Linear Attribution: When Equal Credit Is the Honest Answer

What linear attribution does

If a sale had four attributed touches, linear attribution gives each one 25% of the credit — a display impression, a sponsored brands click, a sponsored products click and a branded search click each get an equal quarter of the sale's value, no matter which came first, which came last, or how close each one sat to the moment of purchase. It's the plainest of the multi-touch models: no decay function, no positional weighting, no statistical inference. Just division.

That simplicity is the whole point. Position-based and time-decay models both encode an assumption about which touches matter more — an assumption that has to be justified, not assumed. Linear attribution makes no such claim. It's the model to reach for specifically when you don't yet have evidence for a more opinionated weighting, and want a baseline that doesn't quietly bake in someone's guess about the funnel.

It's worth being clear about what linear attribution is not, too. It isn't a native Amazon reporting option — none of the platforms compute it for you, which means someone has to pull cross-channel path data, usually through Amazon Marketing Cloud, and do the division deliberately. That extra step is exactly why so many accounts default to whatever attribution their dashboard ships with, last-touch, rather than a model they'd have to build.

A worked example

Take a $200 order with five touches over an 11-day path: a streaming video ad, a DSP display impression, a non-branded Sponsored Products click, a Sponsored Brands click, and a branded search click that closes the sale. Linear attribution assigns $40 to each of the five. Compare that to last-touch, which gives the full $200 to the branded search click alone — a campaign that, on this reading, generated 5x more attributed revenue than the streaming ad that likely started the whole journey, purely because of where it sat in the sequence rather than what it actually did.

Neither number proves causation — that's still a job for a holdout test, not a credit-splitting rule — but the linear number is a more honest starting description of the path than a single-touch model that arbitrarily privileges one position over the other four.

When equal credit is genuinely the right call

Linear attribution earns its place when your funnel is genuinely non-linear in practice — when customers loop back through the same two or three channels repeatedly rather than moving cleanly from awareness to consideration to purchase, and when you have no reliable evidence that, say, the third touch consistently matters more than the first. It's also the right default when you're building a reporting habit for the first time and want something a non-technical stakeholder can understand and trust without a methodology debate — "every touch counted equally" is a sentence that needs no footnote.

When equal credit is the wrong call

Linear attribution flatters channels that show up frequently but contribute little, because appearing in more paths simply earns more $40-sized slices regardless of actual influence. A retargeting campaign with high frequency caps can rack up touches on nearly every path a customer takes without meaningfully changing whether they buy — and linear attribution will reward that frequency with credit indistinguishable from a genuinely persuasive touch. If one of your channels is running an aggressive frequency strategy, check whether linear attribution is quietly crediting volume rather than influence before trusting the split.

It's also the wrong call when path lengths vary wildly across your customer base — a shopper who buys on the first ad they ever see, alongside one who takes nine touches over three weeks, will both get divided evenly under a linear model, which flattens a real difference in how those two purchase decisions actually happened. When path-length variance is high, a single blended linear number across the whole account hides more than it reveals, and it's worth segmenting the analysis by rough path length before drawing conclusions from the average.

The common mistake, including ours

The mistake is treating "equal" as synonymous with "fair" or "accurate," when it's really just the least opinionated of the available guesses. We've defaulted to a linear split in an early client report because it was fast to compute and didn't require a debate about weighting — reasonable for a first pass, but we left it as the standing methodology for two full quarters without revisiting whether the account's actual path lengths and touch frequency still supported it. By quarter three, one retargeting line had grown to appear in nearly every path purely through frequency, and the linear model was quietly overcrediting it as a result.

How linear compares to the alternatives, briefly

Time-decay attribution weights touches closer to the sale more heavily, on the theory that recency correlates with influence. Position-based (U-shaped) attribution gives extra weight to the first and last touch specifically, treating the middle as supporting cast. Both encode a specific, testable claim about how influence is distributed across a path — a claim linear attribution deliberately declines to make. That's not a weakness of linear attribution; it's the reason to reach for it first, before you have the evidence to justify a more opinionated model, and to revisit it once you do.

What to do once the model stops fitting

Recompute the split periodically, not once. Pull path-length and touch-frequency data every quarter and check whether any single channel's touch count has grown disproportionately — if it has, linear attribution is no longer a neutral baseline for that channel, and it's worth moving to position-based or time-decay weighting, or running an incrementality test on the suspect channel specifically to see whether its apparent contribution holds up under a holdout.