Sales agency strategy method for improving customer conversion rate

Abstract

The invention relates to the technical field of sales strategy optimization, in particular to a sales agent strategy method for improving customer conversion rate, which comprises the step of constructing a customer behavior analysis model, a sales agent capability matching model, a resource allocation optimization module and a feedback mechanism updating module. Customer behavior data are collected in real time, matching is performed in combination with sales agent capability characteristics, resource configuration is dynamically optimized by using an adaptive weight adjustment algorithm, and an interactive feedback mechanism is introduced to realize strategy real-time updating. According to the method, the sales resource allocation accuracy and the strategy adaptability can be remarkably improved, and the efficient customer conversion ability is kept in a complex market environment.

Description

Technical Field

[0001] This invention belongs to the field of sales and marketing technology, specifically a sales agency strategy method for improving customer conversion rates. Background: With increasing market competition and diversified customer needs, improving customer conversion rates has become a core issue in corporate sales strategies. Existing sales agency strategies and techniques have made some progress in customer behavior analysis, personalized recommendations, and resource allocation, but still have some shortcomings that limit their effectiveness and efficiency in practical applications.

[0002] A search revealed a patent with publication number CN119515440B, which proposes a method for predicting customer conversion rates using multi-behavioral sparse data, published on May 6, 2025. This method constructs customer feature information sequence data and customer-item interaction graph data, defines and trains a conversion rate prediction model CF4CVR, to predict customer click and conversion probabilities. However, this technical solution primarily relies on historical behavioral data and machine learning model training, lacking the ability to capture and respond to real-time dynamic customer behavior. Furthermore, this method does not fully consider the subjective initiative and flexibility of sales agents in actual operations, potentially leading to a discrepancy between the predicted results and the actual sales scenario, thus affecting the final conversion rate improvement.

[0003] A search revealed that patent CN115099882B discloses a method and apparatus for calculating customer-level marketing budgets, published on December 6, 2022. This method improves marketing accuracy and real-time performance by classifying customers into different marketing stages and calculating the available marketing budget for each customer based on the classification results. However, this technical solution primarily focuses on the static attributes and stage characteristics of customers during budget allocation, failing to fully consider the actual capabilities, experience, and interactions with customers of sales agents, potentially leading to a mismatch between budget allocation and actual sales needs. Furthermore, this method has limited support for dynamic adjustment mechanisms based on customer feedback, which may make it difficult to adapt to rapidly changing customer needs in a complex and volatile market environment.

[0004] The aforementioned problems indicate that existing customer conversion rate improvement technologies still have room for improvement in terms of real-time performance, dynamic adaptability, and sales agent strategy optimization. Therefore, this invention provides a sales agent strategy method to improve customer conversion rates. It aims to combine real-time customer behavior data, the capabilities and characteristics of sales agents, and a dynamic feedback mechanism to optimize sales resource allocation and strategy formulation, thereby more effectively improving customer conversion rates and meeting the needs of enterprises in a highly competitive market. Summary of the Invention

[0005] One of the objectives of this invention is to overcome the shortcomings of existing technologies and provide a sales agency strategy method to improve customer conversion rates.

[0006] The second objective of this invention is to achieve the capture and response to real-time dynamic customer behavior data through this method, thereby optimizing the allocation of sales resources and the formulation of strategies.

[0007] The third objective of this invention is to combine the capabilities and characteristics of sales agents with a dynamic feedback mechanism to form a sales agency strategy system that is more adaptable to market demands.

[0008] To achieve the above objectives, the technical mechanism employed in this invention is as follows: by constructing a multi-dimensional customer behavior analysis model, real-time customer behavior data is correlated and mapped with the capabilities of sales agents, and an adaptive weight adjustment algorithm is introduced to dynamically optimize the sales resource allocation scheme. Based on this, a sales agent strategy update process based on an interactive feedback mechanism is designed to ensure that sales agents can adjust their strategies promptly based on customer feedback during actual operations.

[0009] A sales agency strategy method to improve customer conversion rate, characterized by the following steps:

[0010] 1. Build a customer behavior analysis model and generate a customer behavior feature matrix by collecting real-time customer behavior data such as clicks, browsing, and inquiries;

[0011] 2. Based on the customer behavior feature matrix and combined with the sales agent capability feature data, construct a sales agent capability matching model;

[0012] 3. An adaptive weight adjustment algorithm is introduced to dynamically optimize the sales resource allocation scheme;

[0013] 4. Design an interactive feedback mechanism to update the sales agency strategy in real time based on customer feedback data.

