Cross-platform member unified management and service optimization system

Abstract

The invention discloses a cross-platform member unified management and service optimization system. A data fusion processing module breaks a data island, fuses multi-platform member data, and constructs a complete member portrait; the member value evaluation module comprehensively and accurately evaluates the member value by using a member value evaluation formula based on the comprehensive data, insights the contribution and potential of the member value, and provides a basis for a marketing strategy; the member layering module divides levels according to values and dynamic thresholds, enterprises can formulate personalized management strategies accordingly, resources are reasonably distributed, and the management efficiency and the member satisfaction degree are improved; the service recommendation module calculates and recommends personalized services according to a matching degree formula in combination with the hierarchy and historical behavior data; all the modules are tightly matched, a complete closed loop is formed from data integration, value evaluation, hierarchical division and service recommendation, the member experience is effectively improved, and the recognition sense and stickiness of members to enterprises are enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of member management and service optimization, and particularly relates to a cross-platform member unified management and service optimization system. BACKGROUND

[0002] In the current rapid development of digital business, the member system has become an important means for enterprises to improve user loyalty and promote business growth. However, with the expansion of business to multiple platforms, such as operating e-commerce platforms, mobile applications, and offline stores, member management faces many challenges. Traditional member management methods have been difficult to meet the needs of complex business scenarios.

[0003] In the early days, enterprises independently built member systems on different platforms, and member data on each platform was isolated from each other. For example, e-commerce platforms record members' online shopping behavior and consumption data, offline stores have members' in-store consumption and on-site service information, and social platforms accumulate members' interaction and social data. This scattered data situation makes it difficult for enterprises to fully understand the comprehensive characteristics and behavior preferences of members and form a complete member portrait. SUMMARY

[0004] Therefore, the present application provides a cross-platform member unified management and service optimization system, which can effectively solve the problem that the member data on each platform is isolated from each other, making it difficult to fully understand the comprehensive characteristics and behavior preferences of members.

[0005] The technical solution of the present application is as follows:

[0006] A cross-platform member unified management and service optimization system comprises:

[0007] A member data acquisition module is configured to acquire member basic information, consumption records, and behavior data from different platforms.

[0008] A data fusion processing module is configured to perform fusion processing on the member basic information, consumption records, and behavior data to obtain a member comprehensive data vector.

[0009] A member value evaluation module is configured to calculate the member value based on the member comprehensive data vector using a member value evaluation formula.

[0010] A member layering module is configured to divide members into different levels by comparing the member value with a dynamic threshold.

[0011] A service recommendation module is configured to calculate the matching degree between members and various services using a service recommendation matching degree formula based on the member level and historical behavior data, and recommend personalized services to members according to the matching degree.

[0012] As a further optional solution of the cross-platform member unified management and service optimization system, the member data collection module comprises:

[0013] A platform interface docking unit is configured to establish a standardized data interface with different platforms.

[0014] A data real-time capture unit is configured to capture the changes of member data in real time by using a stream data processing technology and timely transmit the member data to the data fusion processing module.

[0015] A data batch collection unit is configured to periodically collect member data from platforms in batches according to a preset time interval.

[0016] As a further optional solution of the cross-platform member unified management and service optimization system, the data fusion processing module comprises:

[0017] A data cleaning unit is configured to clean member basic information, consumption records and behavior data to obtain cleaned member basic information, consumption records and behavior data.

[0018] A data conversion unit is configured to convert the cleaned member basic information, consumption records and behavior data to obtain standardized member basic information, consumption records and behavior data.

[0019] A data fusion unit is configured to fuse the standardized member basic information, consumption records and behavior data by using a dynamic data fusion formula to obtain a member comprehensive data vector.

[0020] As a further optional solution of the cross-platform member unified management and service optimization system, the member value evaluation formula is specifically:

[0021] ;

[0022] wherein, represents the member value, represents a consumption amount related index of the member in a period of time, represents an interaction behavior index of the member, represents a weight coefficient of the consumption amount related index, represents a weight coefficient of the interaction behavior index.

[0023] As a further optional solution of the cross-platform member unified management and service optimization system, the member layering module comprises:

[0024] A dynamic threshold adjustment unit is configured to dynamically adjust a layering threshold based on a layering threshold dynamic adjustment formula.

[0025] ​The member classification unit is configured to classify members into different levels according to the adjusted hierarchical threshold.

