Commodity selling point generation system and method, device and medium

Abstract

The invention relates to the field of marketing, and discloses a commodity selling point generation system and method, equipment and a medium. According to the scheme, firstly, market competition hotspot analysis and user demand analysis are carried out on multi-modal information of a target commodity; on the basis of a market competition hotspot analysis result, obtaining a demand value model dimension to which key propaganda characteristics of an industry benchmark brand for the target commodity belong; secondly, obtaining a demand value model dimension to which a demand feature of a user for the target commodity belongs based on a user demand analysis result; and then performing difference market opportunity calculation on the demand value model dimension to which the key propaganda features of the industry benchmark brands for the target commodity belong and the demand value model dimension to which the demand features of the user for the target commodity belong so as to obtain selling point information of the target commodity. According to the selling point generation scheme, the input-output ratio of the target commodity in the aspect of network marketing is improved.

Description

Technical Field

[0001] The present application relates to the field of marketing, and in particular to a system and method, device, and medium for generating selling points for a product. Background Art

[0002] In the field of marketing, merchants are concerned about the selling points of products and what selling points are more likely to impress potential users with their purchasing desire. With the development of e-commerce platforms, social networks and other Internet platforms, a large amount of data has been accumulated on the platforms from product display to product promotion to user orders. If these data are analyzed and information such as which user needs are not met and what potential users want is obtained, and the product selling points are refined based on this information, then product marketing will achieve better sales results.

[0003] At present, relevant researchers have proposed some product selling point generation schemes, which analyze product-related information on the Internet platform to derive user needs and attitudes to generate corresponding product selling points. However, the inventors found that the product selling point information generated by the existing product selling point information generation schemes did not avoid the key promotional features of the products by industry benchmark brands, resulting in these features in the generated product selling point information already having a high degree of competitive incentives. If the product selling point information generated by merchants includes the key promotional features of the products by industry benchmark brands, and continue to invest in marketing on these features, it will result in a low input-output ratio for merchants, thereby causing serious damage to the economic interests of merchants. Summary of the invention

[0004] The present application provides a system and method, device, and medium for generating selling points of a product, so as to solve the problem of low input-output ratio of merchants caused by existing selling point generation solutions of the product.

[0005] The first aspect of the present application provides a commodity selling point generation system, the system comprising:

[0006] A commodity information acquisition module, used to acquire multimodal information of a target commodity, wherein the multimodal information of the target commodity indicates information of the target commodity displayed in different data forms;

[0007] The selling point generation module is used to analyze the market competition hot spots of the multimodal information of the target product, and obtain the demand value model dimension to which the key promotional features of the target product by the industry benchmark brand belong based on the analysis results, and to analyze the user demand of the multimodal information of the target product, and obtain the demand value model dimension to which the user demand characteristics for the target product belong based on the analysis results, and to calculate the difference market opportunities of the key promotional features of the target product by the industry benchmark brand and the demand value model dimension to which the user demand characteristics for the target product belong, so as to generate the selling point information of the target product, wherein the difference market opportunity calculation indicates that the relevant features in the user demand characteristics for the target product that are the same as or similar to the key promotional features of the target product by the industry benchmark brand are excluding.

[0008] In some embodiments of the present application,

[0009] The commodity information acquisition module is configured to: convert the multimodal information of the target commodity into textual information corresponding to the multimodal information of the target commodity;

[0010] The selling point generation module is configured to: perform market competition hotspot analysis on the textual information corresponding to the multimodal information of the target product, and obtain the demand value model dimension to which the key promotional features of the target product by the industry benchmark brand belong based on the analysis results; perform user demand analysis on the textual information corresponding to the multimodal information of the target product, and obtain the demand value model dimension to which the user's demand characteristics for the target product belong based on the analysis results; and perform differential market opportunity calculation on the key promotional features of the target product by the industry benchmark brand and the demand value model dimension to which the user's demand characteristics for the target product belong, so as to generate the selling point information of the target product, wherein the differential market opportunity calculation indicates excluding relevant features in the user's demand characteristics for the target product that are the same as or similar to the key promotional features of the target product by the industry benchmark brand.

[0011] In some embodiments of the present application, the selling point generation module includes:

[0012] The market competition hotspot analysis submodule is used to analyze the market competition hotspots of the text information corresponding to the multimodal information of the target product, so as to obtain the prompt words corresponding to the key promotional features of the target product by the industry benchmark brand;

[0013] A user demand analysis submodule is used to perform user demand analysis on the text information corresponding to the multimodal information of the target product to obtain prompt words corresponding to the user's demand characteristics for the target product;

[0014] The large language model submodule is used to obtain the demand value dimension to which the key promotional features of the target product are based on the prompt words corresponding to the key promotional features of the target product by the industry benchmark brand, and to obtain the demand value dimension to which the user's demand features for the target product are based on the prompt words corresponding to the user's demand features for the target product;

[0015] The differential market opportunity calculation submodule is used to perform differential market opportunity calculation on the demand value model dimensions to which the industry benchmark brand's key promotional features for the target product belong and the demand value model dimensions to which the user's demand features for the target product belong, so as to generate selling point information for the target product, wherein the differential market opportunity calculation indicates that relevant features in the user's demand features for the target product that are the same as or similar to the industry benchmark brand's key promotional features for the target product are excluded.

[0016] In some embodiments of the present application, user demand analysis includes at least any one of the following demand analyses: user pre-purchase demand analysis, user post-purchase demand analysis, and user search demand analysis, and the prompt words corresponding to the user's demand characteristics for the target product include at least any one of the following prompt words: prompt words corresponding to the user's expected characteristics for the target product, prompt words corresponding to the characteristics of the target product with low user satisfaction, and prompt words corresponding to the user's attention characteristics for the target product.

[0017] In some embodiments of the present application, the user demand analysis submodule includes:

[0018] The pre-purchase demand analysis unit is used to perform a pre-purchase demand analysis on the text information corresponding to the multimodal information of the target product to obtain prompt words corresponding to the user's expected features of the target product.

[0019] In some embodiments of the present application, the user demand analysis submodule further includes:

[0020] The post-purchase demand analysis unit is used to perform a post-purchase demand analysis on the text information corresponding to the multimodal information of the target product to obtain prompt words corresponding to the characteristics of low user satisfaction with the target product.

[0021] In some embodiments of the present application, the target product multimodal information includes: user demand information related to the target product searched on an e-commerce platform or a social network platform, and the user demand analysis submodule further includes:

[0022] The search demand analysis unit is used to analyze the user's demand related to searching for the target product on the e-commerce platform or the social network platform to obtain the prompt words corresponding to the user's attention features of the target product.

[0023] In some embodiments of the present application, the large language model submodule is configured as follows:

[0024] Input the prompt words corresponding to the key promotional features of the target product by the industry benchmark brand into the pre-trained natural language model to output the demand value model dimension to which the key promotional features of the target product by the industry benchmark brand belong;

[0025] Input the prompt words corresponding to the user's expected features of the target product into the pre-trained natural language model to output the demand value dimension to which the user's expected features of the target product belong;

[0026] Inputting the prompt words corresponding to the features of low user satisfaction with the target product into the pre-trained natural language model to output the demand value dimension to which the features of low user satisfaction with the target product belong;

[0027] The prompt words corresponding to the user's attention features for the target product are input into the pre-trained natural language model to output the demand value model dimension to which the user's attention features for the target product belong.

[0028] A second aspect of the present application provides a method for generating a commodity selling point, the method comprising:

[0029] Acquire multimodal information of a target product, wherein the multimodal information of the target product indicates information of the target product displayed in different data forms;

[0030] Conduct a market competition hotspot analysis on the multimodal information of the target product to obtain the demand value model dimension to which the key promotional features of the target product by the industry benchmark brand belong, and conduct a user demand analysis on the multimodal information of the target product to obtain the demand value model dimension to which the user's demand characteristics for the target product belong, and perform differential market opportunity calculation on the key promotional features of the target product by the industry benchmark brand and the demand value model dimension to which the user's demand characteristics for the target product belong, so as to generate selling point information of the target product, wherein the differential market opportunity calculation indicates excluding relevant features in the user's demand characteristics for the target product that are the same as or similar to the key promotional features of the target product by the industry benchmark brand.

[0031] The third aspect of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in the second aspect of the above embodiment when executing the computer program.

[0032] The fourth aspect of the present application provides a computer-readable storage medium having program instructions stored thereon, and when the program instructions are executed by a processor, the method described in the second aspect of the above embodiment is implemented.

