Methods, devices, electronic equipment, media and products for recovering customers who have had their services suspended

Abstract

This application discloses a method, device, electronic device, storage medium, and computer program product for recovering suspended customers, relating to the field of big data analysis technology. The method includes: for any suspended customer, collecting the suspended customer's operating data and industry dynamic data to determine a customer recovery indicator; inputting the customer recovery indicator into a preset recovery probability model to obtain a predicted recovery probability for the suspended customer; determining whether the suspended customer is recoverable by evaluating the predicted recovery probability; if the suspended customer is determined to be recoverable, tracking and executing the customer recovery plan corresponding to the suspended customer to obtain the recovery results of the customer recovery plan, and revising the preset recovery probability model based on the recovery results of the customer recovery plan. This application solves the technical problem of how to effectively recover suspended customers.

Description

Technical Field

[0001] The present application relates to the field of big data analysis technology, and in particular to a method, device, electronic device, storage medium and computer program product for recovering suspended customers. Background Art

[0002] Against the backdrop of the current complex and volatile global economic environment, numerous industries, such as real estate, manufacturing, retail, and financial services, are facing multiple pressures, including market volatility, rising costs, and slowing demand. In this environment, many companies, in order to control costs and optimize resource allocation, will choose to suspend or reduce investment in non-core business systems, including information system services. For software vendors, this phenomenon has brought unprecedented challenges and opportunities. How to effectively retain customers whose services have been discontinued is a major challenge in the current business process. Summary of the Invention

[0003] The main purpose of this application is to provide a method, device, electronic device, storage medium and computer program product for recovering suspended customers, aiming to solve the technical problem of how to effectively recover suspended customers.

[0004] To achieve the above objectives, the present application proposes a method for recovering suspended customers, the method comprising:

[0005] For any suspended customer, collect the customer's business data and industry dynamics data to determine customer recovery indicators;

[0006] Inputting the customer recovery index into a preset recovery probability model to obtain a predicted recovery probability of the suspended customer;

[0007] Determining whether the suspended customer is recoverable by evaluating the predicted recovery probability;

[0008] If it is determined that the suspended customer can be recovered, the customer recovery plan corresponding to the suspended customer is tracked and executed to obtain the recovery result of the customer recovery plan, and the preset recovery probability model is modified according to the recovery result of the customer recovery plan.

[0009] In one embodiment, the step of determining the customer recovery indicator includes:

[0010] Monitor the operating system of the suspended customer within a preset authorization scope and generate the operating data;

[0011] By presetting a large language model, real-time information of the industry corresponding to the out-of-service customers is collected to generate the industry dynamic data;

[0012] The business data and the industry dynamic data are converted into customer recovery indicators and stored in a local database, and the service-suspended customer recovery indicators are updated according to a preset collection cycle.

[0013] In one embodiment, after obtaining the predicted probability of recovery of the suspended customer, the step further includes:

[0014] By using a preset large language model, the business data is analyzed to obtain customer operation indicators, and the industry dynamic data is analyzed to obtain the latest industry dynamics;

[0015] Outputting the customer operation indicators, the customer information of the corresponding customers, and the predicted probability of recovery of the corresponding customers to the customer recovery interface of the system;

[0016] When an external viewing operation on the predicted probability of recovery is received, specific information of the customer operation indicators and the latest industry trends are output.

[0017] In one embodiment, the step of determining whether the suspended customer is recoverable by evaluating the predicted recovery probability includes:

[0018] Evaluate the level of the predicted probability of recovery according to a preset recovery probability value interval, wherein a customer recovery plan is provided for each level of the predicted probability of recovery;

[0019] When the predicted probability of recovery is greater than a preset level, it is determined that the suspended customer can be recovered;

[0020] When the predicted recovery probability is less than or equal to the preset level, or the out-of-service customer is abandoned, it is determined that the out-of-service customer is irrecoverable.

[0021] In one embodiment, the customer recovery plan includes a service letter stage, a customer system management stage, a service value report stage, and a business negotiation stage. The steps of tracking and executing the customer recovery plan corresponding to the suspended customer include:

[0022] During the service letter phase, a security risk notification letter and a system stability risk notification letter are generated, and the security risk notification letter and the system stability risk notification letter are pushed to the suspended customer;

[0023] During the customer system management phase, fault indicators in the operating system corresponding to the out-of-service customer are detected, and the repair results of the fault indicators are fed back and updated;

[0024] In the service value reporting stage, a chart corresponding to the externally confirmed data set is generated, and the data set is summarized and summarized using a preset large language model to generate a data analysis report;

[0025] During the business negotiation phase, summarize and output the results of the service letter phase, the customer system management phase, and the service value report phase;

[0026] The task completion status of the service letter stage, the customer system management stage, the service value report stage, and the business negotiation stage is monitored, and when the task completion status is completed, the real-time status of the corresponding stage is marked as completed.

[0027] In one embodiment, the step of modifying the preset recovery probability model according to the recovery results of the customer recovery plan includes:

[0028] If the recovery result of the customer recovery plan is successful, the predicted recovery probability value of the suspended customer corresponding to the customer recovery plan is set to 1, and the predicted recovery probability value is returned to the recovery probability model, and the recovery probability model is corrected through feedback adjustment of the recovery probability model;

[0029] If the recovery result of the customer recovery plan is a failure, the predicted recovery probability value is set to 0, and the predicted recovery probability value is returned to the recovery probability model, and the recovery probability model is corrected through feedback adjustment of the recovery probability model.

[0030] In addition, to achieve the above-mentioned purpose, the present application also proposes a device for recovering a suspended customer, the device comprising:

[0031] The data collection module is used to collect the business data and industry dynamic data of any suspended customer and determine the customer recovery indicators;

[0032] A probability generation module, configured to input the customer recovery index into a preset recovery probability model to obtain a predicted recovery probability of the suspended customer;

[0033] A recovery judgment module, configured to judge whether the suspended customer is recoverable by evaluating the predicted recovery probability;

[0034] The feedback correction module is used to track and execute the customer recovery plan corresponding to the suspended customer if it is determined that the suspended customer can be recovered, obtain the recovery result of the customer recovery plan, and correct the preset recovery probability model according to the recovery result of the customer recovery plan.

