AI-driven complete-cycle customer contact platform

Abstract

The invention provides an AI-driven full-cycle customer contact platform which integrates various communication channels and AI technologies and covers a contact scene of the full life cycle of a customer. The platform comprises a multi-channel integration module, an intelligent routing module, a full-scene coverage module, an AI technology fusion module, a unified data management module, a data analysis and optimization module and an openness and expandability module. According to the platform, centralized management of customer requests is realized through multi-channel integration, the customer requests are intelligently distributed by using an intelligent routing algorithm, and the whole-cycle service from potential customer contact to after-sales service is supported. The AI technology is deeply fused to realize intelligent question answering, personalized recommendation and real-time monitoring, and the service automation and the customer experience are improved. The platform realizes unified management and deep analysis of customer data, constructs customer portraits, and predicts customer demands. Through the open API interface and the modular design, the platform has high expansibility, and the requirement of flexible development of enterprises is met.

Description

Technical Field

[0001] The present invention relates to the field of customer service technology, and in particular to an AI-driven full-cycle customer contact platform. Background Art

[0002] With the rapid development of communication technology and the increasing diversification of customer needs, traditional customer service models can no longer meet the needs of the modern market. Existing customer contact platforms usually only support a single communication channel, such as phone, text message or email, and it is difficult to achieve seamless coverage of multiple channels, resulting in poor customer experience. In addition, traditional platforms have limited application in intelligence and automation, and it is difficult to provide efficient and accurate services when facing large-scale customer needs. These platforms have shortcomings in the following aspects: Single communication channel: Most existing platforms only support single channels such as phone, SMS or email. Customer service often lacks cross-channel consistency, resulting in customers repeating information across different channels, inefficient processing, and increased risk of customer churn.

[0003] Low intelligence: Although some platforms have introduced automation functions, most of them are limited to basic automatic replies and FAQ questions, which makes it difficult to cope with complex customer needs. At the same time, these platforms usually lack intelligent routing functions and cannot reasonably allocate appropriate service personnel or AI robots according to the content and priority of customer requests, resulting in delayed resolution of customer issues.

[0004] Incomplete data management: Due to the lack of effective data synchronization and integration, customer information is often scattered in different channels, forming information islands, making it difficult to present a full picture of the customer, affecting service quality. In addition, weak data analysis capabilities make it difficult to conduct in-depth analysis and prediction of customer behavior, missing opportunities for personalized services.

[0005] Disconnected customer service processes: The service processes of existing platforms are mostly static processes, lacking dynamic optimization and personalized configuration capabilities, and are difficult to adapt to the needs of different customer life cycle stages. From the first contact to the after-sales stage after purchase, customers often need to cross multiple systems and service processes. This fragmented service experience greatly reduces customer satisfaction and loyalty.

[0006] Therefore, the market is in urgent need of a full-cycle customer contact platform that integrates multiple communication channels and has highly intelligent data analysis and management functions to meet the needs of modern customer service for efficiency, intelligence and personalization. In response to the above problems, the present invention proposes an AI-driven full-cycle customer contact platform that integrates multiple communication channels, introduces intelligent routing and AI technology, achieves coverage and optimization of the entire customer service process, improves service efficiency, and enhances customer satisfaction and loyalty. Summary of the invention

[0007] The present invention relates to an AI-driven full-cycle customer contact platform, which aims to realize intelligent contact and service throughout the customer life cycle by integrating multiple communication channels and integrating AI technology, thereby improving customer service efficiency and quality. This platform can cover the entire process from the first contact with potential customers to after-sales service and customer care, and improve customer experience and satisfaction through functions such as intelligent routing, data analysis and personalized recommendations.

[0008] 1. Multi-channel integration Multi-channel integration is the basic module of the present invention. It realizes the centralized management and processing of customer requests by connecting multiple communication channels (such as voice calls, instant messages, text messages, emails, etc.) to the platform. The multi-channel integration module includes the following functions: Unified access: The platform connects various communication channels to the platform through configured access parameters (such as API interface, communication protocol and authentication method) for centralized management.

[0009] Intelligent routing: Intelligently assign requests to the most suitable customer service staff or AI robot for processing based on the content, priority and customer type of the customer request.

