The cost of not measuring right
This calculator shows how much money can move when ad-channel lift assumptions are wrong. Start with the default scenario, then adjust the blue cells to match your business, media plan, or experiment results.
How to read it: Budget A is what you spend today. Budget B is what you would spend after learning each channel's true incremental lift. The key comparison is what your current measurement says, what controlled experiments might reveal, and what your budget could do after recalibration.
Incremental revenue by channel
Budget A Assumed vs. Budget A Actual vs. Budget B Actual
Budget shift
Budget A vs. Budget B allocation
How to read this
The formula.
- Reach = MIN(Budget × Reach Efficiency, Max Reach). Linear up to a cap.
- Channel Incremental Revenue = HH TAM × Reach × Avg Spend per HH × Lift.
- Every term has a physical meaning. No multipliers, no fudge factors.
Reach Efficiency (not shown as an editable field). Derived from channel CPM, target frequency, and an assumed 2.5 people per household: (1M / CPM × 1000) / frequency / 2.5 / HH TAM. For example, Audio at a blended $12 CPM with 4 exposures/month yields ~5.5% of HHs reached per $1M, or 0.055. Under the hood, each channel has a reachEff calibrated this way. It's hidden from the UI because most users don't have the inputs to tune it, and tuning it wrong breaks the model.
The three-act story.
- Act 1 — What you THINK your ads drive. Uses Assumed Lift. This is what attribution or MMM tells you today.
- Act 2 — What they ACTUALLY drive (at the same budget). Uses True Lift. This is what controlled experiments would reveal.
- Act 3 — What they COULD drive after reallocating. Uses True Lift at Budget B. Usually: total budget grows or shrinks, but total revenue grows more than it otherwise would.
Simple Model assumes linear reach (no diminishing returns) and independent channels (no audience overlap). For a more realistic view, switch to Diminishing Returns Model.
Incremental revenue by channel
Budget A Assumed vs. Budget A Actual vs. Budget B Actual
Reach saturation curves
S-curve: how reach responds to incremental spend
How this model works
What this is not. This is not a Marketing Mix Model. Real MMMs estimate lift from historical data with controls for seasonality, competition, adstock, and cross-channel interactions. This tool lets you plug in lift values you already believe (or measure via experiment) and see the allocation implications. The insight isn't "what is the lift" — it's "given a lift, how much does allocation matter."
Differences from Simple Model.
- S-curve reach. Reach follows a logistic S-curve: small budgets buy little reach (below the inflection), medium budgets buy a lot, and large budgets approach — but never exceed — the Max Reach ceiling. Each incremental dollar buys less reach as the channel approaches its cap, which is how real media actually behaves.
- Optional reach deduplication. On by default. Overlapping audiences across channels are netted out so a household reached by both TV and CTV isn't counted twice in the combined reach stat. Toggle off to see the "vanity reach" number.
- Equalized-marginal-ROAS optimizer. Adds dollars to whichever channel currently has the highest marginal return per $; stops when the best remaining channel returns less than $1 per $1. Dedup-unaware: it allocates on per-channel True Lift (which is how lift is measured in the real world — via isolated experiments), and lets dedup stay a display adjustment rather than an allocation signal.
The core point stands: the cost of not testing is almost certainly larger than the cost of testing. You can cap the cost of an experiment. You can't cap the cost of a bad allocation.