[0035] In addition, to achieve the above-mentioned purpose, the present application also proposes an electronic device, which includes: a memory, a processor, and a computer program stored on the memory and runnable on the processor, wherein the computer program is configured to implement the steps of the method for recovering suspended customers as described above.

[0036] In addition, to achieve the above-mentioned purpose, the present application also proposes a storage medium, which is a computer-readable storage medium. A computer program is stored on the storage medium. When the computer program is executed by the processor, the steps of the method for recovering suspended customers as described above are implemented.

[0037] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, it implements the steps of the method for recovering suspended customers as described above.

[0038] The present application provides a method for recovering suspended customers, which includes: for any suspended customer, collecting the suspended customer's business data and industry dynamic data to determine the customer recovery index; inputting the customer recovery index into a preset recovery probability model to obtain a predicted recovery probability for the suspended customer; judging whether the suspended customer is recoverable by evaluating the predicted recovery probability; if it is determined that the suspended customer is recoverable, tracking and executing the customer recovery plan corresponding to the suspended customer to obtain the recovery result of the customer recovery plan, and correcting the preset recovery probability model according to the recovery result of the customer recovery plan.

[0039] This application automatically collects operational data and industry dynamic data of suspended customers, and based on the collected data, determines customer recovery indicators that can effectively reflect the customer's recovery potential, thereby ensuring the accuracy of the data and improving the targeted nature of the recovery strategy. The customer recovery indicators are input into a preset recovery probability model that can predict the probability of recovery of suspended customers, thereby obtaining a predicted recovery probability, providing a quantitative basis for decision-making. By evaluating the predicted recovery probability, high-potential recovery customers are screened out, avoiding ineffective investment of resources. For suspended customers determined to be recoverable, the execution process of the corresponding customer recovery plan is tracked to better meet customer needs and improve the success rate of recovery. The results of the recovery plan are fed back into the recovery probability model, and the model is corrected and optimized to ensure the continuous improvement of the model's predictive ability and provide more accurate guidance for future recovery work. Compared with related solutions that do not consider the recovery potential of suspended customers or formulate recovery strategies based solely on experience and intuition and make indiscriminate recovery attempts, this application significantly improves the efficiency and success rate of recovery work through systematic data collection, analysis, prediction, execution and feedback, achieves business growth and market expansion, and effectively solves the technical problem of how to effectively recover suspended customers. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0041] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0042] Figure 1 A flowchart illustrating the first embodiment of the method for recovering a customer who has applied for service suspension;

[0043] Figure 2 A schematic diagram of a basic information interface provided in Example 1 of the method for recovering a customer who has applied for service suspension;

[0044] Figure 3 A schematic diagram of a detailed interface of customer operation indicators provided in Example 1 of the method for recovering a customer who has applied for service suspension;

[0045] Figure 4 A schematic diagram of the latest industry dynamics interface provided in Example 1 of the method for recovering a customer who has applied for service suspension;

[0046] Figure 5 A flowchart illustrating the second embodiment of the method for recovering a customer who has applied for service suspension;

[0047] Figure 6 A schematic diagram of a customer management interface provided in Example 2 of the method for recovering a customer who has applied for service suspension;

[0048] Figure 7 A schematic diagram of a service value report generation interface provided in Example 2 of the method for recovering a customer who has applied for service suspension;

[0049] Figure 8 A schematic diagram of the AI analysis interface provided in Example 2 of the method for recovering a customer who has applied for service suspension;

[0050] Figure 9 A schematic diagram of a customer recovery plan interface provided in Example 2 of the method for recovering a customer who has applied for service suspension;

[0051] Figure 10 This is a schematic diagram of the module structure of the device for recovering out-of-service customers according to an embodiment of the present application;

[0052] Figure 11 Schematic diagram of the device structure of the hardware operating environment involved in the method for recovering suspended customers in the embodiment of the present application.

[0053] The purpose, features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0054] It should be understood that the specific embodiments described herein are merely used to explain the technical solutions of the present application and are not intended to limit the present application.

[0055] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0056] The main solution of the embodiment of the present application is: for any suspended customer, collect the business data and industry dynamic data of the suspended customer to determine the customer recovery index; input the customer recovery index into a preset recovery probability model to obtain the predicted recovery probability of the suspended customer; judge whether the suspended customer is recoverable by evaluating the predicted recovery probability; if it is determined that the suspended customer is recoverable, track and execute the customer recovery plan corresponding to the suspended customer, obtain the recovery result of the customer recovery plan, and modify the preset recovery probability model according to the recovery result of the customer recovery plan.

[0057] In this embodiment, for ease of description, the following description is made with the suspended customer recovery system as the execution entity.

[0058] Most software providers currently fail to fully consider the potential recovery value of suspended customers, lack accurate data-based analysis, or often rely on experience and intuition to recover suspended customers, making indiscriminate recovery attempts that lack scientificity and specificity, resulting in low efficiency and unsatisfactory recovery results.

[0059] This application provides a solution that automatically collects data, determines customer recovery indicators, and predicts the probability of recovery through a model, thereby screening high-potential customers, formulating and implementing personalized recovery plans for them, and continuously optimizing the prediction model based on the results of the recovery plan, thereby improving the pertinence and effectiveness of the recovery strategy, ensuring the efficient use of resources, and significantly improving the success rate of customer recovery.

[0060] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication, and program execution capabilities, such as a tablet computer, personal computer, mobile phone, etc., or an electronic device capable of implementing the above functions, a suspended customer recovery system, etc. The following uses the suspended customer recovery system as an example to illustrate this embodiment and the following embodiments.

[0061] Based on this, the embodiment of the present application provides a method for recovering suspended customers, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the method for recovering customers whose services have been suspended.

