Nividha Jadeja

Established customer rapport through a recommended loyalty program that drove Starbucks consumption up by 11%; Constructed decisions trees to optimize needed algorithms to better target the learning audience by 15%; Created a customer attrition random forest model, improving monthly retention by 6 basis points for customers likely to attribute by servicing relevant product features for them; Received, cleaned, and prepped data using SQL, and Excel to help build marketing mix models that resulted in a lift in ROI of 10 basis points; Deployed a recommendation engine to production to conditionally recommend other menu items based on past order history, increasing average order size by 7%.

Created Python library to parse and reformat data from external vendors, reducing error rate in the data pipeline by 12%; Ingested data from disparate data sources using a combination of SQL, and MicroStrategy using Python to create data views to be used in BI tools like Tableau; Studied and revamped data dictionaries to include a more robust history for developing consistency across domain; Coordinated with the product and marketing teams to determine what kind of client interactions resulted in maximized service opt-ins, increasing conversions by 18%; Compiled and analyzed data surrounding the prototypes for a project in FnB domain.

Collaborated with product managers to perform cohort analysis that identified an opportunity to reduce pricing by 21% for a segment of users to boost yearly revenue by $50,000; Constructed operational reporting in Tableau to improve scheduling contractors, saving $90,000 in the annual budget; Ran, submitted, and reported on monthly client enrollments, services opted in for, and the employees assigned to clients.