Customer Lifetime Value Modeling with Applications in Python and R: Lessons and Experiences from Industry and Research on how to Become a Customer-Centric

Firms and organisations cannot exist without customers. They essentially constitute the key ingredient to make a firm profitable and add shareholder and societal value. Despite recent technological advances in both data storage as well as processing and analysis, many small to large-scale firms are still struggling to quantify customer value, optimise customer relationships, facilitate customer experiences and identify customer journeys. Due to a nearly continuously expanding product portfolio, with new products and services being developed and marketed on an on-going basis, along a diversity of existing as well as innovative channels, modeling customer lifetime value is a far from simple exercise with many challenges and difficulties arising. More specifically, throughout our dealings with firms, we often found that simple questions such as "Who is actually your customer?", "Who are your most valuable customers?", "What is the best way to acquire new customers"?, "Why do your customers leave you?", "What product/service should be offered to what customer?", "How can you sell more to your customers?", "How do you measure customer value?", often provoked intense (if not fierce) discussions with answers not always readily available and uniformly agreed upon by business practitioners across different departments. This book tries to answer exactly these questions using data-driven and analytical techniques and insights. More specifically, we try to provide a clear and to-the-point guide of how to define, quantify, model and deploy Customer Lifetime Value (CLV) models from various perspectives by first identifying and defining the key problems and then offering ways to tackle them using carefully selected data combined with state of the art analytics.