Client data follow-up processing management method and system

Abstract

The invention relates to a customer data follow-up processing management method and system, and relates to the field of data management.The method comprises the steps that a customer data form is built, and a customer follow-up process model is built; constructing a mapping logic relationship between the customer data form and the customer follow-up process model, and storing the mapping logic relationship into a relational database; on the basis of the relational database, client data filling and follow-up processes are monitored, follow-up processing scores are calculated, follow-up management results are obtained, and the technical problems that in the client development process, system theoretical guidance is lacked, the processes are not standard, and resource mismatching and low efficiency are caused by dependence on personal experience of salesmen are solved.

Description

Technical Field

[0001] The present invention relates to the field of data management, and particularly to a method and system for managing customer data follow-up processing. Background Art

[0002] Under the background of the rapid development of globalization and information technology, the competition among enterprises is becoming increasingly fierce, and the market environment is becoming increasingly complex and changeable. Due to the lack of systematic theoretical guidance and standardized operation processes, the traditional customer development methods often rely on the subjective experience and intuition of salespersons, resulting in problems such as non-standard sales behaviors, non-standardized customer development processes, and low efficiency. To solve these problems, enterprises urgently need a more scientific, efficient, and standardized customer development process and method.

[0003] At present, although some enterprises have begun to use information systems to manage customer information, such as CRM systems, these systems usually focus on the basic management of customer information, such as the entry, storage, and query of customer information, and lack the standardization of sales behaviors and the guidance of customer development processes. When using these systems, salespersons still need to rely on personal experience and intuition to formulate customer development strategies, which leads to the lack of consistency and traceability in the customer development process, and also increases the management cost. In addition, the existing information systems often have the problem of low data correlation. Salespersons need to spend a lot of time and energy to integrate and analyze customer information, which not only reduces work efficiency but also may affect the success rate of customer development. Therefore, enterprises urgently need a new customer development method that can centrally manage and associate various types of customer information, and at the same time provide a standardized process and an automated management mechanism. Summary of the Invention

[0004] In view of the technical problems of the lack of systematic theoretical guidance, non-standardized processes, and resource misallocation and low efficiency caused by relying on the personal experience of salespersons in the customer development process in the prior art, the present invention provides a method and system for managing customer data follow-up processing to solve these problems.

[0005] The technical solutions of the present invention for solving the above technical problems are as follows: In a first aspect, the present invention provides a method for managing customer data follow-up processing, the method including: constructing a customer data form, and constructing a customer follow-up process model; constructing a mapping logical relationship between the customer data form and the customer follow-up process model, and storing it in a relational database; based on the relational database, monitoring the filling of customer data and the follow-up process, and calculating a follow-up processing score to obtain a follow-up management result.

[0006] In a second aspect, the present invention provides a customer data follow-up processing management system, which includes: a data construction module for constructing a customer data form and a customer follow-up process model; a relationship mapping module for constructing a mapping logical relationship between the customer data form and the customer follow-up process model and storing it in a relational database; a scoring processing module for monitoring customer data filling and follow-up processes based on the relational database, calculating a follow-up processing score, and obtaining a follow-up management result.

[0007] The beneficial effects of the present invention are as follows: By constructing a customer data form and a customer follow-up process model and establishing a mapping logical relationship between them, systematic management of customer data is achieved. Based on the relational database, this method can monitor the filling and follow-up processes of customer data in real time. By calculating the follow-up processing score, it can effectively evaluate and manage customer follow-up work, thereby ensuring reasonable allocation of resources and improving the efficiency and effectiveness of customer development. Description of the Drawings

[0008] Figure 1 It is a flowchart of a customer data follow-up processing management method provided by the present invention.

[0009] Figure 2 It is a structural diagram of a customer data follow-up processing management system provided by the present invention.

[0010] Description of the reference numerals: data construction module 11, relationship mapping module 12, scoring processing module 13. Detailed Embodiments

[0011] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention.

[0012] In the description of the present invention, the terms "first" and "second" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more, unless otherwise specifically defined.

