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5 Tips to Improve Your CRO Efforts

Posted: Wed Dec 11, 2024 5:36 am
by tanjimajuha100
t is important to measure change here based on the scope of the work and this is only if the change occurred. If our hypothesis is simply changing the wording in the CTA, do not change the shape and color of the button, as this introduces more variables to distort the uncontrolled data.

Illustration of AB testing app bolivia phone numbers determines results by comparing two websites with mobile layout on smartphone in flat style.

A/B testing can be done on cohorts or simultaneously, meaning you can run A over a period of time, followed by B over the same period of time, or you can run them simultaneously by segmenting your users.

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Work with a creative project manager on your team to see how best to run the experiment. However, when you do, be sure to track the impact it has on your conversions, as this can prove or disprove your hypothesis.

Note the discovery
Once you've finalized your A/B test (and likely identified a winner), be sure to keep a record of your hypothesis, how you tested it, and what the metrics did. This will help you avoid duplicate tests and draw informed conclusions for your marketing efforts (perhaps you'll use the same call-to-action approach in your newsletter or landing page).

Remember, the process where you conceptualize your hypothesis, run tests, and track results will be the most rewarding over time, as you can gather insights and take action across all of your marketing efforts. Refining your process is the first step to sustainably improving your results.

1. Set goals for your workflow.
Setting goals throughout your process empowers your team by increasing the overall relevance of granular adjustments that are acceptable. If Q1 is focused on improving your homepage conversion rate, note your current rate at the start of Q1, as well as your target rate for the end of the quarter.

This way, every small improvement leads to a measurable macro-goal that can be celebrated if and when you reach it.

Just make sure you give each of your hypotheses enough time to consider their statistical relevance, as inconsistent testing times and small data sets create a greater ability to skew your data, defeating the purpose of these efforts.