[0010] Data synchronization: Real-time synchronization of data between channels to ensure the integrity of customer information and avoid information silos and duplicate processing.

[0011] 2. Intelligent routing The intelligent routing module dynamically allocates customer requests based on AI algorithms, optimizes resource allocation, and improves service efficiency. Its main functions include: Routing strategy formulation: Platform administrators can formulate intelligent routing strategies based on business needs. For example, requests can be allocated based on factors such as request type, customer level, and agent busyness.

[0012] Intelligent routing algorithm: The intelligent routing algorithm dynamically calculates the best request distribution path, reduces delays and packet loss rates, and improves the response speed of customer service.

[0013] Weight calculation of request features: By calculating the weights of request features (such as user location, behavior, etc.), the best route is intelligently selected to achieve accurate customer request allocation.

[0014] 3. Full scene coverage The full-scenario coverage module supports customers' full life cycle contact needs from first contact to after-sales service, ensuring that customers can receive consistent and personalized services throughout their life cycle. The implementation of full-scenario coverage includes: Scenario identification and analysis: Identify customer needs and divide the customer life cycle by analyzing customer behavior data at different life cycle stages.

[0015] Channel integration and access: Integrate multiple communication channels to ensure that customers can receive seamless services through any channel at all stages of their life cycle.

[0016] Personalized service design: By analyzing customer behavior data, personalized services and recommendations are provided to meet the needs of customers at different stages.

[0017] 4. AI technology integration The AI ​​technology integration module uses AI technology to improve the intelligence and personalization of services. The main functions include: Natural Language Processing (NLP): Use NLP technology to understand customer requests, implement intelligent question and answer, and improve customer consultation experience.

[0018] Intelligent knowledge base: Establish and maintain an intelligent knowledge base of common problems and solutions so that AI robots can automatically obtain answers and improve the accuracy of responses.

[0019] Real-time monitoring and risk warning: Real-time monitoring of the service process, such as conversation content, service duration and customer satisfaction, and timely warning when potential risks arise to ensure service quality.

[0020] 5. Unified data management The unified data management module ensures that the platform can centrally manage and analyze customer data, improve data utilization efficiency, and support personalized services. The main functions include: Multi-channel data collection: The platform synchronizes customer data from various channels such as official website, APP, social media, etc. in real time through API interface or data synchronization technology.

[0021] Data cleaning and standardization: Clean, denoise and standardize data from different sources to ensure consistent data formats for subsequent analysis and processing.

[0022] Data permission management: Protect the security of sensitive data and prevent unauthorized data access based on different roles and permission settings.

[0023] 6. Data analysis and continuous optimization The data analysis and continuous optimization module conducts in-depth analysis of customer behavior data and improves customer experience through customer portrait construction and intelligent recommendations. The main functions include: Customer portrait construction: Build customer portraits based on customer behavior data and basic information, accurately identify customer needs, and provide data support for personalized services.

[0024] Prediction and recommendation: Use AI algorithms to predict customers’ future needs and push personalized service and product recommendations based on customer portraits.

[0025] Data visualization and monitoring: Display customer data and analysis results through dashboards or charts to help management monitor customer status and market trends in a timely manner.

[0026] 7. Openness and scalability The openness and scalability modules ensure that the platform can interact with other systems and support flexible customization and rapid iteration of services to meet the ever-changing market needs. The main functions include: Open API interface and SDK: The platform provides an open API interface, allowing integration with other systems (such as CRM, ERP) to achieve data sharing and process optimization.

[0027] Modular design: The platform adopts a modular design, which allows users to flexibly customize functions according to business needs and supports adding or deleting modules on demand.

[0028] Automatic upgrade: Supports the system's automatic upgrade function to ensure that the platform always maintains the latest version and adapts to changes in business needs. Beneficial Effects

[0029] Omnichannel access and intelligent routing: Omnichannel access is achieved by integrating multiple communication channels, and the intelligent routing function automatically assigns customer requests to the most appropriate seat or AI robot for processing, thereby improving service efficiency.

[0030] Full-scenario coverage and unified data management: Achieve full-cycle contact scenario coverage of pre-sales, sales, and after-sales from marketing to sales follow-up, and uniformly manage various contact data to eliminate information silos and improve service quality.

