Customer data digital management method and system based on CRM system

Abstract

The invention relates to the technical field, in particular to a customer data digital management method and system based on a CRM system, and the method comprises the steps: carrying out the deep integrated non-inductive collection of multi-source heterogeneous data generated by the interaction of customers through a mobile terminal; automatically matching the data with CRM customer information and associating the data with a corresponding customer digital file; performing automatic analysis on the interaction data based on a pre-configured business rule set, and judging a sales follow-up behavior state and compliance; responding to a judgment result, automatically triggering a client management instruction, updating a client state or generating an intervention signal. Multi-source data intelligent acquisition is realized through deep integration of the mobile terminal, full-course digitization and intelligentization of customer data management are realized in combination with automatic matching, rule-driven analysis and a real-time execution mechanism, the accuracy of data integration and the efficiency of sales follow-up are remarkably improved, and the manual intervention cost and the management risk are effectively reduced.

Description

Technical Field

[0001] This invention relates to the technical field of sales customer data management, and in particular to a method and system for digital management of customer data based on a CRM system. Background Technology

[0002] Customer Relationship Management (CRM) systems, as core tools for enterprise customer resource management, play a crucial role in modern sales operations. With the expansion of business scale and the widespread adoption of mobile internet, the ways sales personnel interact with customers have become increasingly diversified, including telephone calls, instant messaging, face-to-face visits, and other channels, generating massive amounts of customer interaction data. This data contains rich business value and is of great significance for analyzing customer behavior and optimizing sales strategies.

[0003] Traditional CRM systems primarily rely on manual data entry for customer data management. Salespeople need to manually input call logs, communication content, visit locations, and other information into the CRM system after interacting with customers. While this method is adequate for handling simple and standardized customer information, it becomes inefficient and inaccurate when faced with the complex and ever-changing interaction scenarios in modern sales. For example, salespeople need to memorize the detailed content of each call and then retroactively enter it, which not only increases their workload but also makes them prone to omissions or errors due to fatigue or negligence. For instant messaging records, copying and pasting chat content is cumbersome and prone to formatting errors. For field visits, manually recording the time and location lacks objective verification.

[0004] Existing automated CRM systems attempt to improve data management through rule engines or template matching, but these are mostly based on fixed rules or preset formats. However, customer interactions during the sales process are highly dynamic and personalized, with significant differences in interaction patterns between different customers, at different sales stages, and even among different sales personnel. Fixed-rule systems struggle to adapt to this variability, often resulting in incomplete data collection, matching errors, or analytical biases. For example, while existing mobile CRM applications can automatically collect call logs, they cannot intelligently identify the correspondence between call participants and the CRM customer database; while they can obtain location information, they lack effective monitoring of sales behavior compliance; and while they can record communication content, they cannot automatically determine the timeliness and effectiveness of follow-ups.

[0005] Therefore, existing technologies have significant shortcomings in terms of processing efficiency, accuracy, real-time performance, and adaptability. There is an urgent need for a digital customer data management solution that can achieve full-process automation and intelligence to meet the development needs of modern sales operations. Summary of the Invention

[0006] To address the aforementioned technical issues, this application provides a method and system for digital management of customer data based on a CRM system.

[0007] The above-mentioned objective of this application is achieved through the following technical solution:

[0008] A method for digital management of customer data based on a CRM system, the method comprising the following steps:

[0009] By deeply integrating with enterprise mobile terminals, multi-source heterogeneous interactive data generated from customer interactions can be collected seamlessly and automatically.

[0010] The multi-source heterogeneous interaction data is automatically matched with customer information in the CRM system, and the successfully matched interaction data is dynamically associated with the corresponding customer's digital profile.

[0011] Based on a pre-configured set of business rules, the interactive data associated with the digital files is automatically analyzed to determine the status and compliance of sales follow-up behavior;

[0012] In response to the judgment result, the system automatically triggers and executes the corresponding customer management instructions to update the customer status or generate management intervention signals.

[0013] By adopting the above technical solutions, and through deep integration with enterprise mobile terminals, seamless and automatic collection of multi-source heterogeneous interactive data is achieved. This includes automatically matching data with CRM customer information and dynamically associating it with digital profiles; automatically analyzing the status and compliance of sales follow-up behavior based on business rule sets; and automatically triggering customer management instructions based on response judgment results. This achieves full automation of customer data management: seamless data collection reduces manual intervention, avoiding data omissions and delays; automated matching and association improve the accuracy and real-time nature of data integration, ensuring the integrity of customer profiles; rule-based analysis enhances the objectivity of sales behavior management and reduces the risk of subjective misjudgment; and automatic instruction triggering improves response speed, supports real-time status updates and intervention, and solves the problems of low efficiency and high error rates caused by manual data entry in traditional CRM systems. This significantly improves the intelligence level of customer management and business collaboration efficiency.

[0014] In a preferred embodiment, this application can be further configured such that: the seamless and automatic collection of multi-source heterogeneous interaction data generated from customer interactions through deep integration with the enterprise's mobile terminal specifically includes:

[0015] Real-time monitoring of call signaling on mobile terminals, collecting and generating structured call data including caller and called numbers, timestamps and call duration;

[0016] Capture communication content within a specified instant messaging application through a secure interface, and generate social interaction data containing session identifiers, timestamps, and content text;

[0017] The geographical coordinates of the mobile terminal are acquired at a preset period to generate continuous spatiotemporal trajectory data. The structured call data, social interaction data, and spatiotemporal trajectory data are standardized and encapsulated to form multi-source heterogeneous interactive data.

[0018] By adopting the above technical solutions, the system generates structured call data through real-time monitoring of call signaling, captures social interaction data through secure interfaces, and acquires and standardizes spatiotemporal trajectory data at preset intervals. Through multi-source data integration, the comprehensiveness and reliability of data collection are enhanced: call signaling monitoring ensures the automatic capture of voice interaction data, avoiding inconsistencies from manual recording; secure interface access to social data ensures the secure extraction of communication content and supports compliance analysis; spatiotemporal trajectory data provides behavioral context, facilitating mobile feature analysis, improving the diversity of data sources, enabling the system to adapt to complex business scenarios, and standardized encapsulation solves the compatibility problem of heterogeneous data, laying a high-quality data foundation for subsequent matching and analysis, and reducing information silos.

