Method and system for analyzing and portraying car purchase behaviors of consumers

Abstract

The invention is suitable for the field of consumer analysis, and provides a consumer car purchase behavior analysis and portraying method and system, and the system comprises a consumer state analysis module, a psychological portraying construction module, a demand prediction module, a car purchase scheme generation module, and a feedback closed loop module. According to the method, through systematic data collection and analysis, the competitiveness and user experience of enterprises in the market are remarkably improved. Firstly, accurate user layering and tagging enable enterprises to deeply understand individual differences of consumers, so that products and services better meeting actual requirements are provided, and the user satisfaction and brand loyalty are directly improved. According to the method, the deep consumer psychological portraits are constructed, so that enterprises can capture the psychological characteristics and potential demands of the users, and then more targeted marketing strategies and personalized services are formulated, the shopping experience of the users is optimized, and the conversion rate is improved.

Description

Technical Field

[0001] The present invention belongs to the field of consumer behavior analysis, and in particular to a method and system for analyzing and profiling consumer car-buying behavior. Background Art

[0002] The field of car purchase consumer behavior analysis technology mainly involves the use of multidisciplinary technologies such as data analysis, psychology and market research to deeply understand consumers' decision-making behavior and psychological motivations in the car purchase process. This field focuses on all aspects of consumers before, during and after car purchases, including information search, brand preference, purchase intention and user experience.

[0003] By leveraging big data analysis, machine learning and artificial intelligence, companies can analyze consumers' historical car purchase data, online behavior and social media interactions to gain insights into consumers' personalized needs and potential preferences. The integration and analysis of this data can help optimize marketing strategies, improve product design and enhance user experience.

[0004] Traditional car sales and services often rely on static data analysis, which fails to effectively capture the dynamic needs of consumers at different stages of life and in different situations, resulting in the inability to adjust products and services in a timely manner to meet real needs. In addition, existing technologies usually lack depth in building user portraits, and fail to fully consider consumers' psychological characteristics and life backgrounds, limiting the targeting and effectiveness of marketing activities. At the same time, decision support systems based on historical data are often relatively simple and difficult to simulate complex car purchase decision paths, resulting in blind spots in companies' understanding of the key factors that consumers choose specific models, affecting market response speed and customer satisfaction. Summary of the invention

[0005] The purpose of the present invention is to provide a method for analyzing and profiling consumer car purchasing behavior, aiming to solve the technical problems existing in the prior art identified in the background technology.

[0006] The present invention is implemented by a method for analyzing and profiling consumer car-buying behavior, the method comprising: Collect consumers’ historical car purchase data and consumer data collected during potential car purchases, and analyze the user’s current life stage and life situation; Build consumer psychological portraits and generate personalized feature sets; Establish a demand forecasting model, analyze the personalized feature set, identify the factors that influence the consumer's car purchase decision, and predict the consumer's car purchase demand in different life stages and situations; Automatically generate personalized car purchase plans and marketing activity plans based on demand forecast results; Synchronously integrate market service plans into car purchase plans, automatically collect consumers' usage experience and feedback after purchasing a car, and integrate the feedback into consumer data to establish a closed-loop data chain.

[0007] As a further solution of the present invention, the collection of consumers' historical car purchase data and consumer data collected during potential car purchases, and analysis of the user's current life stage and life situation, specifically includes: Establish a data collection channel to mark consumers who have obtained historical car purchase data as car purchase users, and mark consumers who have only collected vehicle consultation data as potential car purchase users; Obtain the personal information registered by users who have purchased a car and potential car buyers respectively; Based on the user's personal information obtained, the user's life stage is indirectly inferred. At the same time, combined with the vehicle's feedback information, the user's current life situation is inferred and data labels are added for the user.

[0008] As a further solution of the present invention, the construction of consumer psychological portraits and the generation of personalized feature sets specifically include: Integrate all user personal information, user life stage data and life situation data to form a complete user profile; Based on the vehicle purchase history and the access history of the same brand products in the user's personal information, the user's psychological characteristics are analyzed and a personalized feature set is constructed; Combined with cluster analysis, a consumer portrait is generated for each user based on personalized feature sets and user profiles, and consumer tags are added.

