Jul 2021 – Feb 2022 · 7 mos

• Developed a Demand Forecasting model for Dream11 to predict the 5 yearly weekly trends of Revenue, Users, Retention, ARPU, Sensitivity to Promotional spend, Marketing CAC, and Paybacks. This helped the Dream11's co-founders, CXOs, Product, and Marketing teams in the following ways:
• 1. User lifecycle management decisions were taken to optimize the promotions by understanding the promotions sensitivity across different user segments thereby maximizing long-term ARPU
• 2. Better planning of different products/contest thereby reducing the overlays and refunds across different matches
• 3. Optimise and allocate the marketing spends across different channels thereby maximising their CACs and LTVs; improving capital efficiency
• 4. Understand the health of different cohorts especially Power User by their predicted Payback, ARPU
• This served as the Annual Operating Plan for Dream11
• ARPU Expansion and User Engagement: Increase ARPU (Average Revenue per User) by 5% by upselling the higher-value product by developing a recommendation system. Achieved product adoption of 90% (driving 78% of the Revenue) and Model Recall of 92%

Jul 2020 – Jul 2021 · 1 yr

• Developed User Segmentation Framework (Using Inapp behavior & User Affluence) which helped us achieve the following:
• 1. Decide differential incentive constructs and cool-off periods for different segments to maximize (Incremental revenue / Incremental Promotional spends)
• 2. Measure the Graduation trends (especially for newly acquired Users)
• This will also help the Product and Marketing team to target promotions and retargeting campaigns
• Helped the Customer Success Team in their staffing activity by projecting the queries and optimizing their query selection by categorizing them into different buckets to help them reduce their first response time
• Help the management team drive the business plan for Fancode by helping them build a projection framework for the FanCode team, predicting their Traffic and Revenue for the next 2 years. This helped them in the following ways:
• 1. Forecasting the lifetime values helped the marketing understand the CACs we need to operate with
• 2. Retention and ARPU impact of different content forms which helps them decide on the content acquisition and creation strategy
• The accuracy of the projections is more than 95% for Traffic and 85% for Revenue

Feb 2019 – Jun 2020 · 1 yr 4 mos

• Helped the product team revamp the chat feature in Dream11. Identified potential user segments and created user journeys to increase readoption for the chat feature leading to an increase in the Week 1 retention of cohorts by 10%
• Improved Capital efficiency by optimizing money kept aside in the Escrow Fund. Reduced the average amount kept by 50% by training a linear model to quantify the daily wallet withdrawal trends and predict system liquidity. Achieved a MAPE of less than 5%

Aug 2017 – Feb 2019 · 1 yr 6 mos · Bengaluru Area, India

• Increase Sales by 30% by increasing Sales Force effectiveness: Built a recommendation system for that was consumed by medical representatives to pitch the right factor (deliver the most effective message) for each doctor (HCP) which is important for them when they are prescribing a drug to their patients
• We achieved a 30% increase in sales of cardiovascular drugs for the doctors for which the custom message attribute was delivered compared to the group for which this recommendation was not used
• Creating a framework and automation of the data collection process has been done thereby reducing the project execution time by 70%
• Worked on a project for a computer technology client to showcase the dispatched trends. We did an RCA and classified the most vulnerable products. This helped the clients to reduce the number of dispatches by 10%

Jun 2016 – Jul 2016 · 1 mo · Kolkata

Dec 2014 – Jan 2015 · 1 mo · Mumbai Area, India