[0019] In a preferred embodiment, this application can be further configured to: automatically match the multi-source heterogeneous interaction data with customer information in the CRM system, and dynamically associate the successfully matched interaction data with the corresponding customer's digital profile, specifically including:

[0020] Analyze the received multi-source heterogeneous interaction data and extract the key connection identifiers;

[0021] The key contact identifiers are compared one by one with the official contact information in the customer database of the CRM system to generate comparison results;

[0022] When the comparison result is a successful match, the interaction data is associated with the matched customer digital profile as a new activity record.

[0023] When the comparison result is a failure, the key contact identifier is created as a potential customer lead to be confirmed.

[0024] By adopting the above technical solutions and through intelligent comparison mechanisms, the accuracy and flexibility of data association are improved: parsing and extracting key identifiers avoids data redundancy and ensures the relevance of matching; one-to-one comparison uses algorithm optimization to reduce the false matching rate; dynamic association enables real-time updates of customer profiles, while potential lead creation avoids data waste, solves the bottleneck of data matching relying on manual review in traditional CRM, achieves efficient customer identification and lead conversion, and enhances the system's adaptability.

[0025] In a preferred embodiment, this application can be further configured as follows: the automated analysis of interaction data associated with the digital archive based on a pre-configured set of business rules to determine the status of sales follow-up behavior specifically includes:

[0026] For a single customer interaction event, retrieve its complete interaction data from the associated digital archive;

[0027] Extract the corresponding data type based on the complete number of interactions, and select the appropriate judgment rule based on the data type and the pre-configured business rule set.

[0028] Based on the judgment rules, the status of sales follow-up behavior is determined. If it is determined to be a valid follow-up, a follow-up record is automatically created in the digital file and the last contact timestamp is refreshed.

[0029] Monitoring is initiated based on the last contact timestamp after the update. If there is no new effective interaction within the preset follow-up period, the customer is determined to be in an overdue follow-up status.

[0030] By adopting the above technical solutions and using rule-driven analysis, the timeliness and objectivity of sales follow-up have been improved: complete data acquisition ensures the comprehensiveness of the analysis context; the rule selection mechanism adapts to different interaction types and improves the accuracy of judgment; status monitoring enables proactive early warning, avoids customer churn, solves the problem of follow-up delays being difficult to quantify, reduces management blind spots through automated judgment, ensures the rational allocation of sales resources, and improves the success rate of customer follow-up and team collaboration efficiency.

[0031] In a preferred embodiment, this application can be further configured such that: the automated analysis of interaction data associated with the digital archive based on a pre-configured set of business rules to determine the status of sales follow-up behavior further includes:

[0032] When a customer is determined to be in an overdue follow-up status, a customer pool migration instruction is generated and executed to remove the customer from the personal customer pool of the currently responsible salesperson.

[0033] Removed customers are released to the team's public customer pool and granted access to other members of their team. A system notification that the customer's status has changed is sent to the original sales staff and management.

[0034] By adopting the above technical solutions and automating resource reallocation, the customer management process was optimized: migration instructions triggered timely role adjustments, avoiding resource idleness; opening up access permissions in the public pool promoted internal team collaboration; system notifications enhanced transparency, solved the problem of rigid customer allocation, achieved dynamic load balancing, improved the overall output of the sales team, and reduced communication costs through the notification mechanism.

[0035] In a preferred embodiment, this application can be further configured as follows: the automated analysis of interaction data associated with the digital archive based on a pre-configured set of business rules to determine the compliance of sales follow-up behavior specifically includes:

[0036] Perform real-time semantic analysis on the text content in social interaction data linked to customer digital profiles to detect whether there is any illegal content that matches a pre-set sensitive word database;

[0037] Based on the spatiotemporal trajectory data, the movement characteristics of the mobile terminal during the preset working period are analyzed. When it is detected that the mobile terminal remains stationary in an unauthorized location for more than a preset time, it is determined to be an abnormal loitering behavior.

[0038] Monitor the online status of mobile terminals. If the continuous offline time exceeds a preset time threshold, it is judged as abnormal offline behavior.

[0039] When any of the above analyses is positive, an abnormal behavior alert event is generated that includes the abnormality type, related customer and sales personnel information.

[0040] By adopting the above technical solutions and through multi-dimensional behavioral analysis, compliance management has been strengthened: semantic analysis improves the detection accuracy of text risks; trajectory analysis ensures the compliance of work locations; offline monitoring prevents equipment from going out of control, solves the difficulty of relying on manual supervision of sales behavior, realizes real-time risk identification, reduces the probability of violations, and enhances the company's risk control capabilities.

[0041] In a preferred embodiment, this application can be further configured such that, after generating an abnormal behavior alert event containing anomaly type, associated customer, and sales personnel information, the customer data digitization management method based on the CRM system further includes:

[0042] Receive the abnormal behavior alarm events and automatically determine the alarm level based on their type and trigger frequency;

[0043] Based on the alarm level, a corresponding processing strategy is matched from the preset response strategy library, and the formatted warning information is automatically pushed to one or more designated management terminals according to the processing strategy.

[0044] The entire abnormal behavior alert event and the corresponding handling strategy will be recorded in an unalterable audit log for accountability purposes.

[0045] By adopting the above technical solutions and implementing a tiered response mechanism, the efficiency and traceability of incident handling have been improved: tier determination optimizes resource allocation; strategy matching ensures targeted intervention; and audit logs guarantee accountability transparency. This solves the problem of chaotic alarm handling, achieves closed-loop management, and improves the reliability and compliance of the system.

[0046] Secondly, the above-mentioned inventive objective of this application is achieved through the following technical solutions:

[0047] A customer data digitization management system based on a CRM system, the customer data digitization management system based on a CRM system includes:

[0048] The data acquisition module is used to seamlessly and automatically collect multi-source heterogeneous interactive data generated by customer interactions through deep integration with the enterprise's mobile terminals;

[0049] The data association module is used to automatically match the multi-source heterogeneous interactive data with customer information in the CRM system, and dynamically associate the successfully matched interactive data with the corresponding customer's digital profile.

[0050] The intelligent judgment module is used to automatically analyze the interaction data associated with the digital file based on a pre-configured set of business rules in order to determine the status and compliance of sales follow-up behavior.

