance information for each region is used to guide the new product sales strategy for that region, as well as target demographic feature data for sales methods and campaigns, and sales attention and adaptation information corresponding to target regional feature data for each regional feature type. The product optimization guidance information includes target product feature data from different perspectives, namely, from the perspectives of taste, ingredients, packaging, material ratio, and new product campaigns, to determine the optimization direction and degree of the product.

[0072] Based on the above solution, by comprehensively analyzing the geographical information, new product sales information, user feedback information and other contents in different regional ranges, the sales adaptation information and product optimization strategies of new products are identified for each regional range, thereby generating new product sales guidance information and product optimization guidance information for each regional range, thereby realizing product applicability analysis, user feedback analysis, and market demand analysis of new products in different regional ranges, avoiding the inefficiency and deviation rate of manual analysis, and in actual analysis, combining the regional sales distribution maps of all regional ranges to comprehensively analyze the sales adaptation information of each regional range, which can effectively conduct a comprehensive and targeted comprehensive analysis of the user groups applicable to the new products, thereby improving the analysis accuracy of the sales adaptation of new products, and this solution avoids the process of manual participation in the analysis, eliminates the influence of subjective factors, and the analysis limitations of regional restrictions, thereby improving the market analysis guidance effect of new products.

[0073] Optionally, based on the regional information of each regional range, the regional feature information of each regional range is identified, including: for each regional range, splitting the regional information of the regional range into regional association data of each regional feature type; based on the regional association data of each regional feature type, extracting each regional feature data of each map feature type through a regional feature extraction network; among the each regional feature data of each regional feature type, screening the regional feature data associated with product sales as the target regional feature data of each regional feature type, and using the target regional feature data of all regional feature types as the regional feature information of the regional range.

[0074] In this embodiment, the terminal divides the regional information of each area into regional association data for each regional characteristic type. This regional association data includes, for example, areas with high and low population density; areas with rugged terrain and areas with flat terrain; areas with high temperature, high humidity, high winds, and high rainfall; areas with economic cores and areas with economically deviated areas; and areas with transportation hubs and areas with sparse transportation lines.

[0075] Based on the regional association data of each regional feature type, the terminal extracts the regional feature data of each map feature type through the regional feature extraction network, wherein the regional feature data is the position range of the range of each regional association data within the area range.

[0076] The terminal then selects the regional characteristic data associated with product sales from the regional characteristic data for each regional characteristic type, and uses the target regional characteristic data for each regional characteristic type as the regional characteristic information for the area. The selected regional characteristic data includes the location ranges corresponding to areas with flat terrain, areas with high population density, economic core areas, areas with high temperature climates, and transportation hubs.

[0077] Based on the above solution, by screening the ranges in each region that are suitable for product sales or conducive to product marketing, and using them as the target regional feature data for each regional feature type, the accuracy and adaptability of the range adaptation for marketing, promotion, activities, and exhibition areas when generating new product sales guidance information are improved.

[0078] Optionally, a regional sales distribution map is generated based on the new product sales information of each regional range, including: normalizing the sales data of the new product sales information of each regional range to obtain the standard sales data of new products of each regional range; performing distribution identification processing on the standard sales data of new products of each regional range in the spatial position range corresponding to each regional range to obtain the initial sales distribution map corresponding to each regional range; and performing normalization identification processing on the initial sales distribution map corresponding to each regional range to obtain the regional sales distribution map.

[0079] In this embodiment, the terminal normalizes the sales data of new products in each region to obtain standardized sales data for each new product. This normalization process normalizes the magnitude, unit of measurement, and interval division criteria of all sales data to the same magnitude, unit of measurement, and interval division criteria as those pre-set in the terminal. This improves the accuracy and comprehensiveness of sales data analysis for each region.

