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We recently launched A B tests on our website using Optimizely, but after going live, we started seeing inconsistent results and occasional experiment errors. Some variations are not tracking conversions correctly, and QA reports show differences between staging and production behavior. Our internal team understands basic experimentation but lacks deep platform knowledge.

This is a common challenge when experiments are scaled without advanced configuration checks. Optimizely experts usually start by reviewing event tracking, audience conditions, and experiment setup across environments. They also verify implementation, SDK versions, and data layer consistency to prevent mismatched results. A structured debugging process helps identify root causes quickly and avoids flawed test conclusions.