A method and system for analyzing automobile brand marketing based on big data

Abstract

The present invention is applicable to the field of marketing technology, and provides an automobile brand marketing analysis method and system based on big data. The system includes: an information set collection and processing module, a motor equipment appreciation or depreciation analysis module, a motor equipment recycling model module, and a customer relationship management (CRM) integration module to maximize the value of motor equipment at different stages of its life cycle. By real-time monitoring of market supply and demand, economic conditions, and technological progress, and by optimizing the recycling and reuse process of motor equipment, the present invention can reduce the operating costs of the brand and improve the overall economic benefits. The recycling policy and transaction model can be adjusted according to the brand strategy to ensure the realization of the brand's long-term goals. By designing attractive trade-in transaction models and incentives, the present invention can increase customer participation and the brand's market share, thereby expanding the demand for customer loyalty cultivation and customer stickiness.

Description

Technical Field

[0001] The present invention belongs to the technical field of marketing, and in particular relates to a method and system for analyzing automobile brand marketing based on big data. Background Art

[0002] The ecosystem of marketing technology is becoming more and more integrated, providing a range of solutions from information set management to customer relationship management, automated marketing, content management, etc. The compatibility and integration capabilities between these solutions are becoming stronger and stronger, helping companies to operate cross-channel marketing activities more efficiently. Companies are using technologies such as customer information set analysis, behavioral analysis, and predictive analysis to better understand the target market, customize marketing strategies, and achieve personalized marketing. AI and machine learning are being widely used in the field of marketing to improve customer segmentation, predict consumer behavior, personalized content recommendations, chatbots, and automated customer service.

[0003] The field of marketing has evolved and developed many times since its origins. However, along the way, many companies still focus too much on developing new customers and ignore the need to cultivate loyalty and customer stickiness among existing customers. Summary of the invention

[0004] The purpose of the present invention is to provide an automobile brand marketing analysis method based on big data, aiming to solve the technical problems existing in the prior art identified in the background technology.

[0005] The present invention is implemented as follows: a method for analyzing automobile brand marketing based on big data, the method comprising:

[0006] Collect information sets, process and integrate them into original information sets, and extract information and insights from the original information sets to form analytical features based on business understanding;

[0007] Analyze the appreciation and depreciation trends of mobile equipment using historical information sets, and build a mobile equipment value prediction model to determine the recycling and reuse and second-hand market of mobile equipment through analysis;

[0008] Based on the results of the analysis of the appreciation or depreciation of motor vehicles, formulate a motor vehicle recycling model, provide a trade-in vehicle transaction model, and monitor and evaluate the implementation effect of the motor vehicle recycling model;

[0009] Integrate the information set feedback of the mobile equipment value prediction model and the mobile equipment recycling model into the CRM system, create profiles for customers, formulate communication and marketing strategies based on the customer's mobile equipment situation and behavior information set, develop a customer loyalty incentive program based on mobile equipment recycling participation, and establish a feedback mechanism.

[0010] As a further solution of the present invention, the collected information set is stored in the original information set after processing and integration, and based on business understanding, information and insights are extracted from the original information set to form analysis features, specifically including:

[0011] Capture sales records and service records of internal information sets, market research, social media, competitor analysis, and industry reports of external information sets, and realize real-time collection and processing of information set streams for scenarios that require real-time analysis;

[0012] Identify and correct abnormal information sets, convert information sets from different sources into a unified format, associate and integrate them, build a complete information set view, store it in the information set warehouse, and build new features based on business understanding and information set analysis results.

[0013] As a further solution of the present invention, the method of analyzing the appreciation and depreciation trend of motor equipment using the historical information set and constructing a motor equipment value prediction model, and determining the recycling and reuse of motor equipment and the second-hand market through analysis, specifically includes:

[0014] Filter vehicle age, mileage, brand, model, and maintenance factors from the overall information set, and conduct historical trend analysis to identify patterns in the value of motor equipment over time;

[0015] Select features that have the ability to predict the future value of mobile equipment and build a mobile equipment value prediction model. The specific formula is:

[0016] ,in is the value of mobile equipment, is the intercept term, representing the predicted mobile equipment value when all input variables are zero, are model parameters, each measuring the impact of the corresponding variable on the value of mobile equipment while controlling other variables. is the error term, which is the random variation that cannot be explained by the model.

[0017] As a further solution of the present invention, based on the analysis results of the appreciation or depreciation of the motor equipment, a motor equipment recycling model is formulated, a trade-in vehicle transaction model is provided, and the implementation effect of the motor equipment recycling model is monitored and evaluated, specifically including:

[0018] Using the results of the mobile equipment value prediction model, we analyze the changes in the value of mobile equipment of different models and conditions, and develop a mobile equipment recycling model based on market supply and demand, economic conditions, technological progress, and brand strategy factors. The specific formula is:

[0019]

[0020] in For recycling price, is the value of mobile equipment, is the depreciation rate, is the market adjustment factor, It is a brand strategy factor;

[0021] Design incentives and develop a trade-in model for old cars. The specific formula is:

[0022]

[0023] in For trading benefits, For recycling price, For preferential ratio, For extra incentive;

[0024] Develop strategies to determine the recycling and reuse of mobile equipment based on the appreciation or depreciation analysis of mobile equipment;

[0025] Monitor customer engagement, recycling volume, cost-effectiveness, and implementation effectiveness of recycling policies, and provide quantitative and qualitative analysis reports.

