Omnichannel member integration service system

Abstract

An omni-channel member integration service system relates to the field of information management systems and comprises a multi-source data acquisition module, a data cleaning and standardization module, a member data integration and storage module, a member right and interest unified management module, a personalized marketing and service recommendation module and a cross-channel service collaboration module. According to the method, the members are uniformly collected into a system which is powerful in function and easy to operate, so that an enterprise can clearly insight into complete information of the members from a global perspective, including but not limited to consumption habits, purchase frequency, preference categories and the like, and comprehensively grasp portraits of the members, and meanwhile, the system intelligently identifies and clears repeated data, so that the efficiency of the enterprise is improved. By means of the method, manual repeated input and check work is avoided, the work process is optimized, the overall efficiency of enterprise member management work is remarkably improved, and an enterprise can put more energy into targeted member service optimization and marketing activity planning.

Description

Technical Field

[0001] This invention relates to the field of information management systems, and more specifically to an omnichannel membership integration service system. Background Technology

[0002] In today's surging wave of digital commerce, the forms of interaction between businesses and consumers have become unprecedentedly diverse. Online e-commerce platforms, with their extremely convenient shopping processes—orders can be placed with a simple click of the mouse or touch of the screen—along with a dazzling array of products, from trendy apparel to cutting-edge digital products, have successfully attracted a massive number of users. Offline physical stores, with their enthusiastic and professional face-to-face service and the unique advantage of consumers being able to touch and feel the quality of products, bring a distinctive shopping value to users. The rise of mobile applications has taken convenience to the extreme. Whether in a crowded subway or a quiet suburb, consumers can interact with businesses anytime, anywhere, as long as they have a mobile device, completely breaking down the limitations of time and space.

[0003] However, behind this seemingly prosperous multi-channel operation model lies an extremely thorny problem. The membership systems of different channels are fragmented, operating independently and isolated from each other, resembling information islands. Membership data is scattered across various channels: online e-commerce platforms record members' browsing and purchase history, offline physical stores store members' in-store consumption records, and mobile applications hold some of members' personalized preferences. As a result, companies encounter numerous obstacles when trying to gain comprehensive and in-depth insights into member information, understand member needs from multiple dimensions, and implement precise management and marketing.

[0004] For consumers, the services and benefits they receive when switching between different channels vary significantly. Consumers who painstakingly accumulate points on online e-commerce platforms, only to find they cannot redeem them for gifts or discounts at physical stores, are undoubtedly disappointed. Similarly, exclusive offers obtained in physical stores are difficult to redeem online, and this disjointed experience greatly impacts members' shopping mood, thereby weakening their loyalty to the company. Within the current technological framework, despite numerous innovations, a comprehensive system that effectively and seamlessly integrates omnichannel member data has yet to emerge. This makes it impossible to achieve unified management of member information or accurately extract personalized needs from massive amounts of data to provide highly customized and high-quality services. To address these technical problems, a new technical solution is proposed. Summary of the Invention

[0005] To address the problems raised in the background section, an omnichannel membership integration service system is proposed to solve the problems of fragmented membership systems, inconsistent data, and lack of personalized services, thereby improving the efficiency of enterprise membership management and consumer experience.

[0006] This invention proposes an omnichannel membership integration service system, including a multi-source data acquisition module, a data cleaning and standardization module, a membership data integration and storage module, a unified membership rights management module, a personalized marketing and service recommendation module, and a cross-channel service collaboration module;

[0007] The multi-source data acquisition module is used to collect member data from multiple channels in real time. Let the total number of channels be n, and the amount of data collected by the i-th channel at time t be D. it The total amount of data collected by this module at time t. Meanwhile, to measure the timeliness of data collection, a data collection latency rate is defined. Where t i，acquire For the actual completion time of data collection from the first i-th channel, this module optimizes the collection strategy to make L... t Maintain at the set threshold L threshold one time;

[0008] The data cleaning and standardization module cleans and standardizes the collected raw data. In the data cleaning process, let the original data set be S, and the data set cleaned according to the anomaly identification rule R be S'. This indicates that the data χ does not satisfy the outlier rule R. For the outlier rule R, the statistically based 3σ principle is used: if the data χ satisfies |χ-μ|>3σ, it is considered outlier, where μ is the data mean and σ is the standard deviation. During standardization, for numerical data y, the following is used: Normalization is performed, where Y is the set of values ​​for this type of data, and min(Y) and max(Y) are the minimum and maximum values ​​in Y, respectively. For categorical data, one-hot encoding is used. Let the number of categories be k, and the category set be C = {c1, c2, ..., c3}. k For data χ∈C, its one-hot encoded vector χ one -hot satisfied

[0009] The member data integration and storage module uses algorithms to integrate data and establish a unified member database. Let the member datasets from different channels before integration be M1, M2, ..., M n A unified member database M is obtained through integration algorithm A, i.e., M = A(M1, M2, ..., M). nAlgorithm A integrates a graph database to construct a member relationship graph. Let the set of member nodes be V and the set of relationship edges be E. For any two member nodes v i v j If there exists a similarity in consumption behavior for ∈V, then add an edge e in E. ij Similarity is calculated using cosine similarity. Let member v... i and v j The consumption vectors are respectively and Then similarity when When adding edge e (θ is the set similarity threshold) ij This module adopts a distributed storage architecture, with m storage nodes, N total data blocks, and d data blocks. k Stored on node S j(k) The storage node is determined by the consistent hashing algorithm H, i.e., j(k) = H(d). k )modm;

