Perspectives

People are complicated. So why do we oversimplify attribution?

A discount code can claim credit for a sale it never earned; three things separate companies that know this from those that don't.

Abstact people icons one of whom is a taget in a crosshair
There are some trainers (sneakers, if you prefer) that my daughter has been keen on for a while. Her favourite singer wears them, her best friend has a pair, and they're all over Instagram. Finally, reminded by a great TV ad the brand put out recently, I did a quick search, found a great deal, and bought them for her. At the checkout, there was even a handy 10% discount available: a boon in these troubled times!
Now here's my question for you. To whom do we attribute the sale?

Attribution is about deciding what really caused the outcome

Suppose you're the brand in question. There are many channels at your disposal, but the budget is always tight. Which are the strongest channels? Which produce the best returns? Where's the best place to invest? To answer these questions, you must first solve the problem of attribution.
In my example above, what led to the sale? The singer and their record label? Her best friend and other influencers? Instagram and other social media? The TV ad? The retailer? The search engine? In fact, it's likely that none of these showed up as the sale attribution: the affiliate for that 10% discount likely captured it all. An unwary brand might conclude that affiliates drive sales and shift budget accordingly. Yet the affiliate contributed nothing except last-click proximity.
This isn't a hypothetical problem. Last-click attribution systematically undervalues awareness channels and overvalues conversion mechanics. First-click does the opposite. Linear models assume equal contribution across all touchpoints, which is rarely accurate. Time-decay models improve on this but require you to estimate how awareness degrades, which varies by product category and purchase cycle.
The question becomes: how do you validate that your attribution model reflects reality?

What good attribution actually requires

You need three things.
  1. Instrumentation that captures the full customer journey. This means consistent tracking across channels (web, app, offline if relevant), with identifiers that persist across sessions and devices. Cookie deprecation and privacy regulations make this harder than it was five years ago, but server-side tracking and first-party data strategies can compensate.
  2. A hypothesis about how your channels interact. Does TV drive search volume? Do influencers create latent demand that converts weeks later? You can test these hypotheses by varying channel spend and measuring cross-channel effects. This requires statistical discipline: you're looking for incremental lift, not correlation.
  3. Feedback loops that connect attribution to outcomes. If you shift budget based on an attribution model, you should measure whether overall conversion rate, customer lifetime value, or acquisition cost improves. If the model is wrong, these metrics tell you quickly.
The practical constraint is cost. Building the infrastructure to track billions of events, run holdout tests on channel combinations, and attribute revenue accurately requires significant engineering investment. Machine learning can help here: algorithmic attribution models can identify patterns in complex customer journeys that rule-based models miss. But the model is only as good as the data you feed it and your ability to validate its predictions.
Many companies default to simple attribution because they lack the infrastructure for anything more sophisticated. This is rational if your product has a short consideration cycle and few touchpoints. For complex B2B sales or consumer products with long purchase cycles, the error cost of poor attribution justifies the investment in better measurement.

What good attribution gives you (and what bad attribution costs)

The result of getting this right isn't just better budget allocation. It's clarity about what actually drives your business, which channels create demand versus capture it, and where marginal investment produces returns. That clarity compounds over time as you iterate based on evidence rather than intuition.
That would be a happy ending for your budget allocation – but what about my daughter?
Well, I’m happy to report that she’s delighted with her new trainers, but I expect that somewhere a brand is crediting an affiliate for the sale and putting all their chips on discount code marketing.
What would you do? If you'd like to move beyond that level of insight, I'd be happy to discuss what better attribution could mean for your business. Let's talk!

Tim Benjamin

Chief Technology Officer

02 January 2026

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About the AuthorTim Benjamin

Tim Benjamin—Softwire’s CTO—has over 25 years of experience leading complex transformation initiatives across multiple sectors. He has worked with organisations of all sizes, from startups to global enterprises, specialising in digital transformation, product strategy, and building high-performing technology teams. He brings entrepreneurial vision together with enterprise discipline, and has led teams through rapid growth, scaled platforms to millions of users, and consistently translated emerging technologies into commercial outcomes. His teams excel in implementing AI, DevOps, and Lean practices, delivering robust solutions while operating within complex regulatory environments. Tim’s first successful startup, founded at the turn of the millennium, delivered interactive digital TV services to more than two million European homes. He has since held senior technology leadership roles at organisations including the Continuo Foundation, Fictioneers, part of WPP, and Infinity Works, now part of Accenture. Alongside deep technical expertise, Tim has a background in internationally performed, award-winning classical music composition, bringing an uncommon blend of analytical precision and creative insight to technical leadership.

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