How to avoid 5 testing pitfalls & run trustworthy experiments

Twitter: @robkingston,

Broadly two avenues to increase results from experimentation

  1. Increase Velocity
    • Run more tests, faster
  2. Increase Quality
    • Run better tests, ideas
    • Trustworthy results

Trustworthy experimentation is overlooked

...but it shouldn't be.

  • Outcomes can be worth $Ks/$Ms in revenue
  • Dodgy results enshrine false narratives
  • Undermines credibility in all your work

Let's explore common pitfalls so you can identify & fix them.

Pitfall #1

Finance client testing new CTA design. Mild styling/markup change, passed extensive QA:

<button class="oldBtn">Create an account</button>

Changed to:

<a class="newBtn" href="/signup.html">Signup now</a>

After launching, the SaaS split testing tool reported
checkouts down -70%! 😧 %$#&!

Right, time for us to check what GA was reporting:

  • Overall conversion is fine
  • NO drop in checkouts

So, we go back to the SaaS tool:

And then compared GA's figures:

NB. Subjects = Visitors

The SaaS tool tracked +150% more users bots.

"Checkouts" dropped -70% because bots didn't recognise the new CTA.

Moral #1: Beware of bad tracking / instrumentation

Pitfall #2

Ecommerce client testing major redesign. Used JS redirects for a 50-50 split test that needed back-end changes.

Load times will hurt the treatment, right?

Nope! Conversion rates were dead even...

But, traffic should be split 50-50.
Why does the control group have more?

  1. The redirect thinned out slower browsers
  2. The redirect executed before the tracking ran

Assuming even assignment it could be affected as much as -18%! A far cry from +1.2%...

Moral #2: Avoid selection bias by watching your assignment ratio

Use "SRM" tests & plot your assignment over time

Pitfall #3

Another ecommerce client testing a major SERP listing redesign:

  • Complex test with '000s lines of CSS/JS
  • Passed several rounds of code review
  • Lots of eyeballs on the test

Treatment was delivering a solid lift (Everything stat sig). Just 1-week out from completion...

Days later, we check the results...

Time to investigate...

Errors spiked from the treatment group when a feature deployment broke our test pages.

The whole page was unusable, risking $ooK's/revenue.

Fortunately, we had protection.

Erroring users were booted from the test so they could continue browsing unhindered.

Moral #3: Protect your users & app with error tracking & handling

Pitfall #4

On a lead-gen site, someone wanted to run a really, really important test to lift engagement.

"It's such a good idea - it's full of personalisation and has a
fantastic PIE score!"

So, we built it, QA'd it, tested it and launch the experiment...

With the experiment live, we waited for a result...
2 months passed & no cigar!

Moral #4: Always size experiments to see if it'll produce an outcome

Pitfall #5

Hypothetical: Timmy wants to run a test on an ecommerce payments page (responsible for 100% of revenue).

"Let's redesign this step to make it fit our new brand..."

So, you build, test and QA it thoroughly.

On launch day, you publish it to 100% of traffic...
at 5pm on "Friyay". Job done ¯\_(ツ)_/¯

Yeah, nah.

Imagine a production config flag breaks the test.
Now, half the revenue is at risk.

Moral #5: Protect mission-critical apps with gradual ramp-up

PS. It's easier to launch a contentious idea to 10% traffic, too!

Of course, there are many more pitfalls...

Hopefully now you know how easy they are to spot and solve!

Thanks for your time!

Any questions?

  1. See the slides & links:
  2. Check out Mojito, our open-source split testing tool that eschews these values