[0080] The terminal distributes the standard sales data of new products in each regional range within the spatial location range corresponding to each regional range, obtaining an initial sales distribution map corresponding to each regional range. The initial sales distribution map corresponding to each regional range is then normalized and labeled to obtain a regional sales distribution map. The segment labeling process involves labeling different sales volumes, such as monthly sales of 0-2,000 cups, 2,000-5,000 cups, 2,000-8,000 cups, 8,000-12,000 cups, 12,000-15,000 cups, and so on, or even more than 50,000 cups. The labeling method can be symbolic, color, or graphical. When the initial sales distribution map is normalized, the boundaries of equal range lines within the same range are labeled to obtain a regional sales distribution map.

[0081] Based on the above solution, after normalizing the sales data, different sales levels are range-marked, which improves the accuracy and comprehensiveness of identifying the applicable scope of new products.

[0082] Optionally, based on user feedback information in each regional range, user characteristic data of each regional range and new product feedback characteristic data of each regional range are identified, including: for each regional range, splitting the user feedback information of the regional range into feedback content of each feedback angle, and collecting user information of each feedback user; based on the user information of each feedback user, identifying the user group corresponding to each feedback population type corresponding to the regional range and the population characteristic data of each feedback population type through a user portrait analysis model, and using the population characteristic data of each feedback population type as the user characteristic data of the regional range; for each feedback population type, based on the feedback content of each feedback angle of each feedback user in the user group corresponding to the feedback population type, extracting the feedback characteristic data of each feedback angle of each feedback user through a semantic feature extraction network, and based on the feedback characteristic data of each feedback angle of each feedback user, screening the target feedback characteristic data of each feedback angle through a clustering algorithm; using the target feedback characteristic data of each feedback angle of each feedback population type corresponding to the regional range as the new product feedback characteristic data of the regional range.

[0083] In this embodiment, the terminal divides the user feedback information in each area into feedback content from different feedback angles and collects user information of each feedback user, including but not limited to user attribute information such as age, gender, geographic location, and consumption habits.

[0084] Then, based on the user information of each feedback user, the terminal uses a user portrait analysis model to identify the user groups corresponding to each feedback population type in the regional scope, as well as the population characteristic data of each feedback population type, and uses the population characteristic data of each feedback population type as the user characteristic data of the regional scope. The user portrait analysis model includes a convolutional neural network based on user portrait analysis technology and a feature extraction network based on deep learning. The terminal uses the convolutional neural network based on user portrait analysis technology to identify the user groups corresponding to each feedback population type in the regional scope, and uses the feature extraction network based on deep learning to identify the population characteristic data of each feedback population type. Each feedback population type includes, but is not limited to, types of key information characteristics of individual populations, such as young office workers, product purchasing executives, home-based people, people who try new things, loyal product fans, and bulk consumers.

[0085] For each feedback group type, the terminal uses a semantic feature extraction network to extract feedback feature data for each feedback user's feedback angle based on the feedback content from each feedback user in the user group corresponding to the feedback group type. Furthermore, based on the feedback feature data from each feedback user's feedback angle, the terminal uses a clustering algorithm to filter the target feedback feature data for each feedback angle. Some feedback angles may not have corresponding target feedback feature data.

[0086] The terminal uses the target feedback feature data of each feedback angle for each feedback group type corresponding to the regional range as the new product feedback feature data for the regional range.

[0087] Based on the above solution, by conducting user portrait analysis and then dividing them into user groups corresponding to different feedback population types, we can filter the target feedback feature data for each feedback angle corresponding to each user group, thereby improving the comprehensiveness, granularity, and accuracy of the analysis of product-suitable users.

