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Counterfactual Labs

Causal marketing consultancy

Stop paying customers who were already going to buy.

We estimate — with causal inference, not correlation — how much of your incentive budget goes to people who would have bought anyway. Then we help you spend only on the genuinely persuadable, and prove the lift against a holdout.

Estimate first, proof second. We label every number for what it is.

Your best-converting campaign might be your most wasteful.

Discounts, 0% APR, rebates — a real share goes to “sure-things”: customers who would have purchased with no incentive at all. Standard analytics can't see this, because they measure who converted, not who was moved. The money looks well spent. It isn't.

  • Attribution credits the incentive for sales that would have happened anyway.
  • You can't separate persuasion from coincidence without a counterfactual.
  • The waste compounds every campaign cycle — invisibly.

Four kinds of customer. You should only pay for one.

Every customer falls into a quadrant by how much marketing actually moves them — their causal uplift (CATE).

Persuadable

High +uplift

Marketing causes the purchase. Spend here.

Sure-thing

~0 uplift, high baseline

They'd have bought anyway. This is your wasted incentive.

Lost-cause

~0 uplift, low baseline

Won't convert either way. Don't bother.

Sleeping-dog

Negative uplift

Contact actively hurts. Exclude them.

From a spreadsheet to a defensible number.

  1. 01

    We map your data

    We take your campaign, incentive and outcome data and map it into a causal model — confirming every load-bearing definition with you.

  2. 02

    We estimate the waste

    We estimate uplift per customer and total the incentive spent on customers who would have bought anyway.

    Estimate
  3. 03

    We target the persuadable

    We hand you a ranked audience of the customers marketing actually moves.

  4. 04

    We measure the lift

    We design a randomized holdout, and after your next cycle we measure the real lift against it.

    Proof

The number has to be trustworthy. So the science comes first.

Our work is grounded in causal inference — uplift meta-learners, propensity modelling, holdout randomization and validity checks (overlap, placebo, calibration), not dashboard heuristics. Every figure we hand you is a measured causal estimate, checked for validity — never a guess.

  • Uplift / CATE per customer
  • Holdout-measured lift, not attribution
  • Validity & sensitivity checks on every estimate

Built for big-incentive verticals. Starting with automotive.

Automotive runs enormous incentive budgets — 0% APR, rebates, loyalty offers — with a real fraction spent on buyers who were always going to buy. It’s where proving wasted spend pays for itself fastest. We’re partnering with a leading automotive brand as our anchor client.

Find out what you're wasting.

Book a consultation and we'll walk you through the diagnostic on your own campaign structure.

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