[0033] This application has the following beneficial effects:

[0034] The product selling point generation scheme of the present application first conducts market competition hotspot analysis and user demand analysis on the multimodal information of the target product, and obtains the demand value model dimension to which the industry benchmark brand's key promotional features for the target product belong based on the market competition hotspot analysis results; secondly, obtains the demand value model dimension to which the user's demand features for the target product belong based on the user demand analysis results; then, the demand value model dimension to which the industry benchmark brand's key promotional features for the target product belong and the demand value model dimension to which the user's demand features for the target product belong are differentially calculated for market opportunities to obtain the selling point information of the target product. The selling point generation scheme of the present application excludes relevant features in the user's demand features for the target product that are identical or similar to the key promotional features of the industry benchmark brand for the target product, and the generated target product selling point information avoids the key promotional features of the industry benchmark brand for the target product, thereby improving the competitiveness of the target product and further improving the merchant's input-output ratio on the target product. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The drawings herein are incorporated into the specification and constitute a part of the specification. These drawings illustrate embodiments consistent with the present application and are used together with the specification to illustrate the technical solution of the present application.

[0036] Figure 1 This is a schematic diagram of the first embodiment of the commodity selling point generation system provided by this application.

[0037] Figure 2 This is a schematic diagram of the framework of the second embodiment of the commodity selling point generation system provided by the present application;

[0038] Figure 3 This is a schematic diagram of the framework of the third embodiment of the commodity selling point generation system provided by the present application;

[0039] Figure 4 This is a schematic diagram of the framework of the fourth embodiment of the commodity selling point generation system provided by the present application;

[0040] Figure 5 This is a schematic diagram of the framework of the fifth embodiment of the commodity selling point generation system provided by the present application;

[0041] Figure 6 This is a schematic diagram of the framework of the sixth embodiment of the commodity selling point generation system provided by the present application;

[0042] Figure 7 It is a schematic diagram of the framework of other embodiments of the commodity selling point generation system provided by this application;

[0043] Figure 8 This is a flow chart of an embodiment of an execution method for generating selling point information of a target product by a differential market opportunity calculation submodule provided by the present application;

[0044] Fig. 9 This is a flow chart of an embodiment of a method for generating selling points of a product provided by the present application;

[0045] Fig.10 This is a schematic diagram of a framework of an embodiment of a UI interface design corresponding to the commodity selling point generation system provided by this application;

[0046] Fig.11 It is a schematic diagram of a framework of an embodiment of an electronic device provided by the present application;

[0047] Fig.12 It is a schematic diagram of a framework of an embodiment of a computer-readable storage medium provided by the present application. DETAILED DESCRIPTION

[0048] The scheme of the embodiment of the present application is described in detail below in conjunction with the drawings of the specification.

[0049] In the following description, for the purpose of explanation rather than limitation, specific details such as specific system structures, interfaces, and technologies are provided to facilitate a thorough understanding of the present application.

[0050] The term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there may be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the objects associated before and after are in an "or" relationship. In addition, "many" in this article means two or more than two. In addition, the term "at least one" in this article means any combination of at least two of any one or more of a plurality of, for example, including at least one of A, B, and C, can mean including any one or more elements selected from the set consisting of A, B, and C.

[0051] As described in the background technology, the product selling point information generated by the existing product selling point information generation scheme does not avoid the key promotional features of the products by industry benchmark brands, resulting in a high degree of competitive incentives for these features in the generated product selling point information. If the product selling point information generated by merchants includes the key promotional features of the products by industry benchmark brands, and if they continue to invest in marketing on these features, it will result in a low input-output ratio for the merchants, thereby causing serious damage to the merchants' economic interests.

[0052] In order to solve the above problems, the present application proposes a new product selling point generation scheme. In the scheme of the present application, firstly, market competition hotspot analysis and user demand analysis are performed on the multimodal information of the target product, and based on the market competition hotspot analysis results, the demand value model dimension to which the industry benchmark brand's key promotional features for the target product belong is obtained; secondly, based on the user demand analysis results, the demand value model dimension to which the user's demand features for the target product belong is obtained; then, the demand value model dimension to which the industry benchmark brand's key promotional features for the target product belong and the demand value model dimension to which the user's demand features for the target product belong are calculated for differential market opportunities to obtain the selling point information of the target product. The present application excludes the relevant features in the user's demand features for the target product that are the same as or similar to the key promotional features of the industry benchmark brand for the target product, and the generated target product selling point information avoids the key promotional features of the industry benchmark brand for the target product, thereby improving the competitiveness of the target product, and further improving the input-output ratio of the merchant on the target product.

[0053] The present application is described in detail below with reference to the accompanying drawings and specific embodiments.

[0054] According to one embodiment of the present application, Figure 1 As shown, the present application provides a commodity selling point generation system, which includes: a commodity information acquisition module, which is used to acquire multimodal information of a target commodity and transmit it to a selling point generation module, wherein the multimodal information of the target commodity indicates information of the target commodity displayed in different data forms; a selling point generation module, which is used to perform market competition hotspot analysis on the multimodal information of the target commodity transmitted by the commodity information acquisition module, and obtain the demand value model dimension to which the key publicity features of the target commodity by the industry benchmark brand belong based on the analysis results, and perform user demand analysis on the multimodal information of the target commodity transmitted by the commodity information acquisition module, and obtain the demand value model dimension to which the user's demand features for the target commodity belong based on the analysis results, and perform differential market opportunity calculation on the key publicity features of the target commodity by the industry benchmark brand and the demand value model dimension to which the user's demand features for the target commodity belong, so as to generate selling point information of the target commodity, wherein the differential market opportunity calculation indicates excluding relevant features in the user's demand features for the target commodity that are the same as and similar to the key publicity features of the target commodity by the industry benchmark brand; wherein the commodity information acquisition module is communicatively connected to the selling point generation module.

[0055] According to one embodiment of the present application, different data forms include but are not limited to text, image, sound, video and the like.

[0056] As can be seen from the above description, the above embodiments of the present application can deeply explore the key promotional features of industry benchmark brands and the real demand features of users by integrating and analyzing the multimodal information of the target product (including but not limited to text, images, sounds, videos, etc.), and then accurately exclude features similar or identical to the industry benchmark through differential market opportunity calculation, highlighting the unique selling points of the product. This method not only improves the accuracy and effectiveness of generating product selling points, but also helps merchants find differentiated marketing angles in the fierce market competition, effectively improving the market competitiveness and attractiveness of products.

[0057] According to one embodiment of the present application, Figure 2 As shown, the product information acquisition module of the present application is configured to: convert the multimodal information of the target product into textual information corresponding to the multimodal information of the target product and transmit it to the selling point generation module; the selling point generation module is configured to: perform market competition hotspot analysis on the textual information corresponding to the multimodal information of the target product, and obtain the demand value model dimension to which the key promotional features of the target product by the industry benchmark brand belong based on the analysis results, and perform user demand analysis on the textual information corresponding to the multimodal information of the target product, and obtain the demand value model dimension to which the user's demand characteristics for the target product belong based on the analysis results, and perform differential market opportunity calculation on the key promotional features of the target product by the industry benchmark brand and the demand value model dimension to which the user's demand characteristics for the target product belong, so as to generate the selling point information of the target product, wherein the differential market opportunity calculation indicates that the relevant features in the user's demand characteristics for the target product that are the same as and similar to the key promotional features of the target product by the industry benchmark brand are excluding.

[0058] As can be seen from the above description, the above embodiment of the present application converts the multimodal information (such as text, images, sounds, videos, etc.) of the target product into unified textual information, so that the system can effectively conduct in-depth analysis of this information. This method not only simplifies the information processing process and improves the processing efficiency of the system, but also through market competition hotspot analysis and user demand analysis, combined with differential market opportunity calculation, it can more accurately identify the unique selling points of the target product. This helps merchants to formulate more effective marketing strategies based on market demand, improve the market competitiveness of products and users' purchasing desire.

[0059] The inventor has found through research that the existing product selling point generation scheme generally generates the selling point information of the product based on the conversation information, picture information, etc. of the product, but the samples of these information are relatively small, and the selling points obtained may be of concern to individual users and do not have commonality. Therefore, according to one embodiment of the present application, the multimodal information of the target product includes at least any one of the following: text description information of the target product, search index information of the target product, detailed picture information of the target product, and voice information of the target product. Among them, according to one embodiment of the present application, the text description information of the target product includes at least any one of the following: basic information of the target product, pre-purchase consultation information of the target product, and post-purchase evaluation information of the target product. It should be noted that with the development of data presentation forms, the multimodal information of the target product can be further expanded, and the multimodal information of the target product is not specifically limited here; the text description information of the target product can also be other relevant information about the target product, which is not specifically limited here.