[0088] Optionally, based on the regional sales distribution map, user characteristic data of each regional range, and new product feedback characteristic data of each regional range, the feature analysis network is used to identify the sales adaptation information of the new product for each regional range and the product optimization strategy of the new product, including: based on the regional sales distribution map, identifying the preference level of the new product in each regional range, and based on the population characteristic data of each feedback population type in each regional range and the preference level of the new product in each regional range, identifying the characteristic data of each target population adapted to the new product through the feature adaptation network; based on the characteristic data of each target population adapted to the new product and the population characteristic data of each feedback population type in each regional range, screening the target population characteristic data contained in each regional range as the sales adaptation information of each regional range; for each regional range, based on the target feedback characteristic data of each feedback angle of each feedback population type corresponding to the regional range, identifying the product demand characteristics of the new product adapted to the regional range through the feature analysis network, and using the product demand characteristics of all new products adapted to the regional range as the product optimization strategy for the new product.

[0089] In this embodiment, the terminal identifies the degree of preference for new products in each region based on the regional sales distribution map. Furthermore, based on the demographic data of each feedback group type in each region and the degree of preference for new products in each region, the terminal uses a feature adaptation network to identify the demographic data of each target population for the new product adaptation. The feature adaptation network is used to screen the demographic data for those with a high degree of preference for the new product and whose demographic data appears frequently in each feedback group type in each region. The feature adaptation network is a classifier neural network based on an attention mechanism.

[0090] Based on the target demographic data for the new product and the demographic data for each feedback demographic type within each region, the terminal filters the target demographic data within each region as sales adaptation information for each region. The target demographic data within each region is the demographic data for each target demographic type that overlaps with the demographic data for each feedback demographic type within each region.

[0091] Then, for each regional range, the terminal uses the target feedback feature data of each feedback angle for each feedback population type corresponding to the regional range to identify the product demand features of new products adapted to the regional range through the feature analysis network, and uses the product demand features of all new products adapted to the regional range as the product optimization strategy for the new products. Among them, the product demand features of the new product are the user demand features in each feedback angle of the new product. For example, if the new product is a new tea product, then the product demand features include the demand for tea flavor intensity from the taste perspective, the demand for simple ingredients from the ingredient perspective, the demand for outer packaging related to tea raw materials from the packaging perspective, the demand for increased tea material ratio from the material ratio perspective, and the demand for activities related to tea products from the new product activity perspective (for example, blind boxes of tea products, activities such as tasting and screening out tea products corresponding to new products from multiple tea products, etc.).

[0092] Based on the above solution, by screening the population characteristic data in different regions, the targeted sales guidance effect for the user groups targeted during sales in the region is improved, and the demand characteristics of various feedback angles of new products are identified, thereby improving the optimization direction and optimization angle guidance accuracy of new products.

[0093] Optionally, based on the regional characteristic information of each regional range, the sales adaptation information of the new product for each regional range, and the product optimization strategy of the new product, new product sales guidance information and product optimization guidance information for the new product are generated for each regional range, including: for each regional range, based on the characteristic data of each target population in the regional range, identifying the sales-related characteristics of the regional range through a feature analysis network, and generating new product sales guidance information for the regional range through a sales guidance template based on the sales-related characteristics of the regional range and the target regional characteristic data of each regional characteristic type of the regional range; obtaining the product feature adjustment range of the new product, and identifying the characteristic data of each target product adapted to the regional range based on the product demand characteristics of the new product adapted to the regional range and the product feature adjustment range of the new product; and using the characteristic data of each target product adapted to the regional range as the product optimization guidance information for the new product.

[0094] In this embodiment, for each regional range, the terminal identifies each sales-related feature of the regional range based on the characteristic data of each target population in the regional range through a feature analysis network, and generates regional new product sales guidance information through a sales guidance template based on each sales-related feature of the regional range and the target regional characteristic data of each regional characteristic type of the regional range. The sales guidance template is guidance information preset in the terminal for sales personnel to provide sales guidance to the target population and sales area. The sales-related feature can guide the sales personnel to the population characteristics of the sales population in the regional range through the sales guidance template, such as office workers, females, aged 20-40 years old, ordinary economic groups, etc., and the target regional characteristic data is used for the sales area applicable to the sales personnel, such as a flat area, an area where the sales population appears more frequently, such as an office area, a commercial area, a scenic area, an entertainment and leisure area, etc.

