Decoding the attribution puzzle: why your metrics might be lying to you
Do you know where your marketing dollars are going? Because… spoiler alert: you probably don’t.
I’ve spent over 15 years working with service and ecommerce companies, and there’s a pattern that repeats itself every single time. Teams make critical decisions about where to invest millions based on data that isn’t even well-defined or agreed upon. It’s like flying blind.
The problem isn’t necessarily that you can’t measure. It’s that the way you’re doing it could be skewed from the start.
The problem: when measurement gets too easy
Here’s the thing: when you set up a new web project, the most natural move is to implement Google Analytics. It’s free, it’s everywhere, and… well, everyone uses it. That should be enough, right?
Not quite. That “costs nothing” tag comes with a hidden price tag. Google Analytics is limited, it does data sampling (especially when you’ve got volume), and here’s what matters: the attribution models you can use are practically zero. No transparency, no flexibility, and (let’s call it what it is) the tool tends to favour Google’s ecosystem.
Sure, there are alternatives. Mixpanel, Simple analytics, Amplitude, Contentsquare… the market is packed with options. But here comes the real challenge: convincing the person holding the budget to pay for a tool when they’ve got a free one is… tough. Although if you really want to fine-tune things, there’s no way around it.
The real problem: last click and its lies
Usually, setting up attribution models is treated like a simple administrative task. Check the box, pick whatever comes first, done. And what usually happens is everyone falls into the last click trap.
Suddenly, uncomfortable questions start showing up in meetings:
- Why are we cutting social media ads spend if it’s not bringing in any revenue?
- Why does SEO convert so well when we barely send it traffic?
- Where is all that direct traffic coming from?
- How do we measure foot traffic to a physical store when everyone tracks it through digital?
And then it hits you: the model was wrong. But it’s not fair to blame the last click, because the user hit multiple touchpoints before converting. Every single point of contact mattered.
How to solve the attribution model
Well, first: verify that your traffic is being measured correctly. Sounds obvious, but almost nobody does it. Match your transaction data (what your data warehouse records) against your analytics tool. Look for at least a 95% match rate. If you hit that, your measurement is healthy.
Second: build consensus across your company. Bring together marketing, product, leadership. Get everyone on the same page about how attribution gets measured. Total transparency. This prevents endless conversations that go nowhere.
Third: understand that no universal model exists. It depends on your business. Some put weight on top of the funnel (that crucial first interaction). Others swear by the last click. Many tools let you tweak it. In my experience, a model that usually works well looks like this: 40% of credit to the first click, 20% to the last one, and the remaining 40% spread across all the other touchpoints.
The result? Smarter decisions, better-allocated budgets, and fewer circular conversations around the table.
If measurement and optimization challenges in your ecommerce feel familiar, you’re not alone. At Consumer Services Hub, we’ve helped companies unlock the real potential of their data and build revenue optimization strategies that actually work. You can write us at hi@consumerserviceshub.com and we will be happy to assist you.