[0026] As a further optional solution of the cross-platform member unified management and service optimization system, the service recommendation module comprises:

[0027] The feature vector construction unit is configured to extract key features from the member historical behavior data and the service information, and construct a member historical behavior feature vector and a service feature vector.

[0028] The service recommendation matching degree formula calculation unit is configured to combine the member level and the member historical behavior feature vector, and calculate the matching degree between the member and each service feature vector by using a service recommendation matching degree formula.

[0029] The recommendation result sorting subunit is configured to sort each type of service according to the calculation result of the matching degree, and recommend the service with the highest matching degree to the member.

[0030] As a further optional solution of the cross-platform member unified management and service optimization system, the service recommendation matching degree formula is specifically:

[0031] ;

[0032] wherein, represents the service recommendation matching degree, represents the member historical behavior feature vector, represents the service feature vector, represents the member level and service adaptation degree index, represents the dimension number of the member historical behavior feature vector, represents the number of the member level and service adaptation degree index, represents the weight balance coefficient of the feature similarity and the level adaptation degree.

[0033] A cross-platform member unified management and service optimization method, specifically comprising:

[0034] Collecting member basic information, consumption records and behavior data of different platforms;

[0035] Fusing the member basic information, consumption records and behavior data to obtain a member comprehensive data vector;

[0036] Calculating the member value based on the member comprehensive data vector by using a member value evaluation formula;

[0037] Comparing the member value with a dynamic threshold to classify the member into different levels;

[0038] The service recommendation matching degree formula is used to calculate the matching degree of the member and various services in combination with the member level and historical behavior data, and personalized services are recommended for the member according to the matching degree.

[0039] A computing device comprises a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the steps of the above-mentioned cross-platform member unified management and service optimization method when executing the computer program.

[0040] A computer readable storage medium, the storage medium stores a computer program, and the computer program implements the steps of the above-mentioned cross-platform member unified management and service optimization method when executed by a processor.

[0041] The beneficial effects of the present application are: through the data fusion processing module, the system can effectively fuse the member basic information, consumption records and behavior data from different platforms, can break the data island, gather the data scattered in each platform together, comprehensively cover the information of the member in multiple scenes, and thus build a complete member portrait; the member value evaluation module calculates the member value based on the member comprehensive data vector and using the member value evaluation formula, in this way, the comprehensive value of the member can be more comprehensively and accurately evaluated, the contribution and potential of the member in different aspects can be understood, and a strong basis is provided for the enterprise to formulate targeted marketing strategies; the member layering module compares the member value with the dynamic threshold, divides the member into different levels, different level members have different characteristics and needs, and the enterprise can formulate personalized management strategies for members of each level according to the layering result, which helps the enterprise to reasonably allocate resources, improve the efficiency and effect of member management, and improve the satisfaction and loyalty of the member; the service recommendation module combines the member level and historical behavior data, uses the service recommendation matching degree formula to calculate the matching degree of the member and various services, and recommends personalized services for the member according to the matching degree, and this personalized service recommendation can improve the shopping experience of the member and increase the identification and stickiness of the member to the enterprise. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description only constitute some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0043] Fig. 1 The figure is a schematic diagram of the composition of the cross-platform member unified management and service optimization system of the present application.

[0044] Fig. 2A flow chart of a cross-platform member unified management and service optimization method of the present application;

[0045] Fig. 3 A composition schematic diagram of a computing device of the present application. DETAILED DESCRIPTION

[0046] The technical solutions in the embodiments of the present application will be described clearly and completely below. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the protection scope of the present application.

[0047] REFERENCE Figs. 1 to 3 A cross-platform member unified management and service optimization system, comprising a member data collection module, a data fusion processing module, a member value evaluation module, a member stratification module and a service recommendation module, wherein:

[0048] The member data collection module is used to collect member basic information, consumption records and behavior data of different platforms, wherein the different platforms include but are not limited to e-commerce platforms, social platforms and offline store systems; in some embodiments, the member data collection module comprises:

[0049] The platform interface docking unit is used to establish a standardized data interface with different platforms;

[0050] The data real-time capture unit is used to capture the changes of member data in real time by using a stream data processing technology (such as Apache Kafka) for platforms supporting real-time data update, and timely transmit the member data to the data fusion processing module;

[0051] The data batch collection unit is used to collect member data from platforms in batches at a preset time interval (such as every hour or every day) for platforms not supporting real-time data update, and perform preliminary format conversion and arrangement.