[0031] Deep integration of AI technologies: Deeply integrate AI technologies such as robot question-and-answer, intelligent knowledge base, and gold-medal scripts into the customer service process to achieve intelligent services, and continuously optimize AI services through functions such as robot self-learning.

[0032] Openness and scalability: Provides API, SDK and other interfaces to support integration and expansion with other systems, while supporting flexible customization and rapid iteration of services to meet ever-changing market demands. DETAILED DESCRIPTION

[0033] The present invention is described in detail below in conjunction with specific embodiments. At the same time, it is explained here that in order to make the embodiments more detailed, the following embodiments are the best and preferred embodiments, and those skilled in the art may also adopt other alternatives to implement some known technologies.

[0034] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiments", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0035] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0036] The specific implementation of the present invention includes the following parts: 1. Multi-channel integration: 4.1.1. Goals of multi-channel integration 1. Unified access: All communication channels (such as voice calls, instant messages, text messages, emails, etc.) are unified into the platform for easy management and processing.

[0037] 2. Intelligent routing: Intelligently assign requests to the most appropriate agent or AI robot for processing based on the content and nature of customer requests.

[0038] 3. Data synchronization: Ensure data synchronization between channels to avoid information silos and duplicate processing.

[0039] 4.1.2 Steps and Logic of Multi-channel Integration Channel access configuration 1. Step 1: The platform administrator configures the communication channels that need to be accessed in the background management system.

[0040] 2. Step 2: Set corresponding access parameters for each channel, such as API interface, communication protocol, authentication method, etc.

[0041] 3. Step 3: Test the access configuration of each channel to ensure that customer requests can be received and sent normally.

[0042] Unified request reception 1. Step 1: The platform receives customer requests from various channels in real time through configured access parameters.

[0043] 2. Step 2: Preprocess the received request, such as parsing the protocol, removing redundant information, etc.

[0044] 3. Step 3: Store the pre-processed request in the platform's unified request queue, waiting for further processing.

[0045] Intelligent routing strategy formulation 1. Step 1: The platform administrator formulates intelligent routing strategies based on business needs, such as routing based on factors such as request type, customer level, and agent busyness.

[0046] 2. Step 2: Input the formulated routing strategy into the platform’s intelligent routing engine for configuration and testing.

[0047] Request dispatch and processing 1. Step 1: The intelligent routing engine takes the request from the unified request queue according to the configured routing strategy and determines which seat or AI robot it should be assigned to.

[0048] 2. Step 2: Distribute the request to the corresponding agent or AI robot for processing.

[0049] 3. Step 3: After receiving the request, the agent or AI robot performs specific business processing, such as answering questions, providing solutions, etc.

[0050] Processing result feedback 1. Step 1: After the agent or AI robot processes the request, it returns the processing result to the platform.

[0051] 2. Step 2: The platform records and compiles statistics on the processing results for subsequent analysis and optimization.

[0052] 3. Step 3: The platform will feedback the processing results to the customer through corresponding channels, such as sending SMS, email, etc.

[0053] Data synchronization and integration 1. Step 1: The platform synchronizes data between channels in real time to ensure the accuracy and consistency of information.

[0054] 2. Step 2: Integrate and analyze data to discover valuable customer information and business needs.

[0055] 3. Step 3: Based on the analysis results, optimize the intelligent routing strategy and business processing flow to improve customer service quality.

[0056] 4.1.3 Advantages of multi-channel integration 1. Improve service efficiency: Through unified access and intelligent routing, centralized management and efficient processing of customer requests can be achieved, reducing waiting time and processing costs.

[0057] 2. Improve customer satisfaction: Customers can easily initiate requests through multiple channels and receive timely and accurate responses, which improves customer satisfaction and loyalty.

[0058] 3. Optimize resource allocation: Intelligently allocate seats and AI robot resources according to customer needs and business characteristics to improve resource utilization and overall service level.

[0059] 4.2 Full scene coverage: 4.2.1 Definition and scope of full-scenario coverage Full scenario coverage means that the platform can support all contact scenarios throughout the entire life cycle of customers, from their initial contact with products or services to their eventual becoming loyal customers. This includes but is not limited to: Marketing stage: Potential customers first come into contact with your product or service through advertising, social media, websites, etc.

