Step two · before any modelling

Can this data answer the question at all?

A fit takes minutes and hands back confident-looking numbers whether or not the design could ever have identified them. So we ask whether it could first, and it costs a second. Nothing here has been modelled — this is arithmetic on your file.

The verdict

Your data can answer the question. Nothing here stops the model from running, and nothing will make a channel's number meaningless.

4 things to carry forward, all about how wide the answers will be, not whether you get them: Paid Social, Paid Search, TV — Broadcast and Out of Home.

How this verdict is decided

NO-GOsomething is broken that isn't about one named channel — or every channel is blocked

CAUTIONone or two named channels are blocked, and the rest are fine

GOnothing blocked — cautions on named channels travel with those channels instead

There is no score to improve and no percentage to argue with. A caution about one channel never demotes the whole dataset, because it has somewhere better to live: that channel’s own row, and its trust grade after the fit.

Set my expectations →

Takes about two minutes, and it is the step that catches impossible assumptions before they become results.

Five questions we ask of every dataset

History

Is there enough data to work out this many things at once?

208 rows · 6.1× headroom

34 things to estimate

Separability

Can the channels be told apart from each other, and from ordinary demand?

3 channels flagged

Paid Social, Paid Search and Paid Search

Variation

Does each channel's spend move enough for its effect to be visible?

2 channels flagged

TV — Broadcast and Out of Home

Demand control

Is there a column for the demand that prompted the spend? Whether it is a good one can't be established from this data — only that it is there.

Present

a demand control is mapped

Sales series

Is the sales line itself clean enough to model?

No extreme weeks

nothing the outlier check flagged

Where channels moved together

Paid Social moved with Paid Search (r=0.78)

Five things to know, each with what to do about it

Worth watchingTwo channels move closely together0.777 measured · 0.70 threshold

Paid Social and Paid Search correlate at r=0.78; their individual ROIs will trade off.

What to do: Vary their budgets independently for a quarter if you can. Until then, trust the pair's combined return more than either individual number.

Two channels move closely together0.777 measured · 0.70 thresholdOpen

Paid Social and Paid Search correlate at r=0.78; their individual ROIs will trade off.

What to do: Vary their budgets independently for a quarter if you can. Until then, trust the pair's combined return more than either individual number.

Worth watchingElevated variance inflation6.804 measured · 5.00 threshold

Paid Search has VIF 6.8 after transformation.

What to do: Worth watching rather than fixing. If this channel also shows low contraction on the Evidence tab, treat its individual number as unreliable.

Worth watchingTV — Broadcast is off air most weeks0.615 measured · 0.50 threshold

It runs in 38% of weeks, in bursts rather than at the start or end of the window. The effect is identified from the handful of on/off transitions rather than from the whole period, so its interval comes back wide even when the fit itself is sound.

What to do: 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.

Out of Home is off air most weeks0.524 measured · 0.50 thresholdOpen

It runs in 44% of weeks, in bursts rather than at the start or end of the window. The effect is identified from the handful of on/off transitions rather than from the whole period, so its interval comes back wide even when the fit itself is sound.

What we will do about it

Findings can change the model, never your numbers — we never impute, winsorise or drop a row. A change a finding calls for is only a candidate until a full fit has tested it on your own spend, and every entry says what it would cost you.

A full fit will test these on your own data before choosing

None of these is a change yet. Before fitting, a full fit hides returns we chose in copies of your own spend and tries setups built from these changes on them, side by side with your model exactly as you specified it. It uses a change only if its setup does better, and holds back from the budget plan any channel whose return it cannot find. A quick preview tests nothing and uses none of them.

One combined return for two channels bought together
To be testedraised by two channels bought together
Paid Social + Paid Search
These two channels were bought so closely together that the data cannot tell their effects apart, so any split between them would be a guess. We estimate one return for the pair from their combined spend, starting from an assumption that blends the two channels' own, weighted by how much each spent.
What it would cost you, if a full fit uses it
You get one number for the pair and no split within it. If you need each channel's own return, only an experiment that moves one without the other can give it to you.
A firmer starting assumption for a channel the data barely sees
To be testedraised by spend that runs in short bursts
for each ofTV — BroadcastOut of Home
This channel's spend barely moves, runs in short bursts or covers only part of the window, so your data holds little evidence about what it returned. We narrow its starting assumption, so a few weeks of noise cannot hand it credit that belongs to the baseline or to another channel.
What it would cost you, if a full fit uses it
This channel's number leans on the starting assumption more than on your data. Read it as an informed default, not a measurement.
A return that can change from year to year
To be testedraised by a window long enough for returns to change from year to year
Your data covers at least two years, and each channel ran through most of every year it was on — long enough for its return to have changed over the window. We let each channel's return drift from one year to the next, starting from the same assumption a single average would.
What it would cost you, if a full fit uses it
Each year's number is less certain than one average across the whole window would be, because each year has only its own weeks to go on.
A shared starting assumption for two channels bought together
To be testedraised by two channels bought together
Paid Social + Paid Search
These two channels were bought so closely together that the data struggles to tell their effects apart. Each keeps its own return, but both start from the assumption that they return about the same, so the split between them leans on that rather than on noise.
What it would cost you, if a full fit uses it
The two returns are pulled toward each other. If one channel genuinely returns far more than the other, this setting narrows the gap between them.
An assumed diminishing-returns curve for a channel the data barely sees
To be testedraised by spend that runs in short bursts
for each ofTV — BroadcastOut of Home
This channel's spend barely moves, runs in short bursts or covers only part of the window, so your data cannot trace how its returns fall off as spend rises. We fix that curve at the starting assumption — half its full effect at about its typical weekly spend — and estimate only how much the channel returned.
What it would cost you, if a full fit uses it
How fast this channel runs into diminishing returns is assumed, not measured, so its number, and any budget advice built on its curve, leans on the starting assumption.

How much each channel's spend moves about

Ups and downs are what let the model see what a channel does. Spend the same amount every week and there is nothing to learn from — the return comes back as whatever we assumed going in, however large the budget.

Channel	Spend share	Variation	Overlap cost	Weeks with nothing spent
TV — Broadcast	28.0%	1.37	1.2	62% of weeks had no spend
Video — CTV	15.7%	0.60	3.2	spent every week
Paid Social	19.4%	0.33	3.0	spent every week
Paid Search	16.9%	0.42	6.8	spent every week
Programmatic Display	11.4%	0.32	1.9	spent every week
Out of Home	8.6%	1.17	1.2	56% of weeks had no spend

How closely your channels move together

Read a cell as: when this channel went up, did that one go up too? 1.00 means always, 0.00 means never. Anything above 0.80 and your data cannot tell the two apart. Measured after allowing for advertising that keeps working once it stops running and for the tenth million doing less than the first — checking raw spend instead is the usual reason a tool declares your data fine and then fails to separate anything.

TV — Broadcast	1.00	0.06	0.18	0.19	0.04	0.08
Video — CTV	0.06	1.00	0.17	0.12	0.22	0.11
Paid Social	0.18	0.17	1.00	0.78	0.51	-0.04
Paid Search	0.19	0.12	0.78	1.00	0.58	0.09
Programmatic Display	0.04	0.22	0.51	0.58	1.00	-0.02
Out of Home	0.08	0.11	-0.04	0.09	-0.02	1.00
	TV — Broadcast	Video — CTV	Paid Social	Paid Search	Programmatic Display	Out of Home

How do I read this?