[0062] In this embodiment, the method for recovering a suspended customer includes steps S01 to S04:

[0063] Step S01: For any suspended customer, collect the customer's business data and industry dynamic data to determine the customer recovery index;

[0064] It should be noted that discontinued customers refer to customers who have stopped using the systems or services provided by software vendors. Operating data refers to data reflecting the customer's operating status, including cooperation data and system dimension data. Cooperation data is used to indicate the cooperation record between discontinued customers and software vendors, including cooperation amount, cooperation arrears, historical service revenue, etc. System dimension data is used to indicate the discontinued customers' usage record of products provided by software vendors, including purchased products, user activity, data anomalies, program failures, report applications, approval applications, third-party system integration, etc. Industry dynamic data is data reflecting the development trends and changes in the industry in which the discontinued customers are located, such as the nature of the enterprise, performance data, properties for sale, the latest information, supplier status, etc. Customer recovery indicators are quantitative indicators used to evaluate the recovery potential of discontinued customers, which are determined by analyzing operating data and industry dynamic data.

[0065] In addition, it should be noted that for any suspended customer, comprehensive data collection work is carried out. The scope of data collection includes but is not limited to the operating data and industry dynamic data of the suspended customer. After the data collection is completed, data analysis technology is used to extract key indicators that can effectively reflect the customer's potential for recovery from massive data, namely customer recovery indicators. Customer recovery indicators are the basis for evaluating the possibility of recovering suspended customers.

[0066] It is understandable that since existing solutions often rely on subjective judgment or limited data to evaluate the potential for recovering suspended customers, they lack systematicity and accuracy. Therefore, step S01 is performed to ensure the accuracy and comprehensiveness of the data through automated data collection and determination of customer recovery indicators, providing a solid data foundation for the subsequent prediction of the probability of recovery, thereby improving the pertinence and effectiveness of the recovery strategy.

[0067] Step S02: Input the customer recovery index into a preset recovery probability model to obtain the predicted recovery probability of the suspended customer;

[0068] It should be noted that the preset recovery probability model is a prediction model based on a neural network, which is used to predict the recovery probability of suspended customers. It includes an input layer, a hidden layer and an output layer. The predicted recovery probability refers to the quantitative value of the possibility of recovery of suspended customers output by the preset recovery probability model.

[0069] In addition, it should be noted that a cluster analysis is performed on the customer recovery indicators obtained in step S01, and the customer recovery indicators of the suspended customers are classified (for example, into 5 categories) to facilitate subsequent analysis and processing. The customer recovery indicators of the suspended customers are normalized and organized into a format suitable for neural network training, which is divided into a training set and a test set. The test set is input as a training sample into the input layer of the preset recovery probability model, and is transmitted to the output layer after calculation through the hidden layer. According to the error value obtained from the output layer, the weights and thresholds in the neural network are adjusted in reverse, and the weights are adjusted repeatedly until the error is reduced to a minimum and remains unchanged. By adjusting the training set and the prediction set, the neural network model is continuously trained, and finally the predicted recovery rate of each customer is obtained, and the accuracy of the preset recovery probability model (78%) is obtained.

[0070] It is understandable that, since existing solutions often rely on manual judgment or simple statistical models, step S02 is performed to automate and intelligentize the prediction process through a neural network, which greatly improves work efficiency and accuracy. At the same time, through continuous training and feedback, the neural network model can continuously optimize its prediction ability and adapt to changes in the market environment and customer behavior.

[0071] Step S03: Determine whether the suspended customer is recoverable by evaluating the predicted recovery probability;

[0072] It should be noted that, based on the predicted probability of recovery obtained in step S02, a certain gear threshold is set to determine whether the suspended customer can be recovered, and customers whose probability is above the threshold are regarded as high-potential recovery customers.

[0073] It is understandable that, since existing solutions make it difficult to accurately determine which suspended customers have recovery value, resulting in a waste of resources, step S03 is performed to effectively screen out high-potential recovery customers, avoid ineffective investment of resources, and improve the efficiency and success rate of customer recovery work.

[0074] Step S04: If it is determined that the suspended customer can be recovered, the customer recovery plan corresponding to the suspended customer is tracked and executed to obtain the recovery result of the customer recovery plan, and the preset recovery probability model is modified according to the recovery result of the customer recovery plan.

[0075] It should be noted that for customers who are determined to be recoverable after suspension of service, a personalized recovery plan will be formulated based on the customer's specific situation and recovery needs. The customer recovery plan is a personalized recovery strategy and implementation plan formulated for recoverable suspended customers, including the service letter stage, the customer system governance stage, the service value report stage, and the business negotiation stage. Each stage is responsible for different work content to achieve the recovery of suspended customers. At the same time, the implementation process of the customer recovery plan is tracked and the recovery results are collected. The recovery results are a judgment on the actual effect obtained after executing the customer recovery plan, including recovery success and recovery failure. The recovery results are fed back into the preset recovery probability model to correct and optimize the model.

[0076] It is understandable that the existing solutions often lack personalized recovery plans and continuous model optimization mechanisms, resulting in unsatisfactory recovery results. Therefore, step S04 is performed to better meet customer needs and improve the recovery success rate through personalized recovery plans and continuous model optimization. At the same time, the continuous improvement of the model's prediction capabilities provides more accurate guidance for future recovery work, forming a virtuous circle.

[0077] In a feasible implementation, in step S01, the step of determining the customer recovery index includes steps A01 to A03:

[0078] Step A01: Monitor the operating system of the suspended customer within the preset authorization scope and generate operating data;

[0079] It should be noted that the preset authorization scope clearly defines the boundaries of data collection, avoiding the risk of data abuse. Operational system monitoring not only focuses on infrastructure alarm data, but also goes deep into multiple dimensions such as system usage and historical downtime, thereby improving the comprehensiveness and depth of data analysis.

[0080] In addition, it should be noted that within the preset authorization scope, the software supplier conducted in-depth monitoring of the operating systems of customers whose services were suspended, covering the acquisition of alarm data of infrastructure such as customer server databases, current system usage (such as the number of concurrent users, resource utilization, etc.), and key operating data such as historical downtime. These data are collected through monitoring tools and converted into operating indicators, such as program failures and user activity.

