Ask five people on a marketing team which channel deserves credit for a conversion, and you'll often get five different answers — not because anyone's wrong, but because "credit" depends entirely on which attribution model you're using. Understanding the models, and their tradeoffs, matters more than picking a single "correct" one.
The common models, briefly
Last-click gives full credit to the final touchpoint before conversion. It's simple and widely supported, but it systematically overvalues bottom-funnel channels like branded search and undervalues the channels that introduced the customer earlier in the journey.
First-click does the opposite — full credit to the first touchpoint. Useful for understanding what drives initial awareness, but it ignores everything that happened between discovery and purchase.
Linear splits credit evenly across every touchpoint in the journey. It's fairer in a rough sense, but it treats a passing ad impression the same as an email someone actually engaged with.
Position-based (U-shaped) weights the first and last touchpoints more heavily, with the middle touchpoints splitting the remainder. This tends to reflect real buying behavior better for longer consideration journeys.
Data-driven distributes credit based on actual patterns in your conversion data rather than a fixed rule. It's the most accurate in principle, but it requires enough volume of conversion data to be statistically meaningful — a low-traffic store may not have enough signal for it to outperform a simpler model.
Why multi-channel journeys make this harder
Most purchases today don't happen in one session from one channel. A customer might see a paid social ad, later click a retargeting ad, then convert weeks after that from an email. Any attribution approach that only looks at the final session is throwing away most of the story — but capturing the whole story requires stitching together sessions across time, which is where a lot of reporting setups quietly fall short.
A practical way to use attribution models
Rather than picking one model and treating its output as ground truth, it's more useful to view the same conversion data through two or three models side by side. If a channel looks strong under last-click but weak under linear, that's a channel doing well at closing but not much else — useful to know, and different from a channel that looks strong under both.
What to actually do with this
Attribution isn't just a reporting exercise — it should change budget decisions. A channel that consistently shows up early in multi-touch journeys but never gets last-click credit is easy to under-invest in if you're only looking at last-click numbers. Reviewing attribution across models on a regular cadence, not just once during a planning cycle, is what keeps budget allocation honest.