Method and system for accurate operation of customers of direct jurisdiction group

Abstract

The invention discloses a method and a system for accurate operation of customers in a direct jurisdiction group, relates to the technical field of sales operation, and mainly solves the problems of low sales efficiency, poor customer satisfaction and high marketing cost. The method for accurate operation of customers in the direct jurisdiction group comprises the following steps: sorting and analyzing customer information; making a marketing activity strategy; determining a target customer group; the product is positioned and served; maintaining and managing customer relationships; the marketing effect is evaluated and optimized, and the direct jurisdiction group members are accurately matched for the customer group through a prediction evaluation algorithm, so that the sales conversion rate is improved; personalized recommendation and service are generated by analyzing customer data, so that customer satisfaction and loyalty are improved; and marketing advertisements and promotion activities are accurately put through a dynamic analysis algorithm, so that the marketing cost is reduced.

Description

Technical Field

[0001] The present invention relates to the field of sales and marketing technology, and in particular to a method and system for precise management of direct-controlled group customers. Background Art

[0002] In the insurance sales industry, the direct-controlled group is the smallest operating unit, and its operating efficiency and effectiveness are directly related to the business development of the entire company. However, the traditional customer management model has problems such as low sales efficiency, poor customer satisfaction and high marketing costs, and it is difficult to adapt to the needs of modern market competition. Therefore, how to improve the precision management level of direct-controlled group customers has become an important issue for the company's business development. With the development of information technology, especially in the era of the Internet and big data, the business activities of enterprises are increasingly dependent on the collection and analysis of customer data. Precision management is a data-driven marketing strategy that provides personalized products and services by analyzing customer behavior and preferences, thereby improving operating efficiency and customer satisfaction.

[0003] Therefore, the present invention provides a method and system for precise management of direct group customers, which improves the sales conversion rate by accurately matching direct group members with customer groups through predictive evaluation algorithms; improves customer satisfaction and loyalty by generating personalized recommendations and services through analysis of customer data; and reduces marketing costs by accurately delivering marketing advertisements and promotional activities through dynamic analysis algorithms. Summary of the invention

[0004] The purpose of the present invention is to solve the problems of low sales efficiency, poor customer satisfaction and high marketing costs in the prior art, and to propose a method and system for precise management of direct-controlled group customers.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for precise management of direct-administered group customers includes the following steps:

[0007] Step 1: Sort and analyze customer information;

[0008] Organize customer data, including personal information, purchase records, and interaction records, generate structured customer profiles through the customer grooming module, and analyze customer data to understand customer needs, behaviors, and preferences;

[0009] Step 2: Develop marketing campaign strategy;

[0010] Design marketing activities to attract target customer groups through the marketing module to reach target customers through activity channels;

[0011] Step 3: Determine the target customer group;

[0012] Create detailed customer portraits through the customer selection module, identify the target customer groups for sales, and classify them according to customer needs and potential so as to reasonably allocate them to members of the direct management team;

[0013] Step 4: Position and serve the product;

[0014] Communicate with customers through product service modules, adjust and optimize products and services to provide personalized insurance solutions;

[0015] Step 5: Maintain and manage customer relationships;

[0016] Track and manage customer information through the relationship management module to improve customer satisfaction and loyalty;

[0017] Step 6: Evaluate and optimize marketing effectiveness;

[0018] Continuously improve marketing strategies through evaluation and optimization modules to ensure that the system adapts to market changes and customer needs to achieve long-term business growth.

[0019] As a further description of the above technical solution, the customer combing module includes a data collection unit, a labeling unit and a value assessment unit. The data collection unit collects potential customer data through the CRM system and social media, and loads it into the database for storage; the labeling unit adds descriptive labels to customer groups based on customer attributes, behaviors and interaction history; the value assessment unit evaluates customer value through statistical and regression analysis to optimize resource allocation.

[0020] As a further description of the above technical solution, the marketing module includes a marketing planning unit, a multi-channel execution unit and an effect monitoring unit. The marketing planning unit formulates precise marketing strategies based on the target market, product positioning, pricing strategy, promotional activities and marketing channels; the multi-channel execution unit executes marketing activities through e-mail, social media, text messages, telephone and offline activity channels to ensure that marketing information can reach target customers accurately and efficiently; the effect monitoring unit monitors the marketing activities in real time based on the click-through rate, conversion rate, engagement and product sales of the marketing activities, and the output end of the marketing planning unit is connected to the input end of the multi-channel execution unit, and the output end of the multi-channel execution unit is connected to the input end of the effect monitoring unit.