[0010] The unified membership benefits management module centrally sets and manages membership benefits. Let l be the number of membership levels, and B be the set of benefits for the j-th membership level. j If member u's level is r(u), then member u can enjoy the following benefits: B. r(u) When adjusting equity, let the set of equity before adjustment be B. old The adjusted set of rights is B. new This is achieved through the equity adjustment function f, i.e., B new =f(B old p1, p2, ..., p s ), where p1, p2, ... p s To adjust parameters, including factors such as region, time period, and market activities;

[0011] The personalized marketing and service recommendation module provides personalized content based on data analysis and uses a collaborative filtering algorithm for recommendations. Let R be the member-product rating matrix, where R... ij Let represent the rating of member i for product j. For the target member u, we find the set of neighboring members N(u) that are similar to it. The similarity of neighboring members is calculated using the Pearson correlation coefficient. Let the rating vectors of members i and j be respectively... and Then Pearson correlation coefficient in and Let N(u) be the average ratings of members i and j, respectively, and select members with a similarity of approximately a threshold T. The predicted rating of the target member for the unrated item v is... Products are ranked and recommended based on predicted ratings. Simultaneously, considering real-time feedback optimization, let P be the probability of a member clicking on the recommended content. click The probability of purchasing is P. buy Based on the Bayesian update formula, the recommendation model parameters θ are updated after receiving member feedback. Let the prior probability distribution be P(θ), and the likelihood function be P(D / θ), where D is the feedback data and the feedback probability distribution is... Adjust the recommendation strategy based on posterior probability;

[0012] The cross-channel service collaboration module enables seamless service switching for members across different channels. Let c be the number of channel switches, s be the number of successful switches, and the service switching success rate be... By optimizing service processes and data synchronization mechanisms, P success Maintain the target value P target The above describes the establishment of a unified customer service work order system, assuming a total number of work orders of W and an average processing time of T. avg Work order processing efficiency Improve E by optimizing the work order allocation algorithm and customer service response mechanism.

[0013] As a preferred technical solution, the multi-source data acquisition module uses an advanced data encryption algorithm, AES (Advanced Encryption Standard), to encrypt the acquired raw data, converting sensitive information into ciphertext. Simultaneously, it employs Transport Layer Security (TLS) technology to establish a secure channel throughout the data transmission process, preventing data theft and tampering during transmission, thus ensuring data security. Let the encrypted ciphertext be C, the original data be P, the encryption key be K, and the AES encryption method be E. The encryption process can be represented as C = E(P, K). In the TLS secure transmission process, let the bit error rate during data transmission be BER, and the total number of bits transmitted be N. total The number of error bits is N error ,but The TLS protocol and related security mechanisms are used to ensure that the BER is below the acceptable bit error rate threshold. threshold This is to ensure the accuracy and integrity of data transmission.

[0014] As a preferred technical solution, the member data integration and storage module adopts an advanced distributed storage architecture. By distributing data across multiple nodes, it can effectively improve the reliability and scalability of data storage. When faced with massive amounts of member data, this architecture can not only ensure efficient data storage, but also significantly improve data read and write speed through parallel processing mechanisms, thereby providing solid data storage support for the smooth operation of the omnichannel member integration service system.

[0015] As a preferred technical solution, the personalized marketing and service recommendation module relies on advanced big data analysis technology to deeply mine and carefully organize massive amounts of member data. Using cutting-edge artificial intelligence algorithms, it meticulously segments members from multiple dimensions such as consumption behavior, preferences, purchase frequency, consumption time, and geographical distribution. Through precise modeling, it comprehensively considers every behavioral detail and characteristic information of members to generate a unique and highly accurate profile for each member, providing a solid data foundation for subsequent personalized marketing and service recommendations.

[0016] As a preferred technical solution, the cross-channel service collaboration module utilizes an advanced information technology architecture to build a unified customer service ticket system. This system can comprehensively integrate customer service requests from various channels, unifying service needs generated from different channels into a standardized ticket processing flow. Through intelligent routing algorithms, based on multiple factors such as the type of customer problem, urgency, and customer history, the system accurately assigns tickets to the most suitable customer service personnel, ensuring service continuity and providing customers with a seamless, efficient, and consistent service experience.

[0017] As a preferred technical solution, the data cleaning and standardization module is equipped with advanced intelligent algorithms and has a keen ability to identify abnormal data patterns. This module can deeply mine historical data, establish accurate data analysis models based on its inherent laws, automatically mark data points that deviate from the normal range, and, based on the marking, use preset rules and intelligent correction strategies to quickly and accurately correct abnormal data points, ensuring the accuracy and reliability of the data.

[0018] As a preferred technical solution, the unified membership benefits management module can flexibly and dynamically adjust membership benefits rules based on the consumption characteristics of different regions, market fluctuations over time, and the actual needs of various marketing activities. Whether it's the percentage of membership points earned or the discount on specific products, it can accurately adapt to the current market situation. Furthermore, this module has efficient information push capabilities. Once there are changes to the benefits rules, the adjustment information can be pushed to relevant members in real time and accurately through various channels such as SMS, in-site messages, and app push notifications, ensuring that members are aware of the latest benefits updates immediately and improving the membership service experience.

[0019] As a preferred technical solution, the multi-source data acquisition module has excellent adaptive capabilities, enabling it to sensitively and automatically perceive and adapt to the dynamic changes of data interfaces from different channels. With the help of carefully preset interface adaptation templates, the module has been precisely designed to match various common data interface types and has a built-in intelligent update mechanism. This mechanism can monitor changes in data interfaces in real time. Once an interface update or adjustment is detected, the automated process can be quickly started without manual intervention, continuously and stably collecting data, ensuring the efficiency and consistency of data acquisition.

