What would have happened anyway?
Incrementality is the share of conversions your marketing actually caused — not the ones it merely accompanied. We measure the gap between what happened and what would have happened if you had done nothing. That gap is the only number that proves your marketing worked.
A report tells you the number went up. We tell you whether you’re the reason it did.
The three questions every play has to answer
Estimate before, measure after, explain why — anything less is a guess with a chart on top.
Estimate — before
What could this realistically move, and what is it worth? A range, not a fake‑precise single number.
e.g. “Floor 12%, likely 28%, ceiling 45% — tied to your close rate.”Measure — after
What did it actually move, proven against the “what would’ve happened anyway” baseline?
e.g. “+38% lift vs a held‑back control region.”Explain — why here
Why does the same play work brilliantly for one business and barely move the needle for another?
e.g. “Headroom and margin, not luck.”A dashboard answers none of these — it shows the total and stops.
Estimate: sizing the prize before you spend a dollar
We’ll tell you the floor, the likely, and the ceiling — and we’ll tell you which one we’d bet on.
We don’t promise 10×. Honest forecasting comes as a conservative / expected / optimistic band, not a single number. It’s grounded in your reality: search demand, your close rate, what a customer is worth to you, and how much headroom you have left — you can’t capture demand that isn’t there.
Returns aren’t a straight line. First effort pays back fastest, then it flattens — we plan for the curve, not infinite growth. The estimate is what lets us rank plays before spending: high payoff, low cost, do‑able with your team.
| Scenario | Assumption | Modelled outcome |
|---|---|---|
| Conservative (floor) | Low demand capture, average close rate | +12% qualified enquiries |
| Expected (likely) | Realistic capture given current headroom | +28% qualified enquiries |
| Optimistic (ceiling) | High capture, strong execution, low competition | +45% qualified enquiries |
Illustrative figures to show the shape of an honest range — your numbers are modelled from your own economics in the tool below.
Measure: proving it moved
A counterfactual is the estimate of what would have happened without the marketing — the do‑nothing baseline. Because you can’t observe both outcomes for the same customer, you approximate it with a randomized control, a geo holdout, or a synthetic‑control model.
“It went up” is not proof. Seasonality, a Google update, a competitor’s move, or a concurrent sale can all push the line up without your play doing anything. So we build a counterfactual, then measure the gap — and we lock the baseline before the play launches, not after.
Two honest moves do the work: hold something comparable as a control (an untouched region, page, or channel), and check the forecast against the pre‑period so it’s trustworthy.
Open the incrementality calculator →
Further reading: Lewis & Rao (2015), The Unfavorable Economics of Measuring the Returns to Advertising, Quarterly Journal of Economics.
The Hiilite Measurement Ladder
We don’t claim more certainty than your data can pay for — we tell you which rung of the ladder the proof is standing on.
There’s no single right way to measure — there’s a ladder, and we climb to the highest rung your data can support. Higher rung, stronger proof.
Rung 1 — Before vs after (Interrupted Time Series) · ★
The simplest read: did the line change at the moment you acted? Trustworthy only if nothing else big changed at the same time. Use this when you have clean before/after data and no competing events.
Rung 2 — Treated vs untouched (Difference-in-Differences) · ★★
Run the play in one place, hold a comparable place steady, and compare the change in each. Use this when you have a matched region, page, or segment you can leave alone.
Rung 3 — Modelled forecast (CausalImpact, our default) · ★★★
Build a statistical “what would have happened” line from related signals, then measure the gap. Pioneered by Google’s research team. Use this when you have history and correlated control series but no clean holdout.
Rung 4 — Synthetic control · ★★★★
When there’s no perfect twin, we build one from a blend of comparison cases — the gold standard for a single business. Use this when the result has to stand up to real scrutiny.
Why the same play lands differently
There’s no average business, so we don’t sell you an average result — we estimate yours.
A play that doubled leads for one client might do little for another. That’s not failure, it’s context.
