The cost of not measuring right
Every media plan holds two numbers for each channel: the lift you assume it generates, and the lift a controlled experiment would actually measure. This tool shows what's hiding in the gap — and what reallocating on the truth adds to your top line.
Budget A = what you spend on ads today. Budget B = what you'd spend after learning each channel's true lift (usually larger: more budget flows to the channels that actually work, and those channels can absorb it profitably up to their reach ceiling). Blue cells are editable — change any value and the tool recalculates live. Hover the ? next to any row label for a quick definition.
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.
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, total revenue grows more.
Napkin Math assumes linear reach (no diminishing returns) and independent channels (no audience overlap). For a more realistic view, switch to MMM-Lite.
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
Differences from Napkin Math.
- S-curve reach. Each incremental dollar buys less reach as the channel approaches its Max Reach — how real media actually behaves.
- Optional reach deduplication. When on, overlapping audiences across channels are netted out so a household reached by both TV and CTV isn't counted twice. Watch what happens to total revenue when you flip the toggle — that's the reach inflation hiding in most attribution models.
- 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.
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.