[0014] A sales agency strategy optimization system based on the above method is characterized in that the system consists of a customer behavior data collection module, a sales agency capability matching module, a resource allocation optimization module, and a feedback mechanism update module.

[0015] One specific step to implement the above method is as follows:

[0016] a. The construction of the customer behavior data collection module involves the following steps: a-1. Deploy a data collection interface on the enterprise's online platform using event tracking technology to record customer clicks, browsing, inquiries, and other behavioral data in real time; a-2. Organize the collected behavioral data according to time series to generate a customer behavior feature matrix; a-3. Standardize the customer behavior feature matrix to eliminate the dimensional differences between different behavioral data.

[0017] b. The design of the sales agent capability matching module involves the following steps: b-1. Collect basic information, historical performance, and communication ability evaluation data of sales agents to generate a sales agent capability feature vector; b-2. Map the customer behavior feature matrix to the sales agent capability feature vector and calculate the matching degree between the two; b-3. Based on the matching degree result, assign customers to the most suitable sales agents.

[0018] c. Implementation of the resource allocation optimization module, the specific steps of which are as follows: c-1. Introduce an adaptive weight adjustment algorithm to dynamically adjust the resource allocation weights according to the data changes in the customer behavior feature matrix; c-2. Apply the resource allocation weights to the task priority ranking of sales agents to ensure that high-value customers receive more resource support; c-3. Periodically evaluate the resource allocation scheme and further optimize the weight parameters based on the evaluation results.

[0019] d. The design of the feedback mechanism update module, specifically the following steps: d-1. During the sales process, collect customer feedback data on the sales agent's services through methods such as questionnaires and telephone follow-ups; d-2. Input the feedback data into the feedback mechanism update module to generate sales agent strategy adjustment suggestions; d-3. The sales agent updates its own sales strategy in real time according to the adjustment suggestions and reapplies the updated strategy to subsequent customer interactions.

[0020] The specific implementation method of step a-1 above is as follows: a-1-1. Deploy tracking code in key pages and functional modules of the enterprise's online platform to record behavioral data such as customer click count, dwell time, and page jump path; a-1-2. Upload the recorded behavioral data to the data storage server and sort it according to the timestamp to form time series data; a-1-3. Clean the time series data to remove outliers and duplicate data to ensure the accuracy and integrity of the data.

[0021] The specific implementation of step b-2 above is as follows: b-2-1. Use the cosine similarity algorithm to calculate the matching degree between the customer behavior feature matrix and the sales agent capability feature vector; b-2-2. Output the matching degree result in numerical form and classify the matching result according to the preset threshold; b-2-3. Use the classification result as the basis for customer allocation, and prioritize the allocation of customers with high matching degree to the corresponding sales agents.

[0022] The specific implementation of step c-1 above is as follows: c-1-1. Define the core formula of the adaptive weight adjustment algorithm, and use the key indicators in the customer behavior feature matrix as input variables; c-1-2. Dynamically adjust the weight coefficients of each indicator according to the changing trend of the input variables to ensure the flexibility of the resource allocation scheme; c-1-3. Apply the adjusted weight coefficients to the resource allocation model to generate a new resource allocation scheme.

[0023] The specific implementation of step d-2 above is as follows: d-2-1. Classify and organize the collected customer feedback data according to dimensions such as satisfaction rating and problem category; d-2-2. Use association rule mining algorithms to analyze the potential patterns in the feedback data and generate sales agent strategy adjustment suggestions; d-2-3. Present the adjustment suggestions in a visual form to facilitate quick understanding and implementation by sales agents.

[0024] This invention's sales agency strategy optimization system, through multi-module collaborative operation, achieves real-time capture and analysis of customer behavior data. Simultaneously, by combining the capabilities of sales agents and a dynamic feedback mechanism, it significantly improves the accuracy of sales resource allocation and the adaptability of sales strategies. In practical applications, the system demonstrates high stability and scalability, and after multiple iterations and optimizations, it maintains high customer conversion rates in complex and ever-changing market environments. (See attached figures.)

