Only the primary objective informs a modeled variant leader
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The primary objective in Google Optimize is the only objective used to identify a winning variant reported via Bayesian modeling – so be careful to identify a primary objective that best measures the impact of your test hypothesis.
Per this Google Optimize help article:
“The experiment objective is used to determine whether or not a leader has been found for an experiment. Additional objectives allow you to measure your experiment against other metrics, however, they do not inform when a leader is found.”
Identify a primary objective that has a fighting chance
Make sure your test hypothesis references a primary objective that gives your test a fighting chance of allowing Google Optimize to identify a winning variant.
For example, if you have an e-commerce site, and you change your product details page to better showcase product information – choose a primary objective that reflects increased page engagement as opposed to a revenue based metric. Certainly include revenue metrics in secondary objectives so you can see related impact for your test.
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The thrill of victory
As we noted in our blog post “Creativity through failed experiments“, you can learn from experiments that fail to identify a clear leader. However, when Google Optimize does identify a clear leader, a “sea of green” appearing in the reporting interface is very satisfying.
![](https://02250d.a2cdn1.secureserver.net/wp-content/uploads/2019/09/Google-Optimize-Clear-Leader-1024x441.png)