A model trained on your own purchase history predicts what each customer will be worth over the next 12 or 24 months, from what they did in their first days — the products they chose, the channel they came from, the discount they needed, how they paid. The prediction is pushed back into the ad platforms as a conversion value, so bidding optimises toward customers who stay instead of customers who convert cheaply and leave.
A discount hunter and a customer who will reorder for three years both send back the same conversion value. The algorithm faithfully buys more of the cheap one, because that is what you asked it for.
A single average across the whole base hides that one channel brings customers worth four times the other. Blended LTV is a comforting number that supports no decision.
Real LTV is known when the cohort matures — which is exactly when the budget that created it has already been spent.
Twelve or twenty-four months, revenue or contribution margin, returns and refunds netted or not. We settle this with your finance team first, because a model that predicts the wrong quantity is worse than no model.
First basket contents, category, discount depth, acquisition channel and campaign, device, payment method, delivery outcome, early support contact. The point is to predict from what you know at day one, not day ninety.
The model is trained on older cohorts and tested on ones it never saw, then compared against the naive baseline — first-order value. If it does not beat the baseline meaningfully, we say so and stop.
Predicted value goes back to Meta and Google as a conversion value through their APIs, and into your CRM and BI. Bidding then optimises toward long-term value with the same machinery it already uses.
Cohorts age, the mix changes, a new channel behaves differently. Accuracy is tracked monthly against realised value, and the model is refit on a schedule rather than when someone loses trust in it.
We build in your stack rather than moving you onto ours. The list below is what this solution most often connects to — other systems are a scoping question, not a blocker.
Accurate enough to rank customers, rarely accurate per individual. We report how much better it is than first-order value on a held-out cohort, and that comparison is what matters for bidding.
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