[0021] As a further description of the above technical solution, the customer selection module includes a portrait construction unit, a customer selection unit, a customer classification unit and a customer matching unit. The portrait construction unit forms a detailed customer portrait through structured customer files; the customer selection unit evaluates the customer's potential needs and purchasing intentions through RFM analysis to ensure that marketing resources are effectively allocated; the customer classification unit classifies customer groups into different customer segments through cluster analysis to accurately provide product services; the customer matching unit achieves efficient matching between customers and directly managed group members through a predictive evaluation algorithm, the output end of the portrait construction unit is connected to the input end of the customer selection unit, the output end of the customer selection unit is connected to the input end of the customer classification unit, and the output end of the customer classification unit is connected to the input end of the customer matching unit.

[0022] As a further description of the above technical solution, the working steps of the prediction and evaluation algorithm are:

[0023] a) Data collection and preprocessing

[0024] Collect relevant data about customers and direct team members from multiple sources such as CRM systems, sales records, and customer service records to ensure the comprehensiveness and accuracy of the information; pre-process the data, including processing missing values ​​and outliers; and convert non-numeric data into numeric data through feature hashing;

[0025] b) Feature Engineering

[0026] Select feature values ​​with high correlation with matching degree from the preprocessed data, including customer's historical purchase records, customer preferences, service capabilities and sales performance of direct team members, to construct composite features;

[0027] c) Model selection and training

[0028] A prediction model based on a neural network is selected, and the processed data is trained using the neural network prediction model. The model performance is evaluated by a verification function, and the formula expression of the verification function is:

[0029]

[0030] In formula (1), K represents the verification function, ν represents the tolerance value, and α i represents the sample value of the direct group member data, β i represents the customer data sample value, i represents the order of the data sample to be verified, represents the function evaluation factor;

[0031] d) Model evaluation and optimization

[0032] Use precision and recall metrics to evaluate the accuracy of model prediction matches, and improve model prediction performance through parameter tuning, feature selection, and ensemble methods;

[0033] e) Prediction and matching

[0034] Use the trained neural network model to predict the matching degree between customers and direct team members, and prioritize matching customers and team members with high matching degree based on the prediction results;

[0035] f) Result verification and iteration

[0036] Collect follow-up feedback, verify the actual effect of the matching decision according to the evaluation function, and adjust the model and strategy according to the feedback results to continuously optimize the matching effect. The formula expression of the evaluation function is shown as follows:

[0037]

[0038] In formula (2), P represents the evaluation function, ω represents the evaluation label value corresponding to the function, and ρ represents the predicted value of the model output. Indicates known actual results.

[0039] As a further description of the above technical solution, the product service module includes a market research unit, a personalized service unit and a satisfaction evaluation unit. The market research unit identifies potential market demands and product improvement directions based on customer preferences, competitor analysis and industry trends; the personalized service unit generates personalized recommendations and services by analyzing customer data through collaborative filtering; the satisfaction evaluation unit evaluates customer satisfaction with products and services through a customer feedback collection system to identify service shortcomings. The output end of the market research unit is connected to the input end of the personalized service unit, and the output end of the personalized service unit is linked to the input end of the satisfaction evaluation unit.

[0040] As a further description of the above technical solution, the relationship management module includes a cycle management unit, a relationship maintenance unit and a cross-departmental collaboration unit. The cycle management unit identifies the needs and behavior patterns of customers at each stage through the CRM system; the relationship maintenance unit maintains and deepens customer relationships through accompanying visits, communication and value provision; the cross-departmental collaboration unit ensures information sharing and collaboration between sales, marketing, services and products through REP management software.

[0041] As a further description of the above technical solution, the evaluation and optimization module includes an indicator evaluation unit, a trend monitoring unit and an optimization unit. The indicator evaluation unit evaluates sales, customer satisfaction and customer churn rate through a dynamic data analysis algorithm to measure the effectiveness of the business strategy; the trend monitoring unit tracks industry dynamics, consumer behavior and competitor strategies through the big data analysis tool RapidMiner to ensure that the company can respond to market trends in a timely manner; the optimization unit ensures that the marketing strategy keeps pace with the market through linear programming to achieve optimal utilization of resources.

[0042] As a further description of the above technical solution, the working principle of the dynamic analysis algorithm is: the dynamic analysis algorithm collects and cleans key indicator data to ensure data consistency, analyzes and evaluates sales, customer satisfaction and customer churn rate through the ARIMA model, and at the same time, identifies the reasons for sales slumps and decreased customer satisfaction based on the ARIMA model.

[0043] As a further description of the above technical solution, a system for precise management of direct-controlled group customers includes: the customer sorting module, marketing module, customer selection module, product service module, relationship management module and evaluation optimization module, characterized in that:

[0044] The customer combing module is used to collect, integrate and analyze customer data, understand customer needs, behaviors and preferences, and provide a basis for subsequent precision marketing and services;

[0045] The marketing module is used to formulate and implement marketing strategies, reach customers through multiple channels and monitor marketing results to ensure the accuracy and efficiency of marketing activities;

[0046] The customer selection module is used to identify and select target customers and classify customers to ensure optimal allocation of resources;

[0047] The product service module is used to understand market demand, provide personalized products and services, and continuously optimize product and service experience to improve customer satisfaction;

[0048] The relationship management model is used to maintain and deepen customer relationships and enhance customer loyalty;

[0049] The evaluation and optimization module is used to monitor key indicator data and track market trends to continuously optimize business strategies to ensure business results and market adaptability.