[0081] Step A02: Using a preset large language model, collect real-time information on the corresponding industries of the out-of-service customers to generate industry dynamic data;

[0082] It should be noted that the preset large language model (such as GPT) uses advanced artificial intelligence technology to automatically collect industry dynamic data. It has been pre-built into the software provider's own operation and maintenance platform. Through the preset large language model, based on the keywords of the industry related to the suspended customers, real-time information on the industry in which the suspended customers are located is automatically collected, including but not limited to changes in industry policies, technological innovations, market competition trends, etc. This information is converted into industry dynamic data and used together with business data as a reference for customer recovery strategies.

[0083] Step A03: Convert the business data and industry dynamic data into customer recovery indicators and store them in the local database. Update the service-disabled customer recovery indicators according to the preset collection cycle.

[0084] It should be noted that the basic data obtained from the business system is integrated with the industry dynamic data collected through large language models such as GPT, and calculated and converted into customer recovery indicators with clear business meanings. The business data and industry dynamic data together constitute the customer recovery indicators. The converted customer recovery indicators are stored in the local database, and data update operations are automatically performed according to the preset collection cycle (such as daily, weekly, etc.). This includes re-obtaining the latest business data from the customer business system, collecting the latest industry dynamic data through large language models such as GPT, and recalculating and updating the customer recovery indicators.

[0085] In this implementation, the compliance of data collection is ensured by pre-setting the authorization scope, avoiding the risk of infringing on customer privacy or violating relevant laws and regulations. At the same time, through real-time monitoring and historical data analysis, software suppliers can have a more comprehensive understanding of the customer's business conditions and provide accurate data support for subsequent customer recovery strategies. By pre-setting a large language model to quickly respond and accurately extract key content from massive network information, software suppliers can keep abreast of industry trends and avoid decision-making errors caused by information asymmetry. Through the preset data processing logic and storage mechanism, software suppliers can efficiently manage large amounts of customer data and provide a solid data foundation for subsequent data analysis and decision support.

[0086] In a feasible implementation, in step S02, after obtaining the predicted probability of recovery of the suspended customer, steps A11 to A13 are further included:

[0087] Step A11: Analyze business data to obtain customer operation indicators through a preset large language model, and analyze industry dynamic data to obtain the latest industry trends;

[0088] It should be noted that customer operation indicators reflect a series of quantitative indicators of the current operating status of the suspended customers, including but not limited to business health, dependence on specific software or services provided by software suppliers, and system risk. The latest industry trends are information about the latest policies, technologies, markets, etc. in the customer's industry.

[0089] In addition, it should be noted that the collected business data and industry dynamics data are deeply analyzed using a preset large language model (such as GPT or other NLP models). The large language model uses natural language processing technology to understand and extract key information, and then determine customer operation indicators and the latest industry trends.

[0090] Step A12: Output the customer operation indicator, the customer information of the corresponding customer, and the predicted probability of recovery of the corresponding customer to the customer recovery interface of the system;

[0091] It should be noted that customer information refers to basic information about the customer and historical cooperation records; the customer recovery interface refers to the user interface in the customer recovery system used to display key information such as customer operation indicators, predicted recovery probability, and the latest industry trends.

[0092] In addition, it should be noted that the customer operation indicators determined in step A11, the customer information of the corresponding customer (such as company name, contact information, historical cooperation records, etc.) and the predicted recovery probability calculated by the preset recovery probability model are output to the customer recovery interface for displaying key information to the user.

[0093] Step A13: When receiving an external request to view the predicted probability of recovery, output detailed information of the customer operation indicators and the latest industry trends.

[0094] It should be noted that the external party can be a software supplier. The viewing operation refers to requesting to view detailed information such as the operating indicators, recovery probability, and industry trends of a specific customer through the button or link on the customer recovery interface. When the software supplier performs a viewing operation on the predicted recovery probability on the customer recovery interface, the system responds to this operation and outputs specific information on customer operating indicators related to the predicted recovery probability and the latest industry trends. This information may be displayed in charts, text, or a mixed form so that users can deeply understand the specific situation and industry background of each customer.

[0095] For example, to help understand the technical concept or technical principle of this application, please refer to Figure 2 , Figure 2A scenario diagram of the basic information interface is provided. Module A includes the total number of recovered customers, the number of customers recovered this month, the number of customers with a high probability of recovery, and the total number of lost customers, which more intuitively shows the system's recovery of lost customers. Module B contains customer information of suspended customers, module C contains the predicted probability of recovery of suspended customers, module D contains customer operation indicators of suspended customers, and module E contains the recovery operations for each suspended customer. By clicking on the corresponding probability of recovery of any suspended customer in module C, the detailed content of the customer operation indicators of this suspended customer can be output to the interface, such as Figure 3 As shown, Figure 3 A scenario diagram of the detailed content interface of the customer operation indicator is provided. Module P1 is the detailed content of the corresponding customer operation indicator that pops up after clicking button b. By clicking buttons a1 and a2, the detailed content of the customer operation indicator and the latest industry trends can be displayed respectively. When button a1 is clicked, the corresponding update time module t will be displayed in module P1. Modules p1~p4 display the predicted recovery probability and collected customer operation indicators determined by the system as of the update time. Module p1 is the recovery probability of the corresponding suspended customer. Module p2 includes the business health and its detailed content in the customer operation indicator. Module p3 includes the degree of dependence on the system and its detailed content in the customer operation indicator. Module p4 includes the system risk and its detailed content in the customer operation indicator. When button a2 is clicked, as shown in Figure 4 As shown, Figure 4 A scenario diagram of the latest industry dynamics interface is provided. Module p5 displays the latest industry dynamics corresponding to suspended customers, allowing users to intuitively understand the latest information on related industries and related customer information.

[0096] In this implementation, the generated customer operation indicators more accurately reflect the actual operation status and industry trends of suspended customers. The real-time capture of the latest industry dynamics provides timely information support for decision-making. Through the automation and intelligence of data processing, efficiency and accuracy are greatly improved. By displaying key information on one interface, not only can the surface recovery probability be seen, but also the reasons and basis behind it can be deeply understood, so as to make more informed decisions. Through external viewing operations, the interactivity of the system is enhanced, the user experience is improved, and it is convenient for users to quickly understand customer status and recovery potential.