[0051] The automated execution module is used to automatically trigger and execute corresponding customer management instructions in response to the judgment result, so as to update the customer status or generate management intervention signals.

[0052] By adopting the above technical solutions, and through deep integration with enterprise mobile terminals, seamless and automatic collection of multi-source heterogeneous interactive data is achieved. This includes automatically matching data with CRM customer information and dynamically associating it with digital profiles; automatically analyzing the status and compliance of sales follow-up behavior based on business rule sets; and automatically triggering customer management instructions based on response judgment results. This achieves full automation of customer data management: seamless data collection reduces manual intervention, avoiding data omissions and delays; automated matching and association improve the accuracy and real-time nature of data integration, ensuring the integrity of customer profiles; rule-based analysis enhances the objectivity of sales behavior management and reduces the risk of subjective misjudgment; and automatic instruction triggering improves response speed, supports real-time status updates and intervention, and solves the problems of low efficiency and high error rates caused by manual data entry in traditional CRM systems. This significantly improves the intelligence level of customer management and business collaboration efficiency.

[0053] Thirdly, the above-mentioned objectives of this application are achieved through the following technical solutions:

[0054] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described customer data digital management method based on a CRM system.

[0055] Fourthly, the above-mentioned objectives of this application are achieved through the following technical solutions:

[0056] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the above-described customer data digitization management method based on a CRM system.

[0057] In summary, this application includes at least one of the following beneficial technical effects:

[0058] 1. By deeply integrating with enterprise mobile terminals, the system seamlessly and automatically collects multi-source heterogeneous interactive data, automatically matches the data with CRM customer information and dynamically associates it with digital profiles, automatically analyzes the status and compliance of sales follow-up behavior based on business rule sets, and automatically triggers customer management instructions based on response judgment results. This achieves full automation of customer data management: seamless collection reduces manual intervention and avoids data omissions and delays; automated matching and association improve the accuracy and real-time performance of data integration, ensuring the integrity of customer profiles; rule-based analysis enhances the objectivity of sales behavior management and reduces the risk of subjective misjudgment; automatic instruction triggering improves response speed, supports real-time status updates and intervention, and solves the problems of low efficiency and high error rate caused by manual input in traditional CRM systems, significantly improving the intelligence level of customer management and business collaboration efficiency.

[0059] 2. Real-time monitoring of call signaling generates structured call data, captures social interaction data through secure interfaces, and acquires and standardizes spatiotemporal trajectory data at preset cycles. Through multi-source data integration, the comprehensiveness and reliability of data collection are enhanced: call signaling monitoring ensures automatic capture of voice interaction data, avoiding inconsistencies from manual recording; secure interface access to social data ensures secure extraction of communication content and supports compliance analysis; spatiotemporal trajectory data provides behavioral context, facilitating mobile feature analysis, improving the diversity of data sources, enabling the system to adapt to complex business scenarios, while standardized encapsulation solves the problem of heterogeneous data compatibility, laying a high-quality data foundation for subsequent matching and analysis, and reducing information silos.

[0060] 3. Rule-driven analysis improves the timeliness and objectivity of sales follow-up: complete data acquisition ensures the comprehensiveness of the analysis context; the rule selection mechanism adapts to different interaction types and improves the accuracy of judgment; status monitoring enables proactive early warning, avoids customer churn, solves the problem of follow-up delays being difficult to quantify, reduces management blind spots through automated judgment, ensures the rational allocation of sales resources, and improves the success rate of customer follow-up and team collaboration efficiency.

[0061] 4. Automated resource reallocation optimizes customer management processes: migration instructions trigger timely role adjustments, preventing resource idleness; open access to the public pool promotes internal team collaboration; system notifications enhance transparency, solve the problem of rigid customer allocation, achieve dynamic load balancing, improve the overall output of the sales team, and reduce communication costs through the notification mechanism. Attached Figure Description

[0062] Figure 1 This is a flowchart of a customer data digital management method based on a CRM system according to one embodiment of this application;

[0063] Figure 2 This is a flowchart illustrating the implementation of step S10 in a customer data digitization management method based on a CRM system according to an embodiment of this application.

[0064] Figure 3 This is a flowchart illustrating the implementation of step S20 in a customer data digitization management method based on a CRM system according to an embodiment of this application.

[0065] Figure 4 This is a flowchart illustrating the implementation of step S30 in a customer data digital management method based on a CRM system according to an embodiment of this application.

[0066] Figure 5 This is another implementation flowchart of step S30 in the customer data digital management method based on a CRM system in one embodiment of this application;

[0067] Figure 6 This is another implementation flowchart of step S30 in the customer data digital management method based on a CRM system in one embodiment of this application;

[0068] Figure 7 This is another implementation flowchart of the customer data digital management method based on a CRM system in one embodiment of this application;

[0069] Figure 8 This is a schematic diagram of a customer data digitization management system based on a CRM system in one embodiment of this application;

[0070] Figure 9This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0071] The present application will be further described in detail below with reference to the accompanying drawings.

[0072] In one embodiment, such as Figure 1 As shown, this application discloses a method for digital management of customer data based on a CRM system, which specifically includes the following steps:

[0073] S10: Through deep integration with enterprise mobile terminals, it can automatically and seamlessly collect multi-source heterogeneous interactive data generated by customer interactions.

[0074] In this embodiment, this step aims to achieve seamless and automated data collection through deep integration with enterprise mobile terminals (such as smartphones or tablets), avoiding manual intervention and improving the efficiency and real-time nature of data collection. Multi-source heterogeneous interactive data includes call logs, instant messaging messages, and geolocation information. This data originates from sales personnel's daily interactions with customers and is characterized by diversity (such as structured call data and unstructured social data) and real-time nature. Deep integration means that the system interfaces with the operating system or application layer of the mobile terminal at a low level, ensuring the anonymity of data collection and reducing interference with user operations.

