Real-Time Sentiment Analysis for Sales Concern Resolution
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Solution Overview
Problem
Current systems lack the ability to efficiently identify and address potential sales concerns in real-time during telecommunication sessions with entities such as restaurants, leading to suboptimal sales processes and relationships.
Innovation Solution
A method that aggregates entity data from various sources, classifies entities into clusters, and uses predictive models to generate responses that improve sentiment, enabling real-time concern resolution and sales process optimization during telecommunication sessions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If real-time sentiment analysis and response generation are implemented during telecommunication sessions, then sales concern resolution capability is improved, but system complexity and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing entity data, classifying entities into clusters, and generating predictive models before telecommunication sessions occur. This allows the system to have pre-computed sentiment analysis frameworks and response strategies ready, reducing the computational burden during real-time interactions while maintaining high resolution capability
Solution Approach 2:
The system segments the sales process by identifying and addressing specific concerns separately rather than treating all interactions uniformly. By classifying entities into clusters and analyzing sentiment for specific concepts, the system divides complex sales conversations into manageable segments that can be handled with targeted responses, improving reliability without proportionally increasing overall system complexity
2Measurement precision
If comprehensive entity data aggregation from multiple sources is performed, then accuracy of sentiment analysis is improved, but data processing time and computational resources increase
Solution Approach 1:
The system aggregates entity data from multiple sources and performs preliminary analysis before telecommunication sessions. By pre-processing and classifying entity data into clusters in advance, the system establishes a ready framework for accurate sentiment analysis during interactions, achieving high measurement precision without incurring processing delays during critical sales moments
Solution Approach 2:
The system applies local quality by focusing computational resources on analyzing specific concepts and sentiments relevant to each entity cluster rather than uniformly processing all data. This targeted approach maintains high sentiment analysis accuracy for relevant parameters while reducing overall data processing time by concentrating computational effort where it provides the most value
3Productivity
If predictive models are used to generate responses in real-time, then sales engagement effectiveness is improved, but computational load and response generation time increase
Solution Approach 1:
The system generates predictive models and prepares response strategies in advance based on entity clusters and historical data. By having pre-computed models ready before telecommunication sessions, the system reduces real-time computational load while maintaining the ability to generate effective responses that improve sales engagement productivity
Solution Approach 2:
The system changes parameters by adapting its response generation approach based on the specific entity cluster and detected sentiment. Rather than using a single high-computational model for all situations, the system selects and adjusts models appropriate to each context, maintaining high engagement effectiveness while optimizing computational load by matching model complexity to situation requirements
Data Source
AI summary
A method includes, during a telecommunication session between a first device associated with a organization and a second device associated with an entity: accessing a set of audio data in an audio data stream of the telecommunication session; detecting a set of language signals based on the set of audio data; accessing a model correlating language signals with concepts and sentiment values associated with concepts; based on the model, correlating the set of language signals with a first concept and a first sentiment value associated with the first concept; based on the first concept and the first sentiment value, identifying a subset of responses predicted to result in a second sentiment value associated with the first concept, the second sentiment value exceeding the first sentiment value; generating a notification specifying the first concept and the subset of responses; and serving the notification at the first device.