TL;DR: Multi-touch attribution isn’t a fit for everyone. If your marketing team is small, your data is relatively simple, and you don’t have dedicated marketing ops resources, multi-touch attribution may add complexity without improving decisions. For many SMBs, simpler attribution models can be effective until the business reaches a certain level of scale and complexity.
As attribution grew in prevalence and popularity, so too, grew backlash—the ”Attribution is Dead” refrain.
Why “Attribution Is Dead” Keeps Coming Back
Much of this came from a common misconception that there’s one “best” model for any business.
But here’s the critical thing every marketer should know: in both marketing analytics, and beyond even to any marketing discipline, there’s simply no one-size-fits-all approach.
Think of the differences between a small startup and a large enterprise organization.
If you have 2 marketers, a handful of campaigns run by one individual, and a budget in the ballpark of $100K-$1M, that’s a vastly different ecosystem than if you were at an organization of >10,000 employees, a marketing department of 50+, and countless campaigns across channels.
So to assume both organizations should just pick the objective “best model” is to ignore the differences in resources, complexity, internal expertise, and budget.
What happens if you over-engineer your marketing attribution, is that it becomes hard to maintain with your current resources, and if you can’t maintain it, it won’t help you make better decisions. And if it’s not helping you make better decisions, the budget you spent on it could’ve served you better elsewhere. (This is where many might throw up their hands and say “Attribution is dead”)
The Playbook Pitfall
Marketers often move between small and large companies throughout their career, and a hard lesson to learn is that often, this means you have to throw out your old playbook.
This is as true in marketing analytics as it is in demand gen. If something worked previously for you but in a completely different environment (different industry, TAM, company size, marketing team structure, etc.), there’s no guarantee it will work in your new one.
In practice, this means that if you come from a large organization to a smaller organization, you may have to become more agile, and make decisions with less data.
What attribution models are companies using at different sizes?
Taking a look at your peers by revenue is a great place to start. BenchmarkIt did a great study of B2B Marketing Benchmarks, and in it, they asked practitioners what attribution model they use and plotted that by their company revenue.
Attribution Models by Company Size
| Revenue | < $5M | $5M – $20M | $20M – $50M | $50M – $100M | $100M – $250M | $250M – $1B |
|---|---|---|---|---|---|---|
| First Touch | 29% | 29% | 25% | 50% | 35% | 33% |
| Last Touch | 21% | 25% | 50% | 43% | 38% | 20% |
| Inbound | 25% | 44% | 25% | 17% | 12% | 7% |
| Multi-Touch | 44% | 40% | 38% | 33% | 42% | 73% |
*Category “other” not shown
Source: BenchmarkIt
But when you look at the above, you’ll see an interesting phenomenon. The companies with the least revenue are using the same attribution—multi-touch, as the companies with the most revenue. And on the flip side, half of the companies with $50-100m or $20-50m in revenue are using a single touch model.
Pitfall: Over-Engineering Attribution
This is typically seen in small teams. They may be forward thinking and tech savvy, but they may not have the resources to fully execute on the best practices they wish to follow.
Symptoms of over-engineered attribution include:
- Maintaining data quality is a constant challenge
- The multi-touch model doesn’t have sufficient data
- Few people in the organization understand the attribution model and tech stack, and the rest tend to doubt its validity
- You spend more time explaining reports instead of acting on them
- Your sales cycle is shorter with fewer touches
This might sound like “I’m the only person who understands our model, it takes a ton of effort to maintain, and yet because only I understand it, no one trusts it.”
Pitfall: Over-Simplifying Attribution
This is typically seen in larger teams. They may have outgrown a simple attribution model, and as their organization scaled, they launched more campaigns across channels that can’t be accurately measured with a simple single touch model.
Symptoms of over-simplified attribution include:
- The model undervalues campaigns you know are driving meaningful engagement
- You have a long sales cycle
- Marketing’s impact isn’t well understood
- You have a sizable marketing budget, but when it comes time to allocate it, you’re doing a lot of guesswork.
