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The Power of A/B Testing in Email Marketing

3 Mins read

As business owners, we’ve all been in at least one of these situations:

  1. You’re just starting, still bootstrapped, and aiming to get as much ROI as possible, as quickly as possible from any and all of your marketing channels;
  2. Your business has been around for a while, your tried-and-true marketing channels are doing okay, but not driving the revenue it used to (or you hoped it would);
  3. You’ve been sending marketing emails to a big list of contacts for a while now, ad hoc, but have no idea how to make the most of it.

Enter email – more specifically, properly executed email. No, I’m not talking about sending daily to your entire list (please don’t do that), rather, the testing of email, data analysis, and planned execution.

As with any marketing channel, testing is key and crucial to garnering any real insights into your audiences, including what works best for individual cohorts as well as actually defining your audiences as well. Does this sound like a cart-before-the-horse situation? Well, no – as you iterate and implement A/B testing in email, you’ll notice a clear trend very early on as to which of your existing segments (please tell me that you are segmenting, not batch-blasting to everyone in your database) are most receptive to your tests. From there, you can further segment these audiences into new, higher-value audiences that can bring you even further insights. It’s the snowball effect, in short.

Now, with all of this out of the way, you’re probably asking yourself: “okay, cool, but how do I actually get there?” Great question, and the answer is testing. Testing, testing, testing! How do I A/B test, why is it effective, and how do I continue iterating new ones that are actually valuable to my company? Easy: put on your scientist hat and follow the scientific method:

  1. State an observation (My contacts seem to prefer less text than more”)
  2. Ask a question (“Are my contacts clicking through to my site at a high rate, and don’t need a long article to see the value in my product/service?”)
  3. Craft a hypothesis (“I believe that my contacts are already far enough in the funnel to see the immediate value in my product/service and don’t need to be sold on the merits of it in every single email, hence, they don’t need a long story to understand my value-add”)
  4. Create a prediction based on the hypothesis (“My contacts will click-through to my site at a higher rate if I keep my content short and sweet”)
  5. Test the prediction.

From here, you’ll then gather the data, analyze it, and iterate a new test from the insights you’ve just garnered from your test.

To put this into more solid terms and bring an actual example to life here, one of many examples that we have here at CodeCrew is a recent campaign that we had run for one of our clients in the furniture space. Although the test we ran was a relatively simple one – a send-time test between two of their highest open rate-related tests, 7am and 7pm local, the merit of it was obvious.

After gathering initial data that led us to determining these two times of day as being most valued by this client’s contact base (via a simple historical data dump and Excel analysis), we stated an observation: that although both 7am and 7pm local have consistently provided open rates higher than any other time of day, we observed a higher trailing open rate during evening sends. From here, we asked ourselves the question – if we are seeing the trailing effects of evening deployments providing us with a higher multi-hour open rate, can we expect sends at this time of day to provide us with higher sustained open rates as well as higher revenue figures as well?

Now we had our hypothesis: If we are to test between these two highest-opening times of day, we expect the evening deployment to perform better as a whole, not just at the exact time of day at which the email hits the inbox. Our contacts will engage at a higher rate at this time of day, and in turn, we’ll see stronger contact engagement, retention, and LTV.

Testing this hypothesis, we found that indeed open rates had a sustained increase during 2-3 the hours following the 7pm local deployment, and had seen an average of 28% higher open rates during this timeframe, as well as a 75% higher order split toward evening deployments.

Testing isn’t scary, nor should it be. With proper testing strategy in place, it’s even quite easy!

Alexander Melone, Co-Founder and Director of Production: Alexander Melone is the Co-Founder and Chief Production Officer at CodeCrew. Alex has extensive experience in the email marketing industry. 

email” by Sean MacEntee is licensed under CC BY 2.0.

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