[0075] Specifically, the system integrates with the mobile terminal's API (Application Programming Interface) or SDK (Software Development Kit) to continuously monitor terminal activity in the background. For example, for call data, the system utilizes the mobile network signaling interface to capture call events (such as incoming or outgoing calls) in real time and extract structured information such as caller ID, called number, timestamp, and call duration to generate standardized call logs. For social interaction data, the system accesses designated instant messaging applications (such as WeChat or WeChat Work) through secure interfaces (such as the open APIs of enterprise-level IM applications), captures session content, timestamps, and participant identifiers, and converts them into a unified text format to ensure data parsing. For spatiotemporal trajectory data, the system calls the mobile terminal's GPS or network location service at preset intervals (such as every 5 minutes) to obtain geographic coordinate sequences, forming continuous trajectory records. Finally, all collected data is standardized and encapsulated (such as using JSON or XML format), redundant information is removed, and metadata (such as data source identifiers) is added to form multi-source heterogeneous interactive data packets for easy subsequent processing.

[0076] S20: Automatically match the multi-source heterogeneous interaction data with customer information in the CRM system, and dynamically associate the successfully matched interaction data with the corresponding customer's digital profile.

[0077] In this embodiment, the core of this step is to intelligently match the collected interaction data with the customer master data in the CRM system to achieve accurate data association and thus construct a complete customer interaction history. Automated matching is based on key contact identifiers (such as phone numbers or email addresses), and comparison algorithms reduce the need for manual verification, improving the accuracy and efficiency of data association. Dynamic association means that the matching process is real-time; once data collection is complete, the system immediately triggers the matching logic to ensure timely updates to customer digital profiles. Digital profiles are the core data unit in the CRM system, storing basic customer information and interaction records. This step enriches the profile content through association, supporting subsequent analysis.

[0078] Specifically, the system parses the received multi-source heterogeneous interaction data and extracts key contact identifiers (such as caller and called numbers from call data, or session IDs from social data). The extraction process uses regular expressions or natural language processing techniques to ensure the accuracy of the identifiers. Then, the system compares the extracted identifiers one by one with official contact information in the CRM customer database (such as the customer's registered phone number or email address), using string matching or fuzzy matching algorithms to generate a comparison result (successful or unsuccessful). If the comparison is successful, the system associates the interaction data as a new activity record (such as a call or message session) with the matching customer's digital profile and updates the profile's last contact timestamp. If the comparison fails, the system marks the key contact identifier as a potential customer lead to be confirmed and stores it in the lead pool for sales personnel to follow up on.

[0079] S30: Based on a pre-configured set of business rules, automatically analyze the interaction data associated with the digital file to determine the status and compliance of sales follow-up behavior.

[0080] In this embodiment, this step utilizes a pre-configured set of business rules (such as follow-up cycle thresholds or a sensitive word library) to intelligently analyze customer interaction data and automatically assess the effectiveness and compliance of sales personnel's follow-up actions. Status determination focuses on the timeliness of sales follow-ups, while compliance determination focuses on whether the behavior contains any illegal content or abnormal actions. Automated analysis is executed through a rule engine, reducing subjective judgment and improving management objectivity.

[0081] Specifically, this step is divided into two parts: status determination and compliance determination. Status determination includes: for a customer interaction event, the system obtains complete interaction data (such as call duration or message content) from the digital profile and selects pre-configured determination rules based on the data type (such as call or social media) (e.g., effective follow-up requires meeting a minimum interaction duration). Based on the rules, the system determines the follow-up status: if it is a valid follow-up, a follow-up record is automatically created in the digital profile, and the last contact timestamp is refreshed; then monitoring is started, and if there is no new interaction within the preset follow-up period (such as 7 days), the customer is determined to be in an overdue follow-up status, triggering a status change. Compliance assessment includes: real-time semantic analysis of the text content of social interaction data, using NLP models to detect whether it matches a preset sensitive word library (e.g., containing inappropriate language); if a match is found, it is marked as a violation; based on spatiotemporal trajectory data, the movement characteristics of mobile terminals during working hours are analyzed; if a terminal remains stationary in an unauthorized location (e.g., entertainment venue) for more than a preset duration (e.g., 1 hour), it is considered abnormal loitering behavior; the online status of the terminal is monitored, and if a terminal remains offline for an extended period (e.g., 2 hours), it is considered abnormal offline behavior. Any positive result will generate an abnormal behavior alert event, including information on the type, customer, and sales personnel.

[0082] S40: In response to the judgment result, automatically trigger and execute the corresponding customer management instructions to update the customer status or generate management intervention signals.

[0083] In this embodiment, actions are automatically taken based on the determination result of S30 to achieve closed-loop management. Customer management instructions include status updates (such as migrating the customer pool) or generating intervention signals (such as sending alarms), aiming to improve sales efficiency and management transparency. Automatic triggering based on an event response mechanism ensures timeliness and reduces delays caused by manual intervention.

[0084] Specifically, different operations are performed based on the judgment results: For status judgment results (such as failure to follow up within the time limit), the system generates a customer pool migration instruction, moving the customer from the original salesperson's personal pool to the team's public pool, granting access to the customer, and simultaneously sending a system notification to relevant personnel. For compliance judgment results (such as abnormal behavior alerts), the system automatically determines the level (such as low, medium, high) based on the alert type and frequency, matches a processing strategy (such as email notification or system interception) from the preset response strategy library, and pushes a formatted warning message to the management terminal; all events are recorded in an unalterable audit log for traceability.

[0085] In this embodiment, by deeply integrating with the enterprise's mobile terminals to seamlessly and automatically collect multi-source heterogeneous interactive data, automatically match the data with CRM customer information and dynamically associate it with digital profiles, automatically analyze the status and compliance of sales follow-up behavior based on business rule sets, and automatically trigger customer management instructions based on response judgment results, the entire process of customer data management is automated: seamless collection reduces manual intervention and avoids data omissions and delays; automated matching and association improve the accuracy and real-time performance of data integration and ensure the integrity of customer profiles; rule-based analysis enhances the objectivity of sales behavior management and reduces the risk of subjective misjudgment; automatic instruction triggering improves response speed, supports real-time status updates and intervention, and solves the problems of low efficiency and high error rate caused by manual input in traditional CRM systems, significantly improving the intelligence level of customer management and business collaboration efficiency.

[0086] In one embodiment, such as Figure 2 As shown, in step S10, through deep integration with the enterprise's mobile terminals, multi-source heterogeneous interaction data generated from customer interactions is collected automatically and seamlessly, specifically including:

[0087] S11: Monitor the call signaling of mobile terminals in real time, collect and generate structured call data containing the calling and called numbers, timestamps and call duration.

