Exit-Pop Messaging System for Chatbot Data Control
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Solution Overview
Problem
Web retailers face challenges in deploying and managing chatbots, as they rely on third-party services for creation and maintenance, leading to a lack of control over artificial intelligence and limited insights into user interactions and traffic data.
Innovation Solution
The TeamSalesAgent (TSA) system provides a self-learning artificial intelligence engine that integrates with web retailers' websites to create a browser-based chat window, offering real-time interaction and analysis, allowing for customizable and managed AI-driven conversations to reduce cart abandonment and increase sales.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If web retailers use third-party services to create and deploy chatbots, then chatbot functionality is provided, but control over artificial intelligence and access to user interaction data is lost
Solution Approach 1:
The patent introduces an exit-pop messaging system that serves as an intermediary between the web retailer and third-party chatbot services. This system captures user interaction data at the point of exit and provides it back to the retailer, allowing control and oversight of AI-driven conversations while still utilizing external chatbot capabilities.
Solution Approach 2:
The system implements feedback loops by tracking user interactions with chatbots and providing this data back to the web retailer. This includes monitoring conversation quality, user satisfaction metrics, and traffic patterns, enabling retailers to adjust and optimize their AI chatbot deployments based on actual performance data.
2Ease of manufacture
If third-party services manage chatbot scripts and AI, then chatbot deployment is simplified, but information about user traffic and interactions is not available to the web retailer
Solution Approach 1:
The exit-pop messaging system establishes feedback mechanisms that capture and transmit user interaction data back to the web retailer. This includes tracking which users engage with chatbots, the nature of their interactions, and outcomes, thereby preventing information loss while maintaining deployment simplicity.
Solution Approach 2:
The system acts as a data intermediary, collecting information from third-party chatbot services and making it available to the web retailer. This intermediary layer ensures that simplified deployment through third-party services does not result in loss of valuable user traffic and interaction information.
3Productivity
If chatbots are deployed to reduce cart abandonment, then customer service is improved, but deployment complexity and maintenance requirements increase
Solution Approach 1:
The exit-pop messaging system serves as a deployment intermediary that simplifies the integration of chatbots into web retailer infrastructure. It handles the complexity of connecting to third-party services, managing data flows, and coordinating chatbot deployments, thereby reducing the burden on retailers while maintaining high cart recovery rates.
4Reliability
If real-time monitoring of chatbot performance is implemented, then service quality is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system implements targeted feedback mechanisms that monitor essential chatbot performance metrics in real-time without requiring complex monitoring infrastructure. By focusing on key indicators such as user engagement, conversation outcomes, and satisfaction signals, the system maintains high service quality while avoiding unnecessary complexity.
Data Source
AI summary
The present invention is uniquely designed to interact with web retailer's customers with real agent reaction times as they give astute answers directly concerning web retailer's products and goals. The entire process is manageable through a third-party website which includes scripting, settings and other parameters selected by the web retailer. An artificial intelligence engine uses the combination of Bayesian probability keyword selection, natural language parsing and regular expression processing. The technology updates its response database with every client interaction-learning always takes place as it simulates a live agent, in real time. Every client interaction is recorded and analyzed, and as a result of the analysis the changes in the answer database are made.