Model
When to Use
Pros
Cons
Best For
First-Touch
You want to understand which channels and campaigns are generating net-new demand, before any nurture or sales motion begins.
Clean and simple to implement. Gives demand-gen teams a clear read on which sources are opening the most new buyer relationships.
Assigns zero credit to everything that happened after the first visit. If your average deal takes 6 weeks and 12 touchpoints, first-touch tells you almost nothing about what closed it.
Brand awareness campaigns and top-of-funnel channel measurement, where the goal is net-new pipeline entry, not full-journey analysis.
Last-Touch
You need a baseline conversion view and your team isn’t ready for a more complex model yet. Or your sales cycle is short enough that the last interaction genuinely is the most important one.
Zero setup friction, it’s the default in GA4, HubSpot, and most ad platforms. Fast to report on and easy to explain.
In B2B, the last touchpoint before conversion is usually a branded search or a direct visit. Last-touch gives all the credit to that final click and wipes out everything that built the intent beforehand.
Short-cycle, transactional purchases where the buyer’s decision window is measured in hours. Not appropriate for B2B SaaS.
Linear
You want to give every touchpoint in the buyer journey equal credit, and you’re more interested in channel coverage than in identifying which specific interactions closed the deal.
Politically neutral, no channel gets unfairly penalised. Shows mid-funnel content and channels that single-touch models make invisible.
Treats every interaction as equally important regardless of its actual influence. A 90-second blog visit gets the same credit as a pricing page viewed three times in one week.
Teams in the early stages of multi-touch adoption, or where the goal is simply to confirm that mid-funnel content is appearing in buyer journeys at all.
Time-Decay
You believe that the touchpoints closest to the conversion decision carried the most weight, and you want your attribution model to reflect that.
Rewards the content and channels that were active when the buyer was ready to act. Works well in sales-led motions where late-stage demos, case studies, and follow-up sequences do the heavy lifting.
Systematically undervalues the content that created awareness. If a blog post brought a buyer in six months before they converted, time-decay gives it almost no credit, even if that post was the reason they ever heard of you.
B2B SaaS with active SDR sequences and structured sales follow-up, where late-stage touchpoints genuinely drive the final decision.
U-Shaped
You want to give meaningful credit to both the moment a buyer first discovered you and the moment they converted, without completely ignoring what happened in between.
Recognises that the beginning and end of the journey are disproportionately important while still distributing some credit to mid-funnel touches. More accurate than single-touch for most B2B funnels.
The middle-funnel interactions (the nurture emails, the mid-journey blog visits, the feature comparison pages) get a small, fixed share regardless of how much work they actually did. If your nurture motion is a core part of your pipeline, U-shaped will underreport it.
Two-stage demand funnels focused on lead capture and conversion, where first discovery and final conversion are the two moments that matter most to leadership.
W-Shaped
Your funnel has defined stage transitions, typically MQL, SQL, and opportunity creation, and you want attribution credit to reflect each of those milestone moments, not just the first and last touch.
Built specifically for structured B2B sales funnels. Identifies which channels and content drive pipeline progression and influence movement across key revenue stages.
Requires clean, reliable lifecycle stage data in your CRM. If your MQL or opportunity stage definitions are inconsistent, or if deals frequently skip stages, W-shaped attribution will produce misleading output.
B2B marketing teams with formal MQL, SQL, and opportunity stage definitions in HubSpot or Salesforce, and the CRM hygiene to support accurate stage-transition data.
Data-Driven
You have sufficient conversion volume and want the model itself to determine how credit should be distributed, based on patterns in your actual buyer journey data rather than a fixed formula.
Adapts to your specific funnel instead of assuming a universal rule. Can show non-obvious patterns, channels or content types that consistently appear in journeys that convert, even if they don’t look important in rule-based models.
Operates as a black box, you can see the output but not fully explain the weighting logic, which makes it hard to justify budget decisions to stakeholders. Requires substantial monthly conversion volume to produce statistically reliable output. Breaks down at lower volumes.
Mature marketing teams generating 500+ monthly conversions with the analytical infrastructure to interrogate model outputs and the exec buy-in to act on results they can’t fully reverse-engineer.