What is an example of marketing attribution? A B2B journey

Attribution makes more sense with a real journey in front of it than with another definition. So here's one.

A marketing manager at a B2B SaaS company wants to know which of their marketing channels actually drove a particular sale. The customer converted last week. The question is simple: who did this, and why did this happen? What is marketing attribution? A beginner's guide covers what a touchpoint is and why finding the cause of a sale is the whole point of attributing credit. Here, the point is showing what that looks like against an actual customer purchase.

The answer depends entirely on which marketing attribution model gets applied Marketing attribution models: which to choose?, and the same journey can produce five completely different answers depending on the choice.

The customer journey

One prospect. Six weeks. Seven touchpoints across four marketing channels.

Week 1: they see a LinkedIn ad and click through to the website. First contact. This is where initial brand awareness starts.

Week 2: they come back via organic search, read a blog post, and leave.

Week 3: they see a Facebook ad, click it, and browse the pricing page. Consideration starts here.

Week 4: they open a promotional email newsletter and click through to a case study.

Week 5: they return via organic search again and read two more blog posts.

Week 6: they open a second email, book a demo, go through a sales call, and convert.

Seven touchpoints: a LinkedIn ad, an organic search, a Facebook ad, two email opens, a demo booking, and a sales call. Four channels: paid social, organic search, email, and sales. The question is how to split the credit for the conversion across all of them, what's usually called credit splitting, and different models map touchpoints to that credit in very different ways.

Same journey, five different answers

Each marketing attribution model is a different set of rules for dissecting the touchpoints a customer encounters on their purchase journey. Applied to the journey above, here's what each one concludes.

First-touch gives 100% of the credit to the LinkedIn ad. The logic is that without that first interaction, the customer never enters the funnel. By this model, paid social gets all the budget credit and everything else gets nothing.

Last-touch gives 100% of the credit to the second email, the touchpoint right before the demo booking. Everything that built awareness and consideration over the previous five weeks gets zero.

Linear splits the credit evenly, roughly 14% to each of the seven touchpoints. Every interaction counts equally, which is more honest but treats the quick email open the same as the sales call that closed the deal.

Time decay weighs the credit toward the later touchpoints. The demo booking, the second email, and the sales call get the biggest shares, while the week-one LinkedIn ad gets very little, even though it started the whole thing.

Position-based models, like u-shaped and w-shaped, land in between, concentrating credit at a few chosen moments instead of one end or an even split. W-shaped concentrates the credit at three moments here: the LinkedIn ad (first touch), the demo booking (lead creation), and the sales call (final touch before conversion). Each of those gets roughly 30%, with the remaining 10% spread across the blog posts, emails, and Facebook ad in the middle.

First-touch, last-touch, linear, and time decay are all rule-based attribution, following the same rule-based process: a fixed formula applied the same way no matter how the journey actually unfolded. Data-driven, or algorithmic, attribution skips that entirely. It looks at every converting and non-converting journey in the company's data and assigns credit based on which touchpoints statistically correlate with conversion. If prospects who read the case study convert far more often than those who don't, the case study email gets weighted heavily, regardless of where it sat in the sequence.

Why the differences matter

Each model answers the same basic question, which marketing channels deserve credit, in a completely different way. These aren't academic distinctions. Each one points to a different budget allocation.

If the company runs last-touch, they'll conclude email drives conversions and pour budget into it, cutting the LinkedIn spend that generated the awareness in the first place. If they run first-touch, they'll do the opposite: over-invest in top-of-funnel paid social and under-fund the subsequent customer interactions, the emails and sales calls, that actually closed the deal.

The same seven touchpoints, the same single sale, and two models produce opposite conclusions about where the money should go.

A simpler example: single channel

Attribution doesn't always involve seven touchpoints across four channels. A simpler version: a customer sees one Google Ads campaign, one of dozens of online ads running that week, clicks it, and buys the same day.

Here, attribution is almost trivial: one touchpoint, one channel, one conversion. The Google ad gets the credit because it's the only interaction. This is the kind of journey last-touch handles perfectly well. The problem shows up once the journey gets longer and the number of marketing channels grows, which for B2B SaaS, spanning websites, mobile apps, and social media, means conversions across multiple channels are the norm rather than the exception. Cross-channel attribution: connecting the full journey covers what changes once a customer moves across several channels instead of staying inside one.

What this shows

Run enough of these journeys through a model and patterns emerge. Analyzing customer touchpoints at scale shows marketers what's working and what's not, not just for one sale but across the whole marketing engine.

It reveals which channels consistently show up early in converting journeys, which ones close deals, and which ones appear just as often in journeys that go nowhere. Knowing a single sale came from a LinkedIn ad is a data point. Knowing LinkedIn ads reliably start the journeys that convert is what turns that data point into an informed decision about where next quarter's budget goes.

Where to start

A worked example only holds up if the underlying data is clean. That means consistent tracking across every channel, so ad clicks, email opens, and website visits all get captured, and attribution windows wide enough to catch a six-week journey rather than cutting it off at 30 days. How to measure marketing attribution? A step-by-step guide walks through how to get that foundation in place.

Once the data is clean, running journeys through a model like the ones above becomes routine instead of a manual reconstruction. From there it's a process of measure and iterate: watching which channels the model credits, adjusting budget, and checking whether the results follow.

If you'd rather see this mapped out for your own funnel than build it by hand, that's what Cascayd does. Try Cascayd for free.