[0013] In the description of the present invention, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described as "for example" in the present invention is not necessarily to be construed as more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to implement and use the present invention. In the following description, details are set forth for purposes of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be implemented without the use of these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

[0014] Embodiment 1: As Figure 1 shown, an embodiment of the present invention provides a method for managing and processing customer data follow-up, and the method includes: S10: Construct a customer data form and a customer follow-up process model.

[0015] S20: Construct the mapping logical relationship between the customer data form and the customer follow-up process model, and store it in a relational database.

[0016] S30: Based on the relational database, monitor the customer data filling and follow-up process, calculate the follow-up processing score, and obtain the follow-up management result.

[0017] Exemplarily, an enterprise systematically manages and follows up customers by constructing a customer data form and a customer follow-up process model. The customer data form is a structured data collection tool that details the basic information of customers, such as name, contact information, purchase history, etc., and may also involve other important information, such as customer needs, preferences, feedback, etc. The design of this form should be based on the actual needs of the enterprise to ensure that customer information can be comprehensively and accurately captured. Through the customer data form, the enterprise can establish a complete customer information database, providing a basis for subsequent analysis and follow-up. The customer follow-up process model, on the other hand, is a framework that defines the steps and logic of customer follow-up. It stipulates the entire process from the initial contact with the customer, needs analysis, solution provision, negotiation to closing or losing the deal. Each step clearly defines the tasks to be executed, the expected goals, and the personnel or departments that may be involved. Through the customer follow-up process model, the enterprise can ensure that each customer is followed up in a timely and professional manner, improving customer satisfaction and conversion rate.

[0018] Next, in order to effectively combine this customer data form and the customer follow-up process model, the enterprise needs to build the mapping logical relationships between them and store these relationships in a relational database. The mapping logical relationships define how each field in the customer data form is associated with each step in the customer follow-up process model. For example, a customer's purchase history may trigger specific follow-up strategies, while the customer's feedback may affect subsequent communication methods. Through the relational database, the enterprise can efficiently store, query, and process these complex mapping relationships, providing strong support for subsequent data analysis and follow-up management.

[0019] Furthermore, based on the relational database, the enterprise can monitor the filling of customer data and the follow-up process in real time. Whenever there is an update to the customer data or a follow-up step is completed, the system will automatically record and trigger the corresponding processing logic. At the same time, the enterprise can also use this data to calculate the follow-up processing score to evaluate the follow-up effect of each customer. The follow-up processing score is based on multiple factors, such as the timeliness of the follow-up, the customer's satisfaction, and the conversion rate of transactions. By calculating and analyzing these scores, the enterprise can identify potential business opportunities, optimize the follow-up strategies, and ultimately improve the overall sales performance and customer satisfaction.

[0020] In summary, the entire customer data follow-up processing management method realizes the comprehensive management and efficient follow-up of customer data by constructing the customer data form and the customer follow-up process model and establishing the mapping logical relationships between them. This method not only improves the operational efficiency of the enterprise but also optimizes the customer experience, bringing significant economic benefits and competitive advantages to the enterprise.

[0021] In a preferred embodiment, constructing the customer data form includes: Obtaining multiple categories of customer information; setting the editing attributes of the multiple categories of information, where the editing attributes include whether it is a required item and the information length; constructing the customer data form according to the multiple categories of information and the editing attributes.

