Predictive Lifetime Value (CLV) Data Analyst

Job Overview & Business Challenge

Our business collects vast amounts of customer interaction data from various platforms (website, mobile app, email campaigns, and social media). The core challenge is to move beyond simple data collection and build an automated and scalable system for accurately predicting Customer Lifetime Value (CLV) in real-time. The ultimate goal is to identify high-value customers, optimize marketing budgets, and personalize user experiences to increase loyalty.

Key Items to Solve the Challenge

• Cloud-Based Data Pipeline: Design and implement a robust data pipeline in a cloud platform (e.g., AWS, Google Cloud, or Azure) to collect, process, and store customer data from diverse sources. This pipeline must be capable of real-time operation.
• Feature Engineering & Analysis: Identify and extract key features from customer behavioral data (e.g., purchase history, site activity, email open rates, and ad interactions) that are critical for CLV prediction models.

• Feature Engineering & Analysis: Identify and extract key features from customer behavioral data (e.g., purchase history, site activity, email open rates, and ad interactions) that are critical for CLV prediction models.
• Predictive Modeling: Utilize machine learning algorithms (such as regression models or gradient boosting) to build a CLV prediction model. This model should be able to predict the future value of each customer based on historical data. The model must be validated and optimized for accuracy.
• Deployment and Integration: Deploy the model in a scalable cloud environment and integrate it with our operational systems (e.g., CRM and marketing tools). Create an API that allows product and marketing teams to automatically access the model's predictions.
• Dashboard and Reporting: Build a comprehensive dashboard using business intelligence (BI) tools like Tableau or Power BI. This dashboard will allow various teams to monitor customer CLV and visualize the results of the analysis.

Why This Project is Right for You

This challenge allows a data professional to participate in all stages of a data project, from data engineering and modeling to large-scale deployment, and directly contribute to business growth.