Saranya Mohan

• Leading marketplace analytics team (3-4 data analysts) across various strategic sub groups like seller growth, category analytics, new verticals (refurbished, preowned, subscription, flash-deals), marketing, seller services.
• Building scalable analytics solutions (ML model based, Rule based, Heuristic ) to solve various business problems supporting 100+ strategic account managers, 8 product managers and various category/business teams.
• Define strategic pillars for the various analytics subgroups for marketplace business and develop roadmap for the team with clear focus on 1) Foundation- stable data, dashboards, business deep dives 2) Opportunity estimations & impact measurement 3) Product & program support and scaling.

• Retail Analytics Forecasting: Automated forecasting of revenue For WMT.com & Jet.com using Ensemble stacking of Linear Regression, Logistics Regression, Random Forest model, XGBoost model. Outcome: Weekly forecasting of revenue and units helped category planners effectively plan for the quarter and achieve division level targets with less 5% MAPE & orders forecast helped customer support team in effectively staffing which helped in reduced operations cost.
• Customer Acquisition and Monthly Cohorts Revenue Forecasting Outcome: Developing a time series forecast using SARIMAX, ARIMAX model for forecasting the metrics like new customers, repeat customers, retention rate and revenue for repeat and new customers (Error percentage for each metrics within ~20%)
• Automated WBR, QBR & Other Reporting: Outcome: Provided Retail leadership (include CRO) visibility into overall performance of WMT ecommerce in a full automated WBR and QBR report which was considered source of true in the organization
• Campaign Performance & Accuracy Scoring: Developed series of tableau reports for pre/post analysis of top priority SKUs performance for each campaign during peak holiday season.

• Customer Segmentation platform for Ad targeting: Identified customer segments with high propensity for conversion based on past customer behavior for 30,60,90 days
• Outcome: Generated 30% higher ROI from the segmented customers compared to other segments & generating ~10 M revenue monthly
• Visa Ad measurement for evaluating campaign effectiveness: Ad campaign measurement by evaluating key metrics for understanding customer acquisitions and loyalty.

Data Engineering: Developed an efficient data pipeline which improved the redundant data manipulations process and set a smooth reporting Created a data warehouse which reduced the overhead by ~50% and helped

timely refresh of data with reduced run time of (3 hours).

Descriptive analytics for reporting: Performed Ad-hoc analysis for understanding customer demographics and used RFM

segmentation for identifying high, low and medium value customers.