Personalized Call Center Experience via ML Preference Analysis
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Solution Overview
Problem
Current computer-based text/speech analyzers fail to properly recognize user requirements or preferences based on their speech, leading to a generic customer experience in call centers that does not account for personal preferences or previous interactions.
Innovation Solution
A system and method that utilize machine learning models to analyze past interactions and preferences of customers, generating a customer profile that informs call agents about user preferences, allowing for real-time personalization of customer interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional text/speech analyzers are used to process customer interactions, then basic text and sentence recognition is achieved, but the ability to properly recognize user requirements or preferences based on their speech is lost
Solution Approach 1:
The system performs preliminary analysis of customer speech patterns, word choices, and communication styles before the actual customer interaction begins. Historical interaction data is pre-processed to extract preference indicators, enabling the system to anticipate customer needs and personalize the interaction from the start rather than reacting during the conversation.
Solution Approach 2:
The system continuously monitors customer responses during interactions and uses this feedback to refine preference recognition in real-time. By analyzing customer reactions, clarifying questions, and communication patterns, the system updates its understanding of customer preferences dynamically, improving recognition accuracy throughout the interaction.
2Adaptability or versatility
If call centers start each discussion from scratch without utilizing previous interactions, then operational simplicity is maintained, but customer personalization and anticipation of preferences are lost
Solution Approach 1:
The system creates simplified copies or representations of customer preference data in the form of preference profiles and interaction patterns. Instead of managing complex raw interaction data, the system generates condensed preference models that capture essential customer characteristics, making it easier to personalize interactions without proportionally increasing system complexity.
Solution Approach 2:
The preference recognition system is designed to serve multiple functions simultaneously: it analyzes historical interactions, identifies customer preferences, guides real-time agent behavior, and provides training data for continuous improvement. This multi-functionality reduces the need for separate specialized systems, thereby limiting the increase in overall system complexity.
3Ease of operation
If machine learning models analyze past interactions to generate customer profiles, then personalized customer experience is achieved, but data processing time and computational resources increase
Solution Approach 1:
Customer profiles and preference models are generated and updated in advance during periods when computational resources are more readily available. Historical interaction data is pre-processed offline to create ready-to-use preference representations, so that during actual customer interactions, the system only needs to retrieve and apply pre-computed profiles rather than analyzing raw data in real-time.
Solution Approach 2:
Instead of analyzing all customer interaction data uniformly, the system focuses computational resources on analyzing specific aspects of interactions that are most relevant to preference recognition. The system identifies and prioritizes key indicators such as word choice patterns, communication style, and specific interaction contexts, processing only the most informative data points to reduce overall processing time.
Data Source
AI summary
Disclosed herein are system, method, and computer program product embodiments for machine learning systems to process incoming call-center calls based on caller preferences. Text of historical interactive communications of a set of first callers is used to train one or more machine learning models to extract current caller preferences. A first sentiment score of a current caller may be labeled as a complaint and, based on subsequent utterances of the current caller, a first and second sentiment score trend of the current caller are detected relative to the complaint. For a second sentiment score above a complaint threshold, phrasing is generated for a call center agent interacting with the current caller and, for a second sentiment score below the complaint threshold, utterances are labelled as non-complaint utterances, and identified as a call resolution.