[0009] As a further solution of the present invention, the establishment of a demand prediction model, analyzing the personalized feature set, identifying the factors that affect the consumer's car purchase decision, and predicting the consumer's car purchase demand in different life stages and situations specifically includes: Conduct dimensional analysis on consumer portraits and extract personalized feature sets and user information from user profiles; Perform feature analysis on the extracted information, identify the frequency of occurrence of each feature type, and filter out irrelevant items. Then sort all the feature types after filtering out according to the frequency of occurrence, and identify the priority sequence of factors that affect the user's car purchase decision; Setting an advanced priority tag library to increase the importance of a certain feature type in the factor priority ranking, and reanalyzing the user profile based on the advanced priority tag library to determine whether the user has any priority tag included in the advanced limited tag library; If it exists, the frequency of occurrence of the feature type corresponding to the priority label is used as the weight to regenerate the factor priority sequence; The final generated factor priority sequence is typed and classified into functional requirements, economic requirements, and scenario requirements.

[0010] As a further solution of the present invention, the automatic generation of personalized car purchase plans and marketing activity plans based on demand forecast results specifically includes: Based on the type identification results of each user, a demand matrix is ​​created to match each consumer demand type with a specific feature type; Establish a car purchase plan template and automatically fill in the car purchase plan that meets the user's needs based on the priority sequence of factors; Generate matching key recommended car models and promotion plans for the filled car purchase plan.

[0011] As a further solution of the present invention, the market service solution is integrated into the car purchase solution, the consumer's use experience and feedback after the car purchase is automatically collected, and the feedback is integrated into the consumer data to establish a closed-loop data chain, which specifically includes: Generate market service plans based on the consumption results of users for the generated recommended models and promotion plans; Collect customer feedback on each service in the market service plan, and update the consumer profile based on the feedback; Convert feedback information into actionable data, combine it with personalized feature sets and user profiles to form a closed-loop data chain.

[0012] Another object of the present invention is to provide a consumer car purchase behavior analysis and profiling system, the system comprising: The consumer status analysis module is used to collect consumers' historical car purchase data and consumer data collected during potential car purchases, and analyze the user's current life stage and life situation; Psychological portrait construction module, used to construct consumer psychological portraits and generate personalized feature sets; The demand forecasting module is used to establish a demand forecasting model, analyze the personalized feature set, identify the factors that affect the consumer's car purchase decision, and predict the consumer's car purchase demand in different life stages and situations; The car purchase plan generation module is used to automatically generate personalized car purchase plans and marketing activity plans based on demand forecast results; The feedback closed-loop module is used to synchronously integrate market service solutions into car purchase solutions, automatically collect consumers' usage experience and feedback after purchasing a car, and integrate the feedback into consumer data to establish a closed-loop data chain.

[0013] The beneficial effects of the present invention are: This method significantly improves the competitiveness and user experience of enterprises in the market through systematic data collection and analysis. First, accurate user stratification and labeling enable enterprises to deeply understand the individual differences of consumers, so as to provide products and services that better meet actual needs, which directly improves user satisfaction and brand loyalty. By building in-depth consumer psychological portraits, enterprises can capture the psychological characteristics and potential needs of users, and then formulate more targeted marketing strategies and personalized services, optimize users' shopping experience, and improve conversion rates.

[0014] In terms of demand forecasting, companies can identify key factors that influence car purchase decisions and respond quickly based on different life stages and situations. This forward-looking market adaptability enables companies to launch corresponding car purchase plans and marketing activities at the right time, improving the efficiency of resource allocation and the effectiveness of marketing activities. The ability to automatically generate personalized car purchase plans reduces labor costs and improves the accuracy of the plans, allowing consumers to enjoy more convenient and satisfactory services when making choices. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 A flow chart of a method for analyzing and profiling consumer car-buying behavior provided by an embodiment of the present invention; Figure 2 A flowchart of collecting a consumer's historical car purchase data and consumer data collected during potential car purchases, and analyzing the user's current life stage and life situation, provided by an embodiment of the present invention; Figure 3 A flowchart of building a consumer psychological portrait and generating a personalized feature set provided by an embodiment of the present invention; Figure 4 A flowchart for identifying factors that influence the consumer's car purchase decision and predicting the consumer's car purchase needs in different life stages and situations provided by an embodiment of the present invention; Figure 5 A flowchart for automatically generating a personalized car purchase plan and marketing activity plan based on demand forecast results provided by an embodiment of the present invention; Figure 6 A flowchart of automatically collecting consumers' usage experience and feedback after purchasing a car, integrating the feedback into consumer data, and establishing a closed-loop data chain provided by an embodiment of the present invention; Figure 7 This is a structural block diagram of the consumer car purchase behavior analysis and profiling system provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0017] It is understood that the terms "first", "second", etc. used in this application may be used herein to describe various elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish a first element from another element. For example, without departing from the scope of this application, a first xx script may be referred to as a second xx script, and similarly, a second xx script may be referred to as a first xx script.