[0060] Sales follow-up stage: Sales personnel have in-depth communication with customers, answer questions, and provide customized solutions.

[0061] After-sales service stage: After purchasing a product or service, customers may encounter problems and require consultation, repair, or return or exchange.

[0062] Customer care stage: Communicate with customers regularly, understand usage feedback, provide value-added services, and enhance customer loyalty.

[0063] 4.2.2 Implementation steps and logic for full-scenario coverage Scene recognition and analysis Step 1: Collect and analyze customer behavior data at different stages, including browsing records, purchase records, consultation records, etc.

[0064] Step 2: Based on data analysis, identify customers’ needs and pain points at different stages.

[0065] Logic: Through big data and AI technology, customers are segmented to identify which stage of their life cycle they are in, as well as their main needs and pain points at the current stage.

[0066] Channel integration and access Step 1: Integrate multiple communication channels, such as phone, email, instant messaging, social media, website, etc.

[0067] Step 2: Ensure that each channel can seamlessly access the platform to achieve real-time synchronization and sharing of information.

[0068] Logic: Customers may access the platform through different channels, so it is necessary to ensure that the platform can support access from multiple channels and maintain the timeliness and consistency of information.

[0069] Intelligent service design Step 1: Design intelligent service processes based on customer behavior data and needs.

[0070] Step 2: Use AI technologies, such as natural language processing and machine learning, to achieve automated services and personalized recommendations.

[0071] Logic: Improve service efficiency through intelligent service design, while providing personalized service experience to meet the diverse needs of customers.

[0072] Business process optimization Step 1: Sort out and optimize existing business processes to ensure smooth connection between each link.

[0073] Step 2: Introduce automated and intelligent tools to reduce manual intervention and improve business processing efficiency.

[0074] Logic: Reduce operating costs and improve customer satisfaction and loyalty by optimizing business processes.

[0075] Customer feedback and continuous improvement Step 1: Establish a customer feedback mechanism to collect customers’ evaluations and suggestions on platform services.

[0076] Step 2: Based on customer feedback, continuously improve the service quality and functions of the platform.

[0077] Logic: Customer feedback is an important basis for continuously improving platform services. By collecting and analyzing customer feedback, we can promptly discover and solve problems on the platform, and improve service quality and customer satisfaction.

[0078] 4.2.3 Benefits and impacts of full-scenario coverage The implementation of full-scenario coverage will bring the following benefits and impacts: Improve customer satisfaction: Through intelligent services and personalized recommendations, we can meet the diverse needs of customers and improve customer satisfaction.

[0079] Reduce operating costs: Through automated and intelligent tools, reduce manual intervention and reduce operating costs.

[0080] Enhance customer loyalty: Enhance customer loyalty through regular customer care and value-added services, and promote long-term customer retention and repurchase.

[0081] Enhance brand image: High-quality full-scenario coverage services will enhance brand image and attract more potential customers.

[0082] 4.3.AI technology integration: 4.3.1. Goals of AI technology integration Intelligent service: Use AI technology to achieve automated and intelligent customer service and problem solving, reduce manual intervention, and improve service efficiency.

[0083] Personalized experience: By analyzing customer historical data and behavior patterns, we provide personalized service recommendations and solutions to enhance customer satisfaction.

[0084] Real-time monitoring and risk warning: Use AI technology to monitor the service process in real time, promptly detect and warn of potential risks, and ensure service quality.

[0085] 4.3.2 Steps and Logic of AI Technology Integration Data collection and preprocessing Data collection: Through the platform's omni-channel access function, collect customer interaction data on various communication channels, including voice, text, pictures, etc.

[0086] Data preprocessing: Perform preprocessing operations such as cleaning, denoising, and format conversion on the collected data to ensure the accuracy and consistency of the data.

[0087] AI model training and optimization Model selection: Select appropriate AI models according to business needs, such as natural language processing models, image recognition models, etc.

[0088] Data labeling: Label the preprocessed data to provide supervision information for model training.