[0050] Compared with the prior art, the present invention provides a method and system for precise management of directly-administered group customers, which has the following beneficial effects.

[0051] 1. The present invention uses a predictive evaluation algorithm to accurately match direct group members to customer groups, thereby improving the sales conversion rate.

[0052] 2. The present invention generates personalized recommendations and services by analyzing customer data, thereby improving customer satisfaction and loyalty.

[0053] 3. The present invention reduces marketing costs by accurately placing marketing advertisements and promotional activities through dynamic analysis algorithms.

[0054] Other advantages, objectives and features of the present invention will be described in part in the following description; and in part, will be apparent to those skilled in the art based on an examination of the following; or, may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to understand and grasp the technical solution more intuitively and clearly, when describing the embodiments of the present invention or the prior art, drawings are often used to supplement and illustrate. It should be noted that the drawings are only one way of expressing the embodiments of the present invention or the prior art. In fact, the technical solution can also have other implementations and changes, which all fall within the protection scope of the present invention. Therefore, technicians can design other drawings as needed to implement the technical solution of the present invention, among which,

[0056] Figure 1 The figure is a schematic diagram of the process steps for implementing the present invention.

[0057] Figure 2 It is a structural schematic diagram of the marketing module of the present invention.

[0058] Figure 3 This is a schematic diagram of the structure of the customer selection module of the present invention.

[0059] Figure 4 It is a structural schematic diagram of the product service module of the present invention.

[0060] Figure 5 It is a structural schematic diagram of the evaluation and optimization module of the present invention.

[0061] Figure 6 It is a schematic diagram of the structure of the system of the present invention. DETAILED DESCRIPTION

[0062] The following will clearly and completely describe the technical solutions in the embodiments of this invention through the accompanying drawings in the embodiments of this invention. Obviously, the described embodiments are only part of the embodiments of this invention, not all of the embodiments. At the same time, the description of well-known structures and technologies is omitted in the following description to avoid unnecessary confusion of the concepts of the present invention.

[0063] like Figure 1-Figure 6 As shown, a method for precise management of direct-controlled group customers includes the following steps:

[0064] Step 1: Sort and analyze customer information;

[0065] Organize customer data, including personal information, purchase records, and interaction records, generate structured customer profiles through the customer grooming module, and analyze customer data to understand customer needs, behaviors, and preferences;

[0066] Step 2: Develop marketing campaign strategy;

[0067] Design marketing activities to attract target customer groups through the marketing module to reach target customers through activity channels;

[0068] Step 3: Determine the target customer group;

[0069] Create detailed customer portraits through the customer selection module, identify the target customer groups for sales, and classify them according to customer needs and potential so as to reasonably allocate them to members of the direct management team;

[0070] Step 4: Position and serve the product;

[0071] Communicate with customers through product service modules, adjust and optimize products and services to provide personalized insurance solutions;

[0072] Step 5: Maintain and manage customer relationships;

[0073] Track and manage customer information through the relationship management module to improve customer satisfaction and loyalty;

[0074] Step 6: Evaluate and optimize marketing effectiveness;

[0075] Continuously improve marketing strategies through evaluation and optimization modules to ensure that the system adapts to market changes and customer needs to achieve long-term business growth.

[0076] In the above embodiment, the customer combing module includes a data collection unit, a labeling unit and a value assessment unit. The data collection unit collects potential customer data through the CRM system and social media, and loads it into the database for storage; the labeling unit adds descriptive labels to customer groups based on customer attributes, behaviors and interaction history; the value assessment unit assesses customer value through statistical and regression analysis to optimize resource allocation.

[0077] In a specific embodiment, the customer combing module serves as the intelligent center of enterprise precision marketing and customer relationship management. Its depth and breadth expansion not only strengthens customer insight, but also promotes efficient resource allocation and value maximization. The data collection unit realizes the comprehensive capture of potential customer data by integrating the dual-channel data capture mechanism of CRM system and social media. These data are not limited to basic information, but also include customer preferences, interaction trajectories and behavior patterns, ensuring the richness and real-time nature of information. The collected data is then efficiently loaded into the database to form structured storage, providing a solid foundation for subsequent analysis. The labeling unit converts massive customer data into intuitive descriptive labels. These labels cover customer attributes, behavioral characteristics and interaction history, such as purchase frequency, interest preferences, loyalty level, etc. Through precise classification, it provides a basis for subsequent personalized services and precision marketing strategies. The value assessment unit uses advanced data processing methods such as statistics and regression analysis to conduct in-depth assessment of customer value. By quantifying customer contribution, predicting potential value and life cycle value, the enterprise can clearly identify high-value customer groups, optimize resource allocation, ensure that more attention and resources are invested in key customers, and achieve optimization of marketing investment and return.

