Outbound-Marketing Interaction Scoring Through LLM Transcript Analysis
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Solution Overview
Problem
Current technical systems in contact centers lack accurate measurement of agent performance during sales or marketing interactions, as they rely on predefined mathematical calculations and metadata rather than analyzing the content of conversations, failing to provide insights into sales effectiveness and customer interest.
Innovation Solution
A computerized method using Generative Artificial Intelligence (AI) Large Language Models (LLM) to analyze outbound-marketing interactions, identify marketed products, construct prompts based on product features and questions, and calculate scores for customer interest and agent effectiveness, enabling precise evaluation and follow-on actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If predefined mathematical calculations and metadata are used to measure agent performance, then the measurement process is simple and fast, but the accuracy and insight into sales effectiveness are insufficient
Solution Approach 1:
The patent replaces traditional mechanical/mathematical calculation systems with an AI-based semantic analysis system. Instead of using predefined mathematical formulas to calculate performance scores from metadata, the system employs large language models to perform natural language processing on conversation transcripts, enabling accurate understanding of sales effectiveness, customer interest, and agent performance through semantic comprehension rather than numerical computation.
Solution Approach 2:
The patent introduces an AI language model as an intermediary between the raw conversation data and the performance measurement process. This intermediary layer processes the unstructured transcript data, extracts meaningful insights about customer interest and sales effectiveness, and transforms them into structured performance metrics, thereby bridging the gap between simple metadata and accurate performance assessment.
2Productivity
If all interactions are filtered for evaluation based on metadata, then the filtering process is efficient, but the relevant interactions requiring evaluation may be missed
Solution Approach 1:
The patent performs preliminary semantic analysis on interaction transcripts before the final evaluation stage. By using AI models to pre-process and assess the content of conversations, the system identifies which interactions actually require human evaluation based on their semantic content rather than just metadata characteristics, enabling more precise filtering that maintains both efficiency and relevance.
Solution Approach 2:
The patent replaces metadata-based mechanical filtering with AI-driven semantic filtering. Instead of relying on predefined rules that check metadata fields like call duration or channel type, the system uses natural language processing to understand the actual content and context of interactions, thereby identifying which ones truly require evaluation based on their sales effectiveness and customer interest levels.
3Measurement precision
If AI Large Language Models are used to analyze conversation content, then accurate performance measurement is achieved, but the processing time and computational resources increase
Solution Approach 1:
The patent segments the performance measurement process into distinct AI processing stages: transcript analysis, customer interest assessment, sales effectiveness evaluation, and performance scoring. This segmentation allows the system to process different aspects of conversations separately and efficiently, reducing overall processing time while maintaining comprehensive analysis accuracy.
Solution Approach 2:
The patent implements partial processing by analyzing only the most relevant portions of conversation transcripts using AI models. Instead of processing every single word equally, the system identifies and focuses on key segments that contain information about customer interest, product features, and sales outcomes, thereby reducing computational overhead while preserving measurement accuracy.
Data Source
AI summary
A computerized-method for calculating a score of an outbound-marketing interaction. The computerized-method includes: (i) retrieving a transcription of the outbound-marketing interaction; (ii) identifying a product that is being marketed in the outbound-marketing interaction by executing an Artificial Intelligence (AI) Large Language Model (LLM) with a check-product-prompt having the transcription embedded therein; (iii) constructing a prompt based on: (a) the identified product; (b) one or more features of the identified product; (c) the transcription; and (d) one or more questions. Each question is related to a section in one or more preconfigured-sections; (iv) executing the AI LLM with the constructed prompt to yield an answer and a question-score to each question of the one or more questions; (v) calculating the score of the outbound-marketing interaction based on the question-score of each question; and (vi) sending the score to one or more applications for follow-on actions based on the score.