[0089] Model training: Use labeled data sets to train AI models so that they can accurately understand customer questions and give appropriate answers or solutions.

[0090] Model optimization: Improve the accuracy and generalization ability of the model by continuously iterating and optimizing model parameters.

[0091] Intelligent service realization Robot Q&A: Use the trained natural language processing model to enable intelligent conversations between robots and customers and answer customer questions.

[0092] Intelligent knowledge base: Establish an intelligent knowledge base to store common problems and solutions. Robots can get answers directly from the knowledge base.

[0093] Golden Medal Scripts: By analyzing customer feedback and interaction data, we extract efficient and professional golden medal scripts to improve service quality and customer satisfaction.

[0094] Real-time monitoring and risk warning Real-time monitoring: Use AI technology to monitor the service process in real time, including conversation content, service time, customer satisfaction and other indicators.

[0095] Risk warning: Based on real-time monitoring data, timely discover and warn of potential risks, such as customer emotional out-of-control, service timeout, etc., so that timely measures can be taken to deal with them.

[0096] Continuous iteration and optimization of AI technology Data analysis and feedback: Regularly analyze customer feedback and interaction data, evaluate the effectiveness of AI technology, and identify existing problems and areas for improvement.

[0097] Technology iteration and optimization: Based on the analysis results, AI technology is iterated and optimized to continuously improve the level and quality of intelligent services.

[0098] 4.3.3 Logic of AI technology integration The logic of AI technology integration is mainly reflected in the following aspects: Data-driven: The core of AI technology lies in data. By collecting and analyzing customer interaction data, we can continuously optimize AI models and improve the level of intelligent services.

[0099] Continuous iteration: AI technology is a process of continuous iteration and optimization, which requires regular evaluation of results, identification of problems and improvement.

[0100] Personalized service: By analyzing customer historical data and behavior patterns, we provide personalized service recommendations and solutions to enhance customer satisfaction.

[0101] Real-time monitoring and early warning: Use AI technology to monitor the service process in real time, promptly detect and warn of potential risks, and ensure service quality.

[0102] 4.4 Unified Data Management: 4.4.1 Data Integration Multi-channel data collection: The platform needs to be able to uniformly collect customer data from various channels (such as official website, APP, WeChat public account, mini program, telephone, etc.).

[0103] Through API interface or data synchronization technology, ensure that data from each channel can be transmitted to the platform in real time or regularly.

[0104] Data cleaning and standardization: Clean the collected data to remove duplicate, invalid or erroneous data.

[0105] Standardize data to ensure that data from different sources are consistent in format, naming, etc. to facilitate subsequent processing and analysis.

[0106] Data integration and storage: Integrate the cleaned and standardized data into a unified database.

[0107] Use efficient data storage technologies (such as distributed databases, cloud storage, etc.) to ensure data reliability and scalability.

[0108] 4.4.2 Data Management Data classification and labeling: Classify customer data according to business needs, such as basic information, transaction records, service records, etc.

[0109] Set corresponding labels for each category of data to facilitate subsequent data retrieval and analysis.

[0110] Data rights management: Set corresponding data access permissions based on different roles and departments.

[0111] Ensure the security and compliance of sensitive data to prevent data leakage or misuse.

[0112] Data backup and recovery: Back up your data regularly to ensure that it can be quickly restored in the event of data loss or corruption.

[0113] Redundant storage and disaster recovery backup technologies are used to improve data reliability and security.

[0114] 4.4.3. Intelligent Data Analysis Customer portrait construction: Build detailed customer profiles based on the collected customer data.

[0115] The portrait includes the customer’s basic information, preferences, behavioral patterns, etc., providing strong support for subsequent precision marketing and services.

[0116] Data analysis and prediction: Use AI technology to conduct in-depth analysis of customer data and explore potential business opportunities and trends.

[0117] Through machine learning algorithms, customers’ future behaviors and needs can be predicted, providing a basis for corporate decision-making.

[0118] Intelligent recommendation and marketing: Provide customers with personalized product and service recommendations based on customer portraits and data analysis results.

[0119] Improve customer satisfaction and loyalty through intelligent marketing methods (such as email marketing, SMS marketing, social media marketing, etc.).

