The headline, in one sentence
Media produced about $131.6M of your $476.6M — roughly 28 cents in every dollar. That includes five channels whose own share we can’t measure. Your one measurable channel genuinely cannot be called yet.
That is not a hedge and it is not a fault in the model. Three years of weekly history is simply not enough evidence to separate six channels that mostly ran at the same time. Below, the channels are sorted by how much we actually know about them — not by how good their number looks.
We can't put a number on five of your channels on their own
When we hid known returns in your own spend, the setup we chose could not find the returns of Paid Social, Paid Search, Programmatic Display, Affiliate and Retail Media on their own. All of them still count toward the total effect of your media above.
A test that changes one channel’s spend on its own is what would make it measurable. Each channel’s own line in the list of returns below says what it would take.
Show me the statistics
Held back on this run: Paid Social, Paid Search, Programmatic Display, Affiliate, Retail Media. The media total at the top of this page, its share of your sales and the business you would have had anyway still count them — their spend still did something; we just cannot say how much each one did. The budget planner leaves them at today’s spend.
The validation tab lists every change we made to this fit, each of these channels included, in the engine’s own words.
Three years of revenue, and where it came from
Each band is one channel's contribution, stacked on the business you'd have had without any media at all. The white line is what you actually sold.
$476.6M
actually sold
The pale mass underneath is the business you’d have had anyway: $345.0M, three quarters of everything.
The hatched band is other media: Paid Social, Paid Search, Programmatic Display, Affiliate and Retail Media. Its weeks are drawn so the stack still adds up to what you sold, but it is not measured on its own, so it gets no share of its own here.
Your one measurable channel does not have a range that sits entirely above break-even.
That is a real result, not a missing one: it says your data cannot yet separate what each channel did from what the others did at the same time. Nothing below should be cut on this evidence, and nothing below should be scaled on it either.
What would settle it: one channel deliberately switched off, or moved out of step with the others, for six weeks or more. The evidence tab shows how little each range moved away from what you assumed going in.
What each dollar came back as
Sorted by how much your data actually pinned down. The dotted line is break-even — a band crossing it means we can't tell you whether it won or lost.
$0.51 to $2.34 · best-measured channel you have, and it still straddles break-even
You get no return and no budget advice for Paid Social. A test that changes its spend on its own would make it measurable.
You get no return and no budget advice for Paid Search. A test that changes its spend on its own would make it measurable.
You get no return and no budget advice for Programmatic Display. A test that changes its spend on its own would make it measurable.
You get no return and no budget advice for Affiliate. A test that changes its spend on its own would make it measurable.
You get no return and no budget advice for Retail Media. A test that changes its spend on its own would make it measurable.
Do this first
Don't cut anything on this evidence.
Nothing here has shown it lost money — the ranges simply cross break-even in both directions. A cut made on this page would be a cut made on noise, and next year would look like proof that the cut worked.
Do this next
To measure Programmatic Display on its own, pause it everywhere for 13 weeks.
A test this long would add little: the range we could give for Programmatic Display would narrow by only about 10%.
Don't do this
Don't share out the media total by spend.
We could not measure what those channels each did on their own. Splitting the total between them by what each one cost would print exactly the number this page refuses to — a guess wearing a return’s clothes.
Nine things would make next quarter’s answer sharper. Four of them are ours.
Everything the run flagged, in one place and sorted by who actually does the work. Nothing in the first two columns is generic — each item is the engine’s own words about a specific finding in your data. The third column is opinion, and is drawn so you can tell at a glance.
Your media team
changes to how you buy, not to the data
Severe variance inflation
Usually the same cause as near-duplicate channels: this one is reconstructable from the others. Combine it with whichever channel it shadows, or drop a control that is standing in for it. A channel that launched partway through the window hits this too, because the baseline absorbs the period it was dark.
More than met. Retail Media has no individual number on this run at all — when we hid known returns in your own spend, the setup we chose could not find its own — so there is nothing left to treat as unreliable. It counts only toward the total effect of your media.