[0022] Optionally, constructing a customer data form is a systematic process aimed at comprehensively and accurately collecting and managing customer information, providing a basis for subsequent customer service and sales activities. First, the enterprise needs to clarify which information categories it hopes to collect from customers. These information categories generally cover multiple aspects such as customers' basic information, contact information, purchase history, preference settings, feedback, etc. For example, basic information includes name, gender, date of birth, nationality, etc.; contact information includes phone number, email address, mailing address, etc.; purchase history includes records of purchased products / services, purchase time, purchase amount, etc.; preference settings include language preference, communication channel preference, privacy settings, etc.; feedback includes evaluations, suggestions, complaints about products / services, etc. After determining the information categories, the enterprise needs to set corresponding editing attributes for each information category to ensure the integrity and accuracy of the data. Editing attributes usually include whether it is a required field and the information length. Among them, whether it is a required field indicates whether this information category is a mandatory content when the customer fills out the form. Required fields are usually information crucial to the business process, such as name and contact information. And the information length refers to setting the maximum number of characters or word limit for text-type information to prevent data from being overly long or exceeding the system's processing capacity. Subsequently, based on the determined information categories and editing attributes, the enterprise can start constructing the customer data form. The form design should follow the following principles: simple and clear, the form should be easy to understand, avoiding the use of professional terms or complex expressions; logical, the information categories should be arranged in a logical order for easy customer filling; security, ensuring the security of form data transmission and storage to protect customer privacy. In the actual construction process, the enterprise can use form generation tools or software to quickly create form templates. These tools usually provide rich field types (such as text boxes, drop-down menus, check boxes, etc.) and editing attribute setting options, making the form design more flexible and efficient. After completing the form design, the enterprise needs to conduct form testing to ensure that its functions and performance meet expectations. The test content may include: data validation, checking whether the form can correctly identify and process incorrect inputs (such as illegal characters, exceeding the length limit, etc.); user experience, evaluating the usability and friendliness of the form, collecting user feedback and optimizing it; compatibility, testing the display effect and compatibility of the form on different devices and browsers. According to the test results, the enterprise can make necessary adjustments and optimizations to the form to improve its quality and user experience. Finally, the enterprise can deploy the constructed customer data form to relevant channels (such as websites, APPs, CRM systems, etc.) for customers to fill out and submit. At the same time, the enterprise also needs to establish corresponding data processing and analysis mechanisms to promptly obtain and utilize customer information to support business decisions and development. In summary, constructing a customer data form is a process involving multiple links such as determining information categories, setting editing attributes, form design, testing and optimization, and deployment and application.By scientifically and reasonably constructing forms, enterprises can collect and manage customer information more effectively, providing strong support for business development.

[0023] In a preferred embodiment, a customer follow-up process model is constructed, including: Constructing multiple-stage information in customer follow-up, where the multiple-stage information has a sequential order; According to the sequential order, constructing the logical connection relationship of the multiple-stage information to obtain a customer follow-up process model.

[0024] Specifically, constructing a customer follow-up process model is a systematic and structured process, aiming to ensure that customers can receive timely and effective attention and service at each stage of their interaction with the enterprise. First, the enterprise needs to clarify multiple key stages in customer follow-up, which usually reflect the entire life cycle of the customer from the initial contact with the enterprise to the final transaction and even after-sales service. Common follow-up stages may include: initial contact, needs analysis, solution provision and negotiation, signing and execution, after-sales service. Among them, the initial contact is the first interaction between the enterprise and the customer, which may be carried out through salesperson phone calls, email marketing, social media interactions, etc. At this stage, the enterprise needs to collect basic customer information, understand customer needs, and establish a preliminary trust relationship. Based on the initial contact, the enterprise needs to further understand the specific needs of the customer in depth, including functional requirements for products / services, price expectations, purchase time, etc. The goal of this stage is to provide customized solutions or product recommendations for the customer. Furthermore, according to the customer's needs, the enterprise provides a detailed solution or product quotation to the customer and negotiates on aspects such as price and terms. The goal of this stage is to reach an agreement acceptable to both parties. After the negotiation is successful, both parties sign a formal contract and execute the transaction according to the contract provisions. The enterprise needs to ensure the timely delivery and high-quality completion of products / services at this stage. After the transaction is completed, the enterprise also needs to provide necessary after-sales services to the customer, including product usage guidance, fault handling, satisfaction surveys, etc. The goal of this stage is to enhance customer satisfaction and loyalty.

[0025] After defining the various stages of customer follow-up, the enterprise needs to construct the logical connection relationships among them according to the sequence of these stages. These relationships usually reflect the natural process and internal logic of the interaction between the customer and the enterprise. For example: The initial contact is the basis for all subsequent stages. Without the initial contact, it is impossible to conduct subsequent needs analysis, solution provision, etc. The needs analysis follows immediately after the initial contact because only by understanding the customer's needs can a suitable solution be provided. The solution provision and negotiation are based on the results of the needs analysis. After the negotiation is successful, the signing and execution stages can be entered. The signing and execution are the key steps for the transaction to be completed and also the prerequisite for after-sales service. The after-sales service is carried out after the transaction is completed, aiming to consolidate the customer relationship and promote word-of-mouth spread. By constructing these logical connection relationships, the enterprise can form a complete customer follow-up process model. This model not only helps the enterprise better understand and manage the customer interaction process but also improves the work efficiency of employees and customer satisfaction.