[0025] Figure 1 This is a flowchart illustrating the method of the present invention, showing the overall process from customer behavior data collection to sales agency strategy updates, including key steps such as customer behavior analysis, capability matching, resource allocation optimization, and feedback mechanism updates.

[0026] Figure 2 This is a schematic diagram of the system module structure of the present invention, showing that the system consists of a customer behavior data collection module, a sales agent capability matching module, a resource allocation optimization module, and a feedback mechanism update module, and demonstrating the data interaction relationship between each module.

[0027] Figure 3 The flowchart for the implementation of the adaptive weight adjustment algorithm details the specific process of dynamically adjusting the resource allocation weight coefficients based on key indicators in the customer behavior feature matrix.

[0028] The attached diagram is labeled as follows: 1. Customer behavior data collection module; 2. Sales agent capability matching module; 3. Resource allocation optimization module; 4. Feedback mechanism update module; 5. Customer behavior feature matrix; 6. Sales agent capability feature vector; 7. Adaptive weight adjustment algorithm; 8. Resource allocation scheme; 9. Customer feedback data; 10. Sales agent strategy adjustment suggestions. Detailed implementation method.

[0029] This invention provides a sales agency strategy method and its corresponding optimization system for improving customer conversion rates. Its core lies in achieving real-time capture and analysis of customer behavior data through multi-module collaborative work, and optimizing resource allocation and strategy formulation by combining the characteristics of sales agency capabilities and a dynamic feedback mechanism. The following is in conjunction with the appendix... Figure 1 To be continued Figure 3 The part numbers marked in the attached diagrams will be explained in detail.

[0030] In practical applications, the specific implementation process of this invention is divided into four main modules: customer behavior data collection module 1, sales agent capability matching module 2, resource allocation optimization module 3, and feedback mechanism update module 4. These modules are closely connected and cooperate with each other through data flow to complete the entire process from data collection to strategy update. First, the customer behavior data collection module 1 is responsible for recording customer clicks, browsing, inquiries, and other behavioral data in real time, and organizing this data into a time series format to generate a customer behavior feature matrix 5. This module uses tracking technology to deploy data collection interfaces in key pages and functional modules of the enterprise's online platform. Specifically, tracking code is deployed in key pages such as product detail pages, shopping cart pages, and payment pages to record customer click counts, dwell time, and page navigation paths. This data is then uploaded to a data storage server and sorted according to timestamps to form time series data. To ensure the accuracy and integrity of the data, module 1 also includes a data cleaning step to remove outliers and duplicate data.

[0031] The generated customer behavior feature matrix 5 is passed as input to the sales agent capability matching module 2. The core task of this module is to associate and map the customer behavior feature matrix 5 with the sales agent capability feature vector 6, and calculate the matching degree between the two. The sales agent capability feature vector 6 is generated from data such as the sales agent's basic information, historical performance, and communication ability evaluation. In the specific implementation, module 2 uses a cosine similarity algorithm to calculate the matching degree between the customer behavior feature matrix 5 and the sales agent capability feature vector 6, and outputs the matching degree result in numerical form. After classifying the matching results according to a preset threshold, module 2 prioritizes assigning customers with high matching degrees to the corresponding sales agents. For example, if a customer's behavior characteristics show that they have a high interest in high-end products, and a sales agent's historical performance shows that they are good at handling high-end customers, then this customer will be prioritized to be assigned to that sales agent. This data-based matching allocation method can significantly improve the fit between customers and sales agents, thereby increasing customer conversion rates.

[0032] Resource allocation optimization module 3 receives the matching results from sales agent capability matching module 2 and dynamically optimizes resource allocation scheme 8 using adaptive weight adjustment algorithm 7. The implementation process of adaptive weight adjustment algorithm 7 is shown in the appendix. Figure 3 As shown, its core lies in dynamically adjusting the weight coefficients of each indicator based on the key indicators in the customer behavior characteristic matrix 5. For example, when a customer's click frequency and dwell time increase significantly, the algorithm automatically increases the weight coefficients of these indicators, thereby prioritizing the allocation of more resources to support that customer. The resource allocation optimization module 3 applies the adjusted weight coefficients to the task priority ranking of sales agents, ensuring that high-value customers receive more resource support. In addition, module 3 also includes a periodic evaluation function, which further optimizes the weight parameters by evaluating the actual effect of the resource allocation scheme 8, ensuring that the resource allocation scheme can adapt to changes in the market environment.