[0060] It can be seen from the above embodiments that the present application conducts market competition hotspot analysis and user demand analysis on the text description information of the target product, the search index information of the target product, the detailed image information of the target product, and the voice information of the target product, thereby ensuring the comprehensiveness and accuracy of the target product information, discovering differentiated market opportunities through in-depth and comprehensive analysis of user needs and market competition conditions, formulating personalized marketing strategies, and dynamically adjusting to adapt to market changes, thereby effectively improving user trust and conversion rates, and ultimately promoting sales growth and enhancing brand competitiveness.

[0061] According to one embodiment of the present application, Figure 3 As shown, the commodity selling point generation system also includes: a data cleaning module, which is used to remove invalid data from the text form information corresponding to the target commodity multimodal information, so as to complete data cleaning and transmit the cleaned text form information corresponding to the target commodity multimodal information to the selling point generation module. For example, the invalid data removal process includes malicious posts posted by "water army".

[0062] As can be seen from the above description, the above embodiment of the present application can ensure the data quality and reliability of subsequent analysis and processing by setting up a data cleaning module to remove invalid data from the text information corresponding to the multimodal information of the target product. This method not only improves the accuracy of selling point generation and avoids the impact of false information on market analysis results, but also provides enterprises with more real and objective market feedback, helping enterprises make more accurate decisions and improve market competitiveness.

[0063] In order to more clearly illustrate the product selling point generation solution of the present application, the product information acquisition module and the selling point generation module are described in detail below with reference to the accompanying drawings.

[0064] 1. Product Information Acquisition Module

[0065] The product information acquisition module is used to acquire multimodal information of a target product and convert the multimodal information of the target product into text information corresponding to the multimodal information of the target product, wherein the multimodal information of the target product indicates information of the target product displayed in different data forms.

[0066] In order to obtain comprehensive multimodal information of the target product, the inventor obtains relevant information of the target product from different Internet platforms. Figure 4 As shown, the commodity information acquisition module includes: an e-commerce platform commodity information acquisition submodule, which is used to acquire multimodal information of target commodities on the e-commerce platform, and convert the multimodal information of target commodities on the e-commerce platform into textual information corresponding to the multimodal information of target commodities on the e-commerce platform; a social network platform commodity information acquisition submodule, which is used to acquire multimodal information of target commodities on the social network platform, and convert the multimodal information of target commodities on the social network platform into textual information corresponding to the multimodal information of target commodities on the social network platform; wherein the e-commerce platform commodity information acquisition submodule and the social network platform commodity information acquisition submodule are respectively connected to the selling point generation module in communication. It should be noted that the Internet platforms listed in the above embodiments of the present application are only platforms currently related to commodity trading. With the development of trading platforms, the present application can also acquire multimodal information of target commodities on other new platforms, which is not specifically limited here.

[0067] For example, the following is an example of text information obtained from the original information extracted from the product introduction pictures of the e-commerce platform after OCR processing: Whole grain light meal collagen nutrition gift; precious collagen with multiple flavors is more thoughtful; international traceability quality is more reassuring; fish maw; whole grain fish maw porridge 8 bowls gift box; promotional price: ￥268; collagen fish maw family gift box; promotional price: ￥399; buy now>; protect elastic skin; S-grade yellow fish maw series promotional price: from ￥289; buy now>; collagen advanced to maintain good condition; whole grain fish maw porridge 12 bowls ; Promotional price: ￥79; 12 bottles of instant fish maw; Promotional price: ￥478; Promotional price of collagen advanced meal: ￥928; Full traceability certification by China Inspection and Quarantine Institute; One code for each fish, safe and traceable from fishing to production, the entire industry chain is clearly traceable; China's highest quality fish tracing service platform, China's high quality fish maw tracing service platform; Consumer query area; Traceability code; Fish maw code; Anti-counterfeiting code; Famous yellow croaker school; Net total quantity: Shelf life: Storage conditions: Branded cool and dry place; Certificate of origin; Inspection and quarantine certificate; Complete test reports.

[0068] For example, the following is a sample of pre-purchase questions obtained from an e-commerce platform: Q: Can dried fish maw be stewed to release gelatin? A: Yes.

[0069] For example, the following is an example of a post-purchase review: The logistics is very fast, I bought it in the evening and it arrived in the morning. This brand should be quite good. I don’t buy it for myself, but as a gift to others. It is presentable and has a sense of quality. This brand should also be guaranteed!

[0070] From the above description, it can be seen that the embodiments of the present application can comprehensively cover the performance of the target products in the market and user feedback by obtaining multimodal information of the target products from different Internet platforms, ensure the diversity and completeness of the information, and help to more accurately capture user needs, market trends and competitor dynamics, and provide solid data support for generating accurate and attractive product selling points, thereby improving marketing effectiveness and market competitiveness.

[0071] The following is a detailed description of the e-commerce platform product information acquisition submodule and the social network platform product information acquisition submodule.

[0072] (I) E-commerce platform product information acquisition submodule

[0073] The e-commerce platform product information acquisition submodule is used to obtain the multimodal information of the target product on the e-commerce platform, and convert the multimodal information of the target product on the e-commerce platform into textual information corresponding to the multimodal information of the target product on the e-commerce platform.

[0074] Wherein, according to one embodiment of the present application, when the e-commerce platform includes an open interface that can transmit data with other platforms, the e-commerce platform product information acquisition submodule is configured to: take the category of the target product or the brand name of the target product as the input of the e-commerce platform, and crawl the multimodal information of the target product on the e-commerce platform to obtain the multimodal information of the target product. Wherein, according to one embodiment of the present application, when the e-commerce platform does not include an open interface that can transmit data with other platforms, the e-commerce platform product information acquisition submodule is configured to: take the category of the target product or the brand name of the target product as the input of the e-commerce platform, and crawl the multimodal information of the target product on the e-commerce platform by means of a crawler to obtain the multimodal information of the target product. Schematically, the introduction page of the target product is obtained from the e-commerce platform. The content of the introduction page may be text in a table (such as basic product information) or a picture (exquisite picture, including a description of the product selling point), from which the introduction of the product features is extracted and output in text form; or the voice during live broadcast is converted into text form.

[0075] From the above description, it can be seen that the above embodiments of the present application flexibly use the open interface or crawler technology of the e-commerce platform to obtain multimodal information of the target product, which can not only collect data efficiently and accurately, but also adapt to the technical conditions of different e-commerce platforms, thereby ensuring the flexibility and breadth of data acquisition, and providing strong support for subsequent selling point generation and market analysis, thereby improving the accuracy and effectiveness of product marketing.

[0076] (II) Submodule for obtaining product information on social network platforms

[0077] The social network platform product information acquisition submodule is used to acquire the multimodal information of the target product on the social network platform, and convert the multimodal information of the target product on the social network platform into text information corresponding to the multimodal information of the target product on the social network platform.

[0078] According to one embodiment of the present application, when the social network platform includes an open interface that can transmit data to other platforms, the social network platform product information acquisition submodule is configured to: use the category of the target product or the brand name of the target product as the input of the social network platform, obtain posts related to the target product posted on the social network platform, and obtain multimodal information of the target product from the posts related to the target product. According to one embodiment of the present application, when the social network platform does not include an open interface that can transmit data to other platforms, the social network platform product information acquisition submodule is configured to: use the category of the target product or the brand name of the target product as the input of the social network platform, obtain posts related to the target product posted on the social network platform by crawling, and obtain multimodal information of the target product from the posts related to the target product. Schematically, there are mixed text and picture articles related to the target product on the social network platform, which are used to introduce the target product and the user's experience.

[0079] From the above description, it can be seen that the above embodiments of the present application flexibly use the open interface or crawler technology of the social networking platform to obtain multimodal information of the target product, which can not only collect data efficiently and accurately, but also adapt to the technical conditions of different social networking platforms, thereby ensuring the flexibility and extensiveness of data acquisition, and providing strong support for subsequent selling point generation and market analysis, thereby improving the accuracy and effectiveness of product marketing.

[0080] 2. Selling Point Generation Module

[0081] The selling point generation module is used to perform market competition hotspot analysis on the textual information corresponding to the multimodal information of the target product, and obtain the demand value model dimension to which the key promotional features of the target product by the industry benchmark brand belong based on the analysis results, and to perform user demand analysis on the textual information corresponding to the multimodal information of the target product, and obtain the demand value model dimension to which the user's demand characteristics for the target product belong based on the analysis results, and to perform differential market opportunity calculation on the demand value model dimension to which the key promotional features of the target product by the industry benchmark brand and the user's demand characteristics for the target product, so as to generate the selling point information of the target product, wherein the differential market opportunity calculation indicates excluding relevant features in the user's demand characteristics for the target product that are the same as or similar to the key promotional features of the target product by the industry benchmark brand.