[0026] The constructed customer follow-up process model can be applied to the daily operations of the enterprise to guide the work of sales personnel and customer service personnel. At the same time, the enterprise also needs to continuously optimize and adjust the process model according to the actual situation. For example: Collect feedback, collect problems and improvement suggestions in the process of process execution through methods such as customer satisfaction surveys and internal employee feedback; Analyze data, use data analysis tools to deeply analyze the process execution data to find bottlenecks and problems; Optimize the process, adjust and optimize the process model according to the feedback and analysis results to improve work efficiency and customer satisfaction. In short, constructing a customer follow-up process model is a continuous iterative and optimization process. The enterprise needs to continuously explore and practice to find the most suitable customer follow-up method for itself.

[0027] In a preferred embodiment, constructing the mapping logical relationship between the customer data form and the customer follow-up process model and storing it in a relational database includes: Obtain the association relationship between the multiple information categories and the multiple stage information; Based on the association relationship, split the customer data form to obtain multiple customer data sub-tables; Construct the mapping logical relationship between the multiple customer data sub-tables and the multiple stage information in the customer follow-up process model and store it in a relational database.

[0028] Furthermore, establishing the mapping logical relationship between the customer data form and the customer follow-up process model can ensure that enterprises can efficiently manage customer information and track every step of the interaction between customers and the enterprise. First of all, the enterprise needs to clarify the association relationship between multiple information categories in the customer data form and multiple stage information in the customer follow-up process model. This association relationship is usually based on business logic and the natural process of customer interaction. For example, the "basic information" in the customer data form may be associated with the "initial contact" stage in the customer follow-up process model because salespeople need to know this basic information when first contacting customers. Similarly, "purchase history" may be associated with the "needs analysis" stage because understanding the customer's purchase history helps salespeople better grasp the customer's needs. Based on the above association relationship, the enterprise can split the customer data form to obtain multiple customer data sub-tables. These sub-tables are usually divided according to information categories or business logic to facilitate better data management and query. For example, the customer data form can be split into a "basic information table", a "contact information table", a "purchase history table", etc. Each sub-table contains fields related to specific information categories, and these fields have a clear association relationship with the corresponding stage information in the customer follow-up process model.

[0029] Next, the enterprise needs to establish the mapping logical relationship between multiple customer data sub-tables and multiple stage information in the customer follow-up process model. This mapping relationship ensures the correct flow and synchronization of data between the form and the process model. To achieve this, the enterprise can create foreign keys or association tables in a relational database to establish these mapping relationships. For example, a foreign key can be created in the "purchase history table" that points to the "customer ID" field (which usually exists in the "basic information table") to indicate which purchase records belong to which customer. Similarly, a field can be created in each stage information table in the process model to store the ID or key field value of the customer data sub-table related to that stage.

[0030] Finally, the established mapping logical relationship is stored in a relational database. This usually involves steps such as creating database tables, defining fields and data types, setting relationships such as primary keys and foreign keys, and inserting initial data. Before storing in the database, the enterprise also needs to ensure that the database design complies with best practices, including considerations in aspects such as data integrity, security, and scalability. In addition, database performance optimization needs to be considered to ensure good response speed under a large number of data read and write operations. Once the mapping logical relationship is stored in the relational database, the enterprise needs to continuously maintain and optimize it, which includes regularly backing up data, monitoring database performance, handling data conflicts and errors, and adjusting the database structure according to business requirements. At the same time, the enterprise also needs to pay attention to data privacy and compliance issues to ensure compliance with relevant laws, regulations, and industry standards when collecting, storing, and using customer information.