[0020] As a preferred technical solution, the personalized marketing and service recommendation module possesses powerful real-time feedback optimization capabilities. It can keenly capture members' immediate responses to pushed content. Through tracking technology, the system accurately tracks every interaction between members and pushed content. Whether it's a click on a product details page, a successful purchase, or even simply ignoring the content, the system treats it all as key information. The system incorporates advanced machine learning algorithms to deeply analyze this feedback data, uncovering member preferences, behavioral patterns, and potential needs. Through continuous data analysis and model optimization, the system can dynamically adjust subsequent recommendation strategies, accurately filtering and pushing information that matches members' interests and needs from a vast amount of content, effectively improving marketing conversion rates and service satisfaction.

[0021] As a preferred technical solution, the system boasts robust multilingual support, featuring built-in global language packs covering dozens of languages ​​including English, Chinese, Spanish, French, and Arabic. Through precise IP location technology, the system intelligently identifies the member's location and automatically matches the corresponding system interface language. Furthermore, members can customize their preferred language in their personal settings, seamlessly switching between the system interface's interactive language and the language used to display membership benefits details and marketing promotional content. This provides global members with a personalized, convenient, and language-barrier-free high-quality service experience.

[0022] Compared with the prior art, the beneficial effects of the present invention are:

[0023] 1. Improve enterprise membership management efficiency: Through an innovative technical architecture, the omnichannel membership integration service system can automatically capture and aggregate member data scattered across various platforms such as online e-commerce platforms, offline store POS systems, and social media interaction channels, unifying it into a powerful and easy-to-use system. This integration allows enterprises to gain a clear and comprehensive understanding of member information from a holistic perspective, including but not limited to consumption habits, purchase frequency, and preferred product categories, thus comprehensively grasping member profiles. At the same time, the system intelligently identifies and removes duplicate data, avoiding repetitive manual data entry and verification, optimizing workflows, and significantly improving the overall efficiency of enterprise membership management. This allows enterprises to devote more energy to targeted membership service optimization and marketing campaign planning.

[0024] 2. Enhance member experience and loyalty. Through this system, members can enjoy the same benefits across all channels, from exclusive discounts and double points on online platforms to priority service and customized gifts in offline stores. With the help of big data analysis, the system can accurately understand member preferences and provide personalized services, such as pushing product recommendations that match their interests online and providing dedicated shopping guides offline based on past purchase records. Whether browsing online or shopping offline, members can get a consistently high-quality experience, greatly improving member satisfaction and encouraging them to actively build a long-term and stable relationship with the brand, thus significantly enhancing loyalty.

[0025] 3. Innovative personalized marketing model: Leveraging advanced big data analytics, this approach deeply mines members' consumption behavior, preferences, and historical data. Combined with artificial intelligence algorithms, it accurately identifies members' needs. Compared to traditional broad-based marketing and indiscriminate information delivery, this method precisely targets specific customer groups. Businesses can tailor exclusive marketing campaigns to the unique characteristics of different groups, such as high-value members, active members, and potential members. For example, they can push the latest trendy product information to fashionista members and recommend sports equipment promotions to fitness enthusiasts, greatly improving the participation and conversion rate of marketing activities, thereby significantly enhancing marketing effectiveness and return on investment.

[0026] 4. Cross-channel service synergy advantages: Through advanced system architecture and intelligent algorithms, the omnichannel membership integrated service system can efficiently break down data and business barriers between different channels. Whether members place orders online through the official website or consult at offline stores, they can enjoy a consistent and seamless service experience. From order tracking and after-sales service to personalized recommendations, everything is seamlessly integrated. This not only greatly improves member satisfaction but also effectively enhances the company's brand image, helping the company stand out in fierce market competition. Detailed Implementation

[0027] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] A multi-channel membership integration service system includes a multi-source data collection module, a data cleaning and standardization module, a membership data integration and storage module, a unified membership rights management module, a personalized marketing and service recommendation module, and a cross-channel service collaboration module;

[0029] The multi-source data acquisition module is used to collect member data from multiple channels in real time. Let the total number of channels be n, and the amount of data collected by the i-th channel at time t be D. it The total amount of data collected by this module at time t. Meanwhile, to measure the timeliness of data collection, a data collection latency rate is defined. Where t i，acquire For the actual completion time of data collection from the first i-th channel, this module optimizes the collection strategy to make L... t Maintain at the set threshold L thresholdThe multi-source data acquisition module, through its layered architecture design, dynamic interface adaptation, real-time monitoring, and security encryption technology, achieves the core requirements of real-time acquisition, latency optimization, adaptive interface changes, and data security. Its technology toolchain covers the entire process from data source access to data transmission, combining distributed computing and message queue technology to ensure the efficiency and reliability of large-scale multi-channel data acquisition. The multi-source data acquisition module is crucial in the omnichannel membership integration service system, requiring real-time and accurate data acquisition from various data sources. The following details the technical tools and frameworks it may use. Apache Nutch, a highly scalable and configurable open-source web crawler, is suitable for collecting web page data from the internet. It can automatically crawl page content from specified websites according to preset rules and perform preliminary data parsing and processing. In the multi-source data acquisition module, Apache Nutch can be an effective tool for collecting publicly available data from the internet, such as industry news and competitor information. Apache Flume is a distributed, reliable, and highly available system used for efficiently collecting, aggregating, and moving large amounts of log data. Flume features a flexible architecture that allows for real-time data acquisition and transmission by configuring different data sources (such as files, network sockets, etc.) and data receivers (such as HDFS, HBase, etc.). In the multi-source data acquisition module, for log data acquisition, Selenium, a tool for automating browser operations, can simulate various user behaviors in a browser, such as clicking, typing, and scrolling. Selenium can be used to collect data that requires user interaction, such as website data requiring login or dynamically loaded webpage content. The tools used include Octoparse, a powerful webpage data acquisition tool with a visual interface that allows data acquisition tasks to be completed without writing code. Octoparse supports various data acquisition methods, such as automatic acquisition and loop acquisition, and can quickly and accurately collect various data from webpages; and DataMiner, a professional data acquisition and analysis tool that supports data acquisition from multiple data sources (such as databases, files, webpages, etc.) and provides rich data cleaning and transformation functions.DataMiner can perform real-time analysis and processing of collected data, generating visual reports and charts; JDBC (Java Database Connectivity): This is the standard API in the Java language for connecting to databases. JDBC allows for easy data collection from various relational databases (such as MySQL, Oracle, SQL Server, etc.); ODBC (Open Database Connectivity): It is an open database connectivity standard that supports multiple operating systems and database management systems. ODBC allows for data collection from different types of databases through a unified interface.

