Leading Data Science and Campaign Management for Tata Play. Driving Revenue, Reducing Churn and Improving Customer Experience by building and deploying analytical solutions end to endKey Projects : Recommendation Engine, Sentiment Analysis, Churn Models, Cross Sell and Up Sell model

Prepare and execute data driven strategy for rolling out products in various geographiesDefine and implement credit policy to create a healthy bookPrepare Risk Models for identifying risky customersIdentify cross sell opportunities on existing baseProducts: Consumer Durable Loan, Personal Loan, Loan Against Property and Business Loan

Analytics

Marketing Analytics | Predictive Models | Deep Dive AnalysisProducts ownership: Home Loan, PayZapp and Loan Against PropertyIncrease sales of the Home Loan and LAP by building Predictive Models and Micro SegmentsImprove traffic at PayZapp by developing Predictive Model with Personalized Offers

Experienced in understanding data from various industries like DTH, BFSI and NGO.Demonstrated capability in building predictive models like Up Sell, Cross Sell, Lead Scoring Model and Estimation Model. BFSI Industry:1) 13th Month Lapsation Model: a. Build a 13th month lapsation model for an insurance company. Increased 13th month persistency and reduced calling cost b. Close engagement with business (client) to understand persistency concept & factors affecting it2) Up Sell Model: a. Worked closely with the Marketing team of the Insurance company to increase the sales b. Helped Marketing team in achieving up sell target Mortgage Industry:1) Lead Scoring Model a. Build a lead scoring model there by assisting client in targeting right set of customers b. Helped client in identifying right channels (online and offline) to engage with c. Increased call-center efficiency thereby reducing costDTH Industry:Build Predictive scorecards and performed strategic analysis for understanding customer behavior 1) Up Sell Model: a. Helped client in identifying customers who have higher propensity to upgrade their product2) Cross Sell Model: a. Build a predictive model for identifying customers who are most likely to buy a value added service product b. Models were build for two VAS productsStrategic Analysis:- a product is introduced - price hike is done on a product- bonus is removed- offer is introducedSuccessfully prepared proposals for multiple clients there by helping organization in bagging projects.Capable of handling multiple projects, a good team player and always enthusiastic about data challenges.

Data Analytics

SAP Basis Consultant