[0120] 4.4.4 Data visualization and monitoring Data visualization display: Customer data and analysis results can be intuitively displayed through charts, dashboards, etc.

[0121] It helps corporate leaders and business departments to quickly understand customer status and market trends.

[0122] Real-time monitoring and early warning: Real-time monitoring of key data indicators, such as customer churn rate, satisfaction, etc.

[0123] When data fluctuates abnormally, early warnings are issued in a timely manner to facilitate companies to take countermeasures.

[0124] 4.4.5. Continuous Optimization and Iteration Feedback Collection and Evaluation: Collect user feedback on the platform and evaluate the platform's performance and effectiveness.

[0125] Based on the evaluation results, the platform will be continuously optimized and improved.

[0126] Technology updates and upgrades: Keep track of the latest AI and data management technologies to ensure your platform stays ahead of the curve.

[0127] Regularly carry out technical upgrades and updates to the platform to improve its performance and stability.

[0128] 4.5. Openness and scalability: 4.5.1 Openness 1. Multi-channel access capabilities Description: The platform should support communication with customers through multiple channels (such as telephone, website, APP, WeChat public account, mini program, etc.) to ensure a seamless customer experience.

[0129] Steps and logic: Identify and integrate key communication channels.

[0130] Develop a unified interface to allow data from various channels to flow into the platform.

[0131] Use natural language processing (NLP) and speech recognition technology to ensure that data from various channels can be understood and processed by the platform.

[0132] 2. Third-party system integration Description: The platform should be able to quickly integrate with other business systems of the enterprise (such as CRM, ERP, etc.) to form a full-process business closed loop.

[0133] Steps and logic: Define standards and protocols for integration interfaces.

[0134] Develop data exchange middleware to ensure data flow between the platform and enterprise systems.

[0135] Conduct tests to ensure accurate data transfer and synchronization between different systems.

[0136] 3. Open API Description: Provides an open API to allow other developers or enterprises to conduct secondary development or customization based on the platform's functions.

[0137] Steps and logic: Design and publish API documentation, detailing each API's functions, parameters, and return values.

[0138] Establish a management mechanism for API access rights to ensure security.

[0139] Monitor API usage and make continuous improvements based on feedback.

[0140] 4.5.2 Scalability 1. Modular design Description: The platform should be modular in design so that new features or modules can be easily added or removed.

[0141] Steps and logic: Divide the core functions of the platform into independent modules.

[0142] Design interfaces and communication protocols between modules.

[0143] Establish a module management mechanism to allow dynamic loading and unloading of modules.

[0144] 2. Automatic upgrade Description: The platform should support automated upgrades to ensure the system is always up to date.

[0145] Steps and logic: Design a version control mechanism to record changes and updates for each version.

[0146] Develop automated upgrade tools that allow systems to be upgraded while online or offline.

[0147] Set an upgrade strategy, such as regular upgrades or on-demand upgrades.

[0148] 3. Horizontal scalability Description: The platform should support horizontal expansion to meet growing customer demand.

[0149] Steps and logic: Design a load balancing mechanism to ensure that requests are evenly distributed to each server.

[0150] Add new servers or nodes to increase the processing power of the system.

[0151] Monitor system performance and load, and make dynamic adjustments as needed.

[0152] 4. Data Scalability Description: The platform should support the storage and processing of large-scale data to meet the needs of data analysis and insights.

[0153] Steps and logic: Design efficient data storage architectures, such as distributed databases or cloud storage.

[0154] Develop data processing and analysis tools to support complex data query and analysis.

[0155] Establish data backup and recovery mechanisms to ensure data security and reliability.

[0156] 4.6. Full business closed loop: 4.6.1. Customer contact and identification Multi-channel access: The platform supports multiple channels such as telephone, website, APP, social media, etc., ensuring that customers can contact the company anytime and anywhere.

[0157] Intelligent identification: Use AI technologies such as natural language processing (NLP) and speech recognition to automatically identify customer identity and intentions, providing a basis for subsequent services.

[0158] 4.6.2 Consultation and Answers Intelligent customer service: Through AI-driven chatbots, customers are provided with 24 / 7 online consultation and answering services.