[0088] In this embodiment, this sub-step focuses on call data collection, aiming to capture voice interaction information between sales and customers as foundational data for customer follow-up. Call signaling monitoring involves event capture at the mobile network layer, ensuring seamless and real-time data collection and avoiding reliance on manual recording. Structured call data emphasizes standardized formats, facilitating subsequent matching and analysis and improving data consistency within the CRM system.

[0089] Specifically, the system monitors call signaling events in real time, such as incoming calls, outgoing calls, and hang-ups, through the mobile terminal's operating system API. When a call occurs, the system automatically extracts the calling number, called number, call start timestamp, and call end timestamp, and calculates the call duration (in seconds). After the data is generated, it is encapsulated using JSON or XML format.

[0090] S12: Capture communication content within a specified instant messaging application through a secure interface, and generate social interaction data containing session identifiers, timestamps, and content text.

[0091] In this embodiment, this sub-step processes instant messaging data (such as WeChat or WeChat Work messages) to capture text interaction details for compliance analysis and customer intent identification. A secure interface ensures data access complies with corporate security policies, preventing information leakage. Social interaction data contains session context, supporting semantic analysis and enhancing customer insights.

[0092] Specifically, the open API of the integrated instant messaging application securely accesses communication content through an authentication mechanism. The capture process includes: reading the session list, extracting message content, timestamps and participant IDs, and generating standardized data packets.

[0093] S13: Obtain the geographical coordinates of the mobile terminal according to a preset period, generate continuous spatiotemporal trajectory data, and standardize and encapsulate the structured call data, social interaction data, and spatiotemporal trajectory data to form multi-source heterogeneous interactive data.

[0094] In this embodiment, this sub-step integrates geographic location information to construct the spatiotemporal trajectory of sales activities for behavioral compliance determination (such as workplace verification). A preset cycle ensures the continuity of data collection and avoids data gaps. Standardized encapsulation solves the problem of heterogeneous multi-source data and enables unified processing.

[0095] Specifically, the system calls the mobile terminal's location service (GPS or network positioning) at a preset period (e.g., every 5 minutes) to obtain latitude and longitude coordinates, adds timestamps to form a trajectory sequence, and then uses data encapsulation technology to package call data, social data, and trajectory data, adding metadata (such as data source type and version) to form a multi-source heterogeneous interactive data package.

[0096] In one embodiment, such as Figure 3 As shown, in step S20, the multi-source heterogeneous interaction data is automatically matched with customer information in the CRM system, and the successfully matched interaction data is dynamically associated with the corresponding customer's digital profile. Specifically, this includes:

[0097] S21: Parse the received multi-source heterogeneous interaction data and extract the key connection identifiers.

[0098] In this embodiment, this sub-step achieves unified processing of heterogeneous data by constructing a multi-pattern parsing engine. The parsing engine adopts specific parsing strategies for different types of data: for structured call data, it directly reads preset field values; for semi-structured social data, it uses pattern matching based on regular expressions; and for unstructured text content, it applies a named entity recognition (NER) model to extract contact information.

[0099] Specifically, the parsing engine unpacks multi-source data: for call data, it extracts the calling and called numbers; for social data, it extracts phone numbers or user IDs from the conversation; for trajectory data, it generally does not directly extract identifiers, but retains the context. After extraction, the identifiers are normalized and deduplicated.

[0100] S22: Compare the key contact identifier with the official contact information in the customer database of the CRM system one by one to generate a comparison result.

[0101] In this embodiment, the core of this sub-step is identifier matching, which improves accuracy through algorithmic comparison and avoids human error. The comparison results drive subsequent association logic to ensure the correct data flow.

[0102] Specifically, the system queries the customer table in the CRM database to obtain official contact information (such as registered phone numbers), and then uses a string matching algorithm (such as exact match or fuzzy match, with a similarity threshold set to 85%) to compare each match. For example, the extracted number "13800138000" matches successfully with "138-0013-8000" in the database after standardization. The comparison result generates a Boolean value (success / failure), and details of the differences are recorded.

[0103] S23: When the comparison result is a successful match, the interaction data is associated with the matched customer digital profile as a new activity record.

[0104] Specifically, after a successful match, the interaction data (such as call logs) is inserted as a new record into the activity table of the customer's digital profile, and fields such as activity type, timestamp, and content are automatically populated. For example, on the customer details page of the CRM system, a new "Call" activity is added, displaying the duration and participants. The association process uses database transactions to ensure consistency and updates the profile's "Last Contact Time".

[0105] S24: When the comparison result is a failure, the key contact identifier is created as a potential customer lead to be confirmed.

[0106] Specifically, the identifiers of failed matchups are stored in the lead pool, along with metadata (such as source and time). For example, a new number "Unknown_13800138000" is created as a lead with the status set to "Pending Confirmation," which other sales personnel can claim and follow up on through the CRM interface.

[0107] In one embodiment, such as Figure 4 As shown, in step S30, based on a pre-configured set of business rules, the interactive data associated with the digital file is automatically analyzed to determine the status of sales follow-up behavior, specifically including:

[0108] S31: For a given customer interaction event, retrieve its complete interaction data from the associated digital archive.

[0109] Specifically, when analyzing a specific interaction event, the system first locates the corresponding basic record in the CRM database based on the event ID (such as call ID or message ID). Then, it uses a graph query language (such as Cypher) to traverse all data nodes and relationships related to the event: including directly related customer nodes, participant nodes, and activity type nodes, as well as indirectly related historical interaction nodes, opportunity nodes, contract nodes, etc. The system aggregates this scattered data to generate a complete interaction context, including: basic interaction information (time, duration, content, etc.), participant information (customer details, salesperson information), historical interaction sequences (recent related activities), and business context (related opportunity status, order information, etc.). All data is sorted by time series and labeled with data source and confidence level. The system also performs data integrity verification to ensure that key fields are not missing, and assigns appropriate quality labels to incomplete data records.

[0110] S32: Extract the corresponding data type based on the complete number of interactions, and select the appropriate judgment rule based on the data type and the pre-configured business rule set.

[0111] In this embodiment, this sub-step enables dynamic rule selection, improving the targeting of the analysis. Data type classification (such as call, message, or trajectory) ensures that rules match the scenario, reducing false positives.

