Predicted customer lifetime value
Predicted customer lifetime value is an estimate of what a customer will spend with you over a future period, made from her behavior and orders so far. It lets a store put its effort, and its ad budget, where the future value is, not where the last click was.
What it means
Historical lifetime value looks back: what has this customer spent. The predicted version looks forward: given what she has done, what is she likely to do. The second number is the useful one, because it exists for a customer with one order, or none, and it changes as she acts. The model learns from the store's own customers which early signals end in a long relationship.
Why it matters for a store
Ad platforms optimize for the cheapest conversion, not for the customer worth the most. Feeding them audiences built from predicted value changes what they look for. The same number decides who deserves a retention message, a loyalty nudge or a coupon, and who was going to buy anyway.
An example
Two shoppers each place a first order of the same size. One arrived from a brand search, viewed the care guide and signed up for the newsletter. The other arrived from a discount ad and used a coupon. The first gets a higher predicted value and lands in the lookalike seed; the second does not.
How the agent uses it
Ads audiences are built from predicted customer value, synced to Meta, Google and TikTok. How it decides explains what changes when you choose long-term value as the goal.
Related terms
See also RFM segmentation, Incrementality, Holdout group.
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