Index
A B2B site with few conversions may be slow to produce reliable evidence in an A/B test. Before hiring a tool, evaluate whether the volume and decision justify this method.
How to evaluate this decision
Define a hypothesis, the main metric and the effect size that would be relevant to the business. The analysis needs to consider variability and available time. If the sample is insufficient, interviews, usability assessment, and contact analysis can reveal actionable issues. This evidence does not replace a controlled experiment, but it helps to choose the next investment.
Criteria for comparing proposals
- Feasibility: estimate the necessary sample with the support of those who master experimental analysis.
- Metric: distinguish click, contact and qualified opportunity before comparing versions.
- Decision: establish when to end, continue or consider the result inconclusive.
A scenario to discuss with the supplier
Hypothetical example: a page receives few monthly orders and a version generates two more contacts. This isolated difference does not demonstrate that the new design will increase sales consistently.
What to validate upon delivery
Review hypothesis, instrumentation, and analysis rule before launch. Don't change the main metric after seeing the results to turn a random variation into a positive conclusion.
Prepare the conversation about the project
Quantum9 can evaluate the journey and organize improvement hypotheses. Bring traffic, conversions and sales cycle to choose between research, evident fix and viable experiment.
Conversion Optimization · Map the company's priority