How a long-form story in an email affects webinar registrations Experiment ID: #11970


Experiment Summary

Timeframe: 2/13/2019 - 2/20/2019

In this experiment, our original email was trying to communicate the importance of a/b testing by telling a personal story. This is typical of many emails that we write – we often look for a personal story to make help the recipient see that we’re humans (not just marketers) and use that connection to lead into a content offer of some type.

But we wondered if it might be more effective to tell this same story in a much shorter format, and spend more time talking about the specifics of what someone will learn in the webinar, rather than solely relying on the landing page to cover those details.

Research Question

Will spending less time on the story and more time on the content affect webinar registrations?


C: Long Story Format
T1: Detailed Content Focus


Treatment Name Conv. Rate Relative Difference Confidence
C: Long Story Format 1.4%
T1: Detailed Content Focus 2.1% 48.4% 98.4%

This experiment has a required sample size of 2,807 in order to be valid. Since the experiment had a total sample size of 8,489, 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
× 48.4% increase in conversion rate
× 0% increase in average gift

Key Learnings

Our treatment saw a 48.4% increase in webinar registrations. This is a very interesting result as it can make a significant impact on how we structure of emails related to free content offers in the future. It’s often hard for us to validate an experiment like this based on our scale, let alone see an increase this large. So a 98% level of confidence gives us great reassurance that this strategy will be on to continue test and iterate on moving forward.

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Experiment Documented by...

Nathan Hill

Nathan is an Optimization Evangelist at NextAfter. If you have any questions about this experiment or would like additional details not discussed above, please feel free to contact them directly.