[0095] The terminal obtains the product feature adjustment range of the new product and, based on the product demand features of the new product adapted to the regional scope and the product feature adjustment range of the new product, identifies the feature data of each target product adapted to the regional scope. Finally, the terminal uses the feature data of each target product adapted to the regional scope as product optimization guidance information for the new product. The feature data of each target product adapted to the regional scope refers to the feature data that overlaps with the product demand features of the new product adapted to the regional scope within the new product feature adjustment range.

[0096] Based on the above solution, by comprehensively adapting the population characteristics and regional characteristics, new product sales guidance information is generated, and the product feature adjustment range of new products is adapted, so as to screen the feature data of each target product adapted to each regional range, thereby improving the accuracy and comprehensiveness of sales guidance for staff and new product optimization guidance.

[0097] The application also provides an example of market analysis guidance for a new product, such as Figure 2 As shown, the specific processing process includes the following steps:

[0098] Step S201 : Acquire the regional information of each area, the new product sales information of each area, and the user feedback information of each area.

[0099] Step S202 : For each area range, the region information of the area range is split into region-related data of each region feature type.

[0100] Step S203 : Based on the region association data of each region feature type, the region feature data of each map feature type is extracted through a region feature extraction network.

[0101] Step S204 , among the regional feature data of each regional feature type, regional feature data associated with product sales are screened as target regional feature data of each regional feature type, and the target regional feature data of all regional feature types are used as regional feature information of the area.

[0102] Step S205 , normalizing the sales data of the new product in each region to obtain the standard sales data of the new product in each region.

[0103] In step S206, the standard sales data of the new products in each regional range are distributed and identified in the spatial position range corresponding to each regional range to obtain the initial sales distribution map corresponding to each regional range, and the initial sales distribution map corresponding to each regional range is normalized and identified to obtain the regional sales distribution map.

[0104] Step S207 : For each area range, the user feedback information of the area range is divided into feedback content of each feedback angle, and user information of each feedback user is collected.

[0105] Step S208: Based on the user information of each feedback user, the user portrait analysis model is used to identify the user groups corresponding to each feedback population type corresponding to the regional scope, as well as the population characteristic data of each feedback population type, and the population characteristic data of each feedback population type is used as the user characteristic data of the regional scope.

[0106] Step S209: For each feedback group type, based on the feedback content of each feedback user in the user group corresponding to the feedback group type, the feedback feature data of each feedback user at each feedback angle is extracted through a semantic feature extraction network. Based on the feedback feature data of each feedback user at each feedback angle, a clustering algorithm is used to screen the target feedback feature data of each feedback angle.

[0107] Step S210 : Using the target feedback feature data of each feedback angle for each feedback group type corresponding to the regional range as the new product feedback feature data for the regional range.

[0108] Step S211: Based on the regional sales distribution map, identify the preference level of the new product in each regional range, and based on the population characteristic data of each feedback population type in each regional range and the preference level of the new product in each regional range, identify the characteristic data of each target population adapted for the new product through the feature adaptation network.

[0109] Step S212 , based on the characteristic data of each target population adapted for the new product and the characteristic data of each feedback population type in each regional range, the characteristic data of the target population included in each regional range is filtered as sales adaptation information for each regional range.

[0110] Step S213: For each regional range, based on the target feedback feature data of each feedback angle of each feedback population type corresponding to the regional range, the product demand features of new products adapted to the regional range are identified through the feature analysis network, and the product demand features of new products adapted to all regional ranges are used as the product optimization strategy for new products.

[0111] In step S214, for each regional scope, based on the characteristic data of each target population in the regional scope, the sales-related characteristics of the regional scope are identified through the characteristic analysis network, and based on the sales-related characteristics of the regional scope and the target regional characteristic data of each regional characteristic type in the regional scope, new product sales guidance information for the regional scope is generated through the sales guidance template.