[0018] Figure 1 A flowchart of a method for analyzing and profiling consumer car-buying behavior provided by an embodiment of the present invention, such as Figure 1 As shown, the method includes: S100, collects consumers’ historical car purchase data and consumer data collected during potential car purchases, and analyzes the user’s current life stage and life situation; In this step, we first need to establish an efficient data collection channel to ensure the accuracy and comprehensiveness of the information. This step marks consumers with historical car purchase data as car purchase users, and marks consumers who only collect vehicle consultation data as potential car purchase users. This marking process not only helps companies distinguish user types in subsequent analysis, but also helps companies formulate corresponding strategies for different users.

[0019] Next, the personal information registered by car buyers and potential car buyers is obtained, including basic information (such as age, gender, income level), car purchase preferences (such as brand, model), and their life stage. With this information, companies can indirectly infer the user's life stage, such as whether the user is single, married, has children, or is about to retire, so as to better understand the user's demand background. At the same time, combined with the feedback information of the vehicle, the company can infer the user's current life situation, such as whether the user needs a car suitable for family travel, or purchases an economical and practical model for work trips.

[0020] On this basis, data labels are added to users, which may include the user's life stage, car purchase intention, budget range, etc., so that companies can make more accurate portraits of users. Through this detailed user classification and labeling management, companies can conduct more in-depth analysis and understanding of different users.

[0021] Accurate user segmentation and labeling not only help companies better understand individual differences among consumers, but also improve user experience and ensure that the products and services provided are more in line with users' actual needs. This scientific methodology enables companies to be more precise when formulating marketing strategies, thereby improving conversion rates and customer satisfaction. In addition, dynamic analysis of user life stages and situational changes enables companies to respond to market changes in a timely manner and launch products and services that better meet consumer needs, thereby gaining an advantage in the fiercely competitive automotive market.

[0022] like Figure 2 As shown, the collection of consumers' historical car purchase data and consumer data collected during potential car purchases, and analysis of the user's current life stage and life situation, specifically includes: S110, establishing a data collection channel, marking consumers who have acquired historical car purchase data as car purchase users, and marking consumers who have only acquired vehicle consultation data as potential car purchase users; S120, obtaining personal information registered by the car purchasing user and the potential car purchasing user respectively; S130, based on the acquired user personal information, indirectly infer the user's life stage, and at the same time combine the feedback information of the vehicle to infer the user's current life situation and add a data tag for the user.

[0023] S200, builds consumer psychological portraits and generates personalized feature sets; This step forms a complete user profile by integrating user personal information, life stage data, and life situation data. Through this integration, the system can provide a comprehensive view of the user, covering their historical car purchase experience, brand interaction and other related information. This comprehensive user information aggregation lays a solid foundation for subsequent psychological characteristic analysis.

[0024] Next, based on the user's vehicle purchase history and the visit history of the same brand products, the user's psychological characteristics are analyzed to build a personalized feature set. This process not only focuses on the user's past behavior, but also digs deep into their potential needs and preferences to form a more accurate consumer portrait. Combined with cluster analysis, the system classifies users according to similarities, generates a consumer portrait for each user, and adds specific consumer tags. These tags can include the user's preferences, demand type, and potential purchase motivations, making the user portrait richer and more detailed.

[0025] By establishing an in-depth consumer psychological portrait, companies can understand their target users more accurately. This precise insight not only helps companies make smarter decisions in marketing and product development, but also creates more targeted personalized services and promotions to improve user experience and satisfaction. By deeply analyzing the psychological characteristics of users, companies can predict consumer tendencies and achieve higher conversion rates. In addition, the generation of personalized feature sets also provides data support for subsequent demand forecasting and car purchase plan formulation, making the entire process smoother and more efficient. This data-driven decision-making capability will help companies gain a more advantageous position in the fiercely competitive market.