[0082] According to one embodiment of the present application, Figure 5 As shown, the selling point generation module includes: a market competition hot spot analysis submodule, which is used to perform market competition hot spot analysis on the text form information corresponding to the multimodal information of the target product, so as to obtain the prompt words corresponding to the key promotional features of the target product by the industry benchmark brand; a user demand analysis submodule, which is used to perform user demand analysis on the text form information corresponding to the multimodal information of the target product, so as to obtain the prompt words corresponding to the user's demand characteristics for the target product; a large language model submodule, which is used to obtain the demand value dimension to which the key promotional features of the target product by the industry benchmark brand belong based on the prompt words corresponding to the key promotional features of the target product by the industry benchmark brand, and to obtain the demand value dimension to which the user's demand characteristics for the target product belong based on the prompt words corresponding to the user's demand characteristics for the target product; a differential market opportunity calculation submodule, which is used to perform differential market opportunity calculation on the demand value model dimension to which the key promotional features of the target product by the industry benchmark brand belong and the demand value model dimension to which the user's demand characteristics for the target product belong, so as to generate the selling point information of the target product, wherein the differential market opportunity calculation indicates that the relevant features of the user's demand characteristics for the target product that are the same as or similar to the key promotional features of the target product by the industry benchmark brand are excluded.

[0083] According to one embodiment of the present application, Figure 6 As shown, the selling point generation module of the present application also includes a demand value library sub-module, which is used to receive and store the demand value model dimensions to which the industry benchmark brand's key promotional features for the target product belong, the demand value model dimensions to which the user's demand features for the target product belong, and the selling point information of the target product.

[0084] According to one embodiment of the present application, reference is still made to Figure 6As shown, the market competition hotspot analysis submodule is used to perform market competition hotspot analysis on the text form information corresponding to the multimodal information of the target commodity transmitted by the commodity information acquisition module, so as to obtain the prompt words corresponding to the key publicity features of the target commodity by the industry benchmark brand and transmit them to the large language model submodule, and receive the demand value model dimension to which the key publicity features of the target commodity by the industry benchmark brand transmitted from the large language model submodule belong and transmit them to the demand value library submodule; the user demand analysis submodule is used to perform user demand analysis on the text form information corresponding to the multimodal information of the target commodity transmitted by the commodity information acquisition module, so as to obtain the prompt words corresponding to the user's demand features for the target commodity and transmit them to the large language model submodule, and receive the demand value model dimension to which the user's demand features for the target commodity transmitted from the large language model submodule belong and transmit them to the demand value library submodule; the large language model submodule is used to obtain the demand value dimension to which the key publicity features of the target commodity by the industry benchmark brand belong based on the prompt words corresponding to the key publicity features of the target commodity transmitted by the market competition hotspot analysis submodule and transmit them to the market competition hotspot analysis submodule, and receive the demand value model dimension to which the user's demand features for the target commodity transmitted from the large language model submodule belong and transmit them to the demand value library submodule; the large language model submodule is used to obtain the demand value dimension to which the key publicity features of the target commodity by the industry benchmark brand belong based on the prompt words corresponding to the key publicity features of the target commodity transmitted by the market competition hotspot analysis submodule and transmit them to the market competition hotspot analysis submodule, and receive the demand value model dimension to which the user's demand features for the target commodity transmitted from the large language model submodule belong and transmit them to the demand value library submodule The prompt words corresponding to the demand characteristics are used to obtain the demand value dimensions to which the user's demand characteristics for the target product belong and transmit them to the user demand analysis submodule; the demand value library submodule is used to receive and store the demand value model dimensions to which the key publicity characteristics of the target product of the industry benchmark brand transmitted by the market competition hotspot analysis submodule belong, the demand value model dimensions to which the user's demand characteristics for the target product belong transmitted by the user demand analysis submodule, and the selling point information of the target product transmitted by the differential market opportunity calculation submodule; the differential market opportunity calculation submodule is used to transmit the key publicity characteristics of the target product of the industry benchmark brand transmitted by the demand value library submodule The differential market opportunities are calculated based on the demand value model dimension to which the demand value model belongs and the demand value model dimension to which the user's demand characteristics for the target product belong, so as to generate the selling point information of the target product and transmit it to the demand value library sub-module, wherein the differential market opportunity calculation indicates that the relevant characteristics of the user's demand characteristics for the target product that are identical or similar to the key promotional characteristics of the target product by the industry benchmark brand are excluded; wherein the market competition hotspot analysis sub-module and the user demand analysis sub-module are communicated with the product information acquisition module, the large language model sub-module, and the demand value library sub-module respectively, and the demand value library sub-module is also communicated with the differential market opportunity calculation sub-module.

[0085] From the above description, it can be seen that the embodiment of the present application adopts the demand value library sub-module to separately store historical market opportunity data and the generated selling point information of the target products, which can effectively accumulate and manage key market information, improve the accessibility and reusability of data, and accelerate the speed and accuracy of generating selling points for new products. At the same time, it supports in-depth analysis and algorithm optimization, enhances the ability of personalized marketing, and provides a solid data foundation for corporate strategic decision-making, thereby enhancing the adaptability and competitive advantage of enterprises in the highly competitive market environment.

[0086] The following is a detailed description of the market competition hotspot analysis submodule, user demand analysis submodule, large language model submodule, and market difference opportunity calculation submodule.

[0087] (I) Market competition hotspot analysis submodule

[0088] The market competition hotspot analysis submodule is used to perform market competition hotspot analysis on the textual information corresponding to the multimodal information of the target product, so as to obtain the prompt words corresponding to the key promotional features of the target product by the industry benchmark brand.

[0089] Among them, according to an embodiment of the present application, the market competition hotspot analysis sub-module is configured to obtain the prompt words corresponding to the key promotional features of the target product by the industry benchmark brand in the following manner: based on the text form information corresponding to the multimodal information of the target product, analyze the key promotional features of the target product by the industry benchmark brand in the text form information corresponding to the multimodal information of the target product; based on the text form information corresponding to the multimodal information of the target product, the preset first running instructions of the pre-trained natural language model and the preset demand value model, construct the prompt words corresponding to the key promotional features of the target product by the industry benchmark brand and transmit them to the large language model sub-module.

[0090] From the above description, it can be seen that the above embodiment uses the market competition hotspot analysis sub-module to combine the text information of the target product, the running instructions of the pre-trained natural language model and the preset demand value model to accurately construct the prompt words corresponding to the key promotional features of the target product by the industry benchmark brand, and transmit them to the large language model sub-module. This process can not only efficiently extract and utilize the promotional strategies of the industry benchmark brands, but also ensure that the generated prompt words are highly relevant and insightful, thereby providing more accurate and valuable information support for the subsequent selling point generation, significantly improving the work efficiency and effectiveness of the entire system.

[0091] (II) User Needs Analysis Submodule

[0092] The user demand analysis submodule is used to perform user demand analysis on the text information corresponding to the multimodal information of the target product, so as to obtain prompt words corresponding to the user's demand characteristics for the target product.

[0093] Among them, according to an embodiment of the present application, user demand analysis includes at least any one of the following demand analyses: user pre-purchase demand analysis, user post-purchase demand analysis and user search demand analysis; the prompt words corresponding to the user's demand characteristics for the target product include at least any one of the following prompt words: prompt words corresponding to the user's expected characteristics for the target product, prompt words corresponding to the characteristics of the target product with low user satisfaction, and prompt words corresponding to the user's attention characteristics for the target product.

[0094] According to one embodiment of the present application, Figure 7 As shown, the user demand analysis submodule includes: a pre-purchase demand analysis unit, which is used to perform a user pre-purchase demand analysis on the text form information corresponding to the multimodal information of the target product to obtain prompt words corresponding to the user's expected features of the target product.

[0095] As can be seen from the above description, the above embodiment of the present application can accurately capture the user's expectations for the product and generate highly relevant prompt words by setting up a special pre-purchase demand analysis unit to conduct in-depth analysis of user needs in the pre-purchase stage and scenario of the target product. This demand analysis not only helps to gain an in-depth understanding of the user's real needs, but also provides strong data support for product improvement and marketing strategy formulation, thereby improving user satisfaction and market competitiveness.

[0096] According to one embodiment of the present application, reference is still made to Figure 7 As shown, the user demand analysis submodule also includes: a post-purchase demand analysis unit, which is used to perform a post-purchase demand analysis on the text information corresponding to the multimodal information of the target product to obtain prompt words corresponding to the characteristics of low user satisfaction with the target product.