[0030] The data cleaning and standardization module cleans and standardizes the collected raw data. In the data cleaning process, let the original data set be S, and the data set cleaned according to the anomaly identification rule R be S'. This indicates that the data χ does not satisfy the outlier rule R. For the outlier rule R, the statistically based 3σ principle is used: if the data χ satisfies |χ-μ|>3σ, it is considered outlier, where μ is the data mean and σ is the standard deviation. During standardization, for numerical data y, the following is used: Normalization is performed, where Y is the set of values ​​for this type of data, and min(Y) and max(Y) are the minimum and maximum values ​​in Y, respectively. For categorical data, one-hot encoding is used. Let the number of categories be k, and the category set be C = {c1, c2, ..., c3}. k For data χ∈C, its one-hot encoded vector χ one -hot satisfied

[0031] For handling abnormal data, the data cleaning methods employed include: unique identifier matching: using unique identifier fields such as member ID, mobile phone number, and email address, GROUP BY+COUNT is used to detect duplicate records; fuzzy matching: for data without clear unique identifiers (such as member name + phone number manually entered offline), the Levenshtein Distance algorithm is used to match similar records, retaining the latest or most complete entries; deletion: fields with high missing rates (e.g., exceeding 80%) are directly deleted; for records with low missing rates, if they are key fields (e.g., member registration time), the entire record is deleted. The cleaning and preprocessing process after multi-source data collection focuses on "data quality improvement," addressing inconsistencies, errors, and format differences in multi-channel data through steps such as deduplication, anomaly handling, and standardization. This process strictly adheres to the technical methods defined in the documentation (such as the 3σ principle, Min-Max normalization, and one-hot encoding), and combines them with automated tools for efficient processing, laying the foundation for the integration and analysis of omnichannel member data.

[0032] The member data integration and storage module uses algorithms to integrate data and establish a unified member database. Let the member datasets from different channels before integration be M1, M2, ..., M n A unified member database M is obtained through integration algorithm A, i.e., M = A(M1, M2, ..., M). n Algorithm A integrates a graph database to construct a member relationship graph. Let the set of member nodes be V and the set of relationship edges be E. For any two member nodes v i v j If there exists a similarity in consumption behavior for ∈V, then add an edge e in E. ij Similarity is calculated using cosine similarity. Let member v... i and v j The consumption vectors are respectively and Then similarity when When adding edge e (θ is the set similarity threshold) ij This module adopts a distributed storage architecture, with m storage nodes, N total data blocks, and d data blocks. k Stored on node S j(k) The storage node is determined by the consistent hashing algorithm H, i.e., j(k) = H(d). kThe member data integration and storage module is a crucial part of the omnichannel member integration service system. It needs to integrate and efficiently store the data cleaned and preprocessed by the multi-source data acquisition module for subsequent analysis and application. Relational databases, such as MySQL, are open-source relational database management systems characterized by high performance, reliability, and ease of use. In the member data integration and storage module, MySQL can be used to store structured member data, such as basic member information (name, gender, date of birth, etc.) and member consumption records (consumption time, consumption amount, consumption location, etc.). By creating appropriate table structures and indexes, fast querying and statistical analysis of member data can be achieved. Oracle is a powerful commercial relational database management system suitable for large-scale enterprise applications. Oracle provides rich database management functions, such as data backup and recovery, data security management, and distributed database management. In the member data integration and storage module, if the enterprise has high requirements for data security, reliability, and performance, it is suitable. Non-relational databases, such as MongoDB, are open-source document-oriented databases belonging to the NoSQL database category. MongoDB stores data in JSON format documents, featuring a flexible data model and high scalability. In the member data integration and storage module, MongoDB can be used to store semi-structured or unstructured member data, such as member comments, feedback, and behavior logs. By using MongoDB's aggregation framework, complex analysis and processing of this data can be performed. Redis, a high-performance key-value store database, is commonly used in caching, message queues, and real-time data analysis scenarios. In the member data integration and storage module, Redis can act as a caching layer to store frequently accessed member data, such as member points and levels. Using Redis's caching mechanism can improve system response speed and performance.