[0078] In the above embodiment, the marketing module includes a marketing planning unit, a multi-channel execution unit and an effect monitoring unit. The marketing planning unit formulates precise marketing strategies according to the target market, product positioning, pricing strategy, promotional activities and marketing channels; the multi-channel execution unit executes marketing activities through e-mail, social media, text messages, telephone and offline activity channels to ensure that marketing information can reach target customers accurately and efficiently; the effect monitoring unit monitors the marketing activities in real time according to the click-through rate, conversion rate, engagement and product sales of the marketing activities, and the output end of the marketing planning unit is connected to the input end of the multi-channel execution unit, and the output end of the multi-channel execution unit is connected to the input end of the effect monitoring unit.

[0079] In a specific embodiment, the marketing planning unit formulates an efficient marketing strategy based on the demand context of the target market, the market positioning of the product, the exquisite layout of the pricing strategy, the creative planning of promotional activities and the precise selection of marketing channels. It deeply analyzes market trends and has insights into consumer psychology to ensure that each strategy is accurate and correct; the multi-channel execution unit builds a comprehensive marketing network through personalized reach through e-mail, the wide influence of social media, the immediacy of text messages, direct communication through telephone, and the immersive experience of offline activities; the effect monitoring unit captures the feedback of each marketing action by real-time monitoring of key indicators such as the click-through rate, conversion rate, participation and product sales of marketing activities. It can not only quickly evaluate the effectiveness of marketing activities, but also adjust strategies in time according to data feedback to ensure that marketing activities are always in the best state and maximize ROI.

[0080] In the above embodiment, the customer selection module includes a portrait construction unit, a customer selection unit, a customer classification unit and a customer matching unit. The portrait construction unit forms a detailed customer portrait through structured customer files; the customer selection unit evaluates the customer's potential needs and purchasing intentions through RFM analysis to ensure that marketing resources are effectively allocated; the customer classification unit classifies the customer groups into different customer segments through cluster analysis so as to accurately provide product services; the customer matching unit achieves efficient matching between customers and direct group members through a predictive evaluation algorithm, the output end of the portrait construction unit is connected to the input end of the customer selection unit, the output end of the customer selection unit is connected to the input end of the customer classification unit, and the output end of the customer classification unit is connected to the input end of the customer matching unit.

[0081] In a specific embodiment, the customer selection module serves as the intelligent brain of the enterprise's refined operation, and its depth and accuracy determine the success or failure of the marketing strategy. The portrait construction unit uses structured customer files to deeply mine the customer's basic information, behavioral habits, interest preferences and other multivariate data to form a comprehensive and three-dimensional customer portrait. These portraits not only capture the static characteristics of customers, but also dynamically reflect the customer's behavioral trajectory, providing a solid data foundation for subsequent precision marketing. The customer selection unit conducts an in-depth analysis of customers, evaluates their recent transaction behavior, transaction frequency and consumption amount, and accurately identifies high-value customers and potential lost customers. By quantifying the customer's potential needs and purchasing intentions, it ensures that marketing resources are effectively allocated, achieves the optimal ratio of marketing input and output, and improves customer conversion rate and retention rate. The customer classification unit uses advanced clustering analysis technology to finely divide customer groups to form multiple customer segments with similar characteristics. This process is not only based on the basic attributes of customers, but also takes into account their behavioral patterns and consumption preferences, ensuring that each customer segment can obtain tailored products and services, realizing the transformation from mass marketing to precision marketing; the customer matching unit uses predictive evaluation algorithms to achieve efficient matching between customers and direct team members. It not only considers customer needs and preferences, but also deeply analyzes the service capabilities and styles of team members to ensure that each match can maximize customer expectations and improve customer satisfaction and loyalty.

[0082] In the above embodiment, the working steps of the prediction and evaluation algorithm are:

[0083] a) Data collection and preprocessing

[0084] Collect relevant data about customers and direct team members from multiple sources such as CRM systems, sales records, and customer service records to ensure the comprehensiveness and accuracy of the information; pre-process the data, including processing missing values ​​and outliers; and convert non-numeric data into numeric data through feature hashing;

[0085] b) Feature Engineering

[0086] Select feature values ​​with high correlation with matching degree from the preprocessed data, including customer's historical purchase records, customer preferences, service capabilities and sales performance of direct team members, to construct composite features;

[0087] c) Model selection and training

[0088] A prediction model based on a neural network is selected, and the processed data is trained using the neural network prediction model. The model performance is evaluated by a verification function, and the formula expression of the verification function is:

[0089]

[0090] In formula (1), K represents the verification function, v represents the tolerance value, and α i represents the sample value of the direct group member data, β i represents the customer data sample value, i represents the order of the data sample to be verified, represents the function evaluation factor;