[0112] Step S215 , obtaining the product feature adjustment range of the new product, and identifying each target product feature data adapted to the regional range based on the product demand features of the new product adapted to the regional range and the product feature adjustment range of the new product.

[0113] Step S216: Using the characteristic data of each target product adapted to the regional scope as product optimization guidance information for the new product.

[0114] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0115] Based on the same inventive concept, the present application also provides a new product market analysis guidance device for implementing the above-mentioned new product market analysis guidance method. The solution provided by this device is similar to the solution described in the above-mentioned method. Therefore, the specific limitations of one or more new product market analysis guidance device embodiments provided below can be found in the above-mentioned limitations of the new product market analysis guidance method, and will not be repeated here.

[0116] In an exemplary embodiment, Figure 3 As shown, a market analysis guidance device for a new product is provided, comprising: an acquisition module 310, an identification module 320 and a generation module 330, wherein:

[0117] An acquisition module 310 is configured to acquire geographical information of each area, new product sales information of each area, and user feedback information of each area, and identify geographical feature information of each area based on the geographical information of each area;

[0118] Identification module 320, configured to generate a regional sales distribution map based on the new product sales information of each of the regional areas, and identify user feature data of each of the regional areas and new product feedback feature data of each of the regional areas based on user feedback information of each of the regional areas;

[0119] Generation module 330 is used to identify the sales adaptation information of the new product for each regional range and the product optimization strategy of the new product based on the regional sales distribution map, the user characteristic data of each regional range, and the new product feedback characteristic data of each regional range through a feature analysis network, and generate new product sales guidance information for each regional range and product optimization guidance information for the new product based on the geographical characteristic information of each regional range, the sales adaptation information of the new product for each regional range, and the product optimization strategy of the new product.

[0120] Optionally, the acquisition module 310 is specifically configured to:

[0121] For each area range, the regional information of the area range is split into regional association data of each regional feature type;

[0122] Based on the regional association data of each of the regional feature types, extracting the regional feature data of each map feature type through a regional feature extraction network;

[0123] Among the regional feature data of each regional feature type, regional feature data associated with product sales are screened as target regional feature data of each regional feature type, and the target regional feature data of all regional feature types are used as the regional feature information of the area range.

[0124] Optionally, the identification module 320 is specifically configured to:

[0125] Normalize the sales data of new products in each region to obtain the standard sales data of new products in each region;

[0126] The standard sales data of new products in each regional range are distributed and identified in the spatial position range corresponding to each regional range to obtain the initial sales distribution map corresponding to each regional range, and the initial sales distribution map corresponding to each regional range is normalized and identified to obtain the regional sales distribution map.

[0127] Optionally, the identification module 320 is specifically configured to:

[0128] For each regional range, user feedback information in the regional range is divided into feedback content of each feedback angle, and user information of each feedback user is collected;

[0129] Based on the user information of each feedback user, identifying the user groups corresponding to each feedback population type corresponding to the regional range and the population characteristic data of each feedback population type through a user portrait analysis model, and using the population characteristic data of each feedback population type as the user characteristic data of the regional range;

[0130] For each feedback group type, based on the feedback content of each feedback user in the user group corresponding to the feedback group type, the semantic feature extraction network is used to extract the feedback feature data of each feedback user at each feedback angle. Based on the feedback feature data of each feedback user at each feedback angle, a clustering algorithm is used to screen the target feedback feature data of each feedback angle.

[0131] The target feedback feature data of each feedback angle for each feedback group type corresponding to the regional range is used as the new product feedback feature data for the regional range.

