Generating hypotheses on that data

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Based on the results of the analysis, the Analyst begins to infer tests through hypotheses. For example:

"It was observed through the number X and through the feedback Y that the buttons with the blue color received more clicks. From that, we will test blue buttons in our quote requests for a month."

The Analyst will always create this hypothesis with data that is relevant to the execution of the test.

Test Implementation
At this stage, the Conversion Optimization Analyst starts making optimizations based on the hypotheses.

Here you start using the testing tools. Among them, we can highlight:

Optimizely
Unbounce
Visual Website Optimizer
Adobe Target
Apps Flyer
Ion
Yozio
It is very common for these optimizations to be implemented by making russia phone data comparisons between two or more versions of a given element.

In the case of a contact button, for example, it may be interesting for the Analyst to make the variation based on the displayed texts "Take your evaluation here" vs. "Receive a free evaluation".

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These are the famous A/B tests.

In this test category, the Analyst will observe which of the two options generates more conversion, keeping the one that worked most effectively in the end.

The more testing the analyst does, the more he or she will understand how the business's audience interacts with that business.