[0091] d) Model evaluation and optimization

[0092] Use precision and recall metrics to evaluate the accuracy of model prediction matches, and improve model prediction performance through parameter tuning, feature selection, and ensemble methods;

[0093] e) Prediction and matching

[0094] Use the trained neural network model to predict the matching degree between customers and direct team members, and prioritize matching customers and team members with high matching degree based on the prediction results;

[0095] f) Result verification and iteration

[0096] Collect follow-up feedback, verify the actual effect of the matching decision according to the evaluation function, and adjust the model and strategy according to the feedback results to continuously optimize the matching effect. The formula expression of the evaluation function is shown as follows:

[0097]

[0098] In formula (2), P represents the evaluation function, ω represents the evaluation label value corresponding to the function, and ρ represents the predicted value of the model output. Indicates known actual results.

[0099] In a specific embodiment, the core function of the prediction and evaluation algorithm is to accurately match customers with direct team members, improve service efficiency and customer satisfaction by analyzing customer preferences, historical behaviors and team member capabilities, and ultimately promote sales performance and customer loyalty. The problems that the prediction and evaluation algorithm needs to solve include: inaccurate matching between customers and team members, resulting in low service efficiency; uneven distribution of sales resources, and insufficient attention to high-potential customers; lack of effective data analysis methods, making it difficult to quantify matching effects and optimization strategies. i , customer data sample value β i and function evaluation factor Get the verification function to evaluate the performance of the prediction model to verify the accuracy of the model prediction; through the evaluation label value ω corresponding to the function, the predicted value ρ output by the model and the known actual results The formula of the evaluation function is obtained, and the actual effect of the matching decision is verified to check the accuracy of the matching decision. At the same time, the difference between the prediction evaluation algorithm and the traditional algorithm in terms of accuracy and recall is shown in Table 1:

[0100] Table 1. Differences between prediction evaluation algorithms and traditional algorithms

[0101]

[0102] In summary, the prediction and evaluation algorithm shown is superior to the traditional algorithm in both accuracy and recall. It can be seen that the prediction and evaluation algorithm is the best choice for the present invention.

[0103] In the above embodiment, the product service module includes a market research unit, a personalized service unit and a satisfaction evaluation unit. The market research unit identifies potential market demands and product improvement directions based on customer preferences, competitor analysis and industry trends; the personalized service unit generates personalized recommendations and services by analyzing customer data through collaborative filtering; the satisfaction evaluation unit evaluates customer satisfaction with products and services through a customer feedback collection system to identify service shortcomings. The output end of the market research unit is connected to the input end of the personalized service unit, and the output end of the personalized service unit is linked to the input end of the satisfaction evaluation unit.

[0104] In a specific embodiment, the market research unit not only conducts in-depth mining based on customer preferences to understand the real needs and potential wishes of customers, but also grasps industry dynamics and trends through a comprehensive analysis of competitors, thereby identifying market gaps and opportunities for product improvement. This unit provides a solid basis for corporate decision-making through a data-driven approach to ensure that products can be accurately positioned to meet market demand. The personalized service unit uses advanced collaborative filtering technology to deeply analyze customer data, including purchase history, browsing behavior, preference settings, etc., to generate highly personalized product recommendations and service solutions. This customized service not only improves customer experience, but also enhances customer stickiness, allowing companies to stand out from many competitors and establish a unique and difficult-to-copy competitive advantage. The satisfaction evaluation unit regularly collects and analyzes customer evaluations of products and services by building a customer feedback collection system. This unit not only evaluates customer satisfaction, but also identifies potential service shortcomings through in-depth analysis, providing direction for continuous improvement. Through timely adjustment and optimization, ensure that customer experience is always maintained at a high level, promote positive interaction between companies and customers, and build long-term and stable customer relationships. Through the coordinated operation of these three units, the product service module can not only respond quickly to market changes and provide personalized services, but also continuously optimize customer experience, ensuring that the company stays ahead in the fierce market competition and achieves sustainable development.

[0105] In the above embodiment, the relationship management module includes a cycle management unit, a relationship maintenance unit and a cross-departmental collaboration unit. The cycle management unit identifies the needs and behavior patterns of customers at each stage through the CRM system; the relationship maintenance unit maintains and deepens customer relationships through accompanying visits, communication and value provision; and the cross-departmental collaboration unit ensures information sharing and collaboration between sales, marketing, services and products through REP management software.

