Marketing Attribution Model Examples: Five Models, One Deal, Five Very Different Stories

Let’s start with a thought experiment. You close a $48,000 ARR deal. Six people from your team touched the account over nine weeks. Five distinct marketing interactions happened before the contract was signed.

Now run that deal through five different attribution models. You’ll get five completely different answers about which channel deserves credit. And each of those answers, if you use it to guide budget decisions at scale, will send money in a different direction.

What Is a Marketing Attribution Model?

A marketing attribution model is the set of rules that determines which marketing touchpoints receive credit for a conversion, and how much credit each one gets. It’s the system that translates your customer journey data into budget recommendations.

Attribution models fall into two categories. Single-touch models give all the credit to one interaction: either the first touchpoint a prospect had with your brand (first-touch) or the final one before conversion (last-touch). Multi-touch models distribute credit across multiple interactions using different weighting rules.

For most B2B companies, buying decisions do not happen in one step or through one person. They unfold over time and involve multiple touchpoints across the journey. That is why multi-touch attribution is usually a better fit than last-touch attribution. Last-touch models often end up overvaluing branded search, while the channels that first built awareness and interest get far less credit than they deserve.

The Real B2B Journey: A Buying Committee in Action

This is a deal that closed at $48,000 ARR. Three stakeholders, nine weeks, five meaningful touchpoints.

• Week 1: A CFO reads a LinkedIn thought leadership post shared by a peer in their industry network. They don’t click through, but they register the brand.
• Week 3: A VP of Sales searches ‘marketing attribution platforms’ on Google, clicks an organic result, and signs up for the newsletter.
• Week 5: The VP of Sales clicks a link in the weekly newsletter to read a case study about a company similar to theirs.
• Week 7: A Technical Lead sees a LinkedIn retargeting ad, clicks through, and attends a live webinar. After the webinar, they share the recording internally with the CFO and VP of Sales.
• Week 9: A sales rep sends a personalized follow-up with a proposal. All three stakeholders join the call. The deal closes the following week.

Five touchpoints. Three people. One deal. Here’s what each attribution model does with it.

Example 1: What Is Last-Touch Attribution?

What is last-touch attribution?

Last-touch attribution gives 100% of the conversion credit to the final marketing interaction before the sale. In this example, that’s the sales follow-up email in week 9.

This is the default in most analytics platforms and CRMs, and it’s consistently misleading in B2B. Yes, the sales email closed the deal. But it gives you zero visibility into what made that email land. The six weeks of content, brand exposure, and relationship-building don’t appear anywhere. Over time, budgets built on last-touch data cut the awareness channels that were doing the demand-creation work.

Example 2: What Is First-Touch Attribution?

What is first-touch attribution?

First-touch attribution gives 100% of the credit to the very first interaction a prospect had with your brand. Here, that’s the LinkedIn peer share from week 1.

First-touch is genuinely useful for one narrow purpose: understanding which channels bring new audiences into your orbit. But it completely ignores what happened next. Use it as a diagnostic slice within a broader model rather than the primary lens.

Example 3: What Is Linear Attribution?

What is linear attribution?

Linear attribution distributes credit equally across every touchpoint in the customer journey. With five touchpoints, each receives 20% of the $48,000.

Linear attribution is more honest than either single-touch model and is a reasonable first step for teams moving away from last-touch. The limitation is equal weighting: the LinkedIn post the CFO saw but didn’t click gets the same credit as the webinar the Technical Lead shared internally.

Example 4: What Is Position-Based (U-Shaped) Attribution?

What is position-based attribution?

Position-based attribution gives 40% of the credit to the first touchpoint, 40% to the last, and distributes the remaining 20% across everything in the middle.

U-shaped attribution recognizes two specific moments that B2B marketers care most about: the point where an account first discovered you, and the point where they decided to buy. The content team can point to the LinkedIn post. The sales team can point to the follow-up email. Both sides of the organization see their work reflected in the data.

Example 5: What Is Time-Decay Attribution?

What is time-decay attribution?

Time-decay attribution gives more credit to touchpoints that happened closer to the conversion.

Time-decay credits the full journey while acknowledging that the webinar and the proposal conversation probably did more to build conviction than the LinkedIn post from nine weeks earlier. Time-decay emerged as the most reliable model for most B2B SaaS companies in 2025. (Source: Sprut Agency/Medium, December 2025)

Which Attribution Model Should You Choose?

Match the model to your actual sales cycle length and the stage of your business.

• Short cycles under 30 days: Last-touch or time-decay with a short half-life. When buyers convert quickly, the final touchpoint genuinely does carry most of the weight.
• Mid-length cycles of 30 to 90 days: U-shaped or time-decay. These balance acquisition and conversion credit reasonably well for journeys with a manageable number of meaningful touchpoints.
• Long cycles over 90 days, or deals involving buying committees: Time-decay with a longer half-life, or W-shaped attribution. These are built for the reality that touchpoints from months ago still mattered.
• New to multi-touch: Start with U-shaped. It’s intuitive, gives both the marketing team and the sales team fair representation in the data, and doesn’t require a complex statistical setup.

The DiGGrowth Edge: Attribution That Learns From Your Data

Static attribution models apply fixed rules. They don’t update based on what your actual customer journeys show is working.

DiGGrowth’s AI-driven attribution analyzes your real conversion data and continuously surfaces which touchpoint combinations are most predictive of high-value deals in your specific business. Rather than picking a U-shaped model and applying it uniformly across every deal type, DiGGrowth identifies the patterns: which channel sequences close faster, which content types appear most consistently in large-contract journeys, and which early intent signals predict which leads are worth prioritizing for sales outreach.