[0159] Manual intervention: When the intelligent customer service cannot solve the problem, it will be automatically transferred to the manual customer service to ensure that the customer's problem is solved in a timely manner.

[0160] Knowledge base support: Establish a comprehensive knowledge base to provide accurate information support for customer service and improve answering efficiency.

[0161] 4.6.3. Purchase and Conversion Product recommendations: Based on customers’ historical purchase records and browsing behaviors, AI algorithms are used to recommend related products to improve purchase conversion rates.

[0162] Shopping cart management: Support customers to manage shopping carts on the platform, so that they can add, delete or modify products at any time.

[0163] Payment and Settlement: Integrate multiple payment methods to ensure that customers can complete payments safely and conveniently.

[0164] 4.6.4 After-sales service Work order management: After a customer submits an after-sales issue, a work order is automatically generated and assigned to the corresponding after-sales team for processing.

[0165] Progress tracking: Customers can check the progress of after-sales issues at any time to improve customer satisfaction.

[0166] Satisfaction evaluation: After the after-sales problem is solved, invite customers to conduct satisfaction evaluation, collect feedback, and continuously optimize services.

[0167] 4.6.5. Repeat purchase and loyalty improvement Personalized marketing: Based on the customer’s historical purchase records and preferences, personalized marketing information is pushed to increase repurchase rate.

[0168] Membership system: Establish a membership system to provide members with exclusive discounts, points redemption and other benefits to enhance customer loyalty.

[0169] Customer portrait: Use AI technology to build customer portraits, gain an in-depth understanding of customer needs and preferences, and provide data support for precision marketing.

[0170] 4.6.6 Data Analysis and Optimization Data analysis: Collect various data from customers when using the platform, such as browsing history, purchase history, after-sales history, etc., and conduct in-depth analysis.

[0171] Optimization suggestions: Based on the data analysis results, make optimization suggestions, such as improving product recommendation algorithms, optimizing after-sales service processes, etc.

[0172] Continuous iteration: Based on optimization suggestions, we continuously iterate platform functions and services to enhance customer experience.

[0173] 5. Innovation Intelligent routing 5.1 Intelligent Routing Algorithm Intelligent routing algorithm is a key technology for improving network performance and reliability. It optimizes network resource utilization, reduces delays and packet loss, and ensures network reliability and stability by dynamically calculating and selecting the best data transmission path. The basic principles of intelligent routing algorithm include the following aspects: Network topology and link status information collection: The intelligent routing algorithm first collects and maintains the topological relationship and link status information between various devices in the network, including bandwidth utilization, delay, packet loss rate, etc.

[0174] This information can be obtained through link detection, network monitoring, and link monitoring.

[0175] Routing strategy formulation: Routing selection strategy is the core of intelligent routing algorithm.

[0176] It selects appropriate routing algorithms to calculate paths based on different network requirements and optimization goals.

[0177] Commonly used routing selection strategies include the shortest path algorithm (such as Dijkstra algorithm), minimum load algorithm, minimum hop algorithm, minimum delay algorithm, etc.

[0178] These strategies can be combined and adjusted according to specific circumstances to meet different network performance and reliability requirements.

[0179] Dynamic path adjustment and optimization: The link status and topology of the network change dynamically.

[0180] Intelligent routing algorithms need to monitor and perceive network changes in real time and dynamically adjust and optimize paths based on the changes.

[0181] For example, when a link in the network fails or becomes congested, the intelligent routing algorithm can recalculate the path and select an available alternative path to avoid packet loss and increased latency.

[0182] Network security considerations: When calculating and selecting paths, intelligent routing algorithms also need to consider the security and defense of the network.

[0183] It can protect the network from attacks and malicious behaviors through security policies and mechanisms.

[0184] 5.2 Intelligent Routing Formula Intelligent routing formulas are mainly used to calculate routing strategies, weights of request features, weights of service instance status, etc. The following are some common intelligent routing formulas: The weight calculation formula for request features is: textweightrequest=f(featurerequest) Among them, weightrequest represents the weight of the request, and featurerequest represents the feature information of the request (such as user location, user behavior, etc.).