[0026] As a further solution of the present invention, the information set feedback of the mobile equipment value prediction model and the mobile equipment recycling model is integrated into the CRM system to create a profile for the customer, specifically including:

[0027] Collect customer personal information, vehicle purchase records, service records, feedback and behavior data, classify and summarize the collected information sets, merge the information sets of motor vehicle appreciation or depreciation analysis and motor vehicle recycling model with the customer information sets, generate an information set library containing all-round information of the customer, and establish a detailed personal profile for the customer based on the merged information sets;

[0028] Segment customers based on the information set in the customer profile, interact with customers regularly based on the characteristics of the customer groups, and monitor the effectiveness of communication activities;

[0029] Develop customer loyalty incentive programs, track program execution, establish an effective feedback loop mechanism, and analyze customer feedback.

[0030] Another object of the present invention is to provide an automobile brand marketing analysis system based on big data, the system comprising:

[0031] The information set collection and processing module is used to collect information sets, process and integrate them into the original information set, and extract information and insights from the original information set based on business understanding to form analytical features;

[0032] The motor equipment appreciation or depreciation analysis module is used to analyze the appreciation and depreciation trends of motor equipment using historical information sets, and to build a motor equipment value prediction model to determine the recycling and reuse of motor equipment and the second-hand market through analysis;

[0033] A motor vehicle recycling model module is used to formulate a motor vehicle recycling model based on the motor vehicle appreciation or depreciation analysis results, provide a trade-in vehicle transaction model, and monitor and evaluate the implementation effect of the motor vehicle recycling model;

[0034] The customer relationship management (CRM) integration module is used to integrate the information set feedback of the mobile equipment value prediction model and the mobile equipment recycling model into the CRM system, create profiles for customers, formulate communication and marketing strategies based on the customer's mobile equipment situation and behavior information set, develop a customer loyalty incentive program based on mobile equipment recycling participation, and establish a feedback mechanism.

[0035] As a further solution of the present invention, the information set collection and processing module specifically includes:

[0036] The information set collection unit is used to capture sales records and service records of internal information sets, market research, social media, competitor analysis, and industry reports of external information sets. For scenarios that require real-time analysis, it realizes real-time collection and processing of information set flows;

[0037] The information set preprocessing unit is used to identify and correct abnormal information sets, convert information sets from different sources into a unified format, associate and integrate them, build a complete information set view, store it in the information set warehouse, and build new features based on business understanding and information set analysis results.

[0038] As a further solution of the present invention, the mobile equipment appreciation or depreciation analysis module specifically includes:

[0039] A trend analysis unit is used to filter the vehicle age, mileage, brand, model, and maintenance factors from the overall information set and conduct historical trend analysis to identify patterns in the value of motor equipment over time;

[0040] The mobile equipment value prediction model building unit is used to select features that have the ability to predict the future mobile equipment value and build a mobile equipment value prediction model. The specific formula is:

[0041] ,in is the value of mobile equipment, is the intercept term, representing the predicted mobile equipment value when all input variables are zero, are model parameters, each measuring the impact of the corresponding variable on the value of mobile equipment while controlling other variables. is the error term, which is the random variation that cannot be explained by the model.

[0042] As a further solution of the present invention, the mobile equipment recovery model module specifically includes:

[0043] The recycling policy formulation unit is used to use the results of the mobile equipment value prediction model to analyze the value changes of mobile equipment of different models and conditions, and formulate a mobile equipment recycling model based on market supply and demand, economic conditions, technological progress, and brand strategy factors. The specific formula is:

[0044]

[0045] in For recycling price, is the value of mobile equipment, is the depreciation rate, is the market adjustment factor, It is a brand strategy factor;

[0046] The trade-in car transaction model design unit is used to design incentives and formulate a trade-in car transaction model. The specific formula is:

[0047]

[0048] in For trading benefits, For recycling price, For preferential ratio, For extra incentive;

[0049] The value maximization strategy unit is used to formulate strategies to determine whether to recycle or reuse the mobile equipment or sell it in the second-hand market based on the appreciation or depreciation analysis of the mobile equipment;

[0050] The recycling effectiveness evaluation unit is used to monitor customer participation, recycling volume, cost-effectiveness, and the implementation effect of recycling policies, and provide quantitative and qualitative analysis reports.

[0051] As a further solution of the present invention, the customer relationship management (CRM) integration module specifically includes:

[0052] A customer information set integration unit is used to collect customer personal information, car purchase records, service records, feedback and behavior data, classify and summarize the collected information sets, merge the information sets of motor vehicle appreciation or depreciation analysis and motor vehicle recycling model with the customer information set, generate an information set library containing all-round information of the customer, and establish a detailed personal profile for the customer based on the merged information set;

[0053] Personalized communication unit, which is used to segment customers according to the information set in the customer profile, regularly interact with customers based on the characteristics of the customer groups, and monitor the effectiveness of communication activities;

[0054] The customer loyalty determination and feedback loop mechanism unit is used to develop customer loyalty incentive plans, track the implementation of the plans, establish an effective feedback loop mechanism, and analyze customer feedback.