[0052] Specifically, it is specified that the different platforms include but are not limited to e-commerce platforms, social platforms and offline store systems, and the data collection is implemented through the specific platform interface docking unit, the data real-time capture unit and the data batch collection unit, which enables the system to widely cover the activity information of members on various platforms, whether it is online shopping, social interaction or offline consumption behavior, which can be effectively collected, thereby eliminating the information blind area caused by platform data isolation, and providing rich data sources for enterprises to comprehensively understand the comprehensive behavior of members;

[0053] For platforms that support real-time data updates, stream processing technology (such as Apache Kafka) is used to capture changes in member data in real time and transmit them to the data fusion processing module in a timely manner. This real-time capture and transmission mechanism ensures that the system can obtain the latest behavior information of members in real time, such as their immediate purchase behavior on e-commerce platforms and their latest interactions on social platforms, ensuring the real-time and accuracy of the data, allowing enterprises to keep up with member dynamics and quickly respond to market changes and member needs;

[0054] For platforms that do not support real-time data updates, member data is collected from the platform in batches at preset time intervals (such as every hour or every day), and preliminary format conversion and organization are performed. This batch collection method fully considers the technical characteristics and data update frequency of different platforms, flexibly adapts to various platforms, ensures timely data collection, avoids system burden and data acquisition failure caused by frequent access to platforms that do not support real-time updates, and ensures the integrity of member data;

[0055] The platform interface docking unit is used to establish standardized data interfaces with different platforms. Standardized interface design enables the system to smoothly interact with various types of platforms, improving system compatibility. When enterprises expand new business platforms, they only need to dock according to the standardized interface to quickly collect data from new platforms, greatly improving system scalability and reducing system upgrade and maintenance costs.

[0056] The data fusion processing module is used to fuse member basic information, consumption records, and behavior data to obtain a member comprehensive data vector. In some embodiments, the data fusion processing module includes:

[0057] The data cleaning unit is used to remove duplicate data, incorrect data, and missing values in collected member data, and uses data interpolation, data correction, and other methods to repair incomplete or incorrect data.

[0058] The data conversion unit is used to convert member data collected from different platforms into a standardized format, unify data types, encoding formats, and naming conventions, and make the data meet the storage requirements of the member data hub.

[0059] The data fusion unit is used to fuse standardized member basic information, consumption records, and behavior data using a dynamic data fusion formula to obtain a member comprehensive data vector. The dynamic data fusion formula is specifically where represents the data vector of the i-th data source, is a weight coefficient dynamically assigned according to the reliability and importance of the data source, It is an exponential parameter that adjusts the degree of data fusion. This formula is used to generate a comprehensive and accurate integrated member data vector. And store it in the member data center.

[0060] Specifically, the data cleaning unit removes duplicate, erroneous, and missing values ​​from the collected member data, and uses methods such as data interpolation and data correction to repair incomplete or erroneous data, greatly improving data quality. Removing duplicate data avoids analytical biases caused by data redundancy; correcting erroneous data ensures data accuracy; handling missing values ​​makes the data more complete; the cleaned data provides a reliable foundation for subsequent analysis and processing, ensuring the accuracy of information such as comprehensive member characteristics and behavioral preferences derived from this data.

[0061] The data conversion unit converts the format of member data collected from different platforms, unifying data types, encoding formats, and naming conventions to ensure that the data meets the storage requirements of the member data hub. This process achieves data standardization, eliminates obstacles caused by differences in data formats across different platforms, and facilitates unified management and processing by the system. This improves system compatibility, enabling seamless integration and interaction of data from various platforms within the system. At the same time, it also facilitates subsequent data analysis and mining work, enhancing the system's operability and efficiency.

[0062] The data fusion unit uses a dynamic data fusion formula to fuse standardized basic member information, consumption records, and behavioral data to obtain a comprehensive member data vector. In the dynamic data fusion formula, weighting coefficients... The index parameters are dynamically allocated based on the reliability and importance of the data source. Adjusting the degree of data fusion, this dynamic fusion method fully considers the characteristics and value of different data sources. It can flexibly adjust the contribution of each data source in the fusion process according to the actual situation. The comprehensive member data vector generated by this formula comprehensively and accurately reflects the comprehensive characteristics and behavioral preferences of members, providing strong support for building accurate member profiles.

