ID 2330: Predicting customer lifetime value in e-commerce

Master thesis

In collaboration with Re-In.

The purpose of this thesis is to model the customer lifetime value (CLV) of a company from a holistic perspective, by integrating customer interactions from various channels and databases. Specifically, we will be analyzing customer data from SAP data (such as marketing channel, revenue, products information, …) and interaction data (behavior on newsletters and websites). The objective is to identify the most valuable customers, predict which marketing actions will create more value, and ultimately, improve the company’s revenue and profit.

Tasks

• Literature and patents review.
• Data organization and preprocessing.
• Design and training of models to predict revenue and profit.
• Models comparison and evaluation.

Requirements

• Proficiency in Python
• Good experience in machine learning.

Supervisors

Dr. Dario Zanca

Tobias Hipp

Data scientist at Re-In