[0055] The beneficial effects of the present invention are: maximizing the value of mobile equipment at different stages of its life cycle. By real-time monitoring of market supply and demand, economic conditions and technological progress, and by optimizing the recycling and reuse process of mobile equipment, the present invention can reduce the operating costs of the brand and improve the overall economic benefits. The recycling policy and transaction model can be adjusted according to the brand strategy to ensure the realization of the brand's long-term goals. By designing attractive trade-in transaction models and incentives, the present invention can increase customer participation and the brand's market share, thereby expanding the demand for cultivating customer loyalty and customer stickiness. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 A flowchart of a method for analyzing automobile brand marketing based on big data provided by an embodiment of the present invention;

[0057] Figure 2 The collected information set provided by the embodiment of the present invention is stored in the original information set after processing and integration, and based on business understanding, information and insights are extracted from the original information set to form a flow chart of analysis features;

[0058] Figure 3 A flowchart for analyzing the appreciation and depreciation trends of motor equipment using historical information sets provided by an embodiment of the present invention, and constructing a motor equipment value prediction model, and determining the recycling and reuse of motor equipment and the second-hand market through analysis;

[0059] Figure 4 A flowchart for formulating a motor equipment recycling model based on the motor equipment appreciation or depreciation analysis results provided in an embodiment of the present invention, providing a trade-in vehicle transaction model, and monitoring and evaluating the implementation effect of the motor equipment recycling model;

[0060] Figure 5 A flowchart of integrating the information set feedback of the mobile equipment value prediction model and the mobile equipment recycling model into the CRM system to create a profile for the customer provided by an embodiment of the present invention;

[0061] Figure 6 A structural block diagram of an automobile brand marketing analysis system based on big data provided by an embodiment of the present invention;

[0062] Figure 7A structural block diagram of an information set collection and processing module provided in an embodiment of the present invention;

[0063] Figure 8 A structural block diagram of a mobile equipment appreciation or depreciation analysis module provided by an embodiment of the present invention;

[0064] Fig. 9 A structural block diagram of a mobile equipment recovery model module provided by an embodiment of the present invention;

[0065] Fig.10 This is a structural block diagram of the customer relationship management (CRM) integration module provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0066] 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.

[0067] 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.

[0068] Figure 1 A flowchart of a method for analyzing automobile brand marketing based on big data is provided in an embodiment of the present invention, such as Figure 1 As shown, a car brand marketing analysis method based on big data, the method includes:

[0069] S100, collect information sets, process and integrate them into original information sets, and extract information and insights from the original information sets based on business understanding to form analytical features;

[0070] S200 uses historical information sets to analyze the appreciation and depreciation trends of motorized equipment and builds a motorized equipment value prediction model to determine the recycling and re-use and second-hand market for motorized equipment through analysis;

[0071] This step aims to extract variables and patterns that have a significant impact on the value of mobile equipment from a large set of historical information.

[0072] Information set screening: Filter out key factors that may affect the value of motor equipment from the overall information set, such as vehicle age, mileage, brand, model, and maintenance records. Historical trend analysis: Analyze how these factors affect the value of motor equipment over time, and use statistical analysis and information set visualization tools to reveal the degree and trend of the impact of different variables on the value of motor equipment. Pattern recognition: Use machine learning algorithms (such as cluster analysis, principal component analysis (PCA)) to identify typical and abnormal patterns of changes in the value of motor equipment over time. Predictor variable extraction: Based on the results of trend analysis and pattern recognition, determine which factors are most important for predicting the future value of motor equipment.

[0073] This step aims to build an accurate prediction model for the value of mobile equipment based on the predictive variables extracted by the trend analysis unit. Select the features identified by the trend analysis unit that have predictive power for the future value of mobile equipment. These may include vehicle age, mileage, brand value, and model value. Select a suitable machine learning algorithm (such as linear regression, random forest, gradient boosting tree, etc.) to build a prediction model for the value of mobile equipment. The general form of the model can be expressed as:

[0074] ,in is the value of mobile equipment, is the intercept term, representing the predicted mobile equipment value when all input variables are zero, are model parameters, each measuring the impact of the corresponding variable on the value of mobile equipment while controlling other variables. is the error term, which is the random variation that cannot be explained by the model. Use historical information sets to train the model. Evaluate the prediction performance of the model through cross-validation and other techniques to ensure the accuracy, stability and generalization ability of the model. According to the feedback of model performance, adjust the model parameters or choose a more appropriate algorithm to further improve the prediction accuracy.