[0031] In a preferred embodiment, based on the relational database, the process of monitoring the filling and follow-up of customer data includes: Set multiple stage processing times corresponding to multiple stage information; through the spark listener, monitor the filling status of the sub-tables of customer data corresponding to each stage information. When the proportion of the filled data volume in the sub-table of customer data is greater than 0.5 or reaches the stage processing time, enter the next stage information. Among them, calculate the ratio of the filled data volume to the data volume of the sub-table of customer data. When it is less than or equal to 0.5, give a reminder at a preset frequency; until the proportion of the filled data volume in the sub-tables of customer data for multiple stage information is greater than 0.5 or all reach the stage processing time, the monitoring is completed.

[0032] Specifically, monitoring the filling and follow-up process of customer data based on the relational database ensures the accuracy and timeliness of customer information, and at the same time promotes the smooth progress of the sales process. First, the enterprise needs to set corresponding processing times for each stage according to the multiple stage information in the customer follow-up process model. These processing times are usually determined based on historical data, industry standards or business logic, aiming to ensure that each stage can be completed within a reasonable time. For example, for the "initial contact" stage, the enterprise may set a processing time within 3 days, which means that the salesperson needs to complete the initial contact with the customer and collect basic information within 3 days. For the "needs analysis" stage, the processing time may be longer because in-depth communication and exchange with the customer are required.

[0033] Next, use the listener function of Apache Spark to monitor the filling status of the sub-tables of customer data corresponding to each stage information. The Spark listener can capture data changes in real time and trigger the corresponding processing logic. When configuring the listener, the enterprise needs to specify the sub-tables of customer data to be monitored, the monitored fields (such as filling status, filled data volume, etc.), and the trigger conditions (such as the ratio of filled data volume being greater than 0.5 or reaching the stage processing time). Once configured, the Spark listener will start monitoring the filling status of the sub-tables of customer data in real time. It will calculate the ratio of the filled data volume in each sub-table to the total data volume of the sub-table and make a judgment based on the preset trigger conditions. If the ratio of the filled data volume in the sub-table of customer data corresponding to a certain stage information exceeds 50%, that is, the ratio of filled data volume is greater than 0.5, it is considered that the stage information is relatively complete and can enter the next stage. If the processing time of a certain stage information has reached, but the ratio of filled data volume has not reached 0.5, the listener will trigger the corresponding processing logic, such as reminding the salesperson to pay attention or automatically entering the next stage (determined according to business rules). When the ratio of filled data volume is less than or equal to 0.5, the Spark listener will send reminders to the salesperson at a preset frequency (such as every hour, every day, etc.), informing them of the current filling progress and remaining time of the stage. This helps the salesperson to follow up in a timely manner and speed up the filling speed. If the stage processing time is reached and the ratio of filled data volume still does not meet the standard, the enterprise can decide whether to automatically advance to the next stage according to business rules. If it is decided to automatically advance, the listener will update the process status and trigger the processing logic of the next stage. Finally, when the ratio of filled data volume in the sub-tables of customer data for all stage information is greater than 0.5 or all reach the stage processing time, the monitoring process will be completed. At this time, the enterprise can consider that the customer data filling and follow-up process has ended smoothly and is ready to carry out subsequent sales or service activities.

[0034] During the entire monitoring process, the enterprise needs to continuously monitor the performance and stability of the system to ensure that the Spark listener can accurately and timely capture data changes and trigger the corresponding processing logic. At the same time, the enterprise also needs to continuously optimize the monitoring process according to business requirements and feedback to improve the monitoring efficiency and accuracy. In short, by monitoring the customer data filling and follow-up process based on a relational database and a Spark listener, through steps such as reasonably setting the stage processing time, configuring the listener, monitoring the filling status and trigger conditions, reminder and automatic advancement, and continuous monitoring and optimization, the enterprise can ensure the accuracy and timeliness of customer information and promote the smooth progress of the sales process.

[0035] In a preferred embodiment, multiple stage processing times corresponding to multiple stage information are set, including: Based on the historical processing times of multiple stage information within the historical time, multiple stage processing times are calculated as follows: where H is the stage processing time, n is the number of follow-up processes within the historical time of the stage information, and h i is the i-th historical processing time.

