Figuring out how someone found your business used to be very easy. Today, however, buyer journeys stretch across weeks or months and bounce between half a dozen channels. Consequently, it has become tough to know what actually worked in your marketing strategy.
If you only look at the final click before someone converts, you are missing everything that warmed them up along the way. Deciding where to invest next, then, feels like a guessing game.
Guessing carries so much risk. As such, it is time to turn to multi-touch attribution.
Attribution is basically about understanding influence. Instead of giving all the credit to a single moment, it looks at the entire journey and asks which interactions helped move someone closer to buying.
In 2026, that clarity matters more than ever. Why?
Attribution is about assigning credit, and marketing attribution models are the rules that decide how that credit gets shared. Each model makes different assumptions about what matters most in a buyer’s journey.
First-click gives all the credit to the very first interaction someone had with your brand. It is great if your main goal is awareness and top-of-funnel growth. However, it ignores everything that happens after that first moment.
Last-click, in contrast, gives 100% of the credit to the final touch before conversion. It is helpful for spotting which channels tend to “close,” but it undervalues the work that made the prospect ready to convert in the first place.
The linear model allocates credit evenly across all touchpoints. When there are five interactions, each gets 20%, highlighting the importance of consistency and showing that multiple channels work together. The problem is that not all touches are created equal, but linear treats them as if they are.
Time decay gives more weight to interactions that happen closer to conversion. Recent touches are assumed to be more influential than older ones. It works well for longer buying cycles. The trade-off is that early discovery efforts can end up undervalued, even though they kick-started the entire process.
This model — also known as the U-shaped model — emphasizes discovery and conversion. The first touch gets 40% of the credit, the last touch gets 40%, and everything in between shares the remaining 20%. It acknowledges both introduction and closing without completely ignoring the middle, which is reasonable for sales cycles that are not extremely long.
Data-driven attribution uses machine learning to analyze patterns across converting and non-converting journeys. Instead of following preset rules, it looks at what actually changes the likelihood of conversion.
Credit is assigned based on incremental impact, which makes this approach the most objective and nuanced. The catch, though, is that it needs a lot of clean data and more advanced tooling.
Attribution models are starting points; you can adapt them, combine them, or build custom logic. The right choice depends on your sales cycle length, deal size, and which channels are most meaningful at each stage.
If your buyers interact with your brand only a few times before converting, simpler approaches may be enough. However, if your journey involves dozens of touches across months, you will want something that captures that complexity.
One important tip is not to overcomplicate things by cramming too many variables into your model. Small changes in the data yield wildly different outputs, and your model becomes impossible to trust or explain.
How do you implement your chosen marketing attribution model? Here are three steps to follow.
UTM parameters are essential for identifying where traffic comes from and how campaigns are structured. Beyond UTMs, assign unique IDs to recognize the same person across devices and sessions when tying analytics data to CRM revenue.
Pull conversion and revenue data from your CRM. Load everything into a customer data platform (CDP) or central warehouse via automated data extraction and clean up inconsistencies. Then you can calculate ROI rather than just engagement.
If your buyers take weeks or months to decide, a short lookback window will misrepresent influence. Historical performance, deal length, and industry norms should guide this decision.
At Connection Model, we make attribution understandable to leadership. Take these reminders from our team:
As a digital marketing agency, Connection Model helps you generate qualified leads and turn them into new customers. We handle the frameworks, tool selection, and dashboards that connect marketing activity directly to the pipeline.
Get clarity on what is driving growth! Let us talk about your multi-touch attribution framework today.