[0033] The feedback mechanism update module 4 is responsible for collecting customer feedback data 9 on sales agent services and generating sales agent strategy adjustment suggestions 10 based on this data. In practice, module 4 obtains customer feedback data 9 through questionnaires, telephone follow-ups, etc., and categorizes and organizes the data according to dimensions such as satisfaction rating and problem category. Subsequently, module 4 uses association rule mining algorithms to analyze the potential patterns in the feedback data and generate specific sales agent strategy adjustment suggestions 10. These suggestions are presented in a visual format for easy understanding and implementation by sales agents. For example, if a sales agent has a slow response time when handling customer inquiries, module 4 will generate corresponding improvement suggestions, prompting the sales agent to optimize the response process. The sales agent updates their sales strategy in real time according to the adjustment suggestions and reapplies the updated strategy to subsequent customer interactions, thus forming a closed-loop optimization process.

[0034] The data interaction relationships between the four modules mentioned above are shown in the appendix. Figure 2 As shown, the customer behavior feature matrix 5 generated by the customer behavior data collection module 1 is passed as input to the sales agent capability matching module 2. The matching results output by module 2 are further passed to the resource allocation optimization module 3. The resource allocation scheme 8 generated by module 3 guides the actual operation of the sales agents. At the same time, the feedback mechanism update module 4 generates sales agent strategy adjustment suggestions 10 by collecting customer feedback data 9, and feeds these suggestions back to the sales agents, forming a closed-loop structure of dynamic optimization.

[0035] Throughout the process, the specific implementation of each module depends on the data output of the preceding and following modules. For example, the output of the customer behavior data collection module 1 directly affects the matching result of the sales agent capability matching module 2, and the matching result determines the weight adjustment direction of the resource allocation optimization module 3. The feedback mechanism update module 4 dynamically corrects the entire process by collecting customer feedback data 9, ensuring that the system can maintain efficient operation in a complex and ever-changing market environment.

[0036] As can be seen from the above embodiments, the system of the present invention achieves real-time capture and analysis of customer behavior data through the collaborative work of multiple modules. Simultaneously, by combining the characteristics of sales agency capabilities and a dynamic feedback mechanism, it significantly improves the accuracy of sales resource allocation and the adaptability of sales strategies. To better enable those skilled in the art to fully understand and implement the present invention, the specific implementation principles of the present invention are further explained below with reference to a specific application scenario.

[0037] In practice, the customer behavior data collection module 1 first performs the data collection task. Using event tracking technology, data collection interfaces are deployed in key locations on the enterprise's online platform, such as product detail pages, shopping cart pages, and payment pages. The event tracking code records behavioral data such as customer click counts, dwell time, and page navigation paths. This data is then uploaded to a data storage server and sorted by timestamp to form time-series data. To ensure the accuracy and completeness of the data, module 1 also includes a data cleaning step to remove outliers and duplicate data, ultimately generating a customer behavior feature matrix 5. This matrix serves as the core input data for subsequent modules, describing customer behavior patterns and potential needs.

[0038] Next, the sales agent capability matching module 2 receives the customer behavior feature matrix 5 and maps it to the sales agent capability feature vector 6. The sales agent capability feature vector 6 is generated from data such as the sales agent's basic information, historical performance, and communication skills evaluation. Module 2 uses a cosine similarity algorithm to calculate the matching degree between the customer behavior feature matrix 5 and the sales agent capability feature vector 6, and outputs the matching degree result in numerical form. After classifying the matching results according to a preset threshold, module 2 prioritizes assigning customers with high matching degrees to the corresponding sales agents. For example, if a customer's behavior characteristics show a high interest in high-end products, and a sales agent's historical performance indicates expertise in handling high-end customers, then this customer will be prioritized for assignment to that sales agent. This process significantly improves the fit between customers and sales agents through precise data matching, thus laying the foundation for improving customer conversion rates.

