3 ms·

Don't fool yourself with A/B testing

29 points by squiggy22 16y ago

RBr 16y ago
This grain of salt article about A/B testing is smart. Specifically, this is something that I learned too: "Do user testing. Get people in and ask them to use your product. You’re going to get a lot more information a lot faster and have a higher degree of confidence in the results." Expanding on this, I think that it's important not just to rally a group of your friends or other webheads. The closer that you can get to assembling a group of target users, the better. On ecommerce sites with a checkout, one of my favorite tricks is to send a manual e-mail to a handful of people who have become customers. Clear and simple, I say that I'd like to talk to them about the checkout process, mention that there will be 3-5 other customers on the conference call and offer them a significant coupon (25% +free shipping or something) to participate. During the call, I talk to them about their shopping experience and show them a revision or two of different designs. I ask them to (fake) buy a product using the revisions. The feedback (especially with early designs) is consistently some of the most valuable I receive and it's surprising how much of a positive effect talking to people has. With one phone call asking for feedback, you often gain a repeat customer.
jyothi 16y ago
The blog reads don't do A/B tests for "small changes". On the contrary I actually think A/B tests over large number of users work great for small changes than user testing with a small set. Many users cannot articulate the benefit of the small change, eye tracking or getting mental map of what the user thinks is very hard, with small set of users concluding something might be so skewed a decision. Additionally the blog is misguiding >> After 11,000 tests and 400 conversions it clearly showed that the instructions made a 30% difference. Below are the actual A/B test numbers from the blog: Original: 193/5943 => 3.24% Combination 1: 236/6058 => 3.89% 1. quoting this as 30% improvement is wrong. Google only said if you use combination 1 you would see 20% higher leads. 2. Since the difference between the conversion numbers is not very high one has to run it for a very long time and even on the first instance combination1 was no better than original. Given 1 & 2 Google did mark the results as inconclusive telling us not to be fooled. Edit: rephrased
patio11 16y ago
If you don't understand how A/B testing works, and are unwilling to read the clear explanation on the dashboard telling you that your results are not significant, your problem is in your chair, not with A/B testing.
aresant 16y ago
A crucial element left out of the discussion with A/B testing is the importance of user segmenting. You'll see drastic swings by the day of the week, male / female users, traffic sources, entrance paths, browser, region, etc. A/B tests can be terribly misleading without awareness of such factors. A simple illustration of this is eMail response rates which show that Sunday's are far and away the best day to get people's attention (highest aggregate open / click rate by 20% over worst day of week). But inversly we've found through the experience of managing thousands of tests that while opens & clicks are high, purchase conversion rates are on the low end of the scale. Just food for thought - it's absolutely positively rolling up your sleeves and deep diving into the data and segmenting tests or at least being aware of external influences before you take your results to the bank. http://www.mailermailer.com/resources/metrics/daily-rates.rwp http://www.mailermailer.com/resources/metrics/daily-rates.rw... http://conversionvoodoo.com/blog/2010/07/personalizing-your-email-subjects-can-drop-your-conversion-rate/ http://conversionvoodoo.com/blog/2010/07/personalizing-your-...
- dillydally 16y ago
  http://en.wikipedia.org/wiki/Simpsons_paradox http://en.wikipedia.org/wiki/Simpsons_paradox
  - aresant 16y ago
    Had not seen that thank you!

29 points by squiggy22 16y ago

RBr 16y ago

This grain of salt article about A/B testing is smart. Specifically, this is something that I learned too: "Do user testing. Get people in and ask them to use your product. You’re going to get a lot more information a lot faster and have a higher degree of confidence in the results." Expanding on this, I think that it's important not just to rally a group of your friends or other webheads. The closer that you can get to assembling a group of target users, the better. On ecommerce sites with a checkout, one of my favorite tricks is to send a manual e-mail to a handful of people who have become customers. Clear and simple, I say that I'd like to talk to them about the checkout process, mention that there will be 3-5 other customers on the conference call and offer them a significant coupon (25% +free shipping or something) to participate. During the call, I talk to them about their shopping experience and show them a revision or two of different designs. I ask them to (fake) buy a product using the revisions. The feedback (especially with early designs) is consistently some of the most valuable I receive and it's surprising how much of a positive effect talking to people has. With one phone call asking for feedback, you often gain a repeat customer.

jyothi 16y ago

The blog reads don't do A/B tests for "small changes". On the contrary I actually think A/B tests over large number of users work great for small changes than user testing with a small set. Many users cannot articulate the benefit of the small change, eye tracking or getting mental map of what the user thinks is very hard, with small set of users concluding something might be so skewed a decision. Additionally the blog is misguiding >> After 11,000 tests and 400 conversions it clearly showed that the instructions made a 30% difference. Below are the actual A/B test numbers from the blog: Original: 193/5943 => 3.24% Combination 1: 236/6058 => 3.89% 1. quoting this as 30% improvement is wrong. Google only said if you use combination 1 you would see 20% higher leads. 2. Since the difference between the conversion numbers is not very high one has to run it for a very long time and even on the first instance combination1 was no better than original. Given 1 & 2 Google did mark the results as inconclusive telling us not to be fooled. Edit: rephrased

patio11 16y ago

If you don't understand how A/B testing works, and are unwilling to read the clear explanation on the dashboard telling you that your results are not significant, your problem is in your chair, not with A/B testing.

aresant 16y ago

A crucial element left out of the discussion with A/B testing is the importance of user segmenting. You'll see drastic swings by the day of the week, male / female users, traffic sources, entrance paths, browser, region, etc. A/B tests can be terribly misleading without awareness of such factors. A simple illustration of this is eMail response rates which show that Sunday's are far and away the best day to get people's attention (highest aggregate open / click rate by 20% over worst day of week). But inversly we've found through the experience of managing thousands of tests that while opens & clicks are high, purchase conversion rates are on the low end of the scale. Just food for thought - it's absolutely positively rolling up your sleeves and deep diving into the data and segmenting tests or at least being aware of external influences before you take your results to the bank. http://www.mailermailer.com/resources/metrics/daily-rates.rwp http://www.mailermailer.com/resources/metrics/daily-rates.rw... http://conversionvoodoo.com/blog/2010/07/personalizing-your-email-subjects-can-drop-your-conversion-rate/ http://conversionvoodoo.com/blog/2010/07/personalizing-your-...

dillydally 16y ago
http://en.wikipedia.org/wiki/Simpsons_paradox http://en.wikipedia.org/wiki/Simpsons_paradox
- aresant 16y ago
  Had not seen that thank you!