The Marketing Architects
Nerd Alert: Where A/B Testing Goes Wrong
Sep 12, 2024
Dive into the pitfalls of A/B testing where sophisticated algorithms can mislead marketers. Discover the concept of divergent delivery in online ads and how it complicates performance analysis. Elena and Rob use a pizza party analogy to clarify these concepts. Learn about the impact of user demographics and targeting on results. They also discuss using synthetic audiences to enhance testing reliability and overcome biases. This engaging discussion bridges the gap between complex research and practical marketing insights.
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Quick takeaways
- Divergent delivery in online advertising can lead to misleading A/B test results by skewing audience composition and engagement.
- Understanding user types and targeting policies is crucial for accurately interpreting A/B test outcomes and ad performance.
Deep dives
Limitations of A-B Testing
A-B testing is commonly used in advertising to compare different versions of an ad to determine which performs better. However, traditional A-B tests may not provide clear answers due to a phenomenon called divergent delivery. This occurs when algorithms on platforms like Meta or Google show ads to different groups of users in a way that skews results. For instance, if one ad is shown primarily to users who are generally more responsive to ads, it may seem more effective than another ad simply because of the audience composition rather than inherent ad quality.
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