[0063] The member value assessment module is used to calculate member value based on a comprehensive member data vector and a member value assessment formula; in some embodiments, the member value assessment formula is specifically as follows:

[0064] ;

[0065] in, Indicates member value. This indicates that the member has been active for a period of time. Indicators related to consumption amount within the country Indicators representing members' interactive behavior. This represents the weighting coefficient of indicators related to consumption amount. This represents the weighting coefficient of the interactive behavior indicator.

[0066] Specifically, the membership value assessment formula comprehensively considers the member's performance over a period of time. Internal consumption amount related indicators and interactive behavior indicators Consumption amount directly reflects the member's direct economic contribution to the enterprise, while interactive behavior (such as browsing, commenting, sharing, etc.) reflects the member's attention and participation in the brand. By evaluating the value of members from multiple dimensions, compared with the evaluation method based on only a single indicator (such as consumption amount), this comprehensive approach can more accurately reflect the comprehensive value of members and avoid misjudgment of value due to one-sided evaluation.

[0067] The formula includes weighting coefficients for indicators related to consumption amount. Weighting coefficients of interaction behavior metrics Businesses can flexibly adjust these two weighting coefficients based on different business stages, market environments, and marketing strategies. For example, during promotional activities, to encourage members to increase spending, the weighting coefficients can be appropriately increased. The value; however, during the brand promotion phase, if the goal is to increase member engagement, then the value can be increased. The proportion of weighting, this flexible weighting method allows member value assessment to better adapt to business changes and provides strong support for enterprises to formulate targeted marketing strategies;

[0068] Because the assessment is based on members over a period of time The formula dynamically reflects changes in member value by using behavioral data and adjusting weighting coefficients according to business needs. As member consumption and interaction behaviors change, the value assessment results will be updated accordingly. Businesses can conduct precise marketing to members based on real-time value assessment results. For example, for members whose value increases, higher-level services and discounts can be provided; for members whose value decreases, timely measures can be taken to win them back and reactivate them, thereby improving the efficiency of marketing resource utilization.

[0069] A membership tiering module is used to divide members into different tiers based on a comparison between member value and a dynamic threshold; in some embodiments, the membership tiering module includes:

[0070] A dynamic threshold adjustment unit is used to dynamically adjust the hierarchical threshold based on a hierarchical threshold dynamic adjustment formula, wherein the hierarchical threshold dynamic adjustment formula is specifically as follows: , It is the stratification threshold of the previous period. It is an adjustment factor. It represents the value of the member within the current period. It is the average value of members within the current period. Members are divided into different levels based on the adjusted tiering thresholds, and personalized management strategies are formulated accordingly.

[0071] Member segmentation unit, used to divide members into different levels based on the adjusted stratification threshold.

[0072] Specifically, the dynamic threshold adjustment unit dynamically adjusts the stratified threshold based on the stratified threshold dynamic adjustment formula, which comprehensively considers the stratified threshold of the previous period. Adjustment coefficient The value of members in the current period and the average value of members during the current period. Through this dynamic adjustment mechanism, the tiering threshold can be updated in real time according to the actual changes in member value. For example, when the overall member value increases, the tiering threshold will also increase accordingly, ensuring that the tiering standard keeps pace with the dynamic changes in member value, so that the tiering results more accurately reflect the actual value level of members.

[0073] The membership segmentation unit divides members into different tiers based on the adjusted tiering thresholds. Because the tiering thresholds are dynamically adjusted, the membership tier division is more reasonable and accurate. Members at different tiers have different characteristics and needs, and companies can develop personalized management strategies for each tier. For example, for high-value members, exclusive VIP services, high discounts, and priority purchasing rights can be provided; for members at the potential value tier, a nurturing plan can be developed, guiding them to convert to the high-value tier through personalized marketing information and promotional activities. This personalized management can improve member satisfaction and loyalty, and enhance the company's service quality and market competitiveness.

[0074] A service recommendation module is used to combine membership level and historical behavior data, calculate the matching degree between a member and various services using a service recommendation matching degree formula, and recommend personalized services to the member based on the matching degree; in some embodiments, the service recommendation module includes:

[0075] The feature vector construction unit is used to extract key features from member historical behavior data and service information, and to construct member historical behavior feature vectors and service feature vectors.

[0076] The service recommendation matching degree formula calculation unit is used to combine the member level and the member's historical behavior feature vector, and use the service recommendation matching degree formula to calculate the matching degree between the member and each service feature vector.

[0077] The recommendation results ranking subunit is used to rank various services based on the matching degree calculation results and recommend the services with the highest matching degree to members.