[0106] In a specific embodiment, the cycle management unit uses an advanced CRM system to gain accurate insights into every stage of the customer journey. It can keenly capture the changes in customer needs and behavior patterns, and can provide just the right support to ensure the consistency and personalization of the customer experience, whether it is the exploration period of initial contact or the mature period of long-term cooperation. The relationship maintenance unit continuously deepens the emotional bond with customers through regular visits, in-depth communication and value co-creation. It not only focuses on the completion of transactions, but also attaches more importance to the cultivation of relationships. By providing services and solutions that exceed expectations, customers can feel the sincerity and professionalism of the company, thereby establishing unshakable loyalty. The cross-departmental collaboration unit achieves seamless docking between departments such as sales, marketing, service and products through the use of REP management software. It breaks down traditional work barriers, ensures real-time sharing of information and efficient collaboration, and enables each link to work closely together to provide customers with an integrated high-quality experience. In summary, these three units complement each other and jointly build a customer relationship management system that is customer-centric, cross-departmental collaborative, and cyclically managed, which not only improves customer satisfaction and loyalty, but also promotes the company's continued growth and improvement of market competitiveness.

[0107] In the above embodiment, the evaluation and optimization module includes an indicator evaluation unit, a trend monitoring unit and an optimization unit. The indicator evaluation unit evaluates sales, customer satisfaction and customer churn rate through a dynamic data analysis algorithm to measure the effectiveness of the business strategy; the trend monitoring unit tracks industry dynamics, consumer behavior and competitor strategies through the big data analysis tool RapidMiner to ensure that the company can respond to market trends in a timely manner; the optimization unit ensures that the marketing strategy keeps pace with the market through linear programming to achieve optimal utilization of resources.

[0108] In a specific embodiment, the indicator evaluation unit uses cutting-edge dynamic data analysis algorithms to conduct in-depth analysis of key business indicators such as sales, customer satisfaction and customer churn rate. It not only calculates the current values ​​of these indicators, but also has insights into the trends behind them, evaluates the effectiveness of business strategies, and provides intuitive data support for decision makers to ensure that every strategic adjustment has a basis. The trend monitoring unit uses the big data analysis tool RapidMiner to monitor the industry pulse, changes in consumer behavior and the latest trends of competitors around the clock. It can keenly capture the market trend and ensure that enterprises can always take a step ahead in the ever-changing business environment, make forward-looking layouts, and firmly grasp market opportunities. The optimization unit uses advanced mathematical models such as linear programming to scientifically plan marketing strategies to ensure that they are seamlessly connected with market trends. By accurately calculating resource allocation, it achieves the optimization of marketing input and output, allowing every resource of the enterprise to be used to the best effect, maximize marketing effects, and improve overall operational efficiency.

[0109] In the above embodiment, the working principle of the dynamic analysis algorithm is as follows: the dynamic analysis algorithm collects and cleans key indicator data to ensure data consistency, analyzes and evaluates sales, customer satisfaction and customer churn rate through the ARIMA model, and at the same time, identifies the reasons for sales slumps and decreased customer satisfaction based on the ARIMA model.

[0110] In a specific embodiment, the dynamic analysis algorithm first screens out key indicators such as sales, customer satisfaction and customer churn rate from business data through a rigorous data collection mechanism. This process ensures the purity and consistency of the data. Subsequently, the dynamic analysis algorithm introduces the ARIMA model, a predictive analysis tool. The ARIMA is an autoregressive integrated moving average model, which can capture the time series characteristics of data and conduct in-depth analysis of the fluctuations in sales, the ups and downs of customer satisfaction and the dynamic changes in customer churn rate. Through parameter adjustment, the ARIMA model can accurately identify sales troughs, which are often accompanied by changes in the market environment or increased competitive pressure. At the same time, the dynamic analysis algorithm can keenly perceive the precursors of declining customer satisfaction, which may be poor product experience or slow service response, providing valuable early warning signals for decision makers of the enterprise. Furthermore, the dynamic analysis algorithm is not only limited to the description of surface phenomena, but it can also dig deep into the reasons behind the phenomena. Through the analysis results of the ARIMA model, enterprises can gain insight into the deep-seated reasons for the decline in sales troughs and customer satisfaction, whether it is the obsolescence of product lines, mistakes in market strategies, or low operational efficiency. This deep insight allows companies to adjust strategies in a timely manner, optimize products and services, and thus maintain competitiveness in the market. In summary, the dynamic analysis algorithm provides companies with a comprehensive and in-depth market insight solution through its unique data processing and predictive analysis capabilities. It can not only reveal the current business situation, but also predict future trends, help companies make more informed decisions, and lead companies to move forward steadily in the ever-changing market.

[0111] In the above embodiment, a system for precise management of direct-controlled group customers includes: the customer sorting module, marketing module, customer selection module, product service module, relationship management module and evaluation optimization module, characterized in that:

[0112] The customer combing module is used to collect, integrate and analyze customer data, understand customer needs, behaviors and preferences, and provide a basis for subsequent precision marketing and services;

[0113] The marketing module is used to formulate and implement marketing strategies, reach customers through multiple channels and monitor marketing results to ensure the accuracy and efficiency of marketing activities;

[0114] The customer selection module is used to identify and select target customers and classify customers to ensure optimal allocation of resources;

[0115] The product service module is used to understand market demand, provide personalized products and services, and continuously optimize product and service experience to improve customer satisfaction;

[0116] The relationship management model is used to maintain and deepen customer relationships and enhance customer loyalty;

[0117] The evaluation and optimization module is used to monitor key indicator data and track market trends to continuously optimize business strategies and ensure business results and market adaptability.

