How to Calculate Customer Lifetime Value for Data Science Interviews
Customer Lifetime Value (CLV) is one of the most popular business metrics tested in data science and analytics interviews. Whether you’re aspiring to be a product analyst, growth analyst, or business-facing data scientist, you’ll almost certainly face CLV questions. This comprehensive guide will help you master CLV concepts, calculations, and interview techniques—step by step. You’ll learn the theory, formulas, common pitfalls, and how to approach CLV from both a business and technical perspective, so you can ace your next analytics or data science interview.
Customer Lifetime Value is one of the most sought-after business metrics in data analytics interviews. But why? Companies use CLV to understand how much revenue they can expect from a customer over the entire relationship. This directly impacts marketing spend, product decisions, and overall business strategy.
CLV, or Customer Lifetime Value, is the total net profit a business expects to earn from a customer throughout their relationship with the company.