[0078] Specifically, the feature vector construction unit extracts key features from members' historical behavior data and service information to construct member historical behavior feature vectors and service feature vectors. This process allows for in-depth analysis of members' behavioral patterns and key service attributes. For example, extracting features from members' purchase records, browsing history, search keywords, and other historical behavioral data can accurately grasp members' interests, preferences, and consumption habits; extracting features from service information such as service functions, prices, and target audiences can clearly describe the characteristics of the service. Accurate feature extraction provides a high-quality data foundation for subsequent matching degree calculations, making the recommendation results more targeted and reliable.

[0079] The service recommendation matching formula calculation unit combines membership level and historical behavioral feature vectors of members, using a specific formula to calculate the matching degree between members and the feature vectors of each service. By incorporating membership level, the matching degree calculation is based not only on the member's behavioral characteristics but also on the member's status and rights within the enterprise's membership system. For example, higher-level members may have a higher compatibility with high-quality, high-value services, and this is given appropriate weight in the matching degree calculation, enabling a more accurate measurement of the fit between members and services. This scientific calculation method effectively improves the accuracy of service recommendations, avoids blind recommendations, and increases the matching degree between recommended services and member needs.

[0080] The recommendation results ranking subunit sorts various services based on the calculated matching degree, recommending the services with the highest matching degree to members. This ranking allows members to prioritize services that best match their needs and characteristics, reducing the time and effort required to filter through numerous services. For example, in e-commerce scenarios, members can quickly find products that match their interests and spending power; in service booking scenarios, members can easily select the most suitable service. This reasonable ranking method optimizes the member's recommendation experience, increasing member acceptance and satisfaction with the recommended services.

[0081] In some embodiments, the service recommendation matching degree formula is specifically as follows:

[0082] ;

[0083] in, Indicates the service recommendation matching degree. This represents a vector of historical member behavior features. Represents the service feature vector. This indicates the compatibility between membership level and service. This represents the number of dimensions in the feature vector of a member's historical behavior. This indicates the number of metrics related to the compatibility between membership levels and services. This represents the weighting balance coefficient between feature similarity and hierarchical fit.

[0084] Specifically, the service recommendation matching formula incorporates the member's historical behavior feature vector. Service feature vector Feature similarity, and membership level and service compatibility index By calculating the feature similarity component, we can gain a deeper understanding of the correlation between members' historical behavior and service features. For example, we can analyze the relevance between a member's past preferences for a certain type of product and the current recommended service. Additionally, we can consider the average value of the member level and the service fit index. This method combines a member's level within the enterprise's membership system with the compatibility between that level and the services. This comprehensive, multi-dimensional data calculation method makes the calculation of service recommendation matching degree more comprehensive and accurate, and can more accurately reflect the degree of matching between members and various services.

[0085] Weight balance coefficient in the formula To balance the impact of feature similarity and hierarchical fit on service recommendation matching, enterprises can flexibly adjust [their strategies] according to different business objectives and market strategies. The value of [the value of the recommendation strategy]. For example, in a phase where the primary goal is to increase member activity, the weight of feature similarity can be appropriately increased, prioritizing services that are highly relevant to members' historical behavior to increase member interest and engagement. Conversely, when promoting high-end services or marketing to specific member tiers, the weight of tier suitability can be increased to ensure that recommended services better match the characteristics and needs of the member tier. By balancing the influence of different factors, the service recommendation strategy can be optimized and the recommendation effect improved.

[0086] Based on the calculated service recommendation matching degree The system can recommend personalized services to members. Because the matching degree calculation takes into account the member's historical behavior and hierarchical information, the recommended services are more in line with the member's actual needs and spending power. For example, for VIP members who frequently purchase high-end products, the system will prioritize recommending high-quality, high-value-added services; for new members or members with low purchase frequency, some entry-level or promotional services will be recommended based on their behavioral characteristics. This personalized recommendation can improve the member's shopping experience and increase the member's satisfaction and loyalty to the company.

[0087] A method for unified management and service optimization of members across platforms, specifically including:

[0088] Collect basic member information, consumption records, and behavioral data from different platforms;

[0089] The basic information of members, consumption records and behavioral data are integrated and processed to obtain a comprehensive member data vector;

[0090] Member value is calculated based on a comprehensive member data vector and a member value assessment formula.