[0026] like Figure 3 As shown, the construction of consumer psychological portraits and the generation of personalized feature sets specifically include: S210, integrating all user personal information, user life stage data and life situation data to form a complete user profile; S220, based on the vehicle purchase history and the access history of the same brand products in the user's personal information, and based on this, analyzing the user's psychological characteristics, a personalized feature set is constructed; S230, combined with cluster analysis, generates a consumer portrait for each user based on the personalized feature set and user profile, and adds a consumer tag.

[0027] S300, establish a demand prediction model, analyze the personalized feature set, identify the factors that affect the consumer's car purchase decision, and predict the consumer's car purchase demand in different life stages and situations; This step identifies the factors that influence consumers' car purchase decisions and predicts car purchase needs in different life stages and situations by analyzing the personalized feature set. Specifically, first, the consumer portrait is dimensionally analyzed to extract information from the personalized feature set and user profile. This analysis allows the system to gain in-depth understanding of the user's basic attributes, historical behavior, and psychological characteristics, laying the foundation for further feature analysis.

[0028] The extracted information is then analyzed for features, the frequency of each feature is identified, and irrelevant items are screened out. By sorting the effective features, the system can identify the priority sequence of factors that affect the user's car purchase decision. This sequence provides data support for subsequent marketing strategies and product recommendations, ensuring the accuracy and pertinence of the recommendations.

[0029] On this basis, an advanced priority tag library is set to increase the ranking importance of certain features and further optimize the analysis process. If the user exists in the advanced priority tag library, the frequency of occurrence of the feature type corresponding to the priority tag will be used as a weighted basis to regenerate the factor priority sequence. The final generated factor priority sequence will be classified into functional requirements, economic requirements, and scenario requirements. This classification not only reflects the diversified needs of users, but also provides a basis for customized car purchase plans.

[0030] This step provides accurate predictions of car purchase demand through in-depth feature analysis and priority sorting. This enables companies to launch products and services that best meet consumers' needs at the right time, improving user experience. In addition, based on predictions of different life stages and situations, companies can formulate corresponding marketing activities in advance, optimize resource allocation, and improve conversion rates, thereby gaining an advantage in the fiercely competitive market.

[0031] like Figure 4 As shown, the demand prediction model is established to analyze the personalized feature set, identify the factors that affect the consumer's car purchase decision, and predict the consumer's car purchase demand in different life stages and situations, specifically including: S310, performing dimensional analysis on the consumer portrait, and extracting a personalized feature set and user information in the user profile respectively; S320, performing feature analysis on the extracted information, identifying the frequency of occurrence of each feature type, and filtering out irrelevant items, sorting all the filtered feature types according to the frequency of occurrence, and identifying the priority sequence of factors that affect the user's car purchase decision; S330, setting an advanced priority tag library to increase the importance of a certain feature type in the factor priority ranking, and analyzing the user profile again based on the advanced priority tag library to determine whether the user has any priority tag included in the advanced limited tag library; S340, if it exists, the frequency of occurrence of the feature type corresponding to the priority label is used as a weight to regenerate the factor priority sequence; S350, identifying the type of the finally generated factor priority sequence and classifying it into functional requirements, economic requirements and scenario requirements.

[0032] S400, automatically generates personalized car purchase plans and marketing activity plans based on demand forecast results; Based on the type identification results of each user, this step will create a demand matrix to correspond the consumer's demand type to the specific feature type. The establishment of this demand matrix provides a basis for the generation of personalized car purchase plans, so that the unique needs of each consumer can be concretized.

[0033] Then, a car purchase plan template is established, and the user's car purchase plan is automatically filled in according to the priority sequence of factors. This process ensures the personalization of the plan, taking into account the functional needs, economic needs and scenario needs of consumers, so that the recommended car purchase plan is more in line with the user's actual situation. At the same time, the system will also generate matching key recommended models and promotion plans for the filled car purchase plan, which not only improves the relevance of the product, but also enhances the user's desire to buy.

[0034] Through automated personalized solution generation, companies can improve marketing efficiency, reduce labor costs, and provide consumers with more accurate car purchase recommendations. This data-driven approach can respond to changes in user needs in a timely manner, enhance user experience, and improve conversion rates. In addition, personalized marketing activities can attract more potential customers and promote sales growth through targeted promotional programs. Through this approach, companies can not only improve customer relationships, but also gain an advantage in market competition and achieve sustainable development.