[0033] The unified membership benefits management module centrally sets and manages membership benefits. Let l be the number of membership levels, and B be the set of benefits for the j-th membership level. j If member u's level is r(u), then member u can enjoy the following benefits: B. r(u) When adjusting equity, let the set of equity before adjustment be B. old The adjusted set of rights is B. new This is achieved through the equity adjustment function f, i.e., B new =f(B old p1, p2, ..., p s ), where p1, p2, ... p s To adjust parameters, including factors such as region, time period, and market activities;

[0034] The personalized marketing and service recommendation module provides personalized content based on data analysis and uses a collaborative filtering algorithm for recommendations. Let R be the member-product rating matrix, where R... ij Let represent the rating of member i for product j. For the target member u, we find the set of neighboring members N(u) that are similar to it. The similarity of neighboring members is calculated using the Pearson correlation coefficient. Let the rating vectors of members i and j be respectively... and Then Pearson correlation coefficient in and Let N(u) be the average ratings of members i and j, respectively, and select members with a similarity of approximately a threshold T. The predicted rating of the target member for the unrated item v is... Products are ranked and recommended based on predicted ratings. Simultaneously, considering real-time feedback optimization, let P be the probability of a member clicking on the recommended content. click The probability of purchasing is P. buy Based on the Bayesian update formula, the recommendation model parameters θ are updated after receiving member feedback. Let the prior probability distribution be P(θ), and the likelihood function be P(D / θ), where D is the feedback data and the feedback probability distribution is... Adjust the recommendation strategy based on posterior probability;

[0035] The cross-channel service collaboration module enables seamless service switching for members across different channels. Let c be the number of channel switches, s be the number of successful switches, and the service switching success rate be... By optimizing service processes and data synchronization mechanisms, P success Maintain the target value P target The above describes the establishment of a unified customer service work order system, assuming a total number of work orders of W and an average processing time of T. avg Work order processing efficiency Improve E by optimizing the work order allocation algorithm and customer service response mechanism.

[0036] As a preferred technical solution, the multi-source data acquisition module uses an advanced data encryption algorithm, AES (Advanced Encryption Standard), to encrypt the acquired raw data, converting sensitive information into ciphertext. Simultaneously, it employs Transport Layer Security (TLS) technology to establish a secure channel throughout the data transmission process, preventing data theft and tampering during transmission, thus ensuring data security. Let the encrypted ciphertext be C, the original data be P, the encryption key be K, and the AES encryption method be E. The encryption process can be represented as C = E(P, K). In the TLS secure transmission process, let the bit error rate during data transmission be BER, and the total number of bits transmitted be N. total The number of error bits is Nerror ,but The TLS protocol and related security mechanisms are used to ensure that the BER is below the acceptable bit error rate threshold. threshold This is to ensure the accuracy and integrity of data transmission.

[0037] As a preferred technical solution, the member data integration and storage module adopts an advanced distributed storage architecture, with a total number of storage nodes of n and a storage capacity of C for each node. i (i = 1, 2, ..., n), then the total storage capacity of the system Regarding data storage reliability, a redundant storage strategy is adopted. Let the redundancy factor be r, the total number of data blocks be N, and the number of nodes f that can tolerate simultaneous failures satisfy the formula... This redundancy mechanism ensures no data loss when some nodes fail, greatly improving data storage reliability. By distributing data across multiple nodes, it effectively enhances both reliability and scalability. When dealing with massive amounts of member data, this architecture not only ensures efficient data storage but also significantly improves data read / write speeds through parallel processing, thus providing robust data storage support for the smooth operation of the omnichannel member integration service system. When handling massive amounts of member data, this architecture significantly improves data read / write speeds through parallel processing. Let the number of data reading tasks be R, and the number of parallel threads allocated to each task be p. i (i = 1, 2, ..., R), the average time to read a single data block is t. read The total read time Where d i Let be the amount of data for the i-th read task.

[0038] As a preferred technical solution, the personalized marketing and service recommendation module relies on advanced big data analysis technology to deeply mine and carefully organize massive amounts of member data. Using cutting-edge artificial intelligence algorithms, it meticulously segments members from multiple dimensions such as consumption behavior, preferences, purchase frequency, consumption time, and geographical distribution. Through precise modeling, it comprehensively considers every behavioral detail and characteristic information of members to generate a unique and highly accurate profile for each member, providing a solid data foundation for subsequent personalized marketing and service recommendations.

[0039] As a preferred technical solution, the cross-channel service collaboration module utilizes an advanced information technology architecture to build a unified customer service ticket system. This system can comprehensively integrate customer service requests from various channels, unifying service needs generated from different channels into a standardized ticket processing flow. Through intelligent routing algorithms, based on multiple factors such as the type of customer problem, urgency, and customer history, the system accurately assigns tickets to the most suitable customer service personnel, ensuring service continuity and providing customers with a seamless, efficient, and consistent service experience.

[0040] As a preferred technical solution, the data cleaning and standardization module is equipped with advanced intelligent algorithms and has a keen ability to identify abnormal data patterns. This module can deeply mine historical data, establish accurate data analysis models based on its inherent laws, automatically mark data points that deviate from the normal range, and, based on the marking, use preset rules and intelligent correction strategies to quickly and accurately correct abnormal data points, ensuring the accuracy and reliability of the data.

[0041] As a preferred technical solution, the unified membership benefits management module can flexibly and dynamically adjust membership benefits rules based on the consumption characteristics of different regions, market fluctuations over time, and the actual needs of various marketing activities. Whether it's the percentage of membership points earned or the discount on specific products, it can accurately adapt to the current market situation. Furthermore, this module has efficient information push capabilities. Once there are changes to the benefits rules, the adjustment information can be pushed to relevant members in real time and accurately through various channels such as SMS, in-site messages, and app push notifications, ensuring that members are aware of the latest benefits updates immediately and improving the membership service experience.

