How more specific call-to-action affects conversions Experiment ID: #19028

Care Net

Experiment Summary

Timeframe: 6/16/2019 - 7/16/2019

In this email acquisition experiment, the original landing page copy posed signing up for an online course. The page was already using bullet points, testimonials from previous course takers, and reflected personal, rather than organization-centric, language but we wondered if the call-to-action was getting lost or misinterpreted in the shuffle. So for the treatment, we created copy that maintained the personal nature (using the word “you”) and increased the specificity and clarity around the length of the course (a free 6-week online course) was by  and. For the treatment, said “Get the first session in your inbox in five minutes by simply activating your course below!”

Research Question

Will a more specific call-to-action lead to an improved conversion rate?


C: with 2nd step redirect
T1: shorter and clearer copy


Treatment Name Conv. Rate Relative Difference Confidence
C: with 2nd step redirect 21.5%
T1: shorter and clearer copy 24.7% 15.2% 98.0%

This experiment has a required sample size of 1,279 in order to be valid. Since the experiment had a total sample size of 3,617, and the level of confidence is above 95% the experiment results are valid.

Flux Metrics Affected

The Flux Metrics analyze the three primary metrics that affect revenue (traffic, conversion rate, and average gift). This experiment produced the following results:

    0% increase in traffic
× 15.2% increase in conversion rate
× 0% increase in average gift

Key Learnings

The treatment version increased conversion rate on the online course registration page by 15.2% at a 98% level of confidence.

From this, we can learn that when we simplify the call-to-action and bring greater clarity around what a person is signing up for and their expectations, we can decrease the mental friction in the sign-up process resulting in greater conversion on the page.

Experiment Documented by...

Rebekah Josefy

Rebekah is Optimization Associate. If you have any questions about this experiment or would like additional details not discussed above, please feel free to contact them directly.