[0039] Subsequently, the resource allocation optimization module 3 receives the matching results from the sales agent capability matching module 2 and dynamically optimizes the resource allocation scheme 8 using the adaptive weight adjustment algorithm 7. The core of the adaptive weight adjustment algorithm 7 lies in dynamically adjusting the weight coefficients of each indicator based on the key indicators in the customer behavior feature matrix 5. For example, when a customer's click frequency and dwell time increase significantly, the algorithm automatically increases the weight coefficients of these indicators, thereby prioritizing the allocation of more resources to support that customer. The resource allocation optimization module 3 applies the adjusted weight coefficients to the task priority ranking of sales agents, ensuring that high-value customers receive more resource support. Furthermore, module 3 includes a periodic evaluation function, which further optimizes the weight parameters by evaluating the actual effectiveness of the resource allocation scheme 8, ensuring that the resource allocation scheme can adapt to changes in the market environment. This dynamic optimization mechanism makes resource allocation more flexible and efficient, enabling rapid response to changes in customer needs.

[0040] Finally, the feedback mechanism update module 4 is responsible for collecting customer feedback data 9 on sales agent services and generating sales agent strategy adjustment suggestions 10 based on this data. In practice, module 4 obtains customer feedback data 9 through questionnaires, telephone follow-ups, etc., and classifies and organizes the data according to dimensions such as satisfaction rating and problem category. Subsequently, module 4 uses association rule mining algorithms to analyze the potential patterns in the feedback data and generate specific sales agent strategy adjustment suggestions 10. These suggestions are presented in a visual form, making it easy for sales agents to quickly understand and implement them. For example, if a sales agent has a slow response time when handling customer inquiries, module 4 will generate corresponding improvement suggestions, prompting the sales agent to optimize the response process. The sales agent updates its own sales strategy in real time according to the adjustment suggestions and reapplies the updated strategy to subsequent customer interactions, thus forming a closed-loop optimization process. This closed-loop structure not only improves the service quality of sales agents but also enhances the system's self-correction capability.

[0041] The data interaction relationships between the four modules mentioned above are shown in the appendix. Figure 2As shown, the customer behavior feature matrix 5 generated by the customer behavior data collection module 1 is input to the sales agent capability matching module 2. The matching result output by module 2 is further transmitted to the resource allocation optimization module 3. The resource allocation scheme 8 generated by module 3 guides the actual operation of the sales agents. Simultaneously, the feedback mechanism update module 4 generates sales agent strategy adjustment suggestions 10 by collecting customer feedback data 9 and feeds these suggestions back to the sales agents, forming a dynamic optimization closed-loop structure. The specific implementation of each module depends on the data output of the preceding and following modules. For example, the output of the customer behavior data collection module 1 directly affects the matching result of the sales agent capability matching module 2, and the matching result determines the weight adjustment direction of the resource allocation optimization module 3. The feedback mechanism update module 4 dynamically corrects the entire process by collecting customer feedback data 9, ensuring that the system can maintain efficient operation in a complex and ever-changing market environment.

[0042] As can be seen from the above implementation methods, the system of the present invention achieves real-time capture and analysis of customer behavior data through the collaborative work of multiple modules. At the same time, it significantly improves the accuracy of sales resource allocation and the adaptability of sales strategies by combining the characteristics of sales agency capabilities and dynamic feedback mechanisms.

Claims

1. A sales agency strategy method to improve customer conversion rate, characterized in that... The method includes the following steps: a. Constructing a customer behavior analysis model by generating a customer behavior feature matrix through real-time collection of customer click, browsing, and consultation behavior data; b. Constructing a sales agent capability matching model based on the customer behavior feature matrix combined with the sales agent capability feature data; c. Introducing an adaptive weight adjustment algorithm to dynamically optimize the sales resource allocation scheme; d. Designing an interactive feedback mechanism to update the sales agent strategy in real time through customer feedback data.

2. The sales agency strategy method for improving customer conversion rate according to claim 1, characterized in that... The specific implementation method of step a is as follows: a-1. Deploy tracking code in key pages and functional modules of the enterprise's online platform to record customer click counts, dwell time, page navigation path behavior data; a-2. Upload the recorded behavior data to the data storage server and sort it according to timestamps to form time series data; a-3. Clean the time series data to remove outliers and duplicate data to ensure the accuracy and integrity of the data.

3. The sales agency strategy method for improving customer conversion rate according to claim 1, characterized in that... The specific implementation method of step b is as follows: b-1. Collect basic information, historical performance, and communication ability evaluation data of sales agents to generate a sales agent capability feature vector; b-2. Use the cosine similarity algorithm to calculate the matching degree between the customer behavior feature matrix and the sales agent capability feature vector and output the matching degree result in numerical form; b-3. Assign customers to the most suitable sales agents according to the matching degree result.

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