Apply Customer Engagement Strategy

W3C Strategic Highlights September 2019 This report was prepared for the September 2019 W3C Advisory Committee Meeting (W3C Member link). See the accompanying W3C Fact Sheet — September 2019. For th

This project has two primary goals. First, it aims to improve inventory management by using the ARIMA model for accurate daily sales quantity forecasts. Second, it seeks to enhance customer engagement

Predicted customer churn using Netflix engagement data by applying classification models and evaluating their performance to improve retention strategy. Deployed a user-friendly Streamlit app for real

This project applies K-Means Clustering, an unsupervised machine learning algorithm, to group mall customers based on their Annual Income and Spending Score. The goal is to help businesses understand

Customer Journey Analysis using Clustering helps businesses segment customers based on their behavior, such as website visits, product views, cart additions, purchases, and time spent. By applying K-M

Segments credit card customers using behavioral clustering to drive engagement and personalization strategies. Applies K-Means on features like spend, transaction frequency, and payment delays. Includ

Applies the RFM model to analyze customer data, enabling targeted marketing strategies by classifying customers based on their purchasing behavior and engagement.

Explore customer data, apply clustering techniques, and visualize distinct customer groups. Tailor strategies, boost satisfaction, and make informed decisions. Ready to transform customer engagement?