FROM · vif · Retail Media · 34.01 vs 10
Two channels move closely together
Two channels move closely together
Two channels move closely together
Vary their budgets independently for a quarter if you can. Until then, trust the pair's combined return more than either individual number.
FROM · collinearity · Paid Social, Paid Search, Paid Social, Programmatic Display, Paid Search, Programmatic Display
Affiliate spend barely varies
Nothing can be recovered from a flat budget — the model needs the spend to move to see what moves with it. Either vary the budget deliberately for a quarter, or accept that this channel’s number is your assumption reflected back.
FROM · flat_spend · Affiliate · 0.0119 vs 0.05
TV — Broadcast is off air most weeks
Nothing to fix in the data — this is a media plan, not a defect. Expect a wide range on this channel and weight it accordingly; a lift test on a single burst would pin it faster than more history would.
FROM · burst_flighting · TV — Broadcast · 0.596 vs 0.5
Retail Media ran for only part of the window
Nothing to fix in the data — this is what the buy looked like. Treat the channel's return as provisional until it has a full year, and be especially wary of scaling its budget on this fit.
FROM · partial_history · Retail Media · 0.372 vs 0.5
Us
nothing for you to do — one click and we rerun
Lowest bulk ESS 185 at baseline_knots. Under 400 the point estimates carry visible Monte Carlo error.
FROM · scorecard · ess_bulk · 184.71 vs 400
Lowest tail ESS 250 at roi. Under 400 the interval endpoints carry visible Monte Carlo error.
FROM · scorecard · ess_tail · 249.99 vs 400
3 divergences in 1000 draws (0.30%). Divergences mean the sampler could not follow the posterior's curvature, so the region it failed in is under-represented. Raise target_accept_prob (spec section 6) before trusting the numbers.
FROM · scorecard · divergences · 0.003 vs 0.001
Runs the same fit with twice the draws (500 → 1000), and a more careful search (target_accept_prob 0.9 → 0.95). Nothing else moves — same data, same priors, same holdout — so the two are comparable. If the answer shifts, that shift is itself the finding.
MAPE 6.1% (target <10%), R-squared 0.895 (target >0.9). In-sample fit is necessary, not sufficient — a model with enough knots fits anything.
FROM · scorecard · in_sample · 6.09 vs 10
Highest VIF 34.0 on Retail Media (warn 5, hard 10), measured after adstock and saturation because that is what the model sees.
FROM · scorecard · vif · 34.01 vs 5
Outside the prior's 1st-99th percentile: Paid Search (its return is withheld). Either the prior is wrong or the data are being over-read; the fit is a compromise between two claims that disagree.
FROM · scorecard · prior_conflict · 1 vs 0
Those are a property of the data and the priors rather than of the search, so running it again will not move them. Listed here so nobody goes looking for a fix that does not exist.
Nothing to fix in your file
No extreme weeks, no gaps in what you sold, no column that is really an outcome rather than a cause, and a measure of underlying demand is present. Those are the four things that would have landed here, and all four came back clean.
General advice — not from your data
true of most MMMs, not measured on this one
Keep a channel dark somewhere, on purpose
The single cheapest thing you can do for next year's model is leave one region or one fortnight without a channel that normally runs everywhere. Continuous channels are the hardest to measure precisely because they never stop.
Record promotions and price changes weekly
A column of promotion weeks and a column of average price cost nothing to keep, and their absence is the usual reason media ends up with the credit for a price cut. Most uploads arrive without them.
This column is separated on purpose. Everything in the other two is traceable to a finding in your run; nothing in this one is.
Stamped so this can be reproduced
Every fit is written to disk with the data it read and the version that read it, so a number in last quarter’s deck can be traced to the run that produced it — and that run can be produced again. Nothing on these pages is computed when you open them.
- run
- 20261028-155709-0dc4df
- data
- c725e113668a
- engine
- 0.3.0
- holdout
- 0 periods
- dataset
- messy_upload
- fitted
- 28 Oct 2026
- took
- 2 min
- name
- unnamed
Past runs
Every fit is kept, with its verdict and its date, so a number in last quarter’s deck can always be traced back to the run that produced it.
This is the only fit on record. The second one is where this list starts earning its space.