[0075] S300, based on the results of the appreciation or depreciation analysis of motor vehicles, formulate a motor vehicle recycling model, provide a trade-in vehicle transaction model, and monitor and evaluate the implementation effect of the motor vehicle recycling model;

[0076] This step uses the results of the mobile equipment value prediction model to set the recycling price and formulates the most appropriate recycling policy in combination with the current market and brand strategy. Use the output of the mobile equipment value prediction model as input to determine the recycling base value of the mobile equipment under different models and conditions. Determine the depreciation rate based on the actual condition of the mobile equipment (such as age and mileage), and apply market adjustment factors and brand strategy factors to adjust the base value to obtain the final recycling price. Formulate a recycling policy based on the adjusted price, taking into account incentives, market competition conditions and brand goals. Develop incentives to attract customers to participate in the trade-in program.

[0077] This step designs the trade-in transaction model to ensure that customers receive fair value from the transaction and that the brand can achieve long-term value through customer loyalty and effective management of the stock of mobile equipment. Focus on determining which mobile equipment is suitable for recycling and reuse and which is suitable for selling to the second-hand market in order to maximize the value of each vehicle. Analyze the appreciation or depreciation trends of different mobile equipment types and conditions and their performance in the market. Based on the analysis results, formulate strategies to determine which mobile equipment should enter the recycling process, which can be sold directly to the second-hand market, and how to increase the recycling value of mobile equipment through repair and refurbishment. Finally, the evaluation unit is responsible for monitoring the implementation of the recycling program and providing feedback for future improvements. Track customer engagement, recycling volume and cost-effectiveness to ensure that the recycling policy is implemented properly. Provide quantitative and qualitative analysis reports to evaluate the effectiveness of policy implementation, such as customer satisfaction, brand image improvement, etc. Based on market feedback and information set analysis results, continuously optimize the recycling model and transaction model to enhance customer loyalty and brand value.

[0078] S400, integrates the information set feedback of the mobile equipment value prediction model and the mobile equipment recycling model into the CRM system, creates profiles for customers, formulates communication and marketing strategies based on the customer's mobile equipment situation and behavior information set, develops a customer loyalty incentive program based on mobile equipment recycling participation, and establishes a feedback mechanism.

[0079] This step is responsible for creating a 360-degree customer view, collecting customer information sets from multiple channels, including personal basic information, vehicle purchase history, service and maintenance records, customer feedback and behavior information sets, etc. Integrate the collected customer information sets with the information set results of the mobile equipment appreciation or depreciation analysis module and the mobile equipment recycling model module to better understand customer needs and preferences. Using the integrated information sets, create detailed personal profiles for each customer, recording their preferences, purchasing power, mobile equipment usage patterns, service responses, etc. Building on the work of the customer information set integration unit, the goal of this unit is to provide a personalized customer experience, group customers according to the information sets in the customer profile, and create different market segments to design marketing activities more targeted. Design personalized communication plans based on the characteristics and preferences of customer groups, including multiple communication methods such as email, SMS, and social media interactions. Monitor the effectiveness of various communication strategies and analyze customer responses and participation in order to continuously optimize communication strategies.

[0080] The purpose of this step is to improve customer retention and lifetime value through customer loyalty programs. Based on the results of the mobile equipment appreciation or depreciation analysis, design a loyalty incentive program that attracts customers, such as a points system, coupons, exclusive offers, etc. Implement the customer loyalty program, track its execution regularly, and collect customer feedback on the program. Establish and maintain a feedback loop mechanism to systematically collect and analyze customer feedback as a basis for improving customer experience and increasing customer engagement. Dynamically adjust CRM strategies and loyalty programs based on customer feedback and market changes to ensure that they are always aligned with customer needs and corporate goals.

[0081] Figure 2 The collected information set provided in the embodiment of the present invention is stored in the original information set after processing and integration, and information and insights are extracted from the original information set based on business understanding to form a flow chart of analysis features, such as Figure 2 As shown, the collected information set is stored in the original information set after processing and integration, and based on business understanding, information and insights are extracted from the original information set to form analysis features, specifically including:

[0082] S110 captures sales records and service records of internal information sets, market research, social media, competitor analysis, and industry reports of external information sets, and implements real-time collection and processing of information set flows for scenarios that require real-time analysis;

[0083] S120, identifies and corrects abnormal information sets, converts information sets from different sources into a unified format, associates and integrates them, builds a complete information set view, stores it in the information set warehouse, and builds new features based on business understanding and information set analysis results.

[0084] Figure 3 The present invention provides a flowchart for analyzing the appreciation and depreciation trend of motor equipment by using historical information sets, and building a motor equipment value prediction model, and determining the recycling and reuse of motor equipment and the second-hand market through analysis, such as Figure 3 As shown, the use of historical information sets to analyze the appreciation and depreciation trends of mobile equipment and to build a mobile equipment value prediction model, through which the mobile equipment recycling and reuse and second-hand market are judged, specifically includes:

[0085] S210, filter vehicle age, mileage, brand, model, and maintenance factors from the overall information set, and perform historical trend analysis to identify patterns in the value of motor equipment over time;

[0086] S220, select features that have the ability to predict the future value of mobile equipment and build a mobile equipment value prediction model. The specific formula is:

[0087] ,in is the value of mobile equipment, is the intercept term, representing the predicted mobile equipment value when all input variables are zero, are model parameters, each measuring the impact of the corresponding variable on the value of mobile equipment while controlling other variables. is the error term, which is the random variation that cannot be explained by the model.