[0112] Specifically, the metadata fields of the interaction data are parsed and mapped to a pre-configured rule set: for call data, a follow-up duration rule is selected; for message data, a semantic analysis rule is selected; and for social data, it is determined whether the chat content contains a valid reply from the customer.

[0113] S33: Based on the judgment rules, determine the status of the sales follow-up behavior. If it is determined to be a valid follow-up, automatically create a follow-up record in the digital file and refresh the last contact timestamp.

[0114] Specifically, the execution status of each step in this process is determined, prompting an update to the record. Effective follow-up criteria (such as minimum interaction duration) ensure the quality of follow-up actions and avoid invalid records; for example, a call duration exceeding one minute is considered a valid follow-up. Upon successful determination, a follow-up record is created in the digital record (e.g., adding a "Valid Call" log) and the last contact timestamp is updated.

[0115] S34: Start monitoring based on the last contact timestamp after the update. If there is no new effective interaction within the preset follow-up period, the customer is determined to be in an overdue follow-up status.

[0116] Specifically, a monitoring task is initiated after each update of the last contact timestamp. This task first calculates a personalized expected follow-up period based on customer attributes (such as customer level, industry characteristics, historical conversion probability, etc.) and sales strategy settings. For example, the follow-up period for important customers might be set to 3 days, for ordinary customers to 7 days, and for customers who have not made a purchase for a long time to 15 days. The system then starts a countdown timer to check the difference between the current time and the last contact timestamp in real time. When the difference approaches the expected period (e.g., reaching 80% of the period), the system generates an early warning signal; when the difference exceeds the full period, the system officially determines that the customer is in an overdue follow-up status. The determination result includes detailed information such as the number of overdue days, the expected period, the customer ID, and the responsible sales ID.

[0117] In one embodiment, such as Figure 5 As shown, in step S30, which involves automatically analyzing the interaction data associated with the digital file based on a pre-configured set of business rules to determine the status of sales follow-up behavior, the method further includes:

[0118] S35: When a customer is determined to be in an overdue follow-up status, a customer pool migration instruction is generated, and the migration instruction is executed to remove the customer from the personal customer pool of the currently responsible salesperson.

[0119] In this embodiment, this sub-step implements the core mechanism for automated reallocation of customer resources. The system triggers a migration workflow based on the status determination result, ensuring that customer resources are promptly transferred to the appropriate follow-up personnel.

[0120] Specifically, a migration instruction package is generated, containing the customer ID, the original salesperson, and the new pool identifier. This package is then executed via the workflow engine: the customer record is deleted from the individual pool, and a migration log is recorded.

[0121] S36: Release the removed customer to the team's public customer pool, grant other members of the team access to claim the customer, and send a system notification to the original sales staff and management that the customer's status has changed.

[0122] In this embodiment, this sub-step completes the closed loop of intra-team reallocation and notification of customer resources. The public customer pool adopts an access control model based on a permission bitmap, supporting fine-grained permission management. The notification system implements multi-channel, real-time message push to ensure that relevant personnel are promptly informed of status changes.

[0123] Specifically, customers are added to the team's shared customer pool: a new record is inserted into the shared pool relationship table, including expiration time and fields such as automatic recycling after 7 days. Then, the permission matrix is ​​configured: all valid salesperson IDs within the team are traversed, and the "receive" permission bit is set for each in the permission bitmap. The notification generation module simultaneously performs the following operations: generating a notification message for the original salesperson, including basic customer information, reason for migration, and last contact time; generating a summary report for team management, including daily migration customer statistics and trend analysis. Notifications are sent in parallel through multiple channels: unread messages are inserted into the system's message center, instant reminders are pushed via WeChat / DingTalk robots, and additional SMS reminders are sent for important customer migrations. All notification messages are generated using templates, supporting variable substitution and multiple languages ​​to ensure accurate and consistent information.

[0124] In one embodiment, such as Figure 6 As shown, in step S30, based on a pre-configured set of business rules, the interactive data associated with the digital file is automatically analyzed to determine the compliance of sales follow-up behavior, specifically including:

[0125] S301: Perform real-time semantic analysis on the text content in social interaction data associated with customer digital profiles to detect whether there is any illegal content that matches a preset sensitive word library.

[0126] In this embodiment, natural language processing technology is used to automate compliance monitoring of sales communication content. The system employs a deep learning-based text classification model combined with a rule engine to achieve high accuracy in identifying inappropriate content. The sensitive word database supports dynamic updates and multi-dimensional classification to adapt to different compliance requirements.

[0127] Specifically, the social interaction text undergoes preprocessing: word segmentation, stop word removal, part-of-speech tagging, and named entity recognition. Then, a pre-trained BERT model is used to calculate the text vector representation, which is then matched against rules in a sensitive word library in multiple dimensions: first, high-risk keywords based on exact string matching (e.g., "rebate" or "false promise"); second, potential violations based on semantic similarity calculation (e.g., promises such as "guaranteed returns"); and third, violation patterns based on contextual understanding (e.g., repeated mentions of derogatory terms related to competitors). The detection engine employs a multi-model fusion strategy, combining rule matching, keyword weight calculation, and neural network classification results to provide a comprehensive violation probability score. When the score exceeds a threshold (e.g., 0.8), the interaction is marked as containing violation content, and detailed information such as violation type, confidence level, and trigger location is recorded.

[0128] S302: Based on the spatiotemporal trajectory data, analyze the movement characteristics of the mobile terminal during a preset working period. When it is identified that the mobile terminal remains stationary in an unauthorized location for more than a preset duration, it is determined to be an abnormal loitering behavior.

[0129] In this embodiment, intelligent monitoring of field personnel's work behavior is achieved through spatiotemporal trajectory pattern analysis. The system employs a sliding window algorithm and cluster analysis technology to identify abnormal dwelling patterns.

[0130] Specifically, the system loads a preset working time period configuration (e.g., 9:00-18:00) and a set of permitted location geofences. Then, it preprocesses the mobile terminal's trajectory data: coordinate correction, speed calculation, and stop point detection. A clustering algorithm is used to identify persistently stationary areas. When the duration of a stationary cluster exceeds a threshold (e.g., 60 minutes) and the cluster's center point is not within any permitted geofence, an abnormal loitering determination is triggered. The system also considers special cases: such as allowing reasonable loitering at the customer's location, but requiring verification against visit records in the customer's file. The determination result includes detailed information such as the abnormal start time, duration, geographic coordinates, and distance to the nearest permitted location, supporting verification during manual review.

