This project focuses on predicting Customer Lifetime Values (CLV) using advanced analytics techniques. The primary goal is to analyze customer data, prepare it for modeling, and build a predictive model to estimate the potential future value of customers.
The most challenging aspect of the project was data preparation. Various methods were employed to handle outliers and ensure the dataset's quality.
Outliers in the data were addressed using several techniques to enhance model performance and accuracy.
A predictive model was developed to estimate Customer Lifetime Values. The model leverages advanced analytics to provide accurate predictions.
Utilized the CLV predictions to segment customers effectively. Recommendations for customer segmentation were made based on CLV predictions.
Nevzat Taha Ayhan