[0088] Figure 4 A flowchart of formulating a motor equipment recycling model based on the motor equipment appreciation or depreciation analysis results provided by an embodiment of the present invention, providing a trade-in vehicle transaction model, and monitoring and evaluating the implementation effect of the motor equipment recycling model, such as Figure 4 As shown, based on the analysis results of the appreciation or depreciation of motor vehicles, a motor vehicle recycling model is formulated, a trade-in vehicle transaction model is provided, and the implementation effect of the motor vehicle recycling model is monitored and evaluated, specifically including:

[0089] S310, using the results of the mobile equipment value prediction model, analyze the value changes of mobile equipment of different models and conditions, and formulate a mobile equipment recycling model based on market supply and demand, economic conditions, technological progress, and brand strategy factors. The specific formula is:

[0090]

[0091] in For recycling price, is the value of mobile equipment, is the depreciation rate, is the market adjustment factor, It is a brand strategy factor;

[0092] S320, design incentives and develop a trade-in model for old cars. The specific formula is:

[0093]

[0094] in For trading benefits, For recycling price, For preferential ratio, For extra incentive;

[0095] S330, based on the appreciation or depreciation analysis of motor equipment, formulate strategies to determine whether motor equipment should be recycled or reused, or sold in the second-hand market;

[0096] S340 monitors customer engagement, recycling volume, cost-effectiveness, and the effectiveness of recycling policy implementation, and provides quantitative and qualitative analysis reports.

[0097] Figure 5The flow chart of integrating the information set feedback of the mobile equipment value prediction model and the mobile equipment recycling model into the CRM system and creating a profile for the customer provided by the embodiment of the present invention is as follows: Figure 5 As shown, the information set feedback of the mobile equipment value prediction model and the mobile equipment recycling model is integrated into the CRM system to create a profile for the customer, specifically including:

[0098] S410, collecting customer personal information, vehicle purchase records, service records, feedback and behavior data, classifying and summarizing the collected information sets, integrating the information sets of motor vehicle appreciation or depreciation analysis and motor vehicle recycling model with the customer information sets, generating an information set library containing all-round information of the customer, and establishing a detailed personal profile for the customer based on the integrated information sets;

[0099] S420, segment customers based on the information set in the customer profile, interact with customers regularly based on the characteristics of the customer groups, and monitor the effectiveness of communication activities;

[0100] S430, develop a customer loyalty incentive program, track the program's execution, establish an effective feedback loop mechanism, and analyze customer feedback.

[0101] Figure 6 A structural block diagram of an automobile brand marketing analysis system based on big data provided by an embodiment of the present invention, such as Figure 6 As shown, a car brand marketing analysis system based on big data, the system includes:

[0102] The information set collection and processing module 100 is used to collect information sets, store them in original information sets after processing and integration, and extract information and insights from the original information sets based on business understanding to form analytical features;

[0103] The motor equipment appreciation or depreciation analysis module 200 is used to analyze the appreciation and depreciation trend of the motor equipment using the historical information set, and to build a motor equipment value prediction model, so as to determine the recycling and reuse of the motor equipment and the second-hand market through analysis;

[0104] This module aims to extract the variables and patterns that have a significant impact on the value of mobile equipment from large sets of historical information.

[0105] Information set screening: Filter out key factors that may affect the value of motor equipment from the overall information set, such as vehicle age, mileage, brand, model, and maintenance records. Historical trend analysis: Analyze how these factors affect the value of motor equipment over time, and use statistical analysis and information set visualization tools to reveal the degree and trend of the impact of different variables on the value of motor equipment. Pattern recognition: Use machine learning algorithms (such as cluster analysis, principal component analysis (PCA)) to identify typical and abnormal patterns of changes in the value of motor equipment over time. Predictor variable extraction: Based on the results of trend analysis and pattern recognition, determine which factors are most important for predicting the future value of motor equipment.

[0106] This module aims to build an accurate prediction model for the value of mobile equipment based on the predictive variables extracted by the trend analysis unit. Select the features identified by the trend analysis unit that have predictive power for the future value of mobile equipment. These may include vehicle age, mileage, brand value, model value, etc. Select a suitable machine learning algorithm (such as linear regression, random forest, gradient boosting tree, etc.) to build a prediction model for the value of mobile equipment. The general form of the model can be expressed as:

[0107] ,in is the value of mobile equipment, is the intercept term, representing the predicted mobile equipment value when all input variables are zero, are model parameters, each measuring the impact of the corresponding variable on the value of mobile equipment while controlling other variables. is the error term, which is the random variation that cannot be explained by the model. Use historical information sets to train the model. Evaluate the prediction performance of the model through cross-validation and other techniques to ensure the accuracy, stability and generalization ability of the model. According to the feedback of model performance, adjust the model parameters or choose a more appropriate algorithm to further improve the prediction accuracy.