[0097] As can be seen from the above description, the above embodiment of the present application sets up a special post-purchase demand analysis unit to conduct in-depth analysis of user needs in the post-purchase stage and scenario of the target product, which can accurately capture user dissatisfaction with the product and generate highly relevant prompt words. This demand analysis not only helps to deeply understand the real needs of users, but also provides strong data support for product improvement and marketing strategy formulation, thereby improving user satisfaction and market competitiveness.

[0098] According to one embodiment of the present application, reference is still made to Figure 7 As shown, the multimodal information of the target product includes: the user's demand information related to the target product searched on the e-commerce platform or the social networking platform, and the user demand analysis submodule also includes: a search demand analysis unit, which is used to analyze the user's demand related to the target product searched on the e-commerce platform or the social networking platform to obtain the prompt words corresponding to the user's attention features of the target product.

[0099] As can be seen from the above description, the above embodiment of the present application sets up a special search demand analysis unit to conduct in-depth analysis of user needs in the search stage and scenario of the target product, which can accurately capture the user's focus on the product and generate highly relevant prompt words. This demand analysis not only helps to deeply understand the user's real needs, but also provides strong data support for product improvement and marketing strategy formulation, thereby improving user satisfaction and market competitiveness.

[0100] It should be noted that the three types of demand analysis included in the above-mentioned user demand analysis are only some embodiments of the present application for better understanding demand analysis. The user demand analysis of the present application may also be demand analysis in other aspects, which is not specifically limited here.

[0101] In addition, according to one embodiment of the present application, still referring to Figure 7 As shown, the pre-purchase demand analysis unit is used to perform a user pre-purchase demand analysis on the textual information corresponding to the multimodal information of the target product transmitted by the product information acquisition module, so as to obtain the prompt words corresponding to the user's expected features of the target product and transmit them to the large language model submodule, and receive the demand value model dimension to which the user's expected features of the target product transmitted from the large language model submodule belong and transmit them to the demand value library submodule; the post-purchase demand analysis unit is used to perform a user post-purchase demand analysis on the textual information corresponding to the multimodal information of the target product transmitted by the product information acquisition module, so as to obtain the prompt words corresponding to the features of the target product with low user satisfaction and transmit them to the large language model submodule, and receive the demand value model dimension to which the user's expected features of the target product belong from the large language model submodule and transmit them to the demand value library submodule. The model submodule transmits the demand value model dimension to which the user's low satisfaction with the target product belongs and transmits it to the demand value library submodule; the search demand analysis unit is used to analyze the user's demand related to searching for the target product on the e-commerce platform or the social networking platform to obtain the prompt words corresponding to the user's attention features of the target product and transmit them to the large language model submodule, and receive the demand value model dimension to which the user's attention features for the target product transmitted from the large language model submodule and transmit it to the demand value library submodule; wherein the pre-purchase demand analysis unit, the post-purchase demand analysis unit, and the search demand analysis unit are respectively communicated with the product information acquisition module, the large language model submodule, and the demand value library submodule.

[0102] As can be seen from the above description, the above embodiment of the present application sets up a special pre-purchase demand analysis unit, a post-purchase demand analysis unit and a search demand analysis unit to conduct in-depth analysis of user needs at different stages and scenarios of the target product, which can fully cover the user's purchase decision-making process, accurately capture the user's expectations, dissatisfaction and concerns about the product, and generate highly relevant prompt words. This multi-dimensional demand analysis not only helps to gain a deep understanding of the user's real needs, but also provides strong data support for product improvement and marketing strategy formulation, thereby improving user satisfaction and market competitiveness.

[0103] The pre-purchase demand analysis unit, the post-purchase demand analysis unit and the search demand analysis unit are described in detail below.

[0104] 1. Pre-purchase needs analysis unit

[0105] The pre-purchase demand analysis unit is used to perform a pre-purchase demand analysis on the text information corresponding to the multimodal information of the target product to obtain a prompt word corresponding to the user's expected features of the target product. The user's expected features of the target product at least include the product features that the user pays attention to in the pre-purchase consultation information of the target product before purchasing the target product.

[0106] Among them, according to an embodiment of the present application, the pre-purchase demand analysis unit is configured to obtain the prompt words corresponding to the user's expected features of the target product in the following manner: based on the user's pre-purchase consultation information on the target product in the text form information corresponding to the multimodal information of the target product, analyze the user's expected features of the target product; based on the text form information corresponding to the multimodal information of the target product, the preset second running instructions of the pre-trained natural language model and the preset demand value model, construct the prompt words corresponding to the user's expected features of the target product and transmit them to the large language model sub-module.

[0107] From the above description, it can be seen that the pre-purchase demand analysis unit of the above embodiment of the present application can accurately identify and construct prompt words corresponding to the user's expected characteristics of the target product by analyzing the user's pre-purchase consultation information in the text information corresponding to the multimodal information of the target product, combined with the pre-trained natural language model running instructions and the preset demand value model. This method not only improves the accuracy and efficiency of user demand analysis, but also provides more specific and valuable references for subsequent product design and marketing strategies, which helps to better meet user needs and improve user satisfaction and product market performance.

[0108] 2. Post-purchase demand analysis unit

[0109] The post-purchase demand analysis unit is used to perform a post-purchase demand analysis on the text information corresponding to the multimodal information of the target product, so as to obtain a prompt word corresponding to the feature of low user satisfaction with the target product. The feature of low user satisfaction with the target product includes at least any one of the following: product features in the post-purchase evaluation information in which the user expresses dissatisfaction after purchasing the target product, and product features in the post-purchase evaluation information in which the user of the industry benchmark brand expresses dissatisfaction after purchasing the target product.

[0110] Among them, according to one embodiment of the present application, the post-purchase demand analysis unit is configured to obtain the prompt word corresponding to the feature of low user satisfaction with the target product in the following manner: based on the post-purchase evaluation information of the user on the target product in the text form information corresponding to the multimodal information of the target product, analyze the feature of low user satisfaction with the target product; based on the text form information corresponding to the multimodal information of the target product, the preset third running instruction of the pre-trained natural language model and the preset demand value model, construct the prompt word corresponding to the feature of low user satisfaction with the target product and transmit it to the large language model sub-module.

[0111] As can be seen from the above description, the post-purchase demand analysis unit of the above embodiment of the present application can accurately identify the user's dissatisfaction with the target product by analyzing the user's post-purchase evaluation information in the text form information corresponding to the multimodal information of the target product, combined with the running instructions of the pre-trained natural language model and the preset demand value model, and construct prompt words corresponding to the characteristics of low user satisfaction with the product. This method not only helps enterprises to discover problems in products or services in a timely manner and provide a direct basis for product improvement, but also promotes enterprises to optimize customer experience, improve user satisfaction and loyalty, and thus enhance the market competitiveness of enterprises.

[0112] 3. Search demand analysis unit

[0113] The search demand analysis unit is used to analyze the user's demand for searching for a target product on an e-commerce platform or a social network platform to obtain prompt words corresponding to the user's attention features for the target product. The user's attention features for the target product at least include analyzing the popular brand name, product name, and product features from the search index of the target product as the user's attention features for the target product.

[0114] According to one embodiment of the present application, the search demand analysis unit is configured to obtain the prompt word corresponding to the user's attention feature of the target product in the following manner: based on the user's search index information for the target product in the text form information corresponding to the multimodal information of the target product, analyze the user's attention feature for the target product; based on the text form information corresponding to the multimodal information of the target product, the fourth running instruction of the preset pre-trained natural language model and the preset demand value model, construct the prompt word corresponding to the user's attention feature for the target product and transmit it to the large language model submodule. According to one embodiment of the present application, the search index information of the target product includes popular brand names, product names, product features, etc.

[0115] As can be seen from the above description, the search demand analysis unit of the above embodiment of the present application can accurately identify the user's focus on the target product by analyzing the user's search index information in the text form information corresponding to the multimodal information of the target product, combined with the running instructions of the pre-trained natural language model and the preset demand value model, and construct prompt words corresponding to the user's focus features. This method not only helps companies to gain an in-depth understanding of the product characteristics that users are most concerned about during the search process, and provides a scientific basis for optimizing product characteristics and marketing strategies, but also improves the search visibility and attractiveness of the product, thereby increasing sales opportunities and improving market performance.

[0116] (III) Large Language Model Submodule

[0117] The large language model sub-module is used to obtain the demand value dimension to which the key promotional features of the target product are attributed by the industry benchmark brand based on the prompt words corresponding to the key promotional features of the target product, and to obtain the demand value dimension to which the user's demand features for the target product belong based on the prompt words corresponding to the user's demand features for the target product.