[0132] Optionally, the generating module 330 is specifically configured to:

[0133] Based on the regional sales distribution map, identifying the degree of preference for the new product in each region, and based on the demographic data of each feedback group type in each region and the degree of preference for the new product in each region, identifying the demographic data of each target group for the new product through a feature adaptation network;

[0134] Based on the characteristic data of each target population adapted by the new product and the characteristic data of each feedback population type in each regional range, the characteristic data of the target population included in each regional range is filtered as sales adaptation information for each regional range;

[0135] For each regional range, based on the target feedback feature data of each feedback angle of each feedback population type corresponding to the regional range, the product demand features of new products adapted to the regional range are identified through the feature analysis network, and the product demand features of new products adapted to all regional ranges are used as the product optimization strategy for the new products.

[0136] Optionally, the generating module 330 is specifically configured to:

[0137] For each regional range, based on the characteristic data of each target population in the regional range, the characteristic analysis network is used to identify each sales-related characteristic of the regional range, and based on each sales-related characteristic of the regional range and the target regional characteristic data of each regional characteristic type in the regional range, new product sales guidance information for the regional range is generated using a sales guidance template;

[0138] Obtaining a product feature adjustment range of the new product, and identifying target product feature data adapted to the regional range based on the product demand features of the new product adapted to the regional range and the product feature adjustment range of the new product;

[0139] The characteristic data of each target product adapted to the regional range is used as product optimization guidance information for the new product.

[0140] Each module in the aforementioned new product market analysis guidance device may be implemented in whole or in part through software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a computer device memory in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0141] In an exemplary embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. Figure 4As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected via a system bus, and the communication interface, the display unit and the input device are connected to the system bus via the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a market analysis guidance method for a new product is implemented. The display unit of the computer device is used to form a visually visible image, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device casing, or an external keyboard, touchpad or mouse.

[0142] Those skilled in the art will understand that Figure 4 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0143] In an exemplary embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the steps of a market analysis guidance method for a new product when executing the computer program.

[0144] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the market analysis guidance method for new products are implemented.

[0145] In one embodiment, a computer program product is provided, comprising a computer program, which implements the steps of the market analysis guidance method for a new product when the computer program is executed by a processor.

[0146] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0147] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0148] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0149] The above-described embodiments merely represent several implementation methods of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person of ordinary skill in the art may make various modifications and improvements without departing from the spirit of the present application, and these modifications and improvements fall within the scope of protection of the present application. Therefore, the scope of protection of the present application shall be determined by the appended claims.

Claims

1. A market analysis guidance method for a new product, characterized in that: The method comprises: Acquire regional information of each area, new product sales information of each area, and user feedback information of each area, and identify regional feature information of each area based on the regional information of each area; Generate a regional sales distribution map based on the new product sales information of each of the regional areas, and identify user feature data of each of the regional areas and new product feedback feature data of each of the regional areas based on user feedback information of each of the regional areas; Based on the regional sales distribution map, the user characteristic data of each of the regional ranges, and the new product feedback characteristic data of each of the regional ranges, the sales adaptation information of the new product for each regional range and the product optimization strategy of the new product are identified through the feature analysis network, and based on the geographical characteristic information of each regional range, the sales adaptation information of the new product for each regional range, and the product optimization strategy of the new product, the new product sales guidance information for each of the regional ranges and the product optimization guidance information of the new product are generated.

2. The method according to claim 1, characterized in that The identifying of the regional characteristic information of each regional range based on the regional information of each regional range includes: For each area range, the regional information of the area range is split into regional association data of each regional feature type; Based on the regional association data of each of the regional feature types, extracting the regional feature data of each map feature type through a regional feature extraction network; Among the regional feature data of each regional feature type, regional feature data associated with product sales are screened as target regional feature data of each regional feature type, and the target regional feature data of all regional feature types are used as the regional feature information of the area range.

3. The method according to claim 2, characterized in that Generating a regional sales distribution map based on the new product sales information of each regional range includes: Normalize the sales data of new products in each region to obtain the standard sales data of new products in each region; The standard sales data of new products in each regional range are distributed and identified in the spatial position range corresponding to each regional range to obtain the initial sales distribution map corresponding to each regional range, and the initial sales distribution map corresponding to each regional range is normalized and identified to obtain the regional sales distribution map.