[0108] The motor equipment recycling model module 300 is used to formulate a motor equipment recycling model based on the motor equipment appreciation or depreciation analysis results, provide a trade-in vehicle transaction model, and monitor and evaluate the implementation effect of the motor equipment recycling model;

[0109] This module uses the results of the mobile equipment value prediction model to set the recycling price, and combines the current market and brand strategy to develop the most appropriate recycling policy. Use the output of the mobile equipment value prediction model as input to determine the recycling base value of mobile equipment under different models and conditions. Determine the depreciation rate based on the actual condition of the mobile equipment (such as age and mileage), and apply market adjustment factors and brand strategy factors to adjust the base value to obtain the final recycling price. Develop a recycling policy based on the adjusted price, taking into account incentives, market competition conditions and brand goals. Develop incentives to attract customers to participate in the trade-in program.

[0110] This module designs trade-in models to ensure that customers receive fair value from the trade-in and that the brand can achieve long-term value through customer loyalty and effective management of the stock of mobile equipment. Focuses on determining which mobile equipment is suitable for recycling and reuse and which is suitable for selling to the second-hand market in order to maximize the value of each vehicle. Analyzes appreciation or depreciation trends of different mobile equipment types and conditions and their performance in the market. Based on the analysis results, formulates strategies to determine which mobile equipment should enter the recycling process, which can be sold directly to the second-hand market, and how to increase the recycling value of mobile equipment through repair and refurbishment. Finally, the evaluation unit is responsible for monitoring the implementation of the recycling program and providing feedback for future improvements. Track customer engagement, recycling volume and cost-effectiveness to ensure that the recycling policy is implemented properly. Provide quantitative and qualitative analysis reports to evaluate the effectiveness of policy implementation, such as customer satisfaction, brand image improvement, etc. Based on market feedback and information set analysis results, continuously optimize the recycling model and transaction model to enhance customer loyalty and brand value.

[0111] The customer relationship management (CRM) integration module 400 is used to integrate the information set feedback of the mobile equipment value prediction model and the mobile equipment recycling model into the CRM system, create profiles for customers, formulate communication and marketing strategies based on the customer's mobile equipment situation and behavior information set, develop a customer loyalty incentive program based on mobile equipment recycling participation, and establish a feedback mechanism.

[0112] This module is responsible for creating a 360-degree customer view, collecting customer information sets from multiple channels, including basic personal information, vehicle purchase history, service and maintenance records, customer feedback and behavior information sets, etc. Integrate the collected customer information sets with the information set results of the mobile equipment appreciation or depreciation analysis module and the mobile equipment recovery model module to better understand customer needs and preferences. Using the integrated information sets, create detailed personal profiles for each customer, recording their preferences, purchasing power, mobile equipment usage patterns, service responses, etc. Building on the work of the customer information set integration unit, the goal of this unit is to provide a personalized customer experience, group customers according to the information sets in the customer profile, and create different market segments to design marketing activities more targeted. Design personalized communication plans based on the characteristics and preferences of customer groups, including multiple communication methods such as email, SMS, and social media interactions. Monitor the effectiveness of various communication strategies and analyze customer responses and participation in order to continuously optimize communication strategies.

[0113] The purpose of this module is to improve customer retention and lifetime value through customer loyalty programs. Design customer-attractive loyalty incentive programs such as point systems, coupons, exclusive offers, etc. based on the results of the mobile equipment appreciation or depreciation analysis. Implement customer loyalty programs, track their performance regularly, and collect customer feedback on the program. Establish and maintain a feedback loop mechanism to systematically collect and analyze customer feedback as a basis for improving customer experience and increasing customer engagement. Dynamically adjust CRM strategies and loyalty programs based on customer feedback and market changes to ensure that they are always aligned with customer needs and corporate goals.

[0114] Figure 7 The structural block diagram of the information set collection and processing module provided by the embodiment of the present invention is as follows: Figure 7 As shown, the information set collection and processing module specifically includes:

[0115] The information set collection unit 110 is used to capture the sales records and service records of the internal information set, the market research, social media, competitor analysis, and industry reports of the external information set, and to realize the real-time collection and processing of the information set flow for the scenarios requiring real-time analysis;

[0116] The information set preprocessing unit 120 is used to identify and correct abnormal information sets, convert information sets from different sources into a unified format, associate and integrate them, build a complete information set view, store it in the information set warehouse, and build new features based on business understanding and information set analysis results.

[0117] Figure 8 The structural block diagram of the mobile equipment appreciation or depreciation analysis module provided by the embodiment of the present invention is as follows: Figure 8As shown, the mobile equipment appreciation or depreciation analysis module specifically includes:

[0118] a trend analysis unit 210 for filtering vehicle age, mileage, brand, model, and maintenance factors from the overall information set and performing historical trend analysis to identify patterns in the value of motor equipment over time;

[0119] The mobile equipment value prediction model building unit 220 is used to select features that have the ability to predict the future mobile equipment value and build a mobile equipment value prediction model. The specific formula is:

[0120] ,in is the value of mobile equipment, is the intercept term, representing the predicted mobile equipment value when all input variables are zero, are model parameters, each measuring the impact of the corresponding variable on the value of mobile equipment while controlling other variables. is the error term, which is the random variation that cannot be explained by the model.

