Actionable Intelligence for Sales Call Recordings

This research highlights the challenges associated with assessing large volumes of recorded discussions, including issues with data privacy and legal compliance. This research main objective is to increase customer happiness and sales performance by using data-driven insights from sales call records. The dataset consists of sales call recordings transcribed into text, containing customer interactions, questions, objections, and competitor mentions. This work utilizes natural language processing (NLP) techniques such as Named Entity Recognition (NER), sentiment analysis (using VADER), and keyword extraction. The results revealed key insights, such as customer pain points, sentiment trends, competitor mentions, and common objections. The negative statements of text is around 2.7%, neutral statements of text reflects 69.7%, positive statements of text reflects 27.6% and compound statements of text reflects 0.9999%.