Ahmed
Aden

/ How I Work

Most companies don't have a data problem. The data exists. Acting on it is where things break down.

These are the four areas where I've found the most traction across experimentation, analytics, and conversion.

Diagnose before adding more

More tests, more tools, and more dashboards rarely fix the underlying problem. Before anything gets added, it's worth understanding where the current system is leaking. That might be a hypothesis quality issue, a tracking gap, a metric that doesn't connect to anything actionable, or a process that produces insight nobody acts on.

Connect user intent and behaviour to web experiences

Conversion problems are usually understanding problems first. That means looking at how users are actually arriving, what they're looking for, where their expectations break down, where frustration accumulates. From there you can build experiences that meet them where they are.

Put AI where it actually saves time

AI is most useful when it handles the parts of the workflow that are slow, repetitive, and don't require human judgement. "Did we test this before?" used to take days of digging through shared drives and old reports. The right infrastructure answers that in minutes. That recovered time goes back into the work that actually requires thinking.

Make data impossible to ignore

This isn't about better dashboards. It's about the distance between what the data says and what actually gets decided. That distance usually comes down to two things: the infrastructure doesn't connect analytical output to specific actions, or the right people aren't seeing the right numbers at the right moment. Both are fixable. Neither fixes itself.