Attribution Models
By Pros, Cons, and what they’re best for
| Attribution Model | Pros | Cons | Best for |
| First Touch | Simple Requires little data Easy to explain | Overweights top of funnel | Small team Short sales cycle Limited channels Answering questions like: |
| Last Touch | Simple Requires little data Easy to explain | Undervalues awareness and nurture Encourages short-term tactics | Small team Short sales cycle Demand capture is the primary goal |
| Source-Based | Easy to operationalize Directional insight | Can be influenced by bias Can pit marketing and sales against each other | Small team Expected to drive a certain percentage of bookings from inbound / outbound |
| Self-Reported | Easy to operationalize Directional insight on what influenced buyers most | Affected by bias To use at scale, the responses need to be cleaned and categorized | Anyone can use, though does not replace any other model |
| Multi-Touch | Reflects more accurate buyer journey Values both demand generation and capture spend Supports strategic budget decisions | Harder to operationalize Requires more explanation than single touch models | Midsized to large teams Longer sales cycle Teams that work on their data hygiene |
To learn more about each model and how they work, check out the complete guide to attribution.
The Attribution and Brand Awareness Pitfall
This is where attribution choices start to have real downstream consequences.
Most marketers don’t need to be convinced that brand awareness matters. The challenge is that awareness is notoriously difficult to measure in a way that maps cleanly to revenue, especially in shorter timeframes. And when something is hard to measure, it tends to get questioned more aggressively than things that are easy to track.
“What you measure usually gets paid attention to, and what you pay attention to, usually gets better.”
-Seth Godin
What happens next is predictable. If your reporting only captures what converts immediately, then campaigns designed to influence earlier stages of the buyer journey will always appear weaker than they actually are. Awareness, thought leadership, and demand creation efforts rarely show up as last-touch conversions—even when they play a meaningful role in deals that close weeks or months later.
Attribution models don’t just explain performance; they shape it. Every model implicitly tells the organization what “counts.” When awareness never shows up in reporting, it fails to get attention. And what doesn’t get attention doesn’t get optimized. Eventually, it doesn’t get funded.
This dynamic is especially pronounced in small to midsized organizations. With limited budgets and increased scrutiny on every dollar, spend that can’t be clearly defended is often the first to be cut. Not because it isn’t working, but because the reporting framework isn’t equipped to reflect its role in the buyer journey.
The answer isn’t to force multi-touch attribution before an organization is ready for it. But it is a reminder that your attribution model should align with the types of campaigns you’re running. If brand and awareness are a meaningful part of your strategy, your reporting needs to acknowledge that.
The goal isn’t perfect attribution. It’s ensuring that the work required to drive future pipeline doesn’t become invisible simply because it’s harder to measure today.
So… Is Multi-Touch Attribution Right for You?
There isn’t a universally correct answer here.
The goal of attribution isn’t to achieve analytical perfection. It’s to create a system your organization can trust, understand, and actually use to make better decisions. When the model is misaligned with your team size, data maturity, or sales motion, it stops being a decision-support tool and starts becoming a distraction.
For smaller teams, simpler attribution models often work not because they’re more accurate, but because they’re more usable / manageable. They shine a light on the activities that matter most today, without demanding more operational effort than the team can realistically sustain.
As organizations grow in budget, channels, and complexity—the limitations of those simpler models become harder to ignore. That’s usually the right moment to consider multi-touch attribution, not as a badge of sophistication, but as a response to real decision-making gaps.
The common failure mode isn’t choosing the “wrong” model.
It’s choosing a model that doesn’t match your current reality and then judging attribution itself when it inevitably disappoints.
Attribution should evolve alongside your business. What works at $5M in revenue doesn’t need to look like what works at $250M. It’s a sign of growth.
The right question to ask isn’t: “Should we be doing multi-touch attribution?”
It’s: “Given our team, our data, and the decisions we need to make right now—what’s the simplest model that helps us focus on the right things?”
Answer that honestly, and you’ll be much closer to an attribution strategy that actually drives better outcomes.