[0121] Fig. 9 The structural block diagram of the mobile equipment recovery model module provided by the embodiment of the present invention is as follows: Fig. 9 As shown, the mobile equipment recovery model module specifically includes:

[0122] The recycling policy formulation unit 310 is used to use the results of the mobile equipment value prediction model to analyze the value changes of mobile equipment of different models and conditions, and formulate a mobile equipment recycling model based on market supply and demand, economic conditions, technological progress, and brand strategy factors. The specific formula is:

[0123]

[0124] in For recycling price, is the value of mobile equipment, is the depreciation rate, is the market adjustment factor, It is a brand strategy factor;

[0125] The trade-in car transaction model design unit 320 is used to design incentives and formulate a trade-in car transaction model. The specific formula is:

[0126]

[0127] in For trading benefits, For recycling price, For preferential ratio, For extra incentive;

[0128] The value maximization strategy unit 330 is used to formulate a strategy to determine whether to recycle or reuse the motorized equipment, or sell it in the second-hand market, based on the appreciation or depreciation analysis of the motorized equipment;

[0129] The recycling effect evaluation unit 340 is used to monitor customer participation, recycling volume, cost-effectiveness, and the implementation effect of the recycling policy, and provide quantitative and qualitative analysis reports.

[0130] Fig.10 The structural block diagram of the customer relationship management CRM integration module provided by the embodiment of the present invention is as follows: Fig.10 As shown, the customer relationship management CRM integration module specifically includes:

[0131] The customer information set integration unit 410 is used to collect customer personal information, car purchase records, service records, feedback and behavior data, classify and summarize the collected information sets, merge the information sets of motor vehicle appreciation or depreciation analysis and motor vehicle recycling model with the customer information set, generate an information set library containing all-round information of the customer, and establish a detailed personal profile for the customer based on the merged information set;

[0132] a personalized communication unit 420 for segmenting customers according to the information set in the customer profile, regularly interacting with customers based on the characteristics of the customer groups, and monitoring the effectiveness of communication activities;

[0133] The customer loyalty determination and feedback loop mechanism unit 430 is used to develop a customer loyalty incentive plan, track the execution of the plan, establish an effective feedback loop mechanism, and analyze customer feedback.

[0134] 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.

[0135] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the 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, information set library 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. By way of 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), etc.

[0136] 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.

[0137] 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.

[0138] 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. A car brand marketing analysis method based on big data, characterized in that: The method comprises: Collect information sets, process and integrate them into original information sets, and extract information and insights from the original information sets based on business understanding to form analytical features; Analyze the appreciation and depreciation trends of mobile equipment using historical information sets and build a mobile equipment value prediction model; Based on the results of the analysis of the appreciation or depreciation of motor vehicles, formulate a motor vehicle recycling model, provide a trade-in vehicle transaction model, and monitor and evaluate the implementation effect of the motor vehicle recycling model; Integrate the information set feedback of the mobile equipment value prediction model and the mobile equipment recycling model into the CRM system, create profiles for customers, formulate communication and marketing strategies based on the customer's mobile equipment situation and behavior information set, develop customer loyalty incentive programs based on mobile equipment recycling participation, and establish a feedback mechanism; The collected information set is stored in the original information set after processing and integration, and information and insights are extracted from the original information set based on business understanding to form analytical features, specifically including: Capture sales records and service records of internal information sets, market research, social media, competitor analysis, and industry reports of external information sets, and realize real-time collection and processing of information set streams for scenarios that require real-time analysis; Identify and correct abnormal information sets, convert information sets from different sources into a unified format, associate and integrate them, build a complete information set view, store it in the information set warehouse, and build new features based on business understanding and information set analysis results; The method of analyzing the appreciation and depreciation trend of mobile equipment by using the historical information set and constructing a mobile equipment value prediction model specifically includes: Filter vehicle age, mileage, brand, model, and maintenance factors from the overall information set, and conduct historical trend analysis to identify patterns in the value of motor equipment over time; Select features that have the ability to predict the future value of mobile equipment and build a mobile equipment value prediction model. The specific formula is: ,in is the value of mobile equipment, is the intercept term, representing the predicted mobile equipment value when all input variables are zero, are model parameters, each measuring the impact of the corresponding variable on the value of mobile equipment while controlling other variables. is the error term, which is the random variation that cannot be explained by the model; Based on the analysis results of the appreciation or depreciation of motor vehicles, a motor vehicle recycling model is formulated, a trade-in vehicle transaction model is provided, and the implementation effect of the motor vehicle recycling model is monitored and evaluated, including: Using the results of the mobile equipment value prediction model, we analyze the changes in the value of mobile equipment of different models and conditions, and develop a mobile equipment recycling model based on market supply and demand, economic conditions, technological progress, and brand strategy factors. The specific formula is: ； in For recycling price, is the value of mobile equipment, is the depreciation rate, is the market adjustment factor, It is a brand strategy factor; Design incentives and develop a trade-in model for old cars. The specific formula is: ； in For trading benefits, For recycling price, For preferential ratio, For extra incentive; Based on the appreciation or depreciation analysis of mobile equipment, formulate strategies to determine whether to recycle or sell the mobile equipment to the second-hand market; Monitor customer engagement, recycling volume, cost-effectiveness, and implementation of recycling policies, and provide quantitative and qualitative analysis reports; The information set feedback of the mobile equipment value prediction model and the mobile equipment recycling model is integrated into the CRM system to create a profile for the customer, specifically including: Collect customer personal information, vehicle purchase records, service records, feedback and behavior data, classify and summarize the collected information sets, merge the information sets of motor vehicle appreciation or depreciation analysis and motor vehicle recycling model with the customer information sets, generate an information set library containing all-round information of the customer, and establish a detailed personal profile for the customer based on the merged information sets; Segment customers based on the information set in the customer profile, interact with customers regularly based on the characteristics of the customer groups, and monitor the effectiveness of communication activities; Develop customer loyalty incentive programs, track program execution, establish an effective feedback loop mechanism, and analyze customer feedback.

