Hey {{first_name}}
Did you know that only around 20 to 30% of A/B tests end up as statistically significant winners?
That is an insanely low number.
But it is not that surprising either.
Because so often, the wrong solution gets implemented on the back of the right idea.
The insight might be right.
The opportunity might be real.
The logic might be sound.
But if the recommendation is vague, the fix gets left open to interpretation.
And that is where things start going pear-shaped.
Recommendation gets written like this:
“Make the CTA clearer”
“Improve trust”
“Test stronger messaging”
Those are not recommendations.
They are high-level tactics.
The kind you hear thrown around on conference stages and LinkedIn posts.
They leave too much open for personal preferences, opinions, and assumptions.
A real recommendation should document the full solution and carry the insight's logic all the way through to execution.
What changes.
Where it changes.
Why it is worth doing.
What evidence supports it.
How important it is.
What the likely impact is.
How quickly it should move the needle.
Because that is where the value starts compounding.
Not just in spotting the issue.
But in documenting the remedy so clearly that the right action is a tick-box exercise.
Chat soon,
Peter
P.S. Tomorrow, I’ll send over a Recommendation Documentation Template.
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