A/B testing is essential for ad optimization. You run two versions, compare results, and scale the winner. But click fraud makes those results unreliable.
How fraud interferes with A/B testing:
- bots may favor just one version;
- version B might attract more suspicious IPs than A;
- fake clicks create false interest with zero conversions;
- you choose the version with more bot activity, not better performance.
The result:
- wrong creative is scaled;
- budget is wasted;
- no actual improvement is achieved.
What to do:
- Run AntiClick filtering before launching A/B tests.
- Track behavior and traffic sources.
- Exclude IPs and user-agents that behave abnormally.
- Make decisions only from clean, fraud-free data.