[0035] like Figure 5 As shown, the personalized car purchase plan and marketing activity plan are automatically generated according to the demand forecast results, specifically including: S410, based on the type identification result of each user, creating a demand matrix to correspond each consumer demand type with a specific feature type; S420, establishing a car purchase plan template, and automatically filling in a car purchase plan that meets the user's needs based on the priority sequence of factors; S430, generating matching key recommended models and promotion plans for the filled car purchase plan.

[0036] S500 integrates market service solutions into car purchase solutions, automatically collects consumers' usage experience and feedback after purchasing a car, and integrates the feedback into consumer data to establish a closed-loop data chain.

[0037] This step emphasizes the simultaneous integration of market service solutions in the car purchase plan, and automatically collects consumers' experience and feedback after purchasing the car to establish a closed-loop data chain. The core of this process is to connect the user's car purchase experience with after-market services, so as to achieve comprehensive tracking and analysis of consumer behavior. Specifically, after the consumer completes the car purchase, the system will continue to monitor its use, including vehicle feedback information, frequency of use, satisfaction, etc., and obtain the user's experience and feedback in a timely manner. This information will be integrated into the consumer data to form a dynamically updated database.

[0038] Through this closed-loop data chain, companies can not only deeply understand consumers' real experience after purchasing a car, but also adjust their marketing strategies and service plans in real time. When users encounter problems during use or have increased demand for certain functions, companies can respond quickly and provide targeted services, thereby improving user satisfaction and loyalty. In addition, based on the collected usage feedback, companies can optimize product design and functions to ensure that their services and products can better meet the actual needs of consumers.

[0039] This step ensures data tracking throughout the entire process from car purchase to use, allowing companies to make decisions based on real data rather than relying on assumptions or outdated information. This continuous interaction and feedback mechanism strengthens the connection between users and brands, allowing companies to remain agile and forward-looking in a highly competitive market. By adjusting marketing activities and product solutions in a targeted manner, companies can not only increase conversion rates, but also achieve higher customer retention rates and achieve long-term business growth.

[0040] like Figure 6 As shown, the market service solution is integrated into the car purchase solution, the consumer's experience and feedback after purchasing the car are automatically collected, and the feedback is integrated into the consumer data to establish a closed-loop data chain, which specifically includes: S510, generating a market service plan according to the consumption result of the user for the generated recommended car model and promotion plan; S520, collecting customer feedback information on each service in the market service solution, and updating the consumer portrait based on the feedback information; S530, converts the feedback information into actionable data, combines it with the personalized feature set and user profile, and forms a closed-loop data chain.

[0041] Figure 7 The structural block diagram of the consumer car purchase behavior analysis and profiling system provided by the embodiment of the present invention is as follows: Figure 7 As shown, the system comprises: The consumer status analysis module 100 is used to collect the consumer's historical car purchase data and the consumer data collected during the potential car purchase, and analyze the user's current life stage and life situation; A psychological portrait construction module 200 is used to construct a consumer psychological portrait and generate a personalized feature set; The demand prediction module 300 is used to establish a demand prediction model, analyze the personalized feature set, identify the factors that affect the consumer's car purchase decision, and predict the consumer's car purchase demand in different life stages and situations; The car purchase plan generation module 400 is used to automatically generate a personalized car purchase plan and marketing activity plan based on the demand forecast results; The feedback closed-loop module 500 is used to synchronously integrate the market service plan into the car purchase plan, automatically collect the consumer's use experience and feedback after purchasing the car, and integrate the feedback into the consumer data to establish a closed-loop data chain.

[0042] It should be understood that, although each step in the flow chart of each embodiment of the present invention is shown in sequence according to the indication of the arrow, these steps are not necessarily performed in sequence according to the order indicated by the arrow. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be performed in other orders. Moreover, at least a portion of the steps in each embodiment may include a plurality of sub-steps or a plurality of stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0043] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the program can be stored in a non-volatile computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0044] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0045] The above-mentioned embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, which all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

[0046] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. The method of analyzing and profiling consumer car-buying behavior is characterized by: The method comprises: Collect consumers’ historical car purchase data and consumer data collected during potential car purchases, and analyze the user’s current life stage and life situation; Build consumer psychological portraits and generate personalized feature sets; Establish a demand forecasting model, analyze the personalized feature set, identify the factors that influence the consumer's car purchase decision, and predict the consumer's car purchase demand in different life stages and situations; Automatically generate personalized car purchase plans and marketing activity plans based on demand forecast results; Synchronously integrate market service plans into car purchase plans, automatically collect consumers' usage experience and feedback after purchasing a car, and integrate the feedback into consumer data to establish a closed-loop data chain.

