# How to Keep Your Gains from A/B Tests Without Accidentally Killing Them Later

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Limits of A/B Tests …

Limits of A/B Tests
A/B tests don’t give you perfect decisions.
No matter what you do, you’re never 100% certain
If we’re not careful, winners aren’t really winners
Your conversions go up… and then they come back down
The Standard Solution
Run your test until you hit 95% statistical signiﬁcance.
Go to getdatadriven.com if you need a signiﬁcance calculator.
Martin Goodson’s PDF on poor testing methods: kiss.ly/bad-testing
This gives us the best data but not necessarily the best ROI.
So how far do we take this?
Simulation Time!
We modeled several A/B testing strategies. Using Monte Carlo simulations, we tested diﬀerent strategies over 1 million observations (people).
Will Kurt gets full credit for all this. @willkurt
1 Pick the minimal improvement The Scientist: 2 Determine your sample size 3 Determine degree of certainty (95%) 4 Start test but don’t check it early 5 If results aren’t signiﬁcant, keep control
Results for the Scientist:
1 Waits until 80% signiﬁcance The Reckless Marketer: 2 Calls a winner as soon as 80% gets hit
Results for the Reckless Marketer:
1 Waits for 95% signiﬁcance The Impatient Marketer: 2 Moves on to the next test after 500 people
Results for the Impatient Marketer:
The Realist 1 Waits for 99% signiﬁcance 2 Moves on to the next test after 2,000 people
Results for the Realist:
The Persistent Realist 1 Waits for 99% signiﬁcance 2 Moves on to the next test after 20,000 people
Results for the Persistent Realist:
The Blitz Realist 1 Waits for 99% signiﬁcance 2 Moves on to the next test after 200 people
Results for the Blitz Realist:
Let’s compare them using the area under the curve.
Don’t make decisions at less than 95% signiﬁcance.
You’ll waste all the time you spend testing
1 Be a scientist at 95% We have 3 viable strategies for making this work: 2 Only make changes at 99% 3 Sloppy 95% but make it up in volume
1 Pick the minimal improvement Be a scientist when you have lots of data and resources 2 Determine your sample size 3 Determine degree of certainty (95% 4 Start test but don’t check it early 5 If results aren’t signiﬁcant, keep control
If you don’t have the data or resources to be a scientist, go fast at 99%.
And if you still want to play at 95% without being a scientist, never stop testing.
How We A/B Test
First, get volume to 4000+ people/month.
Only make changes at 99% signiﬁcance.
Let the test run at least 1 week before checking results.
If not at 99% after two weeks, launch the next test.
If the next test isn’t ready, let it keep running while you build the next one.
The KISSmetrics A/B Testing Strategy 1 Get to 4,000 people/month for test 2 Only change the control if you reach 99% 3 Check results after 1 week 4 Launch the next test at 2 weeks 5 Let old tests run if you’re still building
This strategy isn’t perfect. It’s a balance between good data and speed.