4. The method according to claim 1, wherein The identifying of user characteristic data of each area and new product feedback characteristic data of each area based on user feedback information of each area includes: For each regional range, user feedback information in the regional range is divided into feedback content of each feedback angle, and user information of each feedback user is collected; Based on the user information of each feedback user, identifying the user groups corresponding to each feedback population type corresponding to the regional range and the population characteristic data of each feedback population type through a user portrait analysis model, and using the population characteristic data of each feedback population type as the user characteristic data of the regional range; For each feedback group type, based on the feedback content of each feedback user in the user group corresponding to the feedback group type, the semantic feature extraction network is used to extract the feedback feature data of each feedback user at each feedback angle. Based on the feedback feature data of each feedback user at each feedback angle, a clustering algorithm is used to screen the target feedback feature data of each feedback angle. The target feedback feature data of each feedback angle for each feedback group type corresponding to the regional range is used as the new product feedback feature data for the regional range.

5. The method according to claim 4, characterized in that The method of identifying sales adaptation information of a new product for each regional range and a product optimization strategy for the new product through a feature analysis network based on the regional sales distribution map, user feature data of each regional range, and feature data of new product feedback in each regional range includes: Based on the regional sales distribution map, identifying the degree of preference for the new product in each region, and based on the demographic data of each feedback group type in each region and the degree of preference for the new product in each region, identifying the demographic data of each target group for the new product through a feature adaptation network; Based on the characteristic data of each target population adapted by the new product and the characteristic data of each feedback population type in each regional range, the characteristic data of the target population included in each regional range is filtered as sales adaptation information for each regional range; For each regional range, based on the target feedback feature data of each feedback angle of each feedback population type corresponding to the regional range, the product demand features of new products adapted to the regional range are identified through the feature analysis network, and the product demand features of new products adapted to all regional ranges are used as the product optimization strategy for the new products.

6. The method according to claim 5, characterized in that Generating new product sales guidance information for each regional range and product optimization guidance information for the new product based on the regional feature information of each regional range, the sales adaptation information of the new product for each regional range, and the product optimization strategy of the new product includes: For each regional range, based on the characteristic data of each target population in the regional range, the characteristic analysis network is used to identify each sales-related characteristic of the regional range, and based on each sales-related characteristic of the regional range and the target regional characteristic data of each regional characteristic type in the regional range, new product sales guidance information for the regional range is generated using a sales guidance template; Obtaining a product feature adjustment range of the new product, and identifying target product feature data adapted to the regional range based on the product demand features of the new product adapted to the regional range and the product feature adjustment range of the new product; The characteristic data of each target product adapted to the regional range is used as product optimization guidance information for the new product.

7. A market analysis and guidance device for new products, characterized in that: The device comprises: an acquisition module, configured to acquire geographical information of each area, new product sales information of each area, and user feedback information of each area, and identify geographical feature information of each area based on the geographical information of each area; an identification module, configured to generate a regional sales distribution map based on the new product sales information of each of the regional areas, and to identify user characteristic data of each of the regional areas and new product feedback characteristic data of each of the regional areas based on user feedback information of each of the regional areas; A generation module is used to identify the sales adaptation information of the new product for each regional range and the product optimization strategy of the new product based on the regional sales distribution map, the user characteristic data of each regional range, and the new product feedback characteristic data of each regional range through a feature analysis network, and generate new product sales guidance information for each regional range and product optimization guidance information for the new product based on the geographical characteristic information of each regional range, the sales adaptation information of the new product for each regional range, and the product optimization strategy of the new product.

8. 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 6 are implemented.

9. 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 6 are implemented.

10. A computer program product comprising a computer program, 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 6 are implemented.

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