Doug asks, “I have an interesting nonrandom case. I have a hard time explaining to the team that this does not count as an A/B test. Also, we ran it and got odd results.
We give a list to sales reps each month that they have to go through to ask to renew their membership. We believe they go through them in order and usually finish about 80% of the list. There is no particular logic to the order in the list – it’s an output of an ETL.
The situation seems very close to random except for a rep could choose to skip someone and the list does have an order that is probably close to random. We wanted to see the success rate between those that were contacted and those that were not. What else is wrong here that I’m missing?”
This is a good question because it underscores the importance of defining test parameters and setting up a proper A/B test. To answer Doug’s question, we need to clearly define what makes for a proper A/B test.
What Is An A/B Test?
An A/B test is a blinded randomized controlled trial. Let’s unpack each of these components in reverse order.
Trial: while it seems silly to need definition, calling something a trial or an experiment indicates you’ve got a formal setup, a formal time and place that a test is occurring, and that you’re measuring for changes. This differs from casual observation.
Controlled: the key to any A/B test is control. What are you testing for? This requires a clear definition of a hypothesis – any provable single variable statement – that you’re testing. “Better sales performance” isn’t a hypothesis. “If we reduce the length of our sales script by 25%, we will see sales performance increase by 25%” is a hypothesis.
This is key: a single, provably true or false statement sets the grounds for any good test. If you’ve got multiple things you’re testing, then by definition you’re not doing an A/B test. For example, if you said “If we reduce the length of our sales script by 25% and call in the mornings, we will see sales performance increase by 25%”, you’ve got two different conditions mixed up. While it’s possible to test that, it’s not an A/B test.
Randomized: a properly done A/B test is intentionally and clearly randomized. Doug’s example above says “probably close to random” which is not the same thing. When you conduct a test, you must make an intentional effort to randomize – and validate that your randomization method works as expected, that your sample is sufficiently mixed. Shuffling a deck of cards once may randomize the deck somewhat, but no casino does that. Casinos use machines to shuffle decks dozens of times at very high speed to ensure true randomization.
Blinded: this is another key part of Doug’s statement. Blinding a trial means removing information from the trial conditions that could influence the trial conditions with a bias. Let’s say Doug’s sales team is all one gender, and the people on that team prefer to speak to people who are their own gender. By permitting his sales team to skip names on the list, there’s a possibility to introduce a gender bias, and thus the trial is no longer random. By removing either the ability to skip or removing the identity of the people being called, we can restore randomness to the trial.
Why Isn’t This an A/B Test?
We now see, based on Doug’s initial description, that what’s happening is clearly not an A/B test.
- The test isn’t blinded. Bias can creep in by allowing trial participants to behave non-randomly.
- The test isn’t randomized sufficiently. Trial participants may or may not be getting truly random testing conditions.
- The test isn’t controlled. There’s no clear hypothesis being tested.
- The test may or may not even be defined as a test.
To repair this test, Doug’s team needs to implement all the features of a true blinded, randomized controlled trial.
- Either prohibit skipping or blind the call data as mcuh as possible to make it a blinded trial.
- Clearly define the hypothesis and the single variable being tested, and make sure that the testing procedure is rigorously designed around only that variable. Don’t let others introduce additional things to test – that should be set up as separate tests.
- Actively randomize and test for randomness.
- Actively define the test period and conditions.
A/B Testing Requires Rigor
To do an A/B test well, we have to stick to what works, what’s proven to constitute a good, proper A/B test. If we don’t, we will end up with test results we can’t rely on – and if we then make business decisions from those incorrect results, we could do substantial harm to our businesses.
You might also enjoy:
Want to read more like this from Christopher Penn? Get updates here:
Get your copy of AI For Marketers