[0036] Exemplarily, setting multiple stage processing times corresponding to multiple stage information is a process of predicting and optimizing future processing times based on historical data and business logic. First, an enterprise needs to collect historical processing time data of multiple stage information within historical time, and this data usually comes from the enterprise's customer relationship management system (CRM), sales automation tools, or other relevant databases. The historical processing time refers to the average or actual time taken for each stage information to start and complete within a past period. Next, the enterprise calculates the average historical processing time of each stage information using the collected historical processing time data. This is usually done through the following formula: where H is the stage processing time, n is the number of follow-up processes within the historical time of the stage information, and h i is the i-th historical processing time. This formula calculates the sum of all historical processing times and then divides it by the number of processes n to obtain the average processing time. This average value can be used as a prediction benchmark for future processing times. Although the average historical processing time provides a useful benchmark, the enterprise also needs to consider business logic and actual situations to adjust these times. For example, if a certain stage has recently undergone process optimization or technology upgrade, the future processing time may be shortened; if the market demand increases, resulting in an increased workload for salespeople, the processing time may be extended; specific industry trends, seasonal factors, or changes in customer groups may also affect the processing time. Subsequently, based on the adjustment of the average historical processing time and business logic, the enterprise sets specific processing times for each stage information. These processing times will serve as benchmarks for salespeople and other relevant personnel to follow to ensure the smooth progress of the customer follow-up process. Finally, the enterprise needs to continuously monitor the execution of the stage processing time and make adjustments and optimizations according to the actual situation, which includes collecting new historical data, analyzing the change trend of the processing time, identifying potential bottlenecks and problems, and taking corresponding measures to improve the process. Through this process, the enterprise can ensure that the setting of the stage processing time not only conforms to historical data but also takes into account changes in business logic and actual situations, thereby providing more efficient and high-quality services to customers.

[0037] In a preferred embodiment, calculating a follow-up process score and obtaining a follow-up management result includes: Obtain the reminder counts in multiple stage information, and calculate multiple reminder scores; obtain the actual stage processing times of multiple stage information, and combine the multiple stage processing times to calculate multiple time scores; obtain the ratio of the final filled data volume of multiple customer data sub-tables to the data volume of the customer data sub-tables, and calculate multiple filling scores; perform weighted calculations on the multiple reminder scores, multiple time scores, and multiple filling scores to obtain multiple stage scores as the follow-up management results.

[0038] Specifically, calculating the follow-up processing score to obtain the follow-up management result is a process of comprehensively evaluating the efficiency and quality of the customer follow-up process. First, the enterprise needs to collect key data in multiple stage information, including reminder counts, actual stage processing times, and the filling situation of customer data sub-tables. These data are the basis for subsequent calculations and evaluations. Among them, the reminder count refers to the number of reminders sent by the system or managers to customers or salespersons in each stage information. The reminder count reflects the communication and supervision in the follow-up process. The actual stage processing time refers to the time taken from the start to the actual completion of each stage information, which reflects the work efficiency of the salespersons. The filling situation refers to the ratio of the final filled data volume in the customer data sub-table to the total data volume of the customer data sub-table, and this ratio reflects the integrity and accuracy of customer information.

[0039] Next, the enterprise calculates multiple reminder scores, multiple time scores, and multiple filling scores respectively based on the collected data. Calculate a reminder score for each stage information according to the comparison between the reminder count and the preset standard (such as the ideal reminder count or industry average level). The fewer the reminder counts and the more timely the response from the customer or salesperson, the higher the reminder score. Compare the actual stage processing time of each stage information with the preset stage processing time to calculate the time deviation. The smaller the time deviation, that is, the closer the actual processing time is to the preset time, the higher the time score. Calculate the filling score according to the ratio of the final filled data volume to the data volume of the customer data sub-table. The higher the ratio, that is, the more complete the filling, the higher the filling score. Then, the enterprise performs weighted calculations on the multiple reminder scores, multiple time scores, and multiple filling scores to obtain multiple stage scores. The weighted calculation can set the weights according to the key points concerned by the enterprise or business requirements. Multiply the reminder score, time score, and filling score of each stage by the corresponding weights respectively, and then sum them to get the total score of this stage. Finally, the multiple stage scores are used as the follow-up management results, reflecting the overall efficiency and quality of the customer follow-up process. The enterprise can conduct performance evaluations on salespersons based on these scores, identify bottlenecks and problems in the process, and take corresponding improvement measures. Through this process, the enterprise can comprehensively evaluate the efficiency and quality of the customer follow-up process, provide strong support for optimizing sales strategies and enhancing customer satisfaction. At the same time, this also provides the enterprise with an opportunity for continuous improvement and optimization to continuously improve sales efficiency and service quality.