What changes the outcome: headroom left to grow, how competitive your search results are, your pricing and margins, customer loyalty, and whether your team can execute the play to a high standard. We don’t report a single industry “average lift” and pretend it’s your number — we estimate your expected result given your situation. That’s why we’re skeptical of “this one trick works for everyone” marketing: recommendations come with the conditions under which they hold.
Borrowing strength: new clients start ahead
You don’t start from zero — you start from everything we’ve learned on businesses like yours, then your own numbers take over.
A brand‑new client has almost no history, so a naïve approach guesses wildly. We start your estimate from everything we’ve learned across many similar businesses, then let your real results pull it toward your truth.
Every play we measure makes the next client’s forecast smarter — the system compounds. It’s the opposite of a blank slate every engagement.
Try it yourself: the four free tools
The method isn’t a secret — here are the exact tools, free, run them on your own numbers.
Incrementality calculator
Did your marketing actually work? Enter before/after numbers, get your real lift.
Measurement method picker
Tells you which rung of the ladder your data can actually support.
SEO ROI calculator
What a play is worth before you run it, as a range.
Next-play prioritizer
Rank your options by payoff, ceiling, and do‑ability.
Glossary
The measurement vocabulary, one dictionary‑clean definition each.
- Incrementality
- The share of conversions your marketing actually caused, not the ones it merely accompanied.
- Counterfactual
- The estimate of what would have happened without the marketing — the do‑nothing baseline.
- Causal attribution
- Crediting an outcome to a cause that actually produced it, proven against a baseline rather than assumed.
- Holdout
- A randomly withheld group that sees no marketing, used as the control to measure lift.
- Geo‑test
- An experiment that runs a campaign in some regions and not others, then compares the outcomes.
- MMM (marketing mix modeling)
- A top‑down statistical model estimating each channel’s contribution from historical spend and sales.
- MTA (multi‑touch attribution)
- A method that distributes conversion credit across the touchpoints a converter saw.
- Synthetic control
- A counterfactual built from a weighted blend of comparison cases when no single twin exists.
- Difference‑in‑differences (DiD)
- Comparing the before/after change in a treated group against the change in an untouched one.
- Lift
- The measured gap between the actual outcome and the counterfactual baseline.
- Statistical significance
- Confidence that a measured difference is real and not just noise.
- Attribution window
- The time period after exposure within which a conversion is credited to a marketing touch.
MMM vs incrementality vs MTA vs A/B
Use MMM to plan the mix; use incrementality to validate it.
The canonical reference block — four methods across identical columns.
| Method | Unit of randomization | What it measures | Best for | Main limitation | Privacy‑safe? |
|---|---|---|---|---|---|
| Incrementality testing | Users, regions, or time (randomized control / holdout) | Causal lift — conversions your marketing actually caused | Validating that a specific campaign truly worked | Needs a held‑back group; forgoes some reach | Yes — geo/time designs need no user tracking |
| MMM (marketing mix modeling) | None — observational, modelled from history | Each channel’s estimated contribution to sales | Budget‑level, top‑down strategy and mix planning | Correlational; needs long, clean history | Yes — uses aggregate spend and sales |
| Multi‑touch attribution (MTA) | None — observational, path‑based | Credit shared across touchpoints on the path | Operational channel reporting at the user level | Assumes everyone who clicked was influenced; over‑credits paid channels | No — relies on user‑level tracking |
| A/B test | Users (randomized at the variant level) | Causal effect of one change vs another | On‑site changes: pages, creative, flows | Limited to what you can randomize on‑site | Yes — first‑party, no cross‑site tracking |
And here’s what that rigor produces for clients
Real, published client outcomes from our portfolio.
Honesty note: these are reported client outcomes (organic traffic, rankings, leads and revenue over the engagement) — correlational portfolio results, not incrementality‑tested lifts. We show them as results we’ve delivered, and we’d measure the causal share of any of them with the methods on this page.
FormulaK8
eCommerce — +400% organic traffic, page‑1 rankings for 30 keywords, $100,000 revenue increase.
Trueline Moulding
Manufacturing — +300% organic traffic, page‑1 rankings for 20 keywords, $50,000 revenue increase.
