Case study
Why users don't buy a €60 add-on — and how one simple explanation raised revenue per user by 8.5%
+8,53%
Revenue / user
85%
Chance to win
70%
Prob. of six-figure yearly uplift
Situation
ALB Filter sells drinking water filters for home and travel. The online shop is optimised continuously.
Together with Heinrich Mahr, I help them test the right things.
Instead of jumping straight into experiments, we run an exploratory user test. Users are asked to visit the site, find a product they like and buy it.
The goal is to better understand how users orient themselves on the site and how they make decisions.
The symptom
On top of the main filter, users can buy a pre-filter (Protect) for €60 that extends the product's lifetime. A high-margin add-on that is rarely bought.
The diagnosis
In the user test we keep observing the same behaviour:
Users open the product page, run into the choice between “with Protect” and “without Protect” and wonder what that even means.
14 out of 32 participants don't understand what “Protect” actually is.
“Version 'with Protect' or 'without Protect'. What is that?”
— Thomas, 41
The add-on is not explained at all in its immediate context. One user even assumes it is an extended warranty rather than a physical product.
The information does exist further down the page, but most users never see it.
The aha moment
Internally it's perfectly clear what “Protect” is. For the user, it isn't.
They're asked to make a purchase decision without understanding what they're buying.
The experiment
At the moment of decision, users lack context. So we give them the information exactly where the decision happens: we add an info icon plus a modal with a detailed explanation.
The result
Revenue per user
+8,53%554 / 8,787 vs. 527 / 8,776 conversions/visitors · 4-week test runtime
Chance to win of the new variant
After four weeks of runtime: we increased revenue per user by 8.53%, and the new variant's chance to win is 85%.
Projections show the change generates a six-figure yearly revenue uplift with 70% probability.
The takeaway
People don't make decisions about things they don't understand. Instead of testing new things, it often pays to first check whether the offer is even clear.
Do your users really understand what you're asking them to buy?
Find out what really works.