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Topic classification and sentiment analysis for a top retailer

Objective A top retailer wanted to understand its customer sentiments to improve customer perceptions and drive differentiation strategies in the market. The goal was to drive brand strategies leveraging insights from social media analytics.
Our Approach
  • Content from customer reviews, surveys, social media posts, etc. were leveraged.
  • Our text classification engine (that leverages various supervised learning algorithms) was used to categorize each comment into classes such as customer service, pricing, in-store experience, etc.
  • Our sentiment analysis engine used an ensemble of algorithms to arrive at a weighted sentiment score for each comment within each of the classes of comments.
Our Technology
  • The solution was built leveraging tcg mcube, our proprietary analytics platform that can analyse large volumes of unstructured data to drive accelerated insights. The built-in data models and data science algorithms drive velocity to value for our customers.
Project Impact and Outcomes
  • Revised brand strategy helped ameliorate customer sentiments, reduced churn by around 8% and increased revenue by 5%.