[0118] In a specific embodiment, the customer combing module not only collects and integrates customer data, but also analyzes customer needs, behaviors and preferences in depth, accurately portrays the portrait of each customer through big data and artificial intelligence technology, and provides a solid data foundation for subsequent marketing strategies and services. The marketing module formulates and implements personalized marketing strategies based on the insights provided by the customer combing module. It reaches customers through multiple channels, whether it is social media, email or text messages, and can accurately locate them, while monitoring the marketing effect in real time to ensure that each activity can achieve the expected goals and achieve marketing accuracy and efficiency. The customer selection module uses advanced algorithms to identify and screen high-potential customer groups, conducts refined classification, ensures efficient use of resources, avoids the inefficient marketing method of "casting a wide net", and allows every investment to bring the maximum return. The product service module focuses on market dynamics and customer needs, and provides personalized products and services. It not only focuses on the product itself, but also pays more attention to the improvement of service experience. Through continuous optimization, it ensures that products and services can meet or even exceed customer expectations, thereby improving customer satisfaction and repurchase rate. The relationship management module is committed to maintaining and deepening relationships with customers. Through regular customer care activities, customized services and loyalty programs, customer loyalty and brand stickiness can be improved, so that every customer can feel the warmth and care of the brand. The evaluation and optimization module monitors key indicator data in real time, tracks market trends, and continuously optimizes business strategies. Through data analysis, the system can adjust the business direction in a timely manner to ensure the continuous improvement of business results and the enhancement of market adaptability.

[0119] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

[0120] In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art can combine and combine different embodiments or examples described in this specification and the features of different embodiments or examples without contradiction.

[0121] Although the embodiments of the present invention have been shown and described above, it is to be understood that the above embodiments are exemplary and are not to be construed as limitations of the present invention. A person skilled in the art may change, modify, replace and vary the above embodiments within the scope of the present invention.

Claims

1. A method for precise management of direct-controlled group customers, characterized in that: The steps include: Step 1: Sort and analyze customer information; Organize customer data, including personal information, purchase records, and interaction records, generate structured customer profiles through the customer grooming module, and analyze customer data to understand customer needs, behaviors, and preferences; Step 2: Develop marketing campaign strategy; Design marketing activities to attract target customer groups through the marketing module to reach target customers through activity channels; Step 3: Determine the target customer group; Create detailed customer portraits through the customer selection module, identify the target customer groups for sales, and classify them according to customer needs and potential so as to reasonably allocate them to members of the direct management team; Step 4: Position and serve the product; Communicate with customers through product service modules, adjust and optimize products and services to provide personalized insurance solutions; Step 5: Maintain and manage customer relationships; Track and manage customer information through the relationship management module to improve customer satisfaction and loyalty; Step 6: Evaluate and optimize marketing effectiveness; Continuously improve marketing strategies through evaluation and optimization modules to ensure that the system adapts to market changes and customer needs to achieve long-term business growth.

2. According to claim 1, a method for precise management of direct-administered group customers is characterized in that: The customer combing module includes a data collection unit, a labeling unit and a value assessment unit. The data collection unit collects potential customer data through the CRM system and social media, and loads it into the database for storage; the labeling unit adds descriptive labels to customer groups based on customer attributes, behaviors and interaction history; the value assessment unit assesses customer value through statistical and regression analysis to optimize resource allocation.

3. According to claim 1, a method for precise management of direct-administered group customers is characterized in that: The marketing module includes a marketing planning unit, a multi-channel execution unit and an effect monitoring unit. The marketing planning unit formulates precise marketing strategies based on target markets, product positioning, price strategies, promotional activities and marketing channels; The multi-channel execution unit executes marketing activities through email, social media, SMS, telephone and offline event channels to ensure that marketing information can reach target customers accurately and efficiently; The effect monitoring unit monitors the marketing activities in real time according to the click rate, conversion rate, participation and product sales of the marketing activities. The output end of the marketing planning unit is connected to the input end of the multi-channel execution unit, and the output end of the multi-channel execution unit is connected to the input end of the effect monitoring unit.

4. According to claim 1, a method for precise management of direct-administered group customers is characterized in that: The customer selection module includes a portrait construction unit, a customer selection unit, a customer classification unit and a customer matching unit. The portrait construction unit forms a detailed customer portrait through structured customer files; the customer selection unit evaluates customers' potential needs and purchasing intentions through RFM analysis to ensure that marketing resources are effectively allocated; the customer classification unit classifies customer groups into different customer segments through cluster analysis to accurately provide product services; the customer matching unit achieves efficient matching between customers and direct group members through a predictive evaluation algorithm, the output end of the portrait construction unit is connected to the input end of the customer selection unit, the output end of the customer selection unit is connected to the input end of the customer classification unit, and the output end of the customer classification unit is connected to the input end of the customer matching unit.

