The Fatal Flaw of A/B Testing

a/b testing

By Kiki Schirr, {grow} Contributing Columnist

Last year I stumbled across one of those customer experiences that makes you want to tear your hair out—and preferably stuff it down the offending company’s throat.

I had started writing more and decided that I would take some time off from the side work I do hourly to support my Fittr/startup lifestyle. So I went to my freelance hour-tracking software service and looked for a “pause” option, so that I wouldn’t have to pay $20/month for 2–3 months.

These were the three options given:

A/B testing pieceAnd there, in the center, was the option I definitely wanted: to “pause” my account.

A/B testing

So I was excited it would be so simple, and clicked the button.

But I got an error message.

UX example for A/B testing piece

Reduce the number of clients to 1

They wanted me to go through and delete all my accounts? I couldn’t do that! I had somewhere around ten customers listed—and two of which were the only places I had stored their company’s contact information.

I looked at the choices again. Was it worth paying $20 to keep that information? Probably, yes.

And I wouldn’t have canceled at all—except that I was pissed. What a poor customer experience! And with righteous indignation, I copied a little information from the account and closed it entirely.

(but not before sending them a nastygram about their UX –not my proudest moment)

UX example for A/B testing piece

A few days later they wrote back to me and very kindly admitted that their cancelation policy was dumb. In fact, they even said their old policy was so flawed that—

“We made it easier for customers to cancel than to stay!”

So I felt a lot better about writing to them. And when I started billing clients again, I went back. Their service really is excellent, and I had been very happy with it. I felt guilty for sending them a note at all, but their UX had been completely illogical, so maybe, my anger and nastygram had helped in a way?

I couldn’t stop thinking about it, though. How had they decided upon a user experience that would enrage a small group of customers?

And then I remembered that thought I’d briefly had: ‘maybe it was worth $20 just to keep the information.’

In an A/B test, I have no doubt that this would have decreased the number of cancellations.

After all, they had increased the pain of leaving.

What an A/B test doesn’t show, though, is how much a customer hates you for it.

…it misses the human element.

I mean, that’s not the only thing flawed about A/B tests. Don’t get me wrong.

There’s also the problem of statistical significance.

If you search significance of A/B testing, Wikipedia gives you the following scenario:

Email test group A: 50 out of 1000 (5%) success rate
Email test group B: 30 out of 1000 (3%) success rate

We’re all going to go with test group A, right?

Kiss Metrics provides this misleading tool, which gives the following analysis:

KissMetric's A/B Testing Tool

But then, when you play around with the calculator a bit, you realize something’s wrong…

KissMetric's A/B Testing Tool

Wait—800 fewer tests, and I still get the same results?

It doesn’t take the size of the pool into account WHATSOEVER. Whoops!

Whereas Optimizely provides this useful tool:

Optimizely's A/B Testing Tool

Basically, to test for significance of a 25% change off of an expected 4% conversion rate… well, you’d need to send it to more than twice as many subscribers than you actually have.

Whoops.

So yeah. Don’t always trust an A/B test without putting some serious thought into it.

My best suggestion? Narrow it down to a few options, sleep on it, and then in the morning pretend you are a new customer seeing your emails for the first time. How do they make you feel?

Empathy will get you a long way in UX.

KikiSchirrKiki Schirr is a cofounder of the fitness app Fittr, and also does the company’s marketing. Her latest project is a lifestyle magazine for women in STEM called Valley Girl — she would love for you to check it out! Kiki is also the author of Tech Doodles, and can be reached easily through Twitter.

Images are screen captures taken by Kiki Schirr

 

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  • I’m not sure I’d blame A/B testing, that feels like a symptom not a cause, but everything else is ON POINT.

    Especially this: “What an A/B test doesn’t show, though, is how much a customer hates you for it.”

    Wish Mark had this Click to Tweet…

    The cause from my perspective is not understanding that every interaction with your business impacts brand perception (or maybe just not valuing brand).

    These guys could have made this decision without A/B testing. Either way it’s a bad decision.

  • I’m not sure I’d blame A/B testing, that feels like a symptom not a cause, but everything else is ON POINT.

    Especially this: “What an A/B test doesn’t show, though, is how much a customer hates you for it.”

    Wish Mark had this Click to Tweet…

    The cause from my perspective is not understanding that every interaction with your business impacts brand perception (or maybe just not valuing brand).

    These guys could have made this decision without A/B testing. Either way it’s a bad decision.

  • Ger van den Buijs

    Dear Kiki,
    wonderful blog. I very much like your sentence “What an A/B test doesn’t show, though, is how much a customer hates you for it.”

    I too think that getting the qualitative information is an important addition to statistical information. My vision is that customers are getting more and more fed up with the data based manipulations. The pain is however just not high enough and there is no alternative yet. But it will come and customers will massively choose for customer friendly alternatives.
    Regards,
    Ger

  • Ger van den Buijs

    Dear Kiki,
    wonderful blog. I very much like your sentence “What an A/B test doesn’t show, though, is how much a customer hates you for it.”

    I too think that getting the qualitative information is an important addition to statistical information. My vision is that customers are getting more and more fed up with the data based manipulations. The pain is however just not high enough and there is no alternative yet. But it will come and customers will massively choose for customer friendly alternatives.
    Regards,
    Ger

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