Skyleigh McCallum PREC
Real estate — +250% organic traffic, page‑1 rankings for 12 keywords, 3× leads.
Scratch Kitchen
Food & beverage — +220% organic traffic, page‑1 rankings for 15 keywords, 3× leads.
Signature Connections
Professional services — +200% organic traffic, page‑1 rankings for 15 keywords, 5× leads.
Okanagan Vision Therapy
Health & beauty — +180% organic traffic, page‑1 rankings for 8 keywords, 2× leads.
Sheridan Ranch Outfitters
Hospitality & tourism — +150% organic traffic, page‑1 rankings for 10 keywords, 4× leads.
FAQ
How do I know if my marketing is actually working?
You measure incrementality — the additional sales your marketing caused that would not have happened anyway. The honest test is a controlled comparison: hold back a randomized group (a holdout or geo‑test) and compare exposed vs unexposed outcomes. The gap is your causal lift; everything else is correlation a dashboard can’t separate from luck or seasonality.
What is incrementality in marketing?
Incrementality is the share of conversions a marketing activity caused rather than merely accompanied. It answers “what would have happened anyway?” by comparing a treated group against an unexposed control. Attribution credits touchpoints on the path to purchase; incrementality isolates the true causal effect against a counterfactual baseline.
What’s the difference between incrementality and attribution?
Attribution distributes credit across the touchpoints a converter saw and assumes everyone who clicked was influenced. Incrementality asks whether they would have converted without the ad at all. Attribution measures correlation along a path; incrementality measures causation against a control group — and frequently shows attribution overstates paid‑channel impact (a finding documented by Lewis & Rao, 2015).
Is MMM or incrementality testing better for a small business?
Use both for different jobs. Marketing mix modeling (MMM) is a top‑down, privacy‑safe model that estimates each channel’s contribution from historical spend and sales — good for planning the budget mix. Incrementality testing is a bottom‑up controlled experiment that proves causal lift for a specific campaign. Use MMM to plan the mix; use incrementality to validate it.
What is a counterfactual, and why does it matter?
A counterfactual is the estimate of what would have happened without the marketing — the do‑nothing baseline. Because you can’t observe both outcomes for the same customer, you approximate it with a randomized control group, a geo holdout, or a synthetic‑control model. Causal measurement is only ever as good as its counterfactual.
Can I measure incrementality without a data team or tracking pixels?
Yes. Three lightweight designs work without analysts: a geo holdout (pause ads in matched regions), a time‑based on/off test (scheduled blackouts), and a customer holdout (withhold a campaign from a random 10%). Each yields a measurable, financials‑bound lift number with no attribution model or tracking pixel required.
How long does an incrementality test take?
Most lightweight geo or holdout tests run for one to two purchase cycles, and cost mainly the foregone reach in the held‑back group. The slower your conversions, the longer the window needed to reach statistical confidence — so the right duration depends on your sales velocity and how large a difference you need to detect.
Does attribution overstate how well my ads work?
Often, yes. Attribution credits any touchpoint a converter saw, including people who would have bought anyway, so it tends to over‑credit paid channels — especially retargeting and branded search. Incrementality testing strips out that baseline and reports only the lift your marketing actually caused, which is frequently lower than the attributed number.
From doctoral research, in the product every day
We measure marketing the way a scientist would — including being willing to tell you it didn’t work. Every play we run declares up front what it should move, how we’ll prove it, and what would count as failure. This rigor comes from doctoral research and runs in the product every day.
References & further reading
- Lewis, R. A., & Rao, J. M. (2015). The Unfavorable Economics of Measuring the Returns to Advertising. The Quarterly Journal of Economics, 130(4), 1941–1973.
- Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing. Cambridge University Press.
- Teece, D. J. (2007). Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 28(13), 1319–1350.
- Google — Meridian: open‑source marketing mix modeling. github.com/google/meridian
- Meta — GeoLift: open‑source geo‑experiment measurement. github.com/facebookincubator/GeoLift
Ready to find out whether your marketing is the reason?
One conversation to map what to measure, and how we’d prove it.