Interactive Chat Response Automation for Social Channels
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Solution Overview
Problem
Existing customer care technologies force users to dedicated websites, mobile apps, or call centers, lacking scalability and automation in social and messaging channels.
Innovation Solution
A social enterprise software platform utilizing machine learning algorithms to automate customer care conversations, monitoring queries through interactive chat features, identifying automatable queries, and providing automated responses across various communication platforms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If human-powered customer care is used in social channels, then customer experience quality is maintained, but scalability is limited
Solution Approach 1:
The system enables self-service through automated chatbots that independently handle customer queries without human intervention. The machine learning model allows the system to autonomously understand queries, select appropriate responses, and manage conversations, achieving scalability while maintaining service quality.
Solution Approach 2:
The patent replaces the mechanical human-operated customer service system with an automated computer-based system using machine learning algorithms. This substitution enables the system to handle multiple customers simultaneously across different social channels, achieving both scalability and automation.
2Productivity
If automated responses are implemented, then scalability is improved, but response accuracy may decrease
Solution Approach 1:
The system incorporates feedback mechanisms where customer responses and interaction outcomes are continuously monitored and fed back into the machine learning model. This feedback loop enables the system to learn from actual customer interactions, refine its response accuracy over time, and adjust to evolving customer needs and preferences.
Solution Approach 2:
The machine learning model dynamically adjusts response parameters based on contextual factors such as customer history, query complexity, and communication channel. The system modifies response tone, detail level, and routing decisions based on real-time analysis of interaction patterns, optimizing both efficiency and accuracy.
3Adaptability or versatility
If multiple communication platforms are integrated, then customer accessibility is improved, but system complexity increases
Solution Approach 1:
The system achieves universality by implementing a unified machine learning framework that operates across multiple communication platforms simultaneously. The same core model handles queries from social media, messaging apps, and other channels, eliminating the need for separate specialized systems for each platform while maintaining platform-specific optimization.
Data Source
AI summary
Systems, media, and methods for automated response to social queries comprising: monitoring queries from users, each query submitted to a vendor via an interactive chat feature of an external electronic communication platform, monitoring human responses to the queries, monitoring subsequent communications conducted via the electronic communication platform until each query is resolved; applying a first machine learning algorithm to the monitored communications to identify a query susceptible to response automation; applying a second machine learning algorithm to the query susceptible to response automation to identify one or more responses likely to resolve the query; and either i) notifying a human to respond to the query susceptible to response automation with the one or more responses likely to resolve the query, or ii) instantiating an autonomous software agent configured to respond to the query susceptible to response automation with the one or more responses likely to resolve the query.