[0131] S303: Monitor the online status of mobile terminals. If the continuous offline time exceeds the preset time threshold, it is determined to be abnormal offline behavior.

[0132] Specifically, a heartbeat service is deployed on the mobile terminal, sending heartbeat packets to the server at fixed intervals (e.g., every 5 minutes), containing device ID, timestamp, network type, battery information, etc. The server maintains an online status table, recording the last online time of each device. The monitoring engine periodically (e.g., every 10 minutes) scans the status table, calculating the difference between the current time and the last online time. When the difference exceeds a threshold (e.g., 2 hours), secondary verification is performed: checking the device's online mode over the past 24 hours, excluding normal off-hours; and sending a network diagnostic request to confirm whether the offline status is a false one caused by a network failure. Only when an abnormal offline status is confirmed is a formal judgment result generated.

[0133] S304: When any of the above analysis results is positive, generate an abnormal behavior alert event that includes the abnormality type, related customer and sales personnel information.

[0134] Specifically, an original record is created for each detected anomaly, and then the event synthesis engine is used to merge and process them: for multiple related anomalies of the same salesperson within the same time period (such as being offline for a long time in an unauthorized location during working hours), a comprehensive alarm event is generated to avoid alarm storms. After the event is generated, the system automatically assigns it to the corresponding processing workflow and updates the real-time monitoring dashboard.

[0135] In one embodiment, such as Figure 7As shown, after step S304, that is, after generating an abnormal behavior alert event that includes the abnormality type, associated customer and sales personnel information, the customer data digital management method based on the CRM system further includes:

[0136] S305: Receive the abnormal behavior alarm event and automatically determine the alarm level according to its type and triggering frequency.

[0137] Specifically, upon receiving an alert, the system first parses the basic information of the event, then executes a level determination process: the base level is determined based on a preset type-level mapping table (e.g., violation content is "high-risk," abnormal retention is "medium-risk"). Next, a frequency correction factor is introduced: the number of similar alerts for the salesperson in the recent period (e.g., within 7 days) is queried, and the level is increased exponentially (e.g., each additional similar alert increases the level by one). Finally, a business impact correction is applied: the importance level of related customers is checked (e.g., alerts related to VIP customers are automatically increased by one level). The level determination algorithm is expressed as follows: Final Level = max(Base Level, Frequency Correction Level, Business Impact Level).

[0138] S306: Based on the alarm level, match the corresponding processing strategy from the preset response strategy library, and automatically push the formatted warning information to one or more designated management terminals according to the processing strategy.

[0139] Specifically, the system maintains a response strategy matrix, including the following dimensions: alert level (low, medium, high, urgent), processing time requirements (e.g., 2 hours, 1 hour, 30 minutes, immediate), notification channel combination (e.g., system messages, email, SMS, telephone), and responsible personnel group (e.g., direct supervisor, department manager, compliance department, etc.). The strategy matching engine selects the most matching strategy instance based on the alert level and then performs the following operations: Sending alert information in parallel according to channel priority, with information content generated using templates, including alert summary, processing link, and timeliness reminder; establishing a delivery confirmation mechanism for each push attempt, requiring recipients to click confirmation for important alerts; setting escalation rules, automatically notifying backup responsible personnel when the primary responsible person fails to respond within a specified time. All push operations are logged in detail, supporting delivery status tracking.

[0140] S307: Record the entire abnormal behavior alert event and the handling strategy implemented in an unalterable audit log for traceability and accountability.

[0141] Specifically, a complete audit log object is constructed, including: full information on alert events, details of the level determination process, matching policy IDs, all push operation records, and response timestamp sequences of relevant personnel. Then, the SHA-256 algorithm is used to calculate the record hash value, which is written to the storage area. Simultaneously, detailed audit logs are stored in a relational database, but each query verification compares the data with the stored hash value to ensure data integrity. The audit log supports multi-condition combined queries and visual analysis; administrators can view the complete processing trajectory by time range, alert type, processing status, and other dimensions.

[0142] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0143] In one embodiment, a customer data digitization management system based on a CRM system is provided, which corresponds one-to-one with the customer data digitization management method based on a CRM system described in the above embodiments. For example... Figure 8 As shown, this customer data digitization management system based on a CRM system includes a data acquisition module, a data association module, an intelligent judgment module, and an automated execution module. Detailed descriptions of each functional module are as follows:

[0144] The data acquisition module is used to seamlessly and automatically collect multi-source heterogeneous interactive data generated by customer interactions through deep integration with the enterprise's mobile terminals;

[0145] The data association module is used to automatically match the multi-source heterogeneous interactive data with customer information in the CRM system, and dynamically associate the successfully matched interactive data with the corresponding customer's digital profile.

[0146] The intelligent judgment module is used to automatically analyze the interaction data associated with the digital file based on a pre-configured set of business rules in order to determine the status and compliance of sales follow-up behavior.

[0147] The automated execution module is used to automatically trigger and execute corresponding customer management instructions in response to the judgment result, so as to update the customer status or generate management intervention signals.

[0148] Specific limitations regarding the customer data digitization management system based on a CRM system can be found in the above description of the limitations on the customer data digitization management method based on a CRM system, and will not be repeated here. Each module in the aforementioned customer data digitization management system based on a CRM system can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in an electronic device, or stored in the memory of an electronic device as software, so that the processor can call and execute the corresponding operations of each module.

[0149] In one embodiment, an electronic device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9 As shown, this electronic device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides the environment for the operation of the operating system and computer programs in the non-volatile storage media. The database stores sales data and customer data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements a CRM-based digital customer data management method.

[0150] In one embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps:

[0151] By deeply integrating with enterprise mobile terminals, multi-source heterogeneous interactive data generated from customer interactions can be collected seamlessly and automatically.

[0152] The multi-source heterogeneous interaction data is automatically matched with customer information in the CRM system, and the successfully matched interaction data is dynamically associated with the corresponding customer's digital profile.