[0118] Among them, according to one embodiment of the present application, the large language model sub-module is configured to: input the prompt words corresponding to the key promotional features of the target product by the industry benchmark brand into the pre-trained natural language model to output the demand-value model dimension to which the key promotional features of the target product by the industry benchmark brand belong; input the prompt words corresponding to the user's expected features for the target product into the pre-trained natural language model to output the demand-value dimension to which the user's expected features for the target product belong; input the prompt words corresponding to the features of the target product with low user satisfaction into the pre-trained natural language model to output the demand-value dimension to which the features of the target product with low user satisfaction belong; input the prompt words corresponding to the features of the target product that the user pays attention to into the pre-trained natural language model to output the demand-value model dimension to which the features of the target product that the user pays attention to belong.

[0119] As can be seen from the above description, the large language model submodule of the above embodiment of the present application can output the demand value model dimension to which these features belong by inputting the prompt words corresponding to various features into the pre-trained natural language model. This method can not only accurately map the demand characteristics of the market and users to specific value dimensions, and provide accurate data support for subsequent market opportunity analysis and selling point generation, but also help enterprises to have a deeper understanding of market demand and user preferences, optimize product design and marketing strategies, improve market response speed and user satisfaction, and thus gain advantages in a highly competitive market environment; at the same time, the demand characteristics are mapped to specific value dimensions using a natural language model, which can automatically and accurately parse complex market and commodity-related data, help enterprises quickly identify and quantify users' real needs and market opportunities, optimize the design of products and services, and improve user experience and satisfaction. This method not only improves the scientificity and efficiency of decision-making, but also promotes continuous innovation of enterprises and enhances market competitiveness.

[0120] Among them, according to an embodiment of the present application, the output format of the natural language model is defined in the prompt word, and the demand value model dimensions to which the industry benchmark brand's key promotional features for the target product belong include demand value number, demand value dimension, and matching degree (divided into 1, 2, and 3 levels, with 1 being the lowest and 3 being the highest); the demand value dimensions to which the user's expected features for the target product belong include demand value dimensions and matching degree (divided into 1, 2, and 3 levels, with 1 being the lowest and 3 being the highest); the demand value dimensions to which the features of the target product that the user is less satisfied with belong include demand value dimensions and satisfaction (divided into 1, 2, and 3 levels, with 1 being the highest and 3 being the lowest); the demand value dimensions to which the features of the target product that the user is paying attention to belong include demand value dimensions and matching degree (divided into 1, 2, and 3 levels, with 1 being the lowest and 3 being the highest).

[0121] As can be seen from the above description, the above embodiment of the present application defines a detailed output format to ensure that the natural language model can accurately parse and process data from different sources, including the key promotional features of the target product by the industry benchmark brand, the user's expected features, the user's satisfaction with the product, and the user's attention features on the product, etc. Each feature contains a specific demand value dimension and a corresponding matching degree or satisfaction score. This not only helps to improve the model's ability to process complex data, but also can more accurately capture the user's real needs and market dynamics, thereby providing more reliable data support for generating selling point information for the target product, helping companies find differentiated advantages in the fierce market competition, and achieve precision marketing and service optimization.

[0122] According to one embodiment of the present application, the preset demand value model includes: experience-based demand dimension, security-based demand dimension, functional demand dimension, and crowd and scenario-based demand dimension, wherein: the experience-based demand dimension indicates information related to the service experience, usage experience, and psychological experience of the target product; the security-based demand dimension indicates information related to user safety and / or security, product quality and / or failure, and monetary rights of the target product; the functional demand dimension indicates information related to the function, form, and composition of the target product; and the crowd and scenario-based demand dimension indicates information related to the crowd label, cognitive scenario, and environmental scenario of the target product. Schematically, Table 1 shows the relevant introduction to each category of demand dimension in the preset demand value model.

[0123] Table 1

[0124]

[0125]

[0126] It can be seen from the above description that the above embodiments of the present application can comprehensively cover the user's multi-faceted needs for goods by defining a detailed preset demand value model, including four demand dimensions: experience, security, functionality, and crowd and scenario, and provide a structured framework for analyzing and understanding user needs. This not only helps companies to accurately locate product features and discover market opportunities, but also promotes the refined development of products and services, and improves user satisfaction and market competitiveness. Specifically, this classification method enables companies to systematically evaluate and optimize products from multiple perspectives such as user experience, security, function realization, and target market to ensure that the diverse needs of different user groups are met.

[0127] (IV) Differentiated Market Opportunity Calculation Submodule

[0128] The differential market opportunity calculation submodule is used to perform differential market opportunity calculation on the demand value model dimensions to which the industry benchmark brand's key promotional features for the target product belong and the demand value model dimensions to which the user's demand features for the target product belong, so as to generate selling point information for the target product, wherein the differential market opportunity calculation indicates excluding relevant features in the user's demand features for the target product that are the same as or similar to the industry benchmark brand's key promotional features for the target product.

[0129] According to one embodiment of the present application, Figure 8As shown, the differential market opportunity calculation submodule is configured to generate the selling point information of the target product by executing the following steps S1-S3: S1, taking the demand value model dimension of the key promotional features of the target product by the industry benchmark brand, the demand value dimension of the user's expected features of the target product, the demand value dimension of the user's low satisfaction features of the target product, and the demand value dimension of the user's attention features to the target product as the first, second, third, and fourth selling points respectively; S2, using non-first selling points and (second selling point or third selling point or fourth selling point) as the judgment principle to generate new market opportunity data for the target product; S3, integrating the new market opportunity data of the target product with the historical market opportunity data to generate the selling point information of the target product.

[0130] Among them, according to one embodiment of the present application, when the demand value library sub-module receives the demand value model dimension to which the industry benchmark brand's key promotional features for the target product belong transmitted by the market competition hotspot analysis sub-module, the demand value dimension to which the user's expected features for the target product belong transmitted by the pre-purchase demand analysis unit, the demand value dimension to which the user's low satisfaction with the target product belongs transmitted by the post-purchase demand analysis unit, and the demand value dimension to which the user's attention features for the target product belong transmitted by the search demand analysis unit, the differential market opportunity calculation sub-module is configured to generate the selling point information of the target product by executing the above-mentioned S1-S3 steps.

[0131] It can be seen from the above description that the above embodiment of the present application combines the key promotional features of the target product by the industry benchmark brand with the user's expectations for the target product, the features of low satisfaction, and the demand value dimension to which the focus features belong through the differential market opportunity calculation submodule, generates new market opportunity data for the target product, and integrates it with the historical market opportunity data to finally generate the selling point information of the target product. This method can not only accurately identify differentiated opportunities in the market and avoid excessive competition with competitors in the same field, but also help companies make full use of users' real needs and market trends, formulate more effective marketing strategies, and enhance product competitiveness and market share. At the same time, through integration with historical data, the decision-making process can be further optimized to ensure the continuous updating and effectiveness of selling point information.

[0132] Among them, according to an embodiment of the present application, in step S3, the new market opportunity data of the target product is fused with the historical market opportunity data. The principle of fusion is that if the demand value dimensions are the same, the values ​​are added; if the demand value dimensions are different, new demand value dimensions are added.

[0133] As can be seen from the above embodiments, the above embodiments of the present application can effectively integrate current and historical market information to form a more comprehensive and continuous data set by fusing the new market opportunity data of the target product with the historical market opportunity data, following the principle of adding values ​​if the demand value dimensions are the same and adding new demand value dimensions if they are different. This method not only helps companies grasp market trends from a long-term perspective and discover persistent demand hotspots, but also can capture emerging market demands in a timely manner, provide a scientific basis for product iteration and marketing strategy adjustment, and enhance the market adaptability and competitiveness of companies.

[0134] According to one embodiment of the present application, in step S3, a preset number of demand value dimensions ranked high among the demand value dimensions to which the features in the text form information corresponding to the multimodal information of the target product belong are used as selling points of the target product.

[0135] As can be seen from the above description, the above embodiment of the present application selects a preset number of demand value dimensions with a high ranking in the demand value dimension to which the features in the text form information corresponding to the multimodal information of the target product belong as selling points. This method can ensure that the selected selling points are the key features that best reflect user needs and market opportunities. By focusing on these high-priority demand value dimensions, companies can more effectively highlight the core advantages of products, improve the pertinence and attractiveness of marketing information, thereby enhancing user interest, conversion rate and market competitiveness.