[0091] Members are divided into different tiers based on a comparison between member value and dynamic thresholds;

[0092] By combining membership levels and historical behavior data, a service recommendation matching degree formula is used to calculate the matching degree between members and various services, and personalized services are recommended to members based on the matching degree.

[0093] A computing 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 aforementioned cross-platform unified member management and service optimization method.

[0094] A computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of the aforementioned cross-platform unified member management and service optimization method.

[0095] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A cross-platform unified membership management and service optimization system, characterized in that, include: The member data collection module is used to collect basic member information, consumption records, and behavioral data from different platforms. The data fusion processing module is used to fuse basic member information, consumption records, and behavioral data to obtain a comprehensive member data vector. The Member Value Assessment Module is used to calculate member value based on a comprehensive member data vector and a member value assessment formula. The membership tiering module is used to divide members into different tiers based on a comparison between member value and dynamic thresholds. The service recommendation module combines membership level and historical behavior data, uses a service recommendation matching degree formula to calculate the matching degree between members and various services, and recommends personalized services to members based on the matching degree.

2. The cross-platform unified membership management and service optimization system according to claim 1, characterized in that, The member data collection module includes: The platform interface interface unit is used to establish standardized data interfaces with different platforms; The real-time data capture unit is used to capture changes in member data in real time using streaming data processing technology and transmit it to the data fusion processing module in a timely manner. The data batch collection unit is used to periodically collect member data from the platform in batches at preset time intervals.

3. The cross-platform unified membership management and service optimization system according to claim 2, characterized in that, The data fusion processing module includes: The data cleaning unit is used to clean member basic information, consumption records and behavioral data to obtain cleaned member basic information, consumption records and behavioral data. The data conversion unit is used to convert the cleaned member basic information, consumption records and behavioral data into standardized member basic information, consumption records and behavioral data. The data fusion unit is used to fuse standardized basic member information, consumption records, and behavioral data using dynamic data fusion formulas to obtain a comprehensive member data vector.

4. The cross-platform unified membership management and service optimization system according to claim 3, characterized in that, The membership value assessment formula is as follows: ； in, Indicates member value. This indicates that the member has been active for a period of time. Indicators related to consumption amount within the country Indicators representing members' interactive behavior. This represents the weighting coefficient of indicators related to consumption amount. This represents the weighting coefficient of the interactive behavior indicator.

5. The cross-platform unified membership management and service optimization system according to claim 4, characterized in that, The membership tier module includes: The dynamic threshold adjustment unit is used to dynamically adjust the stratified threshold based on the stratified threshold dynamic adjustment formula; Member segmentation unit, used to divide members into different levels based on the adjusted stratification threshold.

6. The cross-platform unified membership management and service optimization system according to claim 5, characterized in that, The service recommendation module includes: The feature vector construction unit is used to extract key features from member historical behavior data and service information, and to construct member historical behavior feature vectors and service feature vectors. The service recommendation matching degree formula calculation unit is used to combine the member level and the member's historical behavior feature vector, and use the service recommendation matching degree formula to calculate the matching degree between the member and each service feature vector. The recommendation results ranking subunit is used to rank various services based on the matching degree calculation results and recommend the services with the highest matching degree to members.

7. The cross-platform unified membership management and service optimization system according to claim 6, characterized in that, The service recommendation matching degree formula is as follows: ； in, Indicates the service recommendation matching degree. This represents a vector of historical member behavior features. Represents the service feature vector. This indicates the compatibility between membership level and service. This represents the number of dimensions in the feature vector of a member's historical behavior. This indicates the number of metrics related to the compatibility between membership levels and services. This represents the weighting balance coefficient between feature similarity and hierarchical fit.

8. A method for unified management and service optimization of members across platforms, characterized in that, Specifically, it includes: Collect basic member information, consumption records, and behavioral data from different platforms; The basic information of members, consumption records and behavioral data are integrated and processed to obtain a comprehensive member data vector; Member value is calculated based on a comprehensive member data vector and a member value assessment formula. Members are divided into different tiers based on a comparison between member value and dynamic thresholds; By combining membership levels and historical behavior data, a service recommendation matching degree formula is used to calculate the matching degree between members and various services, and personalized services are recommended to members based on the matching degree.

9. A computing device, characterized in that, It 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 cross-platform unified member management and service optimization method of claim 8.

10. A computer-readable storage medium, characterized in that, The storage medium stores a computer program, which, when executed by a processor, implements the steps of the cross-platform unified member management and service optimization method of claim 8.

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