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• 1. Lars Lofgren and Will Kurt Keep Your Gains from A/B Tests Without Killing Them Later May 2014
• 2. @larslofgren Hit me up
• 3. 1 The limits of A/B tests We’ll cover… 2 The standard solutions 3 Simulations! Woohoo! #KISSwebinar 4 The 3 strategies of A/B testing that work 5 How we A/B test at KISSmetrics
• 4. WATCH WEBINAR RECORDING NOW
• 5. Limits of A/B Tests
• 6. A/B tests don’t give you perfect decisions.
• 7. No ma er what you do, you’re never 100% certain
• 8. If we’re not careful, winners aren’t really winners
• 9. Your conversions go up… and then they come back down
• 10. The Standard Solution
• 11. Run your test until you hit 95% statistical signiﬁcance.
• 12. Go to getdatadriven.com if you need a signiﬁcance calculator.
• 13. 1 Pick the minimal improvement Scientiﬁc A/B testing: 2 Determine your sample size 3 Determine degree of certainty (95%) #KISSwebinar 4 Start test but don’t check it early 5 If results aren’t signiﬁcant, keep control
• 14. Martin Goodson’s PDF on poor testing methods: kiss.ly/bad-testing
• 15. This gives us the best data but not necessarily the best ROI.
• 16. So how far do we take this?
• 17. Simulation Time!
• 18. We modeled several A/B testing strategies. Using Monte Carlo simulations, we tested diﬀerent strategies over 1 million observations (people).
• 19. Will Kurt gets full credit for all this. @willkurt
• 20. 1 Pick the minimal improvement The Scientist: 2 Determine your sample size 3 Determine degree of certainty (95%) #KISSwebinar 4 Start test but don’t check it early 5 If results aren’t signiﬁcant, keep control
• 21. Results for the Scientist:
• 22. 1 Waits until 80% signiﬁcance The Reckless Marketer: #KISSwebinar 2 Calls a winner as soon as 80% gets hit
• 23. Results for the Reckless Marketer:
• 24. 1 Waits for 95% signiﬁcance The Impatient Marketer: #KISSwebinar 2 Moves on to the next test a er 500 people
• 25. Results for the Impatient Marketer:
• 26. The Realist #KISSwebinar 1 Waits for 99% signiﬁcance 2 Moves on to the next test a er 2,000 people
• 27. Results for the Realist:
• 28. The Persistent Realist #KISSwebinar 1 Waits for 99% signiﬁcance 2 Moves on to the next test a er 20,000 people
• 29. Results for the Persistent Realist:
• 30. The Blitz Realist #KISSwebinar 1 Waits for 99% signiﬁcance 2 Moves on to the next test a er 200 people
• 31. Results for the Blitz Realist:
• 32. Let’s compare them using the area under the curve.
• 33. A/B Strategy Scores Strategy Conditions Score Scientist Stats like a pro 67759 Reckless Marketer 80% 57649 Impatient Marketer 95% and 500 people 60532 Realist 99% and 2,000 people 67896 Persistent Realist 99% and 20,000 people 68346 Blitz Realist 99% and 200 people 62836 No Testing Testing? NOPE! 50000 Each score is the area under the curve from the simulation. The higher the score, the more conversions you received.
• 34. 0 17500 35000 52500 70000 Persistent Realist Realist Scientist Blitz Realist Impatient Reckless No Testing 50,000 57,649 60,532 62,836 67,75967,89668,346 A/B Strategy Scores
• 36. 3 Strategies
• 37. Don’t make decisions at less than 95% signiﬁcance.
• 38. You’ll waste all the time you spend testing
• 39. 1 Be a scientist at 95% We have 3 viable strategies for making this work: 2 Only make changes at 99% 3 Sloppy 95% but make it up in volume #KISSwebinar
• 40. 1 Pick the minimal improvement Be a scientist when you have lots of data and resources 2 Determine your sample size 3 Determine degree of certainty (95%) #KISSwebinar 4 Start test but don’t check it early 5 If results aren’t signiﬁcant, keep control
• 41. If you don’t have the data or resources to be a scientist, go fast at 99%.
• 42. And if you still want to play at 95% without being a scientist, never stop testing.
• 43. How We A/B Test
• 44. First, get volume to 4000+ people/month.
• 45. Only make changes at 99% signiﬁcance.
• 46. Let the test run at least 1 week before checking results.
• 47. If not at 99% a er two weeks, launch the next test.
• 48. If the next test isn’t ready, let it keep running while you build the next one.
• 49. The KISSmetrics A/B Testing Strategy 1 Get to 4,000 people/month for test 2 Only change the control if you reach 99% 3 Check results a er 1 week 4 Launch the next test at 2 weeks 5 Let old tests run if you’re still building
• 50. This strategy isn’t perfect. It’s a balance between good data and speed.
• 51. 1 Be a scientist at 95% Remember the 3 strategies: 2 Only make changes at 99% 3 Sloppy 95% but make it up in volume #KISSwebinar
• 52. Q&A Time! Lars Lofgren @larslofgren llofgren@kissmetrics.com