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Getting Better At Optimization: Increasing Testing Velocity

Tagged As: Optimization Process

Increasing Testing Velocity

The more design experiments that you can run, the more likely you are to identify new insights with positive effects that you could benefit from on your site (unless all of your tests flop of course, which is rather unlikely). Stated in another way, if you don’t test, it’s a given that you will not identify any interesting (and possibly profitable) new gains. So the number of tests that you can execute in a given time frame is a key starting criteria for getting better at optimizing sites for conversions. Over the years, here are the ways that we learned to use to improve this important factor:

1) Start That Test Sooner – Start It Today

Chances are that you have a screen (homepage, checkout, search results, landing page, shopping cart page) that is getting exposed to traffic and is not generating any new insights. People are coming and going and you are not learning anything new from that precious traffic. From this perspective, I strongly believe that it’s better to test anything than not (improving win rates and effect size is something we’ll write about in a future article). Non testing days are to be avoided due to regret and opportunity cost. Initially, it’s good practice to start with an easy test and as its running work on something with a greater probability of success.

TOOL RECOMMENDATION: VWO is a great starting a/b testing tool if you are looking for something better than free (Google Optimize) and not as expensive as Optimizely for example.

2) Agile Stop Rules

Some companies test using fixed time frames which limits them to a predictable number of tests, nothing more, nothing less (ex: 1 test per week or 1 test per month are common). Moving to a more agile or flexible time-frame approach allows you to stop less promising tests faster and thus increases your testing velocity.

EXAMPLE: One such stopping rule that we might apply on projects is to stop the test if: 1) either the control or variation reach 100 conversions, and 2) you have a negative result with a p-value of less than 0.03 as in this example. This could be adjusted to what you find acceptable. The point is that if you are getting certain about exposing your site to a loss, there is no need to run a hopeless test for a complete per-established time-frame. Agile stop rules allow you to cut your losses sooner, and move on to more promising tests.

3) Parallel A/B Testing

Another area where huge testing velocity gains are possible is in the number of parallel tests that are run together. Instead of running just a single test, you could be running a test on your checkout, another test on your homepage, and another on a paid traffic landing page all at the same time. This is an area of debate but most experts believe that the benefits are often greater than the risk of data pollution (by increasing variance). We definitely advocate this approach and recommend the following tips when running parallel tests:

4) Prebuild Tests

Finally, we typically see slow downs when a given test completes and the question comes up: so what should we test next? Days or weeks go by before the next test is started which is a big testing velocity opportunity. Instead of slowing down, make sure you have tests that are prebuilt, checked, and ready to run as soon as some other tests stop.

What about you? How have you managed to increase testing velocity?

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Posted by Jakub Linowski on Aug 4, 2017

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2 Comments on "Getting Better At Optimization: Increasing Testing Velocity"

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brian birkhead
Hi Jakub V. interesting article. Just a few points: 1. the process of how you infer & learn from tests is equally as important as how many tests you do 2. these sequential inferences should drive the design of the next set of tests, so the idea of pre-building tests may not always be a good idea 3. at some point there needs to be a balance struck between how many customers get put into tests and how many are treated with winning variants from previous tests (i.e. maximizing the exploitation of learning) 4. I’m one of the strongest advocates… Read more »
Cameron Howieson
These are great ideas that make a lot of sense. Solid post, Jakub. At Opencare, we have this challenge and have started to run design sprints on our tests before we run an A/B test. This allows us to get 4-5 qualitative data points in one day that tell us two primary things: a) Point out any glaring issues with the experiment, e.g. comprehension of copy, unnoticed buttons, unexpected user behaviour. b) Gives us a sense of the enthusiasm for the change. If users don’t notice / act more quickly, or say “yeah this is better / clearer”, it tends… Read more »
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