Authors : Harikaran G.; Vishwash C.; Pavan Kumar Reddy; Sudha S.; Samson Swaroop Paturi; Bharani Kumar Depuru

Volume/Issue : Volume 10 - 2025, Issue 12 - December

Google Scholar : https://tinyurl.com/2ns6ujad

Scribd : https://tinyurl.com/nd3zx9z5

DOI : https://doi.org/10.38124/ijisrt/25dec1539

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The evaluation and enhancement of sales communication skills represent a critical yet challenging aspect of corporate training and development. Traditional methods, which rely on manual observation and subjective feedback, are often inconsistent, time-consuming, and difficult to scale across large sales teams. This research presents an advanced, AI- driven system designed to automate the analysis of sales pitches and provide objective, data-driven recommendations for performance improvement. Our system integrates a suite of sophisticated, self-hosted machine learning models to perform comprehensive speech and behavioral analysis, ensuring data privacy and operational independence from third-party APIs. Key components include a high-accuracy Speech-to-Text (STT) engine based on OpenAI's Whisper model for transcription, a deep learning model for nuanced tone and emotion recognition, and algorithmic detectors for identifying speech patterns such as pause frequency, filler word usage, and speaking pace. The system evaluates sales pitches against a robust set of performance metrics, benchmarking individual performance against data from top-quartile sales professionals. It then generates a detailed report with quantitative scores and qualitative, actionable feedback tailored to the individual. The core contributions of this work include: (1) a fully automated pipeline for multi-modal analysis of sales communications using self-hosted models; (2) a novel scoring mechanism that correlates speech analytics with successful sales outcomes; and (3) a dual-mode recommendation engine that provides both automated improvement plans and a flexible, user-driven interface for manual exploration. Our evaluation demonstrates that the system achieves high accuracy in its analytical components and that its recommendations correlate strongly with performance improvements observed in real-world scenarios. This technology offers a transformative solution for scaling personalized sales coaching, accelerating employee onboarding, and fostering a culture of continuous, data-informed improvement.

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