A sample churn prevention solution for an fintech app
Wrangling & Modeling of Duolingo Data (48-hour Analysis Challenge)
Clean and Apply RFM technique to rank and group clusters to identify the best customers and perform targeted marketing campaigns, using real online transaction data
User profiling refers to creating detailed profiles that represent the behaviors and preferences of users, and segmentation divides the user base into distinct groups with common characteristics, making it easier to target specific segments with personalized marketing, products, or services.
This research study focuses on analyzing user behavior data from Duolingo, a language learning app. The primary objective is to predict user churn using semi-supervised learning techniques.
User segmentation using a sort of classifiers (some quite uncommon like the Fuzzy K-Nearest Neighbours).
In-app survey SDK for Web, iOS, and Android.
My 'Out of PM scopes' data project
Rule-Based User Segmentation
User segmentation API
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