[0153] Based on a pre-configured set of business rules, the interactive data associated with the digital files is automatically analyzed to determine the status and compliance of sales follow-up behavior;

[0154] In response to the judgment result, the system automatically triggers and executes the corresponding customer management instructions to update the customer status or generate management intervention signals.

[0155] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor:

[0156] By deeply integrating with enterprise mobile terminals, multi-source heterogeneous interactive data generated from customer interactions can be collected seamlessly and automatically.

[0157] The multi-source heterogeneous interaction data is automatically matched with customer information in the CRM system, and the successfully matched interaction data is dynamically associated with the corresponding customer's digital profile.

[0158] Based on a pre-configured set of business rules, the interactive data associated with the digital files is automatically analyzed to determine the status and compliance of sales follow-up behavior;

[0159] In response to the judgment result, the system automatically triggers and executes the corresponding customer management instructions to update the customer status or generate management intervention signals.

[0160] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0161] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0162] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A CRM system-based customer data digital management method, characterized by, The CRM-based customer data digital management method includes the following steps: By deeply integrating with enterprise mobile terminals, multi-source heterogeneous interactive data generated from customer interactions can be collected seamlessly and automatically. The multi-source heterogeneous interaction data is automatically matched with customer information in the CRM system, and the successfully matched interaction data is dynamically associated with the corresponding customer's digital profile. Based on a pre-configured set of business rules, the interactive data associated with the digital files is automatically analyzed to determine the status and compliance of sales follow-up behavior; In response to the judgment result, the system automatically triggers and executes the corresponding customer management instructions to update the customer status or generate management intervention signals.

2. The CRM system-based customer data digitalization management method of claim 1, wherein, The aforementioned method, through deep integration with enterprise mobile terminals, enables seamless and automatic collection of multi-source heterogeneous interaction data generated from customer interactions, specifically including: Real-time monitoring of call signaling on mobile terminals, collecting and generating structured call data including caller and called numbers, timestamps and call duration; Capture communication content within a specified instant messaging application through a secure interface, and generate social interaction data containing session identifiers, timestamps, and content text; The geographical coordinates of the mobile terminal are acquired at a preset period to generate continuous spatiotemporal trajectory data. The structured call data, social interaction data, and spatiotemporal trajectory data are standardized and encapsulated to form multi-source heterogeneous interactive data. 3.The CRM system-based customer data digitalization management method of claim 1, wherein, The step of automatically matching the multi-source heterogeneous interaction data with customer information in the CRM system, and dynamically associating the successfully matched interaction data with the corresponding customer's digital profile, specifically includes: Analyze the received multi-source heterogeneous interaction data and extract the key connection identifiers; The key contact identifiers are compared one by one with the official contact information in the customer database of the CRM system to generate comparison results; When the comparison result is a successful match, the interaction data is associated with the matched customer digital profile as a new activity record. When the comparison result is a failure, the key contact identifier is created as a potential customer lead to be confirmed. 4.The CRM system-based customer data digitalization management method of claim 1, wherein, The pre-configured business rule set automatically analyzes the interaction data associated with the digital archive to determine the status of sales follow-up behavior, specifically including: For a single customer interaction event, retrieve its complete interaction data from the associated digital archive; Extract the corresponding data type based on the complete number of interactions, and select the appropriate judgment rule based on the data type and the pre-configured business rule set. Based on the judgment rules, the status of sales follow-up behavior is determined. If it is determined to be a valid follow-up, a follow-up record is automatically created in the digital file and the last contact timestamp is refreshed. Monitoring is initiated based on the last contact timestamp after the update. If there is no new effective interaction within the preset follow-up period, the customer is determined to be in an overdue follow-up status.

5. The CRM system-based customer data digitalization management method of claim 4, wherein, The automated analysis of interaction data associated with the digital archives, based on a pre-configured set of business rules, to determine the status of sales follow-up behavior, also includes: When a customer is determined to be in an overdue follow-up status, a customer pool migration instruction is generated and executed to remove the customer from the personal customer pool of the currently responsible salesperson. Removed customers are released to the team's public customer pool and granted access to other members of their team. A system notification that the customer's status has changed is sent to the original sales staff and management. 6.The CRM system-based customer data digitalization management method of claim 4, wherein, The pre-configured business rule set automatically analyzes the interaction data associated with the digital archive to determine the compliance of sales follow-up behavior, specifically including: Perform real-time semantic analysis on the text content in social interaction data linked to customer digital profiles to detect whether there is any illegal content that matches a pre-set sensitive word database; Based on the spatiotemporal trajectory data, the movement characteristics of the mobile terminal during the preset working period are analyzed. When it is detected that the mobile terminal remains stationary in an unauthorized location for more than a preset time, it is determined to be an abnormal loitering behavior. Monitor the online status of mobile terminals. If the continuous offline time exceeds a preset time threshold, it is judged as abnormal offline behavior. When any of the above analyses is positive, an abnormal behavior alert event is generated that includes the abnormality type, related customer and sales personnel information.

7. The CRM system-based customer data digitalization management method of claim 6, wherein, After generating an abnormal behavior alert event that includes the anomaly type, associated customer, and sales personnel information, the CRM-based customer data digital management method further includes: Receive the abnormal behavior alarm events and automatically determine the alarm level based on their type and trigger frequency; Based on the alarm level, a corresponding processing strategy is matched from the preset response strategy library, and the formatted warning information is automatically pushed to one or more designated management terminals according to the processing strategy. The entire abnormal behavior alert event and the corresponding handling strategy will be recorded in an unalterable audit log for accountability purposes.

8. A CRM system-based customer data digital management system, characterized by, The customer data digitization management system based on the CRM system includes: The data acquisition module is used to seamlessly and automatically collect multi-source heterogeneous interactive data generated by customer interactions through deep integration with the enterprise's mobile terminals; The data association module is used to automatically match the multi-source heterogeneous interactive data with customer information in the CRM system, and dynamically associate the successfully matched interactive data with the corresponding customer's digital profile. The intelligent judgment module is used to automatically analyze the interaction data associated with the digital file based on a pre-configured set of business rules in order to determine the status and compliance of sales follow-up behavior. The automated execution module is used to automatically trigger and execute corresponding customer management instructions in response to the judgment result, so as to update the customer status or generate management intervention signals.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the customer data digital management method based on a CRM system as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the customer data digital management method based on a CRM system as described in any one of claims 1 to 7.

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