[0040] The customer data follow-up processing management method provided by the embodiment of the present invention has at least the following technical effects: 1. By constructing a customer data form and a customer follow-up process model and establishing the mapping logical relationship between them, the structured management of customer data and the automated monitoring of the follow-up process are realized. This greatly improves the efficiency and accuracy of data management, enabling sales personnel to more clearly understand customer information and follow-up status, and thus make more informed decisions.

[0041] 2. Using historical processing time data, the processing times of multiple stage information are intelligently set, providing a reasonable progress reference for sales personnel. At the same time, by calculating the follow-up processing score, including reminder score, time score, and filling score, the follow-up efficiency and data filling quality of sales personnel can be comprehensively evaluated, providing strong data support for performance evaluation and process optimization.

[0042] 3. The Spark listener is used to monitor the filling status of the customer data sub-table in real time, and sales personnel are reminded in a timely manner when the proportion of filled data is insufficient or the stage processing time is reached. This real-time monitoring and reminder mechanism ensures the smooth progress of the customer follow-up process, avoiding customer loss or dissatisfaction caused by untimely follow-up or incomplete information filling. At the same time, this also improves the work efficiency and response speed of sales personnel, further enhancing the customer experience.

[0043] Embodiment 2: As Figure 2 shown, based on the same inventive concept as the customer data follow-up processing management method provided in Embodiment 1, the embodiment of the present invention further provides a customer data follow-up processing management system, and the system includes: A data construction module 11 for constructing a customer data form and a customer follow-up process model.

[0044] A relationship mapping module 12 for constructing the mapping logical relationship between the customer data form and the customer follow-up process model and storing it in a relational database.

[0045] A scoring processing module 13 for monitoring customer data filling and follow-up process based on the relational database, calculating the follow-up processing score, and obtaining the follow-up management result.

[0046] Furthermore, the data construction module 11 is further configured to perform the following steps: Obtain multiple types of information categories of customers; set the editing attributes of the multiple types of information categories, where the editing attributes include whether it is a required item and the information length; construct a customer data form according to the multiple types of information categories and the editing attributes.

[0047] Furthermore, the data construction module 11 is further configured to perform the following steps: Construct multiple stage information in customer follow-up, where the multiple stage information has a sequential order; according to the sequential order, construct the logical connection relationship of the multiple stage information to obtain a customer follow-up process model.

[0048] Furthermore, the relationship mapping module 12 is further configured to perform the following steps: Obtain the association relationship between the multiple information categories and the multiple stage information; based on the association relationship, split the customer data form to obtain multiple customer data sub-forms; construct the mapping logical relationship between the multiple customer data sub-forms and the multiple stage information in the customer follow-up process model, and store it in a relational database.

[0049] Furthermore, the scoring processing module 13 is further configured to perform the following steps: Set multiple stage processing times corresponding to the multiple stage information; through the spark listener, monitor the filling status of the customer data sub-form corresponding to each stage information, and enter the next stage information when the proportion of the filled data volume in the customer data sub-form is greater than 0.5 or the stage processing time is reached. Among them, calculate the ratio of the filled data volume to the data volume of the customer data sub-form, and when it is less than or equal to 0.5, give a reminder at a preset frequency; until the proportion of the filled data volume in the customer data sub-forms of the multiple stage information is greater than 0.5 or all reach the stage processing time, the monitoring is completed.

[0050] Furthermore, the scoring processing module 13 is further configured to perform the following steps: Calculate multiple stage processing times based on the historical processing times of the multiple stage information within the historical time, as shown in the following formula: where H is the stage processing time, n is the number of follow-up processing times of the stage information within the historical time, and h i is the i-th historical processing time.