[0042] As a preferred technical solution, the multi-source data acquisition module has excellent adaptive capabilities, enabling it to sensitively and automatically perceive and adapt to the dynamic changes of data interfaces from different channels. With the help of carefully preset interface adaptation templates, the module has been precisely designed to match various common data interface types and has a built-in intelligent update mechanism. This mechanism can monitor changes in data interfaces in real time. Once an interface update or adjustment is detected, the automated process can be quickly started without manual intervention, continuously and stably collecting data, ensuring the efficiency and consistency of data acquisition.

[0043] As a preferred technical solution, the personalized marketing and service recommendation module possesses powerful real-time feedback optimization capabilities. It can keenly capture members' immediate responses to pushed content. Through tracking technology, the system accurately tracks every interaction between members and pushed content. Whether it's a click on a product details page, a successful purchase, or even simply ignoring the content, the system treats it all as key information. The system incorporates advanced machine learning algorithms to deeply analyze this feedback data, uncovering member preferences, behavioral patterns, and potential needs. Through continuous data analysis and model optimization, the system can dynamically adjust subsequent recommendation strategies, accurately filtering and pushing information that matches members' interests and needs from a vast amount of content, effectively improving marketing conversion rates and service satisfaction.

[0044] As a preferred technical solution, the system boasts robust multilingual support, featuring built-in global language packs covering dozens of languages ​​including English, Chinese, Spanish, French, and Arabic. Through precise IP location technology, the system intelligently identifies the member's location and automatically matches the corresponding system interface language. Furthermore, members can customize their preferred language in their personal settings, seamlessly switching between the system interface's interactive language and the language used to display membership benefits details and marketing promotional content. This provides global members with a personalized, convenient, and language-barrier-free high-quality service experience.

[0045] Example 1

[0046] Bailian Group: Building "iBailian" to Achieve Retail Transformation

[0047] As part of its integration efforts, Bailian Group has consolidated its original 26 membership systems into the Bailian Tong membership system, achieving "one member account, one membership tier standard, and integrated membership points," enabling points to be deposited and redeemed across all platforms. Through the "iBailian" platform, leveraging "+Internet" technology, it has created "delivery to home" and "cloud store" products, breaking down the physical limitations of brick-and-mortar stores and integrating online and offline businesses.

[0048] Application Results: Bailian's membership has reached 25 million, with 2.15 million monthly active users. Members can enjoy digital services such as smart parking, in-store Wi-Fi, and e-invoices at all its stores. In the digital transformation of the Quyang store, through targeted marketing, the offline conversion rate reached 12%, and the online conversion rate reached 18%. The number of followers on Bailian Quyang Shopping Center's WeChat official account doubled, the daily traffic of its mini-program increased 2.4 times, and the number of orders increased tenfold. The retention rate of the member check-in channel reached 53%.

[0049] Example 2

[0050] A high-end domestic beauty brand: Boosting sales through an omnichannel membership system

[0051] Through this integrated approach, the brand launched an omnichannel membership system through Bingkun, connecting membership data from over ten platforms including Tmall, Douyin, WeChat mini-programs, and offline retail stores. Utilizing the One ID feature, it enables members to accumulate points and enjoy benefits across all channels. Leveraging the system's tagging engine, marketing automation engine, and data insight modules, it conducts targeted recommendations and in-depth analysis of marketing campaign data.

[0052] In terms of application effectiveness, taking consumer Nana's case as an example, after registering as a member on Douyin, she received a new user coupon and a free makeup service, and then completed the purchase after experiencing the service at an offline counter. She subsequently received targeted push notifications from the brand on occasions such as her birthday, and made multiple repeat purchases. During the Double 11 shopping festival, several of the brand's best-selling products ranked among the top sellers, and overall sales were within the top 10 on the Douyin platform.

[0053] Example 3

[0054] Branded clothing stores with sponsorship support: Achieving integrated online and offline membership operations

[0055] The integrated approach utilizes Youzan's unified membership management system to connect offline POS system data with the mini-program store's data, enabling real-time data synchronization through data interfaces. Coupons and points are shared, allowing users to accumulate and use points and coupons for both online and offline purchases. Youzan's micro-store data analytics tools are leveraged to conduct targeted marketing based on members' historical spending records.

[0056] The application has proven effective in attracting a large number of users to its membership system and increasing sales through personalized marketing. It has achieved seamless integration of online and offline memberships, enhancing the user experience, and leveraging the mini-program for real-time interaction and feedback, thereby improving service quality.

[0057] The achieved results are as follows: 1. Improve the efficiency of enterprise membership management. Through an innovative technical architecture, the omnichannel membership integration service system can automatically capture and aggregate member data scattered across various locations such as online e-commerce platforms, offline store POS systems, and social media interaction channels, unifying them into a powerful and easy-to-use system. This integration allows enterprises to clearly understand the complete information of members from a global perspective, including but not limited to consumption habits, purchase frequency, and preferred product categories, and to fully grasp member profiles. At the same time, the system intelligently identifies and removes duplicate data, avoiding repetitive manual data entry and verification, optimizing workflows, and significantly improving the overall efficiency of enterprise membership management. This allows enterprises to devote more energy to targeted membership service optimization and marketing campaign planning.

