As discussed in my last blog post, we reviewed how the marketing force of 200 marketers at Optimization Summit was utilized to design the following test:
Control (click to zoom) Treatment (click to zoom)
You can read the experiment details, how we got 200 marketers to agree, and few insightful reader comments in Wednesday’s post. In this second post now, as promised, we will look at the results and what insights might be gained from them.
First, let me just forewarn you, this post will be a little on the detail side of things. I really wanted to give you a clear of a picture of what happened, and that took a little more fleshing out than usual.
So, what were the final results?
According to the final numbers, our focus group of 200+ marketers was able to increase the amount of leads generated from this page by a whopping 0.7% at a 90% confidence level.
This seems like a modest gain to say the least, but keep in mind that we have worked with research partners in which a 1% gain meant a significant increase in revenue. In this case, we were spending many resources to drive people to this page, so even a small gain could potentially generate impressive ROI.
With that said, it is also very interesting to note here the incredibly high conversion rate for this campaign, 47% (meaning nearly one out of every two visitors completes the form). This means that incoming traffic is incredibly motivated, and therefore any gains obtained through testing will most likely be modest.
As taught in the MarketingExperiments Conversion Heuristic, visitor motivation has the greatest influence on the likelihood of conversion. If your visitors are highly motivated, they will put up with a bad webpage in order to get what they want. Not to say either one of these pages are bad, but with such a motivated segment, it will be hard to tell any difference between the designs.
But something still didn’t seem right…
Despite the incremental success, something smelled rotten in Denmark (and it wasn’t Adam Lapp) during the course of the test. We collected data every hour (though some hours are consolidated in the charts below), and by the end of the first day of testing it looked as if we had a clear winner.
At 11:50PM of Day 1, the Treatment was outperforming the Control by 5% and had reached a 93% confidence level. The test was in the bag, and attendees were breaking out the bottles of Champagne (okay… a little stretch there).
However, in the morning, everything had shifted.
The Treatment which had performed with an average 51% conversion rate throughout the previous day, was now reporting performance at a comparatively dismal 34% conversion rate. This completely changed the results, the Control and Treatment were virtually tied, and the confidence level was now under 70%.
What happened? Why the shift? Did visitor behavior change drastically over night? Did some extraneous factor compromise the traffic?
Simple in principle, messy in practice
This leads me to one of the key things I learned during this experiment – testing is messy. When you get real people interacting with a real offer, the results are often unpredictable. There are potentially real-world extraneous factors that can completely invalidate your results. In this test, the aggregate data claimed an increase with a fair level of confidence, but something was interfering with the results. We would have to dig down deep to really figure it out.
Maybe it’s just the thrill seeker inside of me, but it was the unexpected messiness that made this test so exciting. Throughout the conference, I eagerly watched the data come in, not knowing what would happen next. I felt like a kid watching a good sci-fi movie, and at this point of the test in particular, when nothing seemed to make sense, I was on the edge of my seat.
So, how does the story end?
Before moving on, we want to ask you:
- Why do you think the treatment results dropped drastically overnight?
- Have your tests ever done that?
- How would you pinpoint a cause?
- Where would you look first?
Oh and by the way, if the overnight drop hadn’t occurred, the Treatment would have outperforming the Control by 6% with a 96% statistical confidence level. What do you do with that?
Editor’s note: Join us Tuesday on MarketingSherpa when Austin reveals the results of this thrilling trilogy by releasing the entire case study in full. To be notified when that case study is live, you can sign up for the free MarketingSherpa Email Marketing Newsletter.
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