Experimentation & CRO
Read time:
5 min
Focus:
Experimentation & CRO
Engagement Type:
Program build
Ideal For:
Scaling B2B SaaS
Approach:
Research-led
Method:
AI-accelerated
Most scaling SaaS teams are already testing — just without a system. This walks through how I approach an experimentation and CRO engagement: turning scattered, ad hoc tests into a program where research points to the opportunity, every test earns its place, and wins compound instead of evaporating. (Illustrative example.)
Starting point
A typical starting point: a team running the occasional A/B test with no shared prioritization, inconsistent measurement, and no way to tell a real win from a lucky one. Velocity looks busy, but nothing compounds — and the same growth debates resurface every quarter.
Problem solving
Rather than jump to more tests, I start with research: analytics and user behavior to find where revenue is actually leaking. That becomes a prioritized backlog — every hypothesis ranked by revenue impact, effort, and confidence — and a measurement standard so results are trustworthy from the first test onward.
Implementation
From there it's a roadmap in motion: high-impact tests sequenced against revenue goals, sized to real traffic so each one can reach significance, with AI accelerating the slow parts — synthesizing research, drafting variants, surfacing patterns — while the statistical calls stay human.
Results
The output isn't a single win, it's a compounding program: a validated backlog, a measurement standard, and a review cadence that keeps finding and scaling what works — a system that keeps running after handoff. (Illustrative of typical engagement outcomes.)