[0058] 2. Enhance member experience and loyalty. Through this system, members can enjoy the same benefits across all channels, from exclusive discounts and double points on online platforms to priority service and customized gifts in offline stores. With the help of big data analysis, the system can accurately understand member preferences and provide personalized services, such as pushing product recommendations that match their interests online and providing dedicated shopping guides offline based on past purchase records. Whether browsing online or shopping offline, members can get a consistently high-quality experience, greatly improving member satisfaction and encouraging them to actively build a long-term and stable relationship with the brand, thus significantly enhancing loyalty.

[0059] 3. Innovative personalized marketing model: Leveraging advanced big data analytics, this approach deeply mines members' consumption behavior, preferences, and historical data. Combined with artificial intelligence algorithms, it accurately identifies members' needs. Compared to traditional broad-based marketing and indiscriminate information delivery, this method precisely targets specific customer groups. Businesses can tailor exclusive marketing campaigns to the unique characteristics of different groups, such as high-value members, active members, and potential members. For example, they can push the latest trendy product information to fashionista members and recommend sports equipment promotions to fitness enthusiasts, greatly improving the participation and conversion rate of marketing activities, thereby significantly enhancing marketing effectiveness and return on investment.

[0060] 4. Cross-channel service synergy advantages: Through advanced system architecture and intelligent algorithms, the omnichannel membership integrated service system can efficiently break down data and business barriers between different channels. Whether members place orders online through the official website or consult at offline stores, they can enjoy a consistent and seamless service experience. From order tracking and after-sales service to personalized recommendations, everything is seamlessly integrated. This not only greatly improves member satisfaction but also effectively enhances the company's brand image, helping the company stand out in fierce market competition.

[0061] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A multi-channel membership integration service system, characterized in that, It includes a multi-source data acquisition module, a data cleaning and standardization module, a member data integration and storage module, a unified member rights management module, a personalized marketing and service recommendation module, and a cross-channel service collaboration module; The multi-source data acquisition module is used to collect member data from multiple channels in real time. Let the total number of channels be n, and the amount of data collected by the i-th channel at time t be D. it The total amount of data collected by this module at time t. Meanwhile, to measure the timeliness of data collection, a data collection latency rate is defined. Where t i，acquire For the actual completion time of data collection from the first i-th channel, this module optimizes the collection strategy to make L... t Maintain at the set threshold L threshold one time; The data cleaning and standardization module cleans and standardizes the collected raw data. In the data cleaning process, let the original data set be S, and the data set cleaned according to the anomaly identification rule R be S'. This indicates that the data χ does not satisfy the outlier rule R. For the outlier rule R, the statistically based 3σ principle is used: if the data χ satisfies |χ-μ|>3σ, it is considered outlier, where μ is the data mean and σ is the standard deviation. During standardization, for numerical data y, the following is used: Normalization is performed, where Y is the set of values ​​for this type of data, and min(Y) and max(Y) are the minimum and maximum values ​​in Y, respectively. For categorical data, one-hot encoding is used. Let the number of categories be k, and the category set be C = {c1, c2, ..., c3}. k For data χ∈C, its one-hot encoded vector χ one -hot satisfied The member data integration and storage module uses algorithms to integrate data and establish a unified member database. Let the member datasets from different channels before integration be M1, M2, ..., M n A unified member database M is obtained through integration algorithm A, i.e., M = A(M1, M2, ..., M). n Algorithm A integrates a graph database to construct a member relationship graph. Let the set of member nodes be V and the set of relationship edges be E. For any two member nodes v i v j If there exists a similarity in consumption behavior for ∈V, then add an edge e in E. ij Similarity is calculated using cosine similarity. Let member v... i and v j The consumption vectors are respectively and Then similarity when When adding edge e (θ is the set similarity threshold) ij This module adopts a distributed storage architecture, with m storage nodes, N total data blocks, and d data blocks. k Stored on node S j(k) The storage node is determined by the consistent hashing algorithm H, i.e., j(k) = H(d). k )modm; The unified membership benefits management module centrally sets and manages membership benefits. Let l be the number of membership levels, and B be the set of benefits for the j-th membership level. j If member u's level is r(u), then member u can enjoy the following benefits: B. r(u) When adjusting equity, let the set of equity before adjustment be B. old The adjusted set of rights is B. new This is achieved through the equity adjustment function f, i.e., B new =f(B old p1, p2, ..., p s ), where p1, p2, ... p s To adjust parameters, including factors such as region, time period, and market activities; The personalized marketing and service recommendation module provides personalized content based on data analysis and uses a collaborative filtering algorithm for recommendations. Let R be the member-product rating matrix, where R... ij Let represent the rating of member i for product j. For the target member u, we find the set of neighboring members N(u) that are similar to it. The similarity of neighboring members is calculated using the Pearson correlation coefficient. Let the rating vectors of members i and j be respectively... and Then Pearson correlation coefficient in and Let N(u) be the average ratings of members i and j, respectively, and select members with a similarity of approximately a threshold T. The predicted rating of the target member for the unrated item v is... Products are ranked and recommended based on predicted ratings. Simultaneously, considering real-time feedback optimization, let P be the probability of a member clicking on the recommended content. click The probability of purchasing is P. buy Based on the Bayesian update formula, the recommendation model parameters θ are updated after receiving member feedback. Let the prior probability distribution be P(θ), and the likelihood function be P(D / θ), where D is the feedback data and the feedback probability distribution is... Adjust the recommendation strategy based on posterior probability; The cross-channel service collaboration module enables seamless service switching for members across different channels. Let c be the number of channel switches, s be the number of successful switches, and the service switching success rate be... By optimizing service processes and data synchronization mechanisms, P success Maintain the target value P target The above describes the establishment of a unified customer service work order system, assuming a total number of work orders of W and an average processing time of T. avg Work order processing efficiency Improve E by optimizing the work order allocation algorithm and customer service response mechanism.

