In short
Customer lifetime value is the total profit a customer generates over the whole of their relationship with a business, not just their first purchase. Marketing teams use predicted CLV to bid on future value, so that acquiring a customer worth a great deal over years is not judged solely on the margin of the first order. This is exactly the signal that value-based bidding and predictive audiences consume: told what a customer is likely to be worth, platforms can optimize toward high-value acquisition rather than cheap conversions. The dependency is data. A CLV model is only as good as the purchase and value history feeding it, and its output only helps a platform if that value is delivered as a signal the platform can act on. Sending value data, not just conversion counts, is what lets bidding chase profit instead of volume, which is where CLV stops being a report and starts being a lever.
Why platforms want it
Two customers can look identical at first purchase and differ tenfold over two years. Sending predicted value with conversion events lets bidding algorithms pay more for the customers who will be worth more.
From metric to signal
CLV only changes media performance when it leaves the spreadsheet: as value fields on conversion events, or as high-value segments synced to platform audiences for targeting and lookalike seeding.
How does CLV change what you bid on?
A subscription brand sends predicted lifetime value with each signup instead of a flat conversion. Value-based bidding then leans toward the segments that renew for years, rather than the cheapest signups that churn in a month.
Reference: Google Analytics Help, user lifetime (GA4)