[0185] The weight calculation formula of service instance status is: textweightinstance=f(stateinstance) Among them, weightinstance represents the weight of the service instance, and stateinstance represents the status information of the service instance (such as CPU usage, response time, etc.).

[0186] The calculation formula of the routing policy is: textroutepolicy=∑i=1nweightinstance∑i=1nweightrequest×weightinstance Among them, routepolicy represents the routing policy, and n represents the number of service instances. This formula is used to calculate the optimal routing policy based on request characteristics and service instance status.

[0187] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0188] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. An AI-driven full-cycle customer contact platform, characterized in that: include: (1) Multi-channel integration module, which is used to uniformly access multiple communication channels including voice calls, instant messaging, SMS, and email. It receives customer requests in real time through configured access parameters, performs pre-processing, and stores them in a unified request queue to ensure data synchronization between channels and avoid information silos and repeated processing. (2) Intelligent routing module, which is used to allocate customer requests to the most appropriate agent or AI robot for processing based on business needs through intelligent routing strategies, including: Routing strategies are formulated by considering factors such as request type, customer level, agent busyness, etc. Intelligent routing algorithm for dynamically calculating the best data transmission path; Request feature weight calculation and service instance status monitoring are used to achieve intelligent distribution of requests. (3) Full-scenario coverage module, which is used to support the full-cycle customer contact service from marketing to sales follow-up, after-sales service and customer care, and provides the following functions: Scenario recognition and analysis to identify customer needs according to different stages of the customer life cycle; Multi-channel integration and access to ensure that customers can receive seamless services through different channels at all stages; Personalized service design uses AI technology to achieve in-depth analysis of customer data and intelligent recommendations. (4) AI technology integration module, used to implement intelligent services, including: Natural language processing (NLP) and machine learning model training to understand customer requests, implement intelligent question-answering, and provide personalized recommendations; Intelligent knowledge base, which is used to store common questions and standardized responses to improve the accuracy of robot Q&A; Real-time monitoring and risk warning functions monitor the service process in real time, identify potential risks and provide warnings. (5) Unified data management module, used for the integration and management of multi-channel customer data, including the following functions: Data cleaning and standardization: standardize data from different channels to ensure data format consistency; Data permission management: set data access permissions according to different roles to ensure data security; Data backup and recovery mechanism: back up data regularly to ensure that data can be restored in case of data loss. (6) Data analysis and optimization module, used for customer behavior data analysis and platform optimization, including: Customer portrait construction: construct customer portraits based on basic customer information and behavior patterns to provide support for precision marketing; Data prediction function, using machine learning algorithms to predict customers' future behavior; Data visualization and real-time monitoring functions display customer data and analysis results through charts and dashboards to facilitate monitoring and decision-making. (7) Openness and scalability module, providing the following functions: Open API interface and SDK allow integration with other business systems (CRM, ERP system) and support multi-channel access; Modular design makes the platform scalable and flexible; Automatic upgrade function ensures that the system remains up to date to meet the needs of business development.

2. The AI-driven full-cycle customer contact platform according to claim 1, characterized in that: The intelligent routing module also includes a weight calculation formula for request features, a weight calculation formula for service instance status, and a routing strategy calculation formula to achieve an optimal request distribution path.

3. The AI-driven full-cycle customer contact platform according to claim 1, characterized in that: The AI ​​technology fusion module continuously optimizes the accuracy of understanding and responding to customer needs through the self-learning functions of natural language processing and machine learning models.

4. The AI-driven full-cycle customer contact platform according to claim 1, characterized in that: The unified data management module further includes data cleaning, standardization, classification and labeling functions to achieve refined management and efficient use of customer data.

5. The AI-driven full-cycle customer contact platform according to claim 1, characterized in that: The openness and scalability module also includes an automated upgrade mechanism to support regular or on-demand upgrades of the platform.

6. The AI-driven full-cycle customer contact platform according to claim 1, characterized in that: The multi-channel integration module can ensure the real-time or periodic transmission of data from each channel based on an API interface or data synchronization technology to achieve data reliability and scalability.

7. The AI-driven full-cycle customer contact platform according to claim 1, characterized in that: The data analysis and optimization module can perform in-depth analysis of customer data, identify potential business opportunities and provide personalized marketing suggestions.