2. The omnichannel membership integration service system according to claim 1, characterized in that: The multi-source data acquisition module uses an advanced data encryption algorithm, AES (Advanced Encryption Standard), to encrypt the acquired raw data, converting sensitive information into ciphertext. Simultaneously, it employs Transport Layer Security (TLS) technology to establish a secure channel throughout the data transmission process, preventing data theft and tampering during transmission and ensuring data security. Let the encrypted ciphertext be C, the original data be P, the encryption key be K, and the AES encryption method be E. The encryption process can be represented as C = E(P, K). In TLS secure transmission, let the bit error rate during data transmission be BER, and the total number of bits transmitted be N. total The number of error bits is N error ,but The TLS protocol and related security mechanisms are used to ensure that the BER is below the acceptable bit error rate threshold. threshold This is to ensure the accuracy and integrity of data transmission.

3. The omnichannel membership integration service system according to claim 1, characterized in that, The member data integration and storage module adopts an advanced distributed storage architecture. By distributing data across multiple nodes, it can effectively improve the reliability and scalability of data storage. When faced with massive amounts of member data, this architecture can not only ensure efficient data storage, but also significantly improve data read and write speed through parallel processing mechanisms, thereby providing solid data storage support for the smooth operation of the omnichannel member integration service system.

4. The omnichannel membership integration service system according to claim 1, characterized in that, The personalized marketing and service recommendation module relies on advanced big data analytics to deeply mine and meticulously organize massive amounts of member data. Using cutting-edge artificial intelligence algorithms, it meticulously segments members from multiple dimensions, including consumption behavior, preferences, purchase frequency, consumption time, and geographical distribution. Through precise modeling, it comprehensively considers every behavioral detail and characteristic information of members to generate a unique and highly accurate profile for each member, providing a solid data foundation for subsequent personalized marketing and service recommendations.

5. The omnichannel membership integration service system according to claim 1, characterized in that, The cross-channel service collaboration module, as described above, leverages an advanced information technology architecture to build a unified customer service ticketing system. This system can comprehensively integrate customer service requests from various channels, unifying service needs generated from different channels into a standardized ticketing process. Through intelligent routing algorithms, based on multiple factors such as the type of customer problem, urgency, and customer history, the system accurately assigns tickets to the most suitable customer service personnel, ensuring service continuity and providing customers with a seamless, efficient, and consistent service experience.

6. The omnichannel membership integration service system according to claim 1, characterized in that, The data cleaning and standardization module is equipped with advanced intelligent algorithms and has a keen ability to identify abnormal data patterns. This module can deeply mine historical data, establish accurate data analysis models based on its inherent laws, automatically mark data points that deviate from the normal range, and, based on the marking, use preset rules and intelligent correction strategies to quickly and accurately correct abnormal data points, ensuring the accuracy and reliability of the data.

7. The omnichannel membership integration service system according to claim 1, characterized in that, The unified membership benefits management module can flexibly and dynamically adjust membership benefits rules based on the consumption characteristics of different regions, market fluctuations over different time periods, and the actual needs of various marketing activities. Whether it's the percentage of points earned or the discount on specific products, it can accurately adapt to the current market situation. Furthermore, this module has efficient information push capabilities. Once there are changes to the benefits rules, the adjustment information can be pushed to relevant members in real time and accurately through multiple channels such as SMS, in-site messages, and app push notifications, ensuring that members are aware of the latest benefits updates immediately and improving the membership service experience.

8. The omnichannel membership integration service system according to claim 1, characterized in that, The multi-source data acquisition module possesses excellent adaptive capabilities, enabling it to sensitively and automatically perceive and adapt to dynamic changes in data interfaces from different channels. Utilizing carefully pre-designed interface adaptation templates, the module is precisely matched to various common data interface types and incorporates a built-in intelligent update mechanism. This mechanism can monitor changes in data interfaces in real time. Once an interface update or adjustment is detected, the automated process can be quickly initiated without manual intervention, continuously and stably collecting data to ensure the efficiency and consistency of data acquisition.

9. The omnichannel membership integration service system according to claim 1, characterized in that, The personalized marketing and service recommendation module boasts powerful real-time feedback optimization capabilities, enabling it to keenly capture members' immediate responses to pushed content. Through tracking technology, the system precisely tracks every interaction between members and pushed content. Whether it's a click on a product details page, a successful purchase, or even simply ignoring the content, the system treats it all as key information. Built-in advanced machine learning algorithms deeply analyze this feedback data to uncover member preferences, behavioral patterns, and potential needs. Through continuous data analysis and model optimization, the system dynamically adjusts subsequent recommendation strategies, accurately filtering and pushing information that matches members' interests and needs from a vast amount of content, effectively improving marketing conversion rates and service satisfaction.

10. The omnichannel membership integration service system according to claim 1, characterized in that, The system boasts robust multilingual support, featuring built-in global language packs covering dozens of languages ​​including English, Chinese, Spanish, French, and Arabic. Through precise IP location technology, the system intelligently identifies a member's location and automatically matches the corresponding system interface language. Furthermore, members can customize their preferred language in their personal settings, seamlessly switching between the system interface's interactive language and the language used to display membership benefits details and marketing promotional content. This provides global members with a personalized, convenient, and language-barrier-free high-quality service experience.

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