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
Caution
The model will run and most of your channels will come back with numbers you can use. The ones named below will not, and they carry that mark through every screen after this one.
2 channels cannot be measured from this data at all — Retail Media and Affiliate. The rest are fine, and everything else on this page is about how wide those answers will be.
How this verdict is decided
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.
Takes about two minutes, and it is the step that catches impossible assumptions before they become results.
Five questions we ask of every dataset
156 rows · 4.9× headroom
32 things to estimate
2 channels flagged
Two channels move closely together and Retail Media
3 channels flagged
Affiliate, TV — Broadcast and Retail Media
Present
a demand control is mapped
No extreme weeks
nothing the outlier check flagged
Five things to know, each with what to do about it
Retail Media has VIF 34.0 after transformation; it is nearly a linear combination of the other regressors.
What to do: 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.
Coefficient of variation 0.012. Without variation there is nothing for the model to correlate with the KPI; its ROI will be driven entirely by the prior.
What to do: 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.
Paid Social and Paid Search correlate at r=0.82; 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.
It runs in 40% 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.
It was live across 37% of the period covered by this upload. Its return is estimated from that stretch alone, so it rests on far less evidence than the share of spend suggests.
What to do: 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.
What we will do about it
Findings change the model, not your numbers. We never impute, winsorise or drop a row — every entry here alters how the fit is estimated, and states what that costs you.
Some channels held at their current budget
Will applytriggered by vifRetail MediaAffiliateThese channels' spend does not vary enough, did not run for enough of the window, or moves too closely with the rest of the model for the data to say what they returned. Their numbers come mostly from the starting assumption. They stay in the model, so they do not distort the others, but the budget planner will not move them.
What it costs you
You will get no recommendation for these channels — deliberately. A plan that reallocated budget on the strength of an assumption would look exactly like one built on evidence.
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 | 33.3% | 1.38 | 1.1 | 60% of weeks had no spend |
| Paid Social | 23.0% | 0.34 | 3.8 | spent every week |
| Paid Search | 19.8% | 0.44 | 6.8 | spent every week |
| Programmatic Display | 12.8% | 0.32 | 3.2 | spent every week |
| Affiliate | 6.9% | 0.01 | 2.4 | spent every week |
| Retail Media | 4.3% | 1.44 | 34.0 | 63% 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 | 0.18 | 0.21 | 0.13 | -0.08 | -0.09 |
|---|---|---|---|---|---|---|
| Paid Social | 0.18 | 1.00 | 0.82 | 0.70 | 0.15 | -0.02 |
| Paid Search | 0.21 | 0.82 | 1.00 | 0.71 | 0.16 | 0.03 |
| Programmatic Display | 0.13 | 0.70 | 0.71 | 1.00 | 0.36 | -0.11 |
| Affiliate | -0.08 | 0.15 | 0.16 | 0.36 | 1.00 | 0.11 |
| Retail Media | -0.09 | -0.02 | 0.03 | -0.11 | 0.11 | 1.00 |
| TV — Broadcast | Paid Social | Paid Search | Programmatic Display | Affiliate | Retail Media |