Marketing attribution, explained by following one buyer

"So what's actually working?"

It gets asked in a quarterly review, usually by someone who has just seen the marketing spend. It sounds like a simple question. The reason nobody answers it cleanly is that four systems in the room already believe they know, and they do not agree.

Marketing attribution is the practice of deciding which of the things you did gets credit for a customer. That is the whole concept. Everything difficult about it comes from the fact that customers do several things before they buy, and revenue arrives as one indivisible number.

Follow one buyer

Abstract definitions make this harder than it is. Take a single deal.

Now answer the question. Which of those earned the £18,000?

The honest answer is that the question is malformed. The LinkedIn ad is why they had ever heard of you. The branded search is where the money technically came through. Remove either and the deal plausibly does not happen. There is no experiment available that separates them, because you cannot run week zero again without the ad.

So attribution does not discover the answer. It applies a rule. Give it all to the first touch and LinkedIn earns £18,000. Give it to the last and branded search does. Split it evenly across five touches and each gets £3,600. Same buyer, same events, three completely different reports — and none of them is a lie. Choosing between those rules is its own decision with real consequences, and it should be made deliberately rather than inherited from whatever your tooling defaulted to.

Three words that are not synonyms

Most confused attribution conversations are actually three conversations happening at once.

Tracking is capturing what happened. A click identifier lands on a URL, gets written to a form, ends up on a contact record. This is plumbing, it either works or it does not, and it is the part that is usually broken.

Attribution is dividing credit between the things you tracked. It is arithmetic over a table. It is the part people argue about.

Incrementality is what would have happened anyway. This is the question everyone actually wants answered — did the ad cause the sale — and attribution cannot answer it at all. Only an experiment can: hold a region out, turn a channel off for four weeks, and compare. Expensive, slow, and the only method that speaks to causation.

Knowing which of the three you are being asked about saves a great deal of time. "What's working?" is usually an incrementality question being asked of an attribution report.

Why the platform numbers add up to more customers than you have

Here is the thing that makes people assume someone is lying.

Twelve plus nine plus six is twenty-seven. You had fifteen customers.

Nobody is inflating anything. Each platform sees the touches it was involved in and applies its own last-click-within-its-own-window rule. A buyer who clicked a Meta ad and a Google ad is counted once by each, because neither can see the other. They are not reporting on the same population, and they were never designed to sum.

This is why a single blended view has to be built somewhere neither platform controls — your CRM, or a warehouse. Not because the platforms are wrong within their own frame, but because reconciling them is a job only you can do, since only you have the invoice.

Digital marketing attribution and revenue attribution are different jobs

The phrases get used interchangeably and they measure different things.

Digital marketing attribution generally works in the analytics layer, over sessions and conversions — a form submission, a signup, a trial start. It is fast, it has volume, and its unit is an event.

Revenue attribution works over closed money in the CRM or finance system. Its unit is a deal with an amount attached. Slower, far fewer data points, and it is the one that survives a conversation with a CFO, because the total ties to something that was actually invoiced.

For B2B with a sales cycle measured in months, the second is the one worth building. A channel that produces cheap form fills and expensive customers looks excellent in the first view and obvious in the second.

Your report is only as complete as the join

Before improving a model, find out how much revenue it can see at all.

Attribution depends on a chain: the acquisition source reaches the contact record, the contact stays linked to the deal, and the deal carries an amount and a close date. Break any link and that revenue silently drops out of every report you run. It does not appear as an error. It appears as a smaller number for every channel, which nobody notices.

The usual breaks are dull and fixable. Source captured as free text, so linkedin, LinkedIn and LI ads are three channels. A duplicate contact merged, with the surviving record keeping a blank source. A deal typed in by a rep after a good conversation with no contact attached. That last one is the worst, because manually created deals skew heavily towards referral and sales-led paths — so the revenue your report cannot see is systematically different from the revenue it can. Fixing the data underneath shifts the numbers more than any model change.

What to build first

Not a tool. A table.

One row per touch: contact, timestamp, channel, campaign, and whatever click identifier arrived. That table plus the deals table is enough to compute any model you like with a query, and it means you can change your mind about the model later without re-instrumenting anything. Buying an attribution platform before this table exists just relocates the problem — the platform will need the same fields, and it will have the same gaps.

Everything in the wider attribution setup sits on top of that table. Get it populated and honest first.

The check worth doing today

Take last quarter's closed-won deals. Count how many have any recorded first touch at all.

Whatever that percentage is, it is the ceiling on how much of your revenue attribution can currently explain — and every channel figure you have been shown is computed over that subset. If it is 90%, you have a modelling question and it is worth having. If it is 55%, you have a plumbing question, and the model you choose is close to irrelevant until it is fixed.