[0097] In a feasible implementation, in step S03, the step of evaluating the predicted probability of recovery to determine whether the suspended customer is recoverable includes steps A21 to A23:

[0098] Step A21: Evaluate the level of predicted recovery probability according to a preset recovery probability value range, wherein a customer recovery plan is set for each level of predicted recovery probability;

[0099] It should be noted that the preset recovery probability value range refers to a numerical range set in advance based on historical data, industry experience and other factors to divide the recovery probability levels. As shown in Table 1, the preset recovery probability value range can be adjusted according to the software supplier's own needs. The range that is suitable for the actual situation of the current suspended users can be selected to improve the accuracy of the judgment. Under each level of recovery probability, the same strategy can be adopted to recover suspended customers.

[0100] Table 1

[0101]

[0102] Step A22: When the predicted probability of recovery is greater than a preset level, it is determined that the suspended customer can be recovered;

[0103] It should be noted that, as shown in Table 1, when the recovery probability value range is below 0.4, it means that the probability of recovering the suspended customer is extremely low, and the suspended customer is judged to be irrecoverable. When the recovery probability value range is above 0.4, it means that the suspended customer has the possibility of recovery, and the possibility of recovery is proportional to the recovery probability value range.

[0104] In addition, it should be noted that software suppliers can adjust the evaluation criteria for the recovery probability level according to their own needs or the actual situation of the suspended users, and select the evaluation criteria applicable to the current suspended customers for judgment, thereby improving the success rate of customer recovery.

[0105] For example, assuming that the predicted probability of recovery of a suspended customer is 0.75, according to the preset recovery probability value range, the customer is classified as "medium-high" level. Therefore, according to the customer recovery plan for "medium-high" level customers, corresponding strategies can be adopted for recovery.

[0106] Step A23: When the predicted probability of recovery is less than or equal to the preset level, or the suspended customer is abandoned, it is determined that the suspended customer is irrecoverable.

[0107] It should be noted that abandonment operation refers to the operation in which the software supplier explicitly indicates that it will no longer try to recover a suspended customer. It may be because the customer operation indicators of the current suspended customer are too low. After comprehensive consideration, the software supplier proactively abandons the recovery operation for this customer. When receiving the external abandonment operation for the suspended customer, the predicted recovery probability of the customer is recorded as low, indicating that the customer will not be recovered.

[0108] In this embodiment, by dividing the gears, the operator can have a clearer understanding of the difficulty of recovering customers, and formulate a unified recovery plan based on the gears, thereby improving the efficiency and accuracy of the recovery work. By setting the threshold, customers with recovery value can be quickly screened out, thereby improving the efficiency and accuracy of the recovery work. By recording the abandonment operation, it can be ensured that the customer recovery status in the system is consistent with the user's actual intention, avoiding the waste of resources on customers who are not worth recovering, and realizing the accurate assessment, rapid judgment and flexible processing of the probability of customer recovery.

[0109] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above embodiment 1 can be referred to the above introduction and will not be described in detail later. Figure 5 The customer recovery plan includes the service letter stage, the customer system management stage, the service value report stage, and the business negotiation stage. In step S04, the steps of tracking and executing the customer recovery plan corresponding to the suspended customer include steps S11 to S15:

[0110] Step S11: During the service letter phase, a security risk notification letter and a system stability risk notification letter are generated and pushed to the suspended customer.

[0111] It should be noted that during the service letter stage, the risks of security and system stability are communicated to customers through formal letters, and security risk notification letters and system stability risk notification letters are generated and pushed to customers whose services have been suspended, laying the foundation for subsequent recovery work. Security risk notification letters and system stability risk notification letters are used to inform customers of possible risks in security and system stability, as well as the possible consequences of these risks.

[0112] Step S12: During the customer system management phase, fault indicators in the operating system corresponding to the out-of-service customer are detected, and the repair results of the fault indicators are fed back and updated;

[0113] It should be noted that since the customer has not received operation and maintenance services for a long time, there will be major problems with the system stability and security management. During the customer system management stage, the customer's operating system will be comprehensively inspected and maintained to ensure the stability and security of the system. Fault indicators are used to reflect the faults and problems in the customer's system, and the repair results are the results after handling these faults and problems.

[0114] For example, to help understand the technical concept or technical principle of this application, please refer to Figure 6 , Figure 6A scenario diagram of the customer management interface is provided. The input box in module F can be used to classify and search for various fault indicators in the corresponding operating system of the out-of-service customer. Table H displays the inspection name, inspection object, and inspection result of the fault indicator. After the fault indicator is maintained and detected to have returned to normal, the restored fault indicator will be deleted from Table H. For some indicators that are specified by customers as not allowed to be modified, they can be marked through module H. The indicators that are not allowed to be modified can be manually marked as passed to prevent them from affecting the system's judgment of the fault indicator.

[0115] Step S13: In the service value reporting phase, a chart corresponding to the externally confirmed data set is generated, and the data set is summarized using a preset large language model to generate a data analysis report;

[0116] It should be noted that the service value reporting stage generates data analysis reports to demonstrate the effectiveness and value of the service to customers, thereby enhancing their recognition and trust in the service. The data set is the basic data used to generate the data analysis report, and the data analysis report is the result of analyzing and interpreting this data.

[0117] In addition, it should be noted that after the data set is confirmed externally, the system automatically generates various charts. The software supplier can choose the type of chart to be generated, including bar charts, pie charts, line charts, etc., and analyze the data set through a preset large prediction model. The analysis results are output and integrated into a data analysis report.

[0118] For example, to help understand the technical concept or technical principle of this application, please refer to Figure 7 , Figure 7 A scenario diagram of the service value report generation interface is provided. By clicking button c, you can enter the AI analysis interface, such as Figure 8 , Figure 8 A scenario diagram of the AI analysis interface is provided. Module p8 is used to determine the name of the AI analysis. The example here is "Page Visits". Module p9 is used to determine the data set for AI analysis. The data set selected in the example here is "Site Operation Status - Recent Month". Module p10 is used to determine the analysis content of the data set. Through the prompt word content input externally in module p10, the example here is "Analyze Site Operation Status", combined with the preset large language model, the analysis content of the data set can be generated and displayed in module p11. Multiple prompt words can be set in module p10, and there is no need to adjust them after setting. Each time this data set is analyzed, a summary content will be generated based on the prompt word in p10. The analysis content of the data set generated by p11 will eventually be displayed in Figure 7Module p7 in Module p7 provides a text box for displaying the analysis content of the data set generated by p11. At the same time, based on the selected data set, the corresponding analysis chart of the data set is generated and displayed in module p6. When selecting a data set, you can also choose to analyze multiple data sets, integrate the analysis results of multiple data sets, and generate a data analysis report.

