Variation iteration

The process of declining, adjusting or refining a variation tested in an initial A/B test, based on the results obtained, observed behavior or new hypotheses. The aim is todeepen learning,improve performance and validate the robustness of an optimization path, with a view tocontinuous improvement.

🎯 Objective:

Don't be satisfied with a single test result:

• Optimizing a promising (but not yet winning) variation,
• Explore another approach based on the same initial insight,
• Reinforce a positive signal by testing bolder or more targeted versions,
• Adapt a variation to a new context or user segment.

📌 Examples:

• Testing a new CTA slightly improves the click-through rate → iterating with different, more profit-oriented formulations.
• A new layout reduces the bounce rate but not the conversion rate → we iterate by testing a better hierarchy of elements.
• A variation works well on desktop but not on mobile → we create a specific mobile-first iteration.

🔁 Role in a CRO strategy :

Iterations allow :

• Transform a "neutral test" into a learning lever (e.g. by analyzing micro-conversions),
• Build a roadmap of gradual experimentation, with successive small adjustments,
• Reduce the risks associated with radical change,
• Capitalize on past tests to refine insights and maximize impact.

🧪 Best practices :

• Always start with a concrete learning from the previous test (quantitative or qualitative),
• Modify one key variable at a time if possible, to isolate effects,
• Document each iteration in a CRO knowledge base (hypothesis, variation, results),
• Use iterative testing to explore high-potential avenues, especially after "non-significant" but promising results.

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