5. A method for precise management of direct-administered group customers according to claim 4, characterized in that: The working steps of the prediction and evaluation algorithm are: a) Data collection and preprocessing Collect relevant data about customers and direct team members from multiple sources such as CRM systems, sales records, and customer service records to ensure the comprehensiveness and accuracy of the information; pre-process the data, including processing missing values ​​and outliers; and convert non-numeric data into numeric data through feature hashing; b) Feature Engineering Select feature values ​​with high correlation with matching degree from the preprocessed data, including customer's historical purchase records, customer preferences, service capabilities and sales performance of direct team members, to construct composite features; c) Model selection and training A prediction model based on a neural network is selected, and the processed data is trained using the neural network prediction model. The model performance is evaluated by a verification function, and the formula expression of the verification function is: In formula (1), K represents the verification function, ν represents the tolerance value, and α i represents the sample value of the direct group member data, β i represents the customer data sample value, o represents the order of the data sample to be verified, represents the function evaluation factor; d) Model evaluation and optimization Use precision and recall metrics to evaluate the accuracy of model prediction matches, and improve model prediction performance through parameter tuning, feature selection, and ensemble methods; e) Prediction and matching Use the trained neural network model to predict the matching degree between customers and direct team members, and prioritize matching customers and team members with high matching degree based on the prediction results; f) Result verification and iteration Collect follow-up feedback, verify the actual effect of the matching decision according to the evaluation function, and adjust the model and strategy according to the feedback results to continuously optimize the matching effect. The formula expression of the evaluation function is shown as follows: In formula (2), P represents the evaluation function, ω represents the evaluation label value corresponding to the function, and ρ represents the predicted value of the model output. Indicates known actual results.

6. A method for precise management of direct-administered group customers according to claim 1, characterized in that: The product service module includes a market research unit, a personalized service unit and a satisfaction evaluation unit. The market research unit identifies potential market demands and product improvement directions based on customer preferences, competitor analysis and industry trends; the personalized service unit generates personalized recommendations and services by analyzing customer data through collaborative filtering; the satisfaction evaluation unit evaluates customer satisfaction with products and services through a customer feedback collection system to identify service shortcomings. The output end of the market research unit is connected to the input end of the personalized service unit, and the output end of the personalized service unit is linked to the input end of the satisfaction evaluation unit.

7. The method for precise management of direct-administered group customers according to claim 1, characterized in that: The relationship management module includes a cycle management unit, a relationship maintenance unit and a cross-departmental collaboration unit. The cycle management unit identifies the needs and behavior patterns of customers at each stage through the CRM system; the relationship maintenance unit maintains and deepens customer relationships through accompanying visits, communication and value provision; and the cross-departmental collaboration unit ensures information sharing and collaboration between sales, marketing, services and products through REP management software.

8. The method for precise management of direct-administered group customers according to claim 1, characterized in that: The evaluation and optimization module includes an indicator evaluation unit, a trend monitoring unit and an optimization unit. The indicator evaluation unit evaluates sales, customer satisfaction and customer churn rate through a dynamic data analysis algorithm to measure the effectiveness of the business strategy; the trend monitoring unit tracks industry dynamics, consumer behavior and competitor strategies through the big data analysis tool RapidMiner to ensure that the company can respond to market trends in a timely manner; the optimization unit ensures that the marketing strategy keeps pace with the market through linear programming to achieve optimal utilization of resources.

9. A method for precise management of direct-administered group customers according to claim 8, characterized in that: The working principle of the dynamic analysis algorithm is as follows: the dynamic analysis algorithm collects and cleans key indicator data to ensure data consistency, analyzes and evaluates sales, customer satisfaction and customer churn rate through the ARIMA model, and at the same time, identifies the reasons for sales slumps and decreased customer satisfaction based on the ARIMA model.

10. A method for precise management of direct-administered group customers according to any one of claims 1 to 9, characterized in that: A system for precise management of direct-controlled group customers includes: the customer sorting module, marketing module, customer selection module, product service module, relationship management module and evaluation optimization module, characterized in that: The customer combing module is used to collect, integrate and analyze customer data, understand customer needs, behaviors and preferences, and provide a basis for subsequent precision marketing and services; The marketing module is used to formulate and implement marketing strategies, reach customers through multiple channels and monitor marketing results to ensure the accuracy and efficiency of marketing activities; The customer selection module is used to identify and select target customers and classify customers to ensure optimal allocation of resources; The product service module is used to understand market demand, provide personalized products and services, and continuously optimize product and service experience to improve customer satisfaction; The relationship management module is used to maintain and deepen customer relationships and enhance customer loyalty; the evaluation and optimization module is used to monitor key indicator data and track market trends to continuously optimize business strategies and ensure business results and market adaptability.

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