[0119] Step S14: During the business negotiation phase, summarize and output the results of the service correspondence phase, the customer system management phase, and the service value report phase;

[0120] It should be noted that the business negotiation stage is mainly based on the results of the previous work, and business negotiations are conducted with customers to reach a new cooperation agreement. The results of the results report are a summary and display of the results of the previous work, including data analysis reports, promotional posters, annual service reports, etc., to support the progress of business negotiations.

[0121] Step S15, monitoring the task completion status of the service letter stage, customer system management stage, service value report stage, and business negotiation stage, and marking the real-time status of the corresponding stage as completed when the task completion status is completed.

[0122] It should be noted that the system tracks and monitors the completion status of tasks at each stage. The task completion status is used to reflect the completion status of tasks at each stage. When the task is completed, the real-time status of the corresponding stage is updated. The real-time status is used to indicate the status of the current stage (such as completed, in progress, etc.).

[0123] For example, the external Figure 2 The button in module E enters the customer recovery plan interface, such as Figure 9 As shown, Figure 9 A scenario diagram of the customer recovery plan interface is provided, which is divided into four stages from p12 to p15. Module p12 corresponds to the service letter stage, module p13 corresponds to the customer system governance stage, module p13 corresponds to the service value reporting stage, and module p15 corresponds to the business negotiation stage. Each stage has corresponding tasks. For each module, you can view the completion status of each task in each module by clicking the progress report. By clicking "View", you can view the output files generated by each task in each module. When each stage is completed, the module will be adjusted to "Completed (can continue to follow up)". For the stage that is still in progress, the corresponding module will be displayed as "In Progress". For the stage that has not started, the corresponding module will be displayed as "Not Started". When all stages are completed, it means that the customer recovery plan is successful.

[0124] In this implementation, the importance of security and system stability is conveyed to customers through formal letters to attract their attention and encourage subsequent cooperation. Through systematic detection and repair, the stability and security of the customer's business system are improved, providing guarantees for subsequent services. Through the preset large language model, automatic data analysis and report generation are realized, and the accuracy and efficiency of the report are improved to demonstrate the service effect and value. Through systematic results reporting, the results of the previous work are effectively organized and displayed, making business negotiations more reliable and improving the success rate of negotiations. Through real-time task monitoring and status updates, the smooth progress and timely adjustment of customer recovery plans are ensured.

[0125] In a feasible implementation, in step S04, the step of modifying the preset recovery probability model according to the recovery result of the customer recovery plan includes steps B11 to B12:

[0126] Step B11: If the customer recovery plan is successful, the predicted recovery probability value of the suspended customer corresponding to the customer recovery plan is set to 1, and the predicted recovery probability value is returned to the recovery probability model. The recovery probability model is then adjusted and modified through feedback from the recovery probability model.

[0127] It's important to note that feedback adjustment refers to adjusting and optimizing the model based on the discrepancy between the model's actual output (i.e., customer recovery results) and the expected target (i.e., success or failure). This allows the model to learn from this discrepancy and adjust accordingly. After the recovery plan is executed, if the recovery is successful, the recovered customer will be displayed, the predicted recovery probability for this customer will be set to 1, and the predicted recovery probability will be returned to the recovery probability model for a continuous adjustment and correction.

[0128] Step B12: If the recovery result of the customer recovery plan is a failure, the predicted recovery probability value is set to 0, and the predicted recovery probability value is returned to the recovery probability model, and the recovery probability model is corrected through feedback adjustment of the recovery probability model.

[0129] It should be noted that when the recovery plan is executed, if the recovery fails, the customer who failed to be recovered will be displayed, the predicted recovery probability value of this customer will be set to 0, and the recovery probability model will be returned for cyclic adjustment and correction.

[0130] In this embodiment, feedback adjustment is performed immediately based on the actual recovery results, the actual recovery results are continuously compared with the predicted values, and the sample data is updated. The model can learn more about the characteristics of customer recovery behavior, thereby improving the accuracy of the prediction.

[0131] It should be noted that the above examples are only used to understand the present application and do not constitute a limitation on the method for recovering suspended customers of the present application. Simple transformations in more forms based on this technical concept are all within the scope of protection of the present application.

[0132] This application also provides a device for recovering suspended customers. Please refer to Figure 10 , the customer recovery device for out-of-service customers includes:

[0133] The data collection module 10 is used to collect the business data and industry dynamic data of any suspended customer and determine the customer recovery index;

[0134] The probability generation module 20 is used to input the customer recovery index into a preset recovery probability model to obtain the predicted recovery probability of the suspended customer;

[0135] The recovery judgment module 30 is used to judge whether the suspended customer can be recovered by evaluating and predicting the recovery probability;

[0136] The feedback correction module 40 is used to track and execute the customer recovery plan corresponding to the suspended customer if it is determined that the suspended customer can be recovered, obtain the recovery result of the customer recovery plan, and correct the preset recovery probability model according to the recovery result of the customer recovery plan.

[0137] Optionally, the data acquisition module 10 is further configured to:

[0138] Monitor the operating systems of customers who have suspended their services within the preset authorization scope and generate operating data;

[0139] By presetting a large language model, we collect real-time information on the corresponding industries of out-of-service customers and generate dynamic industry data.

[0140] Convert operational data and industry dynamic data into customer recovery indicators and store them in the local database, and update the customer recovery indicators based on the preset collection cycle.

[0141] Optionally, the probability generation module 20 is further configured to:

[0142] By presetting a large language model, we analyze business data to obtain customer operation indicators, and analyze industry dynamic data to obtain the latest industry trends;

[0143] Output the customer operation indicators, the corresponding customer information, and the corresponding customer's predicted probability of recovery to the customer recovery interface of the system;

[0144] When receiving external viewing operations on the predicted probability of recovery, output specific information on customer operation indicators and the latest industry trends.

