Measurement and optimisation
Improvement as a standing process
Operloom sets up the research, prioritisation and testing practice that turns conversion work into a repeatable process rather than a series of opinions.
OPERLOOM / The system in motion
A better journey starts with a better question.
Research becomes a testable hypothesis, a controlled experiment and a documented decision.
Research and friction
Explore the journey ↗Control and variation
Explore the journey ↗Hypothesis → Test → Evidence → Decision
Combine journey research, behaviour and qualitative feedback.
Define success criteria, guardrails and the test before launch.
Record uncertainty and the next action alongside the result.
01Business problems
Most testing programmes fail on the arithmetic
Underpowered tests read as wins, get shipped, and quietly deliver nothing. The discipline matters more than the tool.
- Tests called before they reach significance
- Traffic too low for the effect being sought
- No recorded hypothesis, so nothing is learned
- Results not segmented by device or source
- Winners never re-checked after launch
02Research
Find the friction before changing anything
Research narrows the field so the testing budget is spent where the evidence points.
- 01Funnel and drop-off analysis
- 02Session review and form analytics
- 03Usability testing on the key journeys
- 04Sales and support call themes
- 05Accessibility and performance as conversion factors
OPERLOOM / WORKFLOW
From signal to action, a traceable handoff.
- 01Research and frictionUnderstand the friction✓
- 02Control and variationRun a controlled test✓
- 03Evidence and decisionDecide from evidence✓
03Method
A hypothesis, a power calculation, a decision rule
Every test states what it expects, how long it needs and what result would change the decision, before it starts.
- Written hypothesis and expected mechanism
- Sample size and duration calculated up front
- Decision rule agreed before launch
- Segmented analysis planned in advance
- Result recorded whether it won or lost
04Where the sample is small
When testing is not the right instrument
Below a certain volume, split testing cannot resolve realistic effects. Saying so is more useful than running a test that cannot answer the question.
- 01Sequential and qualitative methods
- 02Fixing known defects rather than testing them
- 03Directional research with usability testing
- 04Improving measurement before optimising
- 05Being explicit about what cannot be proven
OPERLOOM / STACK
The tools, connected by a process.
Understand the friction↗Run a controlled test↗Decide from evidence↗Common questions
Question: How much traffic is needed to test properly?
It depends on the baseline rate and the effect worth detecting, which is why every test starts with a power calculation. Where the numbers do not support a test, that is said plainly and a different method is used.
Related capability
Where this connects
Next step
Build a systemyour team can actually run
Start with the process as it works today, the platforms already in play and the outcome the organisation needs. We will tell you what we would change and in what order.