[0136] According to an embodiment of the present application, the selling points of the target product are normalized to obtain the final selling point information of the target product.

[0137] As can be seen from the above embodiments, the above embodiments of the present application can ensure the comparability and consistency between the various selling points by normalizing the selling points of the target product to obtain the final selling point information, and avoid misleading information caused by uneven weight distribution or inconsistent expressions. Normalization helps to clearly show the importance of each selling point, making marketing information more concise, easy to understand and remember, thereby improving the effectiveness of information transmission, enhancing users' understanding of the core advantages of the product, and promoting purchase decisions.

[0138] Combine the following Fig. 9 The product selling point generation solution of this application is described from the method side.

[0139] According to one embodiment of the present application, Fig. 9As shown, the method includes: T1, obtaining multimodal information of the target product, wherein the multimodal information of the target product indicates that the information of the target product is displayed in different data forms; T2, performing market competition hotspot analysis on the multimodal information of the target product to obtain the demand value model dimension to which the key promotional features of the target product by the industry benchmark brand belong, and performing user demand analysis on the multimodal information of the target product to obtain the demand value model dimension to which the user's demand characteristics for the target product belong, and performing differential market opportunity calculation on the demand value model dimension to which the key promotional features of the target product by the industry benchmark brand and the user's demand characteristics for the target product belong, so as to generate selling point information of the target product, wherein the differential market opportunity calculation indicates excluding relevant features in the user's demand characteristics for the target product that are the same as or similar to the key promotional features of the target product by the industry benchmark brand.

[0140] To sum up, the product selling point generation solution of the present application is based on data collected by e-commerce platforms and social networking platforms. With the help of the processing power of large language models, it analyzes the product features of the target products promoted by industry benchmark brands, as well as the demand value dimensions of industry benchmark brands with low user satisfaction, and selects key selling points suitable for the target products, so that the selling points can more accurately correspond to the needs of potential users, and avoid the key product features promoted by industry benchmark brands for the target products. The product selling point generation solution of the present application can achieve better marketing results with the same marketing investment.

[0141] In addition, for the commodity selling point generation system provided in the above embodiment of the present application, the inventor has designed a corresponding UI interface. Fig.10 As shown, it shows a schematic diagram of an embodiment of the UI interface of the commodity selling point generation system of the present application, from Fig.10 As can be seen in (a), the product information acquisition module, data cleaning module and selling point generation module are the first-level menu bars of the UI interface; when clicking on the product information acquisition module, data cleaning module and selling point generation module, Fig.10 (b) and Fig.10 (c) shows the secondary menu bar, where clicking on the product information acquisition module will bring up Fig.10 (b) and click the e-commerce platform product information acquisition submodule and the social network platform information acquisition submodule in the secondary menu bar respectively, and the product information display area will display the corresponding product information respectively; click the product selling point module to display Fig.10(c) The secondary menu bar is shown in Figure 1. Clicking the market competition hotspot analysis submodule, user demand analysis submodule, large language model submodule, demand value library submodule, and differential market opportunity calculation submodule in the secondary menu bar will trigger the relevant information display area of ​​each submodule to display the information required by the corresponding submodule; clicking the user demand analysis submodule will display Fig.10 (d) and click the pre-purchase demand analysis unit, post-purchase demand analysis unit and search demand analysis unit in the three-level menu bar respectively, which will trigger the relevant information display area of ​​each unit to display the information to be displayed after clicking the corresponding unit. It should be noted that Fig.10 The UI interface design of the commodity selling point generation system is only one embodiment of the present application. The UI interface design of the commodity selling point generation system of the present application can also be in other forms, which are not specifically limited here.

[0142] As can be seen from the above description, the UI interface design of the above embodiment of the present application effectively integrates the core functions such as product information acquisition, data cleaning and selling point generation through an intuitive multi-level menu structure, which not only improves the user's operational convenience and user experience, but also ensures the depth and breadth of data analysis through segmented functional modules and detailed sub-module options, allowing users to quickly locate and obtain the required information, greatly facilitating market analysts and product managers to conduct efficient and accurate product selling point mining and strategy formulation. This design not only meets the needs of professional users, but also keeps the interface simple and easy to use, and is a model of the perfect combination of technology and user experience.

[0143] Based on the inventive concept of the above embodiments, the present application further provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in the above embodiments when executing the computer program. Fig.11 Provide detailed explanation.

[0144] like Fig.11 As shown, it shows the electronic device of the present application, which may specifically include a processor 110 and a memory 120. The memory 120 is coupled to the processor 110.

[0145] The processor 110 is used to control the operation of the electronic device. The processor 110 may also be referred to as a CPU (Central Processing Unit). The processor 110 may be an integrated circuit chip having the ability to process signals. The processor 110 may also be a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components. A general-purpose processor may be a microprocessor or the processor 110 may also be any conventional processor, etc.

[0146] The memory 120 is used to store computer programs, and may be a RAM, a ROM, or other types of storage terminals. Specifically, the memory 120 may include one or more computer-readable storage media, which may be non-transitory or transient. The memory 120 may also include a high-speed random access memory, and a non-volatile memory, such as one or more disk storage terminals, flash memory storage terminals. In some embodiments, the non-transitory computer-readable storage medium in the memory 120 is used to store at least one program code.

[0147] The processor 110 is used to execute the computer program stored in the memory 120 to implement the methods described in the various method embodiments of the present application.

[0148] In some embodiments, the electronic device may further include: a peripheral terminal interface 130 and at least one peripheral terminal. The processor 110, the memory 120 and the peripheral terminal interface 130 may be connected via a bus or a signal line. Each peripheral terminal may be connected to the peripheral terminal interface 130 via a bus, a signal line or a circuit board. Specifically, the peripheral terminal includes: at least one of a radio frequency circuit 140, a display screen 150, an audio circuit 160 and a power supply 170.

[0149] The peripheral terminal interface 130 may be used to connect at least one peripheral terminal related to I / O (Input / output) to the processor 110 and the memory 120. In some embodiments, the processor 110, the memory 120, and the peripheral terminal interface 130 are integrated on the same chip or circuit board; in some other implementations, any one or two of the processor 110, the memory 120, and the peripheral terminal interface 130 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

[0150] The radio frequency circuit 140 is used to receive and transmit RF (Radio Frequency) signals, also known as electromagnetic signals. The radio frequency circuit 140 communicates with the communication network and other IoT devices through electromagnetic signals, and the radio frequency circuit 140 is the communication circuit of the electronic device. The radio frequency circuit 140 converts the electrical signal into an electromagnetic signal for transmission, or converts the received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 140 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, an operator identity module card, and the like. The radio frequency circuit 140 can communicate with other terminals through at least one wireless communication protocol. The wireless communication protocol includes but is not limited to: the World Wide Web, a metropolitan area network, an intranet, various generations of mobile communication networks (2G, 3G, 4G and 5G), a wireless local area network and / or a WiFi (Wireless Fidelity) network. In some embodiments, the radio frequency circuit 140 may also include circuits related to NFC (Near Field Communication), which is not limited in this application.

[0151] The display screen 150 is used to display the UI (User Interface). The UI may include graphics, text, icons, videos, and any combination thereof. When the display screen 150 is a touch display screen, the display screen 150 also has the ability to collect touch signals on the surface or above the surface of the display screen 150. The touch signal can be input to the processor 110 as a control signal for processing. At this time, the display screen 150 can also be used to provide virtual buttons and / or virtual keyboards, also known as soft buttons and / or soft keyboards. In some embodiments, the display screen 150 can be one, set on the front panel of the electronic device; in other embodiments, the display screen 150 can be at least two, respectively set on different surfaces of the electronic device or in a folding design; in other embodiments, the display screen 150 can be a flexible display screen, set on a curved surface or a folding surface of the electronic device. Even, the display screen 150 can also be set to a non-rectangular irregular shape, that is, a special-shaped screen. The display screen 150 can be made of materials such as LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode, organic light-emitting diode).

[0152] The audio circuit 160 may include a microphone and a speaker. The microphone is used to collect sound waves from the operator and the environment, and convert the sound waves into electrical signals and input them into the processor 110 for processing, or input them into the radio frequency circuit 140 to achieve voice communication. For the purpose of stereo acquisition or noise reduction, there can be multiple microphones, which are respectively arranged in different parts of the electronic device. The microphone can also be an array microphone or an omnidirectional acquisition microphone. The speaker is used to convert the electrical signal from the processor 110 or the radio frequency circuit 140 into sound waves. The speaker can be a traditional film speaker or a piezoelectric ceramic speaker. When the speaker is a piezoelectric ceramic speaker, it can not only convert the electrical signal into sound waves audible to humans, but also convert the electrical signal into sound waves inaudible to humans for purposes such as ranging. In some embodiments, the audio circuit 160 may also include a headphone jack.

