Conversation Engagement Scoring From Group Utterance Analysis
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Solution Overview
Problem
Existing digital communication platforms lack analytics and metrics for engagement analysis during remote communication sessions, particularly in sales meetings, failing to provide insights into customer interaction and engagement levels.
Innovation Solution
A system that extracts utterances from communication sessions, associates them with speaker groups, calculates engagement metrics, assigns weights, and determines an engagement score based on statistical analysis, presenting the score to users for improved sales team performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If engagement analytics are added to communication platforms, then customer interaction insights are improved, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary engagement analytics system that sits between the communication platform and the users. This intermediary automatically extracts utterances, calculates engagement metrics, and generates analytics reports, thereby providing customer interaction insights without requiring direct modification of the core communication platform infrastructure.
Solution Approach 2:
The engagement analytics system performs self-service by automatically extracting utterances from communication sessions, computing engagement metrics based on predefined formulas, and generating analytics reports without requiring manual intervention. The system autonomously processes communication data and delivers insights to users.
2Measurement precision
If multiple engagement metrics are calculated and analyzed, then engagement analysis precision is improved, but processing time increases
Solution Approach 1:
The patent implements preliminary action by pre-defining multiple engagement metrics and their calculation formulas in advance. The system is configured with ready-to-use metrics such as speaker turn-taking balance, conversation density, and engagement patterns, allowing it to quickly compute analytics without requiring complex real-time calculations during communication sessions.
Solution Approach 2:
The engagement analytics system employs parameter changes by adjusting the weight and priority of different engagement metrics based on communication session types and user preferences. The system can dynamically modify which metrics are calculated and their relative importance, optimizing processing time while maintaining analysis precision.
Data Source
AI summary
In one embodiment, the system connects to a communication session with a number of participants; receives a transcript of a conversation between the participants; extracts utterances from the transcript; associates a subset of the utterances with a first group of speakers and the remaining subset of the utterances with a second group of speakers; calculates one or more statistical metrics for a number of engagement metrics based on the utterances of the first group of speakers and the utterances of the second group of speakers; assigns a weight to each of the engagement metrics; determines an engagement score for the communication session based on the assigned weights for the engagement metrics; and presents, to one or more users, the engagement score for the communication session.