Customer Lifetime Value Modeling
What you'll learn
- 1Use AI to build CLV models that inform acquisition spend and retention investment
- 2Calculate segment-level CLV to prioritize marketing resources
- 3Create retention strategies calibrated to each segment's lifetime value
Why Customer Lifetime Value Changes Everything
Most retailers think in terms of individual transactions. But the real question is not "How much did this customer spend today?" — it is "How much will this customer spend over their entire relationship with us?" Customer Lifetime Value (CLV) answers this, and it changes how you make decisions about everything.
Building a CLV Model with AI
AI can help you construct and interpret CLV models even without a data science team:
"You are a retail analytics consultant. Help me build a customer lifetime value model for my [store type] with these characteristics: average transaction value of $[X], average purchase frequency of [Y] times per year, average customer lifespan of [Z] years, and gross margin of [W]%. Calculate the basic CLV and then help me build a more sophisticated model that accounts for retention rate decay, discount rate, and referral value."
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What you'll learn:
- Use AI to build CLV models that inform acquisition spend and retention investment
- Calculate segment-level CLV to prioritize marketing resources
- Create retention strategies calibrated to each segment's lifetime value