[0153] The power supply 170 is used to power various components in the electronic device. The power supply 170 can be an alternating current, a direct current, a disposable battery, or a rechargeable battery. When the power supply 170 includes a rechargeable battery, the rechargeable battery can be a wired rechargeable battery or a wireless rechargeable battery. A wired rechargeable battery is a battery that is charged through a wired line, and a wireless rechargeable battery is a battery that is charged through a wireless coil. The rechargeable battery can also be used to support fast charging technology.

[0154] For a detailed description of the functions and execution processes of each functional module or component in the electronic device embodiment of the present application, reference can be made to the description in the above-mentioned method embodiments of the present application, which will not be repeated here.

[0155] In the several embodiments provided in the present application, it should be understood that the disclosed electronic devices and methods can be implemented in other ways. For example, the various embodiments of the electronic devices described above are only schematic. For example, the division of modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0156] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0157] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0158] Based on the inventive concept of the above embodiment, the present application also provides a computer-readable storage medium storing a computer program, which can be executed by a processor to implement the above projection area calculation method. Fig.12 The execution process of the above-mentioned embodiment in a computer-readable storage medium is described.

[0159] like Fig.12 As shown, it shows the computer-readable storage medium of the present application. If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium 200. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions / computer programs to enable an IoT device (which can be a personal computer, server, or network terminal, etc.) or a processor (processor) to perform all or part of the steps of each implementation method of the present application. The aforementioned storage medium includes: various media such as a USB flash drive, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a random access memory (RAM, Random Access Memory), a disk or an optical disk, and electronic terminals such as computers, mobile phones, laptops, tablet computers, cameras, etc. having the above-mentioned storage medium.

[0160] The description of the execution process of the program data in the computer-readable storage medium can refer to the description in the above-mentioned method embodiments of the present application, which will not be repeated here.

[0161] The above descriptions are merely embodiments of the present application and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

[0162] Those skilled in the art will appreciate that, in the above method of specific implementation, the order in which the steps are written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of the steps should be determined by their functions and possible internal logic.

Claims

1. A commodity selling point generation system, characterized in that: The system comprises: A commodity information acquisition module, used to acquire multimodal information of a target commodity, wherein the multimodal information of the target commodity indicates information of the target commodity displayed in different data forms; A selling point generation module is used to perform market competition hotspot analysis on the multimodal information of the target product, and obtain the demand value model dimension to which the industry benchmark brand's key promotional features for the target product belong based on the analysis results, and to perform user demand analysis on the multimodal information of the target product, and obtain the demand value model dimension to which the user's demand features for the target product belong based on the analysis results, and to perform differential market opportunity calculation on the key promotional features of the industry benchmark brand for the target product and the demand value model dimension to which the user's demand features for the target product belong, so as to generate the selling point information of the target product, wherein the differential market opportunity calculation indicates that relevant features in the user's demand features for the target product that are the same as or similar to the key promotional features of the industry benchmark brand for the target product are excluding 2. The commodity selling point generation system according to claim 1, characterized in that: The commodity information acquisition module is configured to: convert the multimodal information of the target commodity into textual information corresponding to the multimodal information of the target commodity; The selling point generation module is configured to: perform market competition hotspot analysis on the textual information corresponding to the multimodal information of the target product, and obtain the demand value model dimension to which the industry benchmark brand's key promotional features for the target product belong based on the analysis results; perform user demand analysis on the textual information corresponding to the multimodal information of the target product, and obtain the demand value model dimension to which the user's demand features for the target product belong based on the analysis results; and perform differential market opportunity calculation on the key promotional features of the industry benchmark brand for the target product and the demand value model dimension to which the user's demand features for the target product belong, so as to generate the selling point information of the target product, wherein the differential market opportunity calculation indicates excluding relevant features of the user's demand features for the target product that are identical or similar to the key promotional features of the industry benchmark brand for the target product.

3. The commodity selling point generation system according to claim 2, characterized in that: The selling point generation module includes: A market competition hotspot analysis submodule is used to perform market competition hotspot analysis on the text information corresponding to the multimodal information of the target product, so as to obtain prompt words corresponding to the key promotional features of the target product by the industry benchmark brand; A user demand analysis submodule, used to perform user demand analysis on the text information corresponding to the multimodal information of the target product, so as to obtain prompt words corresponding to the user's demand characteristics for the target product; The large language model submodule is used to obtain the demand value dimension to which the key promotional features of the target product of the industry benchmark brand belong based on the prompt words corresponding to the key promotional features of the target product of the industry benchmark brand, and to obtain the demand value dimension to which the user's demand features of the target product belong based on the prompt words corresponding to the user's demand features of the target product; The differential market opportunity calculation submodule is used to perform differential market opportunity calculation on the demand value model dimension to which the industry benchmark brand's key promotional features for the target product belong and the demand value model dimension to which the user's demand features for the target product belong, so as to generate selling point information of the target product, wherein the differential market opportunity calculation indicates excluding relevant features in the user's demand features for the target product that are the same as or similar to the industry benchmark brand's key promotional features for the target product.

4. The commodity selling point generation system according to claim 3, characterized in that: The user demand analysis includes at least any one of the following demand analyses: user pre-purchase demand analysis, user post-purchase demand analysis and user search demand analysis. The prompt words corresponding to the user's demand characteristics for the target product include at least any one of the following prompt words: prompt words corresponding to the user's expected characteristics for the target product, prompt words corresponding to the characteristics of the target product with low user satisfaction, and prompt words corresponding to the user's attention characteristics for the target product.

5. The commodity selling point generation system according to claim 4, characterized in that: The user demand analysis submodule includes: The pre-purchase demand analysis unit is used to perform the user's pre-purchase demand analysis on the text information corresponding to the multimodal information of the target product to obtain the prompt words corresponding to the user's expected features of the target product.

6. The commodity selling point generation system according to claim 4, characterized in that: The user demand analysis submodule also includes: The post-purchase demand analysis unit is used to perform the user post-purchase demand analysis on the text information corresponding to the multimodal information of the target product to obtain prompt words corresponding to the characteristics of low user satisfaction with the target product.

7. The commodity selling point generation system according to claim 4, characterized in that: The target product multimodal information includes: user demand information related to the target product searched on an e-commerce platform or a social network platform, and the user demand analysis submodule also includes: The search demand analysis unit is used to analyze the user's demand related to searching for the target product on the e-commerce platform or the social network platform to obtain the prompt words corresponding to the user's attention features of the target product.

8. The commodity selling point generation system according to claim 4, characterized in that: The large language model submodule is configured as follows: Inputting the prompt words corresponding to the key promotional features of the target product by the industry benchmark brand into the pre-trained natural language model to output the demand value model dimension to which the key promotional features of the target product by the industry benchmark brand belong; Inputting the prompt words corresponding to the user's desired features for the target product into the pre-trained natural language model to output the demand value dimension to which the user's desired features for the target product belong; Inputting the prompt words corresponding to the feature of low user satisfaction with the target product into the pre-trained natural language model to output the demand value dimension to which the feature of low user satisfaction with the target product belongs; The prompt words corresponding to the user's attention features for the target product are input into the pre-trained natural language model to output the demand value model dimension to which the user's attention features for the target product belong.

9. A method for generating a commodity selling point based on the commodity selling point generating system according to any one of claims 1 to 8, characterized in that: The method comprises: Acquire multimodal information of a target product, wherein the multimodal information of the target product indicates information of the target product displayed in different data forms; A market competition hotspot analysis is performed on the multimodal information of the target commodity, and based on the analysis results, the demand value model dimension to which the industry benchmark brand's key promotional features for the target commodity belong is obtained; a user demand analysis is performed on the multimodal information of the target commodity, and based on the analysis results, the demand value model dimension to which the user's demand characteristics for the target commodity belong is obtained; and a differential market opportunity calculation is performed on the key promotional features of the industry benchmark brand for the target commodity and the demand value model dimension to which the user's demand characteristics for the target commodity belong, so as to generate selling point information of the target commodity, wherein the differential market opportunity calculation indicates excluding relevant features of the user's demand characteristics for the target commodity that are the same as or similar to the key promotional features of the industry benchmark brand for the target commodity.

10. An electronic device, characterized in that: The method comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the steps of the method according to claim 9 are implemented when the processor executes the computer program.

11. A computer-readable storage medium having program instructions stored thereon, characterized in that: When the program instructions are executed by a processor, the method of claim 9 is implemented.

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