2. The method according to claim 1, characterized in that The collection of consumers' historical car purchase data and consumer data collected during potential car purchases, and analysis of the user's current life stage and life situation, specifically includes: Establish a data collection channel to mark consumers who have obtained historical car purchase data as car purchase users, and mark consumers who have only collected vehicle consultation data as potential car purchase users; Obtain the personal information registered by users who have purchased a car and potential car buyers respectively; Based on the user's personal information obtained, the user's life stage is indirectly inferred. At the same time, combined with the vehicle's feedback information, the user's current life situation is inferred and data labels are added for the user.

3. The method according to claim 2, characterized in that The construction of consumer psychological portraits and generation of personalized feature sets specifically include: Integrate all user personal information, user life stage data and life situation data to form a complete user profile; Based on the vehicle purchase history and the access history of the same brand products in the user's personal information, the user's psychological characteristics are analyzed and a personalized feature set is constructed; Combined with cluster analysis, a consumer portrait is generated for each user based on personalized feature sets and user profiles, and consumer tags are added.

4. The method according to claim 3, characterized in that The demand prediction model is established to analyze the personalized feature set, identify the factors that affect the consumer's car purchase decision, and predict the consumer's car purchase demand in different life stages and situations, specifically including: Conduct dimensional analysis on consumer portraits and extract personalized feature sets and user information from user profiles; Perform feature analysis on the extracted information, identify the frequency of occurrence of each feature type, and filter out irrelevant items. Then sort all the feature types after filtering out according to the frequency of occurrence, and identify the priority sequence of factors that affect the user's car purchase decision; Setting an advanced priority tag library to increase the importance of a certain feature type in the factor priority ranking, and reanalyzing the user profile based on the advanced priority tag library to determine whether the user has any priority tag included in the advanced limited tag library; If it exists, the frequency of occurrence of the feature type corresponding to the priority label is used as the weight to regenerate the factor priority sequence; The final generated factor priority sequence is typed and classified into functional requirements, economic requirements, and scenario requirements.

5. The method according to claim 4, characterized in that The automatic generation of personalized car purchase plans and marketing activity plans based on demand forecast results specifically includes: Based on the type identification results of each user, a demand matrix is ​​created to match each consumer demand type with a specific feature type; Establish a car purchase plan template and automatically fill in the car purchase plan that meets the user's needs based on the priority sequence of factors; Generate matching key recommended car models and promotion plans for the filled car purchase plan.

6. The method according to claim 4, characterized in that The aforementioned synchronous integration of the market service solution into the car purchase solution automatically collects the consumer's experience and feedback after purchasing the car, and integrates the feedback into the consumer data to establish a closed-loop data chain, specifically including: Generate market service plans based on the consumption results of users for the generated recommended models and promotion plans; Collect customer feedback on each service in the market service plan, and update the consumer profile based on the feedback; Convert feedback information into actionable data, combine it with personalized feature sets and user profiles to form a closed-loop data chain.

7. Consumer car purchase behavior analysis and profiling system, characterized by: The system comprises: The consumer status analysis module is used to collect consumers' historical car purchase data and consumer data collected during potential car purchases, and analyze the user's current life stage and life situation; Psychological portrait construction module, used to construct consumer psychological portraits and generate personalized feature sets; The demand forecasting module is used to establish a demand forecasting model, analyze the personalized feature set, identify the factors that affect the consumer's car purchase decision, and predict the consumer's car purchase demand in different life stages and situations; The car purchase plan generation module is used to automatically generate personalized car purchase plans and marketing activity plans based on demand forecast results; The feedback closed-loop module is used to synchronously integrate market service solutions into car purchase solutions, automatically collect consumers' usage experience and feedback after purchasing a car, and integrate the feedback into consumer data to establish a closed-loop data chain.

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