§1 Why attribution matters more in programmatic
Programmatic media is, almost by design, an upper- and mid-funnel channel — it rarely gets the last click. That makes the choice of attribution model not a reporting footnote but a direct lever on how much budget the channel receives. Get the model wrong and you can starve a channel that's quietly doing real work, or fund one that's mostly riding on other channels' coattails.
§2 The three models compared
| Model | Logic | Programmatic credit |
|---|---|---|
| Last-click | 100% to final touchpoint | Lowest |
| Linear | Equal credit across all touchpoints | Medium |
| Data-driven | Algorithmic, based on observed paths | Highest |
§3 Where the numbers diverged
Under last-click attribution, programmatic looked like a poor investment — barely breaking even. Under a data-driven model trained on the advertiser's own conversion paths, the same spend showed a meaningfully positive contribution to conversions that ultimately closed through search or direct. The "true" answer sits somewhere between these, and the gap itself is the useful signal — it tells you how much of the funnel programmatic is actually touching.
§4 What actually changed in budget allocation
Once we trusted the data-driven view — validated against incrementality tests, not just model output — one client increased programmatic budget by roughly 20% and reduced spend on a branded-search campaign that the data-driven model showed was largely capturing demand programmatic had already created.
§5 Caveats worth keeping in mind
Attribution models describe correlation in observed paths — they don't prove causation. Pair any reallocation decision with a holdout or geo-incrementality test before moving meaningful budget. The model told us where to look; the incrementality test told us whether moving budget actually changed outcomes.