Personalized Chat Persona Matching via ML Analysis
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Solution Overview
Problem
Current chat systems restrict personalized experiences by allowing chat clients to initiate communication without considering their specific needs or issues, leading to inefficient customer service solutions compared to in-person interactions.
Innovation Solution
A method and apparatus for personalized Internet chat sessions that enable chat clients to select from a menu of chat personas based on their needs, using machine learning to develop response recommendations iteratively from prior chat logs, allowing for tailored interactions with trained agents or chatbots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a typical phone call center or standard chat system is used, then customer service solutions are provided, but personalized experiences similar to in-store specialists cannot be delivered
Solution Approach 1:
The system performs preliminary actions by analyzing customer data, browsing behavior, and chat history before the chat session begins. This allows the system to pre-select appropriate chat personas and prepare personalized response recommendations, enabling immediate personalized service without requiring manual agent configuration during the interaction.
Solution Approach 2:
The chat system dynamically adapts to each customer's needs by selecting different chat personas based on real-time analysis of customer behavior and preferences. The response recommendations are dynamically generated and updated during the chat session based on the conversation flow, making the system flexible and responsive to changing customer requirements.
2Adaptability or versatility
If chat clients can initiate communication with any available chat agent, then chat functionality is provided, but specific client needs, desires, and issues are not considered in agent selection
Solution Approach 1:
The system performs preliminary analysis of customer profiles, browsing history, and preferences before chat initiation to pre-determine the most suitable chat personas. This preliminary matching process ensures that when customers initiate chat, they are immediately connected with appropriately matched agents rather than any available agent, reducing wait time and improving resolution efficiency.
Solution Approach 2:
The system uses feedback from customer behavior patterns, chat history, and interaction outcomes to continuously improve persona selection algorithms. This feedback mechanism learns from past interactions to refine future agent assignments, ensuring increasingly accurate matching between customer needs and agent expertise over time.
3Adaptability or versatility
If machine learning is used to develop response recommendations iteratively from prior chat logs, then personalized responses are generated, but system complexity increases
Solution Approach 1:
The machine learning system operates autonomously to generate and refine response recommendations without requiring manual intervention. The system automatically analyzes prior chat logs, identifies patterns, and generates personalized response suggestions for chat agents. This self-service capability reduces the need for complex manual configuration and ongoing system management despite the advanced algorithms employed.
Solution Approach 2:
The system creates simplified representations or copies of complex customer profiles and interaction patterns that can be processed by the machine learning algorithms. By working with these condensed models rather than raw data, the system achieves personalized response generation with reduced computational complexity and faster processing times.
Data Source
AI summary
Non-transitory computer-readable media and method for conducting an Internet chat session between an associate device and a client device are disclosed. A chat is enabled between the associate device and the client device in accordance with at least one chat persona. The associate device is instructed to display a plurality of response recommendations wherein the plurality of response recommendations is developed iteratively in response to messages received from the client device, in accordance with the at least one chat persona, and using machine learning of prior chat logs.