[0051] Furthermore, the scoring processing module 13 is further configured to perform the following steps: Obtain the number of reminders in the multiple stage information, and calculate multiple reminder scores; obtain the actual stage processing times of the multiple stage information, and calculate multiple time scores in combination with the multiple stage processing times; obtain the ratio of the final filled data volume of the multiple customer data sub-forms to the data volume of the customer data sub-forms, and calculate multiple filling scores; perform weighted calculation on the multiple reminder scores, multiple time scores, and multiple filling scores to obtain multiple stage scores as the follow-up management results.

[0052] Through the foregoing detailed description of a method for managing customer data follow-up processing, those skilled in the art can clearly know a customer data follow-up processing management system in this embodiment. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For related parts, reference can be made to the description in the method section.

[0053] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to these embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for managing customer data follow-up and processing, characterized in that, The method includes: Constructing a customer data form and a customer follow-up process model; Constructing the mapping logical relationship between the customer data form and the customer follow-up process model and storing it in a relational database; Based on the relational database, monitoring the customer data filling and follow-up process, calculating the follow-up processing score, and obtaining the follow-up management result.

2. The customer data follow-up processing management method according to claim 1, wherein Constructing a customer data form includes: Obtaining multiple types of customer information categories; Setting the editing attributes of the multiple types of information categories, where the editing attributes include whether it is a required item and the information length; Constructing a customer data form according to the multiple types of information categories and editing attributes.

3. The customer data follow-up processing management method according to claim 2, wherein Constructing a customer follow-up process model includes: Constructing multiple stages of information in customer follow-up, where the multiple stages of information have a sequential order; According to the sequential order, constructing the logical connection relationship of the multiple stages of information to obtain a customer follow-up process model.

4. The customer data follow-up processing management method according to claim 3, characterized in that Constructing the mapping logical relationship between the customer data form and the customer follow-up process model and storing it in a relational database includes: Obtaining the association relationship between the multiple types of information categories and the multiple stages of information; Based on the association relationship, splitting the customer data form to obtain multiple customer data sub-forms; Constructing the mapping logical relationship between the multiple customer data sub-forms and the multiple stages of information in the customer follow-up process model and storing it in a relational database.

5. The customer data follow-up processing management method according to claim 1, wherein Based on the relational database, monitoring the customer data filling and follow-up process includes: Setting multiple stage processing times corresponding to multiple stages of information; Through a spark listener, monitoring the filling status of the customer data sub-form corresponding to each stage of information. When the proportion of the filled data volume in the customer data sub-form is greater than 0.5 or the stage processing time is reached, enter the next stage of information. Among them, calculate the ratio of the filled data volume to the data volume of the customer data sub-form. When it is less than or equal to 0.5, give a reminder at a preset frequency; Until the proportion of the filled data volume in the customer data sub-forms of multiple stages of information is greater than 0.5 or all reach the stage processing time, the monitoring is completed.

6. The customer data follow-up processing management method according to claim 5, characterized in that, Setting multiple stage processing times corresponding to multiple stages of information includes: Calculating multiple stage processing times based on the historical processing times of multiple stages of information within the historical time, as follows: Where H is the stage processing time, n is the number of follow-up processes within the stage information history time, and h i is the i-th historical processing time.

7. The customer data follow-up processing management method according to claim 6, wherein Calculating the follow-up processing score and obtaining the follow-up management result includes: Obtaining the number of reminder times in multiple stages of information and calculating multiple reminder scores; Obtaining the actual stage processing times of multiple stages of information, combining with multiple stage processing times, and calculating multiple time scores; Obtaining the ratio of the final filled data volume of multiple customer data sub-forms to the data volume of the customer data sub-forms and calculating multiple filling scores; Performing weighted calculation on the multiple reminder scores, multiple time scores, and multiple filling scores to obtain multiple stage scores as the follow-up management result.

8. A customer data follow-up processing management system, characterized in that For implementing a customer data follow-up processing management method according to any one of claims 1-7, the system includes: A data construction module for constructing a customer data form and a customer follow-up process model; A relationship mapping module for constructing the mapping logical relationship between the customer data form and the customer follow-up process model and storing it in a relational database; A scoring processing module, which is used to monitor the customer data filling and follow-up process based on the relational database, calculate the follow-up processing score, and obtain the follow-up management result.

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