Vijay Narasimha

TCS U.K

Bristol, UK

07.2021 - Current

Analyzed complex datasets using fuzzy logic methodologies while classifying over 25 different attributes associated with changing addresses or marital statuses among pension scheme members within six months of implementation
Implemented a semi-automatic member merging solution that dramatically decreased back office efforts for member matching by 80%, streamlining operations to improve overall efficiency within the team
Applied statistical modelling and visualization techniques to find the hidden patterns and user behavior for resolving the core data fields like First Name, Middle Name, Surname, Date of Birth, National Insurance Number, Postal Address of pension scheme members
Result-Improved the data quality by 85%
Successfully built the segmentation engine to segment the Pension scheme members based on Recency, Frequency & Monetary (RFM), used the Feature Engineering technique to arrive at the scoring of RFM and then applying the life changing circumstances, Demographic features
Result-Successful member segmentation for Personalization recommendations
Engineered a robust sentiment analysis framework that processed over 10,000 customer feedback comments; this initiative empowered the contact center to enhance service quality and response strategies, leading to higher customer satisfaction

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