[0145] Optionally, the recovery judgment module 30 is further configured to:

[0146] According to the preset recovery probability value range, the predicted recovery probability level is evaluated, wherein a customer recovery plan is set under each level of the predicted recovery probability;

[0147] When the predicted probability of recovery is greater than the preset level, the suspended customer is considered recoverable;

[0148] When the predicted probability of recovery is less than or equal to the preset level, or when the suspended customer is abandoned, the suspended customer is determined to be irrecoverable.

[0149] Optionally, the customer recovery plan includes a service letter stage, a customer system management stage, a service value report stage, and a business negotiation stage. The feedback correction module 40 is further used to:

[0150] During the service letter phase, a security risk notification letter and a system stability risk notification letter are generated and pushed to customers whose services have been suspended;

[0151] During the customer system management phase, we detect fault indicators in the operating systems of customers whose services have been suspended, and provide feedback and updates on the repair results of the fault indicators.

[0152] In the service value reporting phase, charts corresponding to the externally confirmed data set are generated, and the data set is summarized and summarized using a preset large language model to generate a data analysis report;

[0153] During the business negotiation phase, summarize and output the results of the service letter phase, customer system management phase, and service value report phase;

[0154] Monitor the task completion status of the service letter stage, customer system governance stage, service value report stage, and business negotiation stage, and mark the real-time status of the corresponding stage as completed when the task completion status is completed.

[0155] Optionally, the feedback correction module 40 is further configured to:

[0156] If the recovery result of the customer recovery plan is successful, the predicted recovery probability value of the suspended customer corresponding to the customer recovery plan is set to 1, and the predicted recovery probability value is returned to the recovery probability model. The recovery probability model is corrected through feedback adjustment of the recovery probability model;

[0157] If the recovery result of the customer recovery plan is a failure, the predicted recovery probability value is set to 0, and the predicted recovery probability value is returned to the recovery probability model, and the recovery probability model is corrected through feedback adjustment of the recovery probability model.

[0158] The device for recovering suspended customers provided in this application utilizes the method for recovering suspended customers described in the aforementioned embodiments, addressing the technical problem of effectively recovering suspended customers. Compared to the prior art, the device for recovering suspended customers provided in this application offers the same beneficial effects as the method described in the aforementioned embodiments. Other technical features of the device are the same as those disclosed in the aforementioned embodiments and are not further elaborated upon here.

[0159] The present application provides an electronic device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for recovering suspended customers in the above-mentioned embodiment one.

[0160] Reference below Figure 11 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present application. The electronic devices in the embodiments of the present application may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, PADs (Portable Application Description: tablet computers), and fixed terminals such as digital TVs and desktop computers. Figure 11 The electronic device shown is merely an example and should not limit the functions and scope of use of the embodiments of the present application.

[0161] like Figure 11As shown, the electronic device may include a processing device 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes based on programs stored in a read-only memory (ROM) 1002 or programs loaded from a storage device 1003 into a random access memory (RAM) 1004. RAM 1004 also stores various programs and data required for the operation of the electronic device. Processing device 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems may be connected to I / O interface 1006: input devices 1007 including, for example, a touchscreen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage device 1003 including, for example, a magnetic tape or hard disk; and communication devices 1009. The communication device 1009 can allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an electronic device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented or have instead.

[0162] In particular, according to the embodiments disclosed in the present application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, the embodiments disclosed in the present application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program comprising program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0163] The electronic device provided in this application, employing the method for recovering suspended customers described in the aforementioned embodiments, can address the technical problem of effectively recovering suspended customers. Compared to the prior art, the electronic device provided in this application achieves the same beneficial effects as the method for recovering suspended customers described in the aforementioned embodiments. Other technical features of this electronic device are the same as those disclosed in the aforementioned embodiments and are not further elaborated upon here.

[0164] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0165] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

[0166] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, a computer program) stored thereon, wherein the computer-readable program instructions are used to execute the method for recovering suspended customers in the above-mentioned embodiment.

[0167] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0168] The computer-readable storage medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0169] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by an electronic device, the out-of-service customer recovery device: for any out-of-service customer, collects the out-of-service customer's business data and industry dynamic data, and determines the customer recovery index; inputs the customer recovery index into a preset recovery probability model to obtain the predicted recovery probability of the out-of-service customer; determines whether the out-of-service customer is recoverable by evaluating the predicted recovery probability; if it is determined that the out-of-service customer is recoverable, tracks and executes the customer recovery plan corresponding to the out-of-service customer, obtains the recovery result of the customer recovery plan, and modifies the preset recovery probability model according to the recovery result of the customer recovery plan.

[0170] Computer program code for performing the operations of the present application may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages such as Java, Smalltalk, C++, and conventional procedural programming languages such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0171] The flow charts and block diagrams in the accompanying drawings illustrate the possible architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flow chart or block diagram can represent a module, program segment or a part of code, and the module, program segment or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flow chart, and the combination of the boxes in the block diagram and / or flow chart can be implemented by a dedicated hardware-based system that performs the specified function or operation, or can be implemented by a combination of dedicated hardware and computer instructions.

[0172] The modules described in the embodiments of the present application may be implemented in software or hardware, wherein the name of a module does not necessarily limit the unit itself.

[0173] The computer-readable storage medium provided in this application stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned method for recovering suspended customers. This computer-readable storage medium can effectively solve the technical problem of recovering suspended customers. Compared to existing technologies, the beneficial effects of the computer-readable storage medium provided in this application are similar to those of the method for recovering suspended customers provided in the aforementioned embodiments, and are not further elaborated here.

[0174] The present application also provides a computer program product, including a computer program, which, when executed by a processor, implements the steps of the above-mentioned method for recovering suspended customers.

[0175] The computer program product provided in this application can solve the technical problem of how to effectively recover customers whose service has been suspended. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the customer recovery method provided in the above embodiment, and will not be repeated here.