2. An automobile brand marketing analysis system based on big data, characterized in that: The system comprises: The information set collection and processing module is used to collect information sets, process and integrate them into the original information set, and extract information and insights from the original information set based on business understanding to form analytical features; A motor equipment appreciation or depreciation analysis module is used to analyze the appreciation and depreciation trends of motor equipment using historical information sets and to build a motor equipment value prediction model; A motor vehicle recycling model module is used to formulate a motor vehicle recycling model based on the motor vehicle appreciation or depreciation analysis results, provide a trade-in vehicle transaction model, and monitor and evaluate the implementation effect of the motor vehicle recycling model; Customer Relationship Management (CRM) integration module, used to integrate the information set feedback of the mobile equipment value prediction model and the mobile equipment recycling model into the CRM system, create profiles for customers, formulate communication and marketing strategies based on the customer's mobile equipment situation and behavior information set, develop customer loyalty incentive programs based on mobile equipment recycling participation, and establish a feedback mechanism; The information set collection and processing module specifically includes: The information set collection unit is used to capture sales records and service records of internal information sets, market research, social media, competitor analysis, and industry reports of external information sets. For scenarios that require real-time analysis, it realizes real-time collection and processing of information set flows; The information set preprocessing unit is used to identify and correct abnormal information sets, convert information sets from different sources into a unified format, associate and integrate them, build a complete information set view, store it in the information set warehouse, and build new features based on business understanding and information set analysis results; The mobile equipment appreciation or depreciation analysis module specifically includes: A trend analysis unit is used to filter the vehicle age, mileage, brand, model, and maintenance factors from the overall information set and conduct historical trend analysis to identify patterns in the value of motor equipment over time; The mobile equipment value prediction model building unit is used to select features that have the ability to predict the future mobile equipment value and build a mobile equipment value prediction model. The specific formula is: ,in is the value of mobile equipment, is the intercept term, representing the predicted mobile equipment value when all input variables are zero, are model parameters, each measuring the impact of the corresponding variable on the value of mobile equipment while controlling other variables. is the error term, which is the random variation that cannot be explained by the model; The mobile equipment recovery model module specifically includes: The recycling policy formulation unit is used to use the results of the mobile equipment value prediction model to analyze the value changes of mobile equipment of different models and conditions, and formulate a mobile equipment recycling model based on market supply and demand, economic conditions, technological progress, and brand strategy factors. The specific formula is: ； in For recycling price, is the value of mobile equipment, is the depreciation rate, is the market adjustment factor, It is a brand strategy factor; The trade-in car transaction model design unit is used to design incentives and formulate a trade-in car transaction model. The specific formula is: ； in For trading benefits, For recycling price, For preferential ratio, For extra incentive; The value maximization strategy unit is used to formulate strategies to determine whether to recycle or reuse the mobile equipment or sell it in the second-hand market based on the appreciation or depreciation analysis of the mobile equipment; Recycling effectiveness evaluation unit, used to monitor customer participation, recycling volume, cost-effectiveness, and the implementation effect of recycling policies, and provide quantitative and qualitative analysis reports; The customer relationship management (CRM) integration module specifically includes: A customer information set integration unit is used to collect customer personal information, car purchase records, service records, feedback and behavior data, classify and summarize the collected information sets, merge the information sets of motor vehicle appreciation or depreciation analysis and motor vehicle recycling model with the customer information set, generate an information set library containing all-round information of the customer, and establish a detailed personal profile for the customer based on the merged information set; Personalized communication unit, which is used to segment customers according to the information set in the customer profile, regularly interact with customers based on the characteristics of the customer groups, and monitor the effectiveness of communication activities; The customer loyalty determination and feedback loop mechanism unit is used to develop customer loyalty incentive plans, track the implementation of the plans, establish an effective feedback loop mechanism, and analyze customer feedback.

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