Last-click attribution is the most common measurement framework in B2B marketing. It is also one of the most misleading. When your reporting tells you that paid search drove 80% of your leads, what it is often really saying is that someone clicked a Google Ad as the final step in a journey that started six weeks earlier on LinkedIn, continued through a downloaded whitepaper, and included three visits to your pricing page.

You optimise for what you can see. If all you can see is the last click, you cut the channels that were actually doing the heavy lifting at the top of the funnel. This is a pattern we see regularly in accounts that have had their budgets trimmed based on incorrect attribution data.

What attribution actually measures

Attribution is the process of assigning credit for a conversion across the touchpoints in a customer journey. The problem is that most B2B journeys are long, involve multiple people, and cross channels that do not talk to each other. A deal that closes in month four may have begun with a cold LinkedIn message, a referral, a piece of organic content, two remarketing ads, and a direct website visit before anyone filled in a contact form.

No single attribution model captures this perfectly. The goal is not to find the perfect model — it is to use one that is honest about its limitations and that informs better decisions than the alternative.

The models worth understanding

First-touch attribution

All credit goes to the first touchpoint — the channel or campaign that brought the lead to you for the first time. Useful for understanding which channels are most effective at generating awareness. Not useful for optimising conversion because it ignores everything that happens after.

Last-touch attribution

All credit goes to the final touchpoint before conversion. This is the default in most analytics platforms and the source of more bad budget decisions than any other single factor in digital marketing. It overvalues channels that exist at the bottom of the funnel and undervalues everything above them.

Linear attribution

Credit is distributed equally across every touchpoint in the journey. This is more honest than first or last touch but still blunt — it treats a ten-second visit to a blog post the same as a thirty-minute session on your pricing page.

Time-decay attribution

Touchpoints closer to the conversion date receive more credit. This reflects the reality that the final stages of a decision are often the most significant, while still giving some credit to earlier interactions. A reasonable choice for sales cycles under 60 days.

Position-based (U-shaped) attribution

40% of credit goes to the first touch, 40% to the final touch before conversion, and the remaining 20% is distributed across everything in between. This is useful when you want to weight both acquisition and conversion without ignoring the middle of the journey.

Data-driven attribution

Uses machine learning to assign credit based on what the data actually shows about which touchpoints influence conversion in your specific account. This is the most accurate model available, but it requires a high volume of conversion data to work reliably. Available in Google Ads accounts with sufficient conversion history.

Which model should you use?

For B2B with long sales cycles (60 days or more): position-based or time-decay. For B2B with shorter cycles or high volume: data-driven if you have the conversion data to support it. For awareness reporting: first touch in isolation alongside your primary model. Never use last-click as your only view.

The honest limitation of all of them

Every attribution model works only with the data it can see. Offline conversations, word of mouth, a podcast mention, a colleague’s recommendation — none of these show up in any attribution report. This is not a reason to abandon measurement. It is a reason to treat attribution data as a useful input into a decision, not the final word on it.

The most useful thing you can do is run two or three models in parallel, look at where they agree and where they diverge, and have an honest conversation with your marketing team about what that tells you. The disagreements are where the most interesting insights live.

At A.A. Dynamics, every client account we manage is set up with attribution reporting built in before spend begins, not bolted on afterwards. If you are working from last-click data and wondering why your decisions do not seem to be improving results, a conversation about your measurement setup is usually the most useful place to start.