[0176] The above description is only part of the embodiments of the present application and does not limit the patent scope of the present application. All equivalent structural transformations made by using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect application in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for recovering suspended customers, characterized in that: The method for recovering suspended customers includes: For any suspended customer, collect the customer's business data and industry dynamic data to determine the customer recovery index, wherein the suspended customer refers to a customer who has stopped using the system or service provided by the software vendor. The steps of determining the customer recovery index include: Monitor the operating system of the customer who has been suspended within the scope of pre-set authorization, obtain infrastructure alarm data, current system usage, and historical downtime information, and generate the operating data; By presetting a large language model and using keywords related to the industry of the suspended customer, real-time information about the industry in which the suspended customer is located is collected and converted into dynamic data about the industry, wherein the real-time information includes changes in industry policies, technological innovations, and market competition trends; Convert the business data and the industry dynamic data into customer recovery indicators and store them in a local database, and update the service-discontinued customer recovery indicators according to a preset collection cycle; Inputting the customer recovery index into a preset recovery probability model to obtain a predicted recovery probability of the suspended customer; Determining whether the suspended customer is recoverable by evaluating the predicted recovery probability; If it is determined that the suspended customer can be recovered, the customer recovery plan corresponding to the suspended customer is tracked and executed to obtain the recovery result of the customer recovery plan, and the preset recovery probability model is modified according to the recovery result of the customer recovery plan, wherein the customer recovery plan includes: During the service letter phase, a security risk notification letter and a system stability risk notification letter are generated and pushed to the suspended customer; During the customer system management phase, the fault indicators in the operating system corresponding to the out-of-service customer are detected, and the repair results of the fault indicators are fed back and updated; In the service value reporting phase, a chart corresponding to the externally confirmed data set is generated, and the data set is summarized and summarized using a preset large language model to generate a data analysis report; During the business negotiation phase, summarize and output the results of the service letter phase, the customer system management phase, and the service value report phase; The task completion status of the service letter stage, the customer system management stage, the service value report stage, and the business negotiation stage is monitored, and when the task completion status is completed, the real-time status of the corresponding stage is marked as completed.

2. The method for recovering a suspended customer according to claim 1, wherein: After the step of obtaining the predicted probability of recovery of the suspended customer, the following steps are further included: By using a preset large language model, the business data is analyzed to obtain customer operation indicators, and the industry dynamic data is analyzed to obtain the latest industry dynamics; Outputting the customer operation indicators, the customer information of the corresponding customers, and the predicted probability of recovery of the corresponding customers to the customer recovery interface of the system; When an external viewing operation on the predicted probability of recovery is received, specific information of the customer operation indicators and the latest industry trends are output.

3. The method for recovering a customer whose service has been suspended according to claim 1, wherein: The step of determining whether the suspended customer is recoverable by evaluating the predicted recovery probability includes: Evaluate the level of the predicted probability of recovery according to a preset recovery probability value interval, wherein a customer recovery plan is provided for each level of the predicted probability of recovery; When the predicted probability of recovery is greater than a preset level, it is determined that the suspended customer can be recovered; When the predicted recovery probability is less than or equal to the preset level, or the out-of-service customer is abandoned, it is determined that the out-of-service customer is irrecoverable.

4. The method for recovering a customer whose service has been suspended according to claim 1, wherein: The step of modifying the preset recovery probability model according to the recovery result of the customer recovery plan includes: If the recovery result of the customer recovery plan is successful, the predicted recovery probability value of the suspended customer corresponding to the customer recovery plan is set to 1, and the predicted recovery probability value is returned to the recovery probability model, and the recovery probability model is corrected through feedback adjustment of the recovery probability model; If the recovery result of the customer recovery plan is a failure, the predicted recovery probability value is set to 0, and the predicted recovery probability value is returned to the recovery probability model, and the recovery probability model is corrected through feedback adjustment of the recovery probability model.

5. A device for recovering out-of-service customers, characterized in that: The device for recovering suspended customers includes: The data collection module is used to collect the operating data and industry dynamic data of any suspended customer and determine the customer recovery index, wherein the suspended customer refers to a customer who has stopped using the system or service provided by the software supplier, and the determination of the customer recovery index includes: monitoring the operating system of the suspended customer within a preset authorization scope, obtaining infrastructure alarm data, current system usage and historical downtime, and generating the operating data; using a preset large language model, based on keywords of the suspended customer's related industry, collecting real-time information on the suspended customer's industry, and converting the real-time information into the industry dynamic data, wherein the real-time information includes changes in industry policies, technological innovations, and market competition trends; converting the operating data and the industry dynamic data into customer recovery indicators and storing them in a local database, and updating the suspended customer recovery indicators according to a preset collection cycle; A probability generation module, configured to input the customer recovery index into a preset recovery probability model to obtain a predicted recovery probability of the suspended customer; A recovery judgment module, configured to judge whether the suspended customer is recoverable by evaluating the predicted recovery probability; A feedback correction module is configured to, if it is determined that the out-of-service customer is recoverable, track and execute the customer recovery plan corresponding to the out-of-service customer, obtain the recovery results of the customer recovery plan, and correct the preset recovery probability model based on the recovery results of the customer recovery plan, wherein the customer recovery plan includes: a service letter stage, generating a security risk notification letter and a system stability risk notification letter, and pushing the security risk notification letter and the system stability risk notification letter to the out-of-service customer; a customer system governance stage, detecting fault indicators in the operating system corresponding to the out-of-service customer, and feeding back and updating the repair results of the fault indicators; a service value report stage, generating a chart corresponding to an externally confirmed data set, and summarizing the data set through a preset large language model to generate a data analysis report; a business negotiation stage, summarizing and outputting the results of the service letter stage, the customer system governance stage, and the service value report stage; monitoring the task completion status of the service letter stage, the customer system governance stage, the service value report stage, and the business negotiation stage, and marking the real-time status of the corresponding stage as completed when the task completion status is complete.

6. An electronic device, characterized in that: The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program is configured to implement the steps of the method for recovering suspended customers according to any one of claims 1 to 4.

7. A storage medium, characterized in that: The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, the steps of the method for recovering suspended customers according to any one of claims 1 to 4 are implemented.

8. A computer program product, characterized in that The computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the method for recovering a suspended customer according to any one of claims 1 to 4 are implemented.

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