1. Ronny Kohavi, Distinguished Engineer, General Manager, Analysis and Experimentation, Microsoft Joint work with Thomas Crook, Brian Frasca, and Roger Longbotham, A&E Team A/B Testing Pitfalls Slides at http://bit.ly/ABPitfallshttp://bit.ly/ABPitfalls

2. Ronny Kohavi 2 A/B Tests in One Slide  Concept is trivial  Randomly split traffic between two (or more) versions o A (Control) o B (Treatment)  Collect metrics of interest  Analyze  A/B test is the simplest controlled experiment  A/B/n refers to multiple treatments (often used and encouraged: try control + two or three treatments)  MVT refers to multivariable designs (rarely used by our teams)  Must run statistical tests to confirm differences are not due to chance  Best scientific way to prove causality, i.e., the changes in metrics are caused by changes introduced in the treatment(s)

3. ConversionXL Audience Statistics Ronny Kohavi 3 83% of attendees ran less than 30 experiments last year. Experimenters at Microsoft use our ExP platform to start ~30 experiments per day

4. Experimentation at Scale  I’ve been fortunate to work at an organization that values being data-driven (video)video  We finish about ~300 experiment treatments per week, mostly on Bing, MSN, but also on Office, OneNote, Xbox, Cortana, Skype, Exchange, OneDrive. (These are “real” useful treatments, not 3x10x10 MVT = 300.)  Each variant is exposed to between 100K and millions of users, sometimes tens of millions  At Bing, 90% of eligible users are in experiments (10% are a global holdout changed once a year)  There is no single Bing. Since a user is exposed to over 15 concurrent experiments, they get one of 5^15 = 30 billion variants (debugging takes a new meaning).  Until 2014, the system was limiting usage as it scaled. Now the limits come from engineers’ ability to code new ideas Ronny Kohavi 4

5. Two Valuable Real Experiments  What is a valuable experiment?  Absolute value of delta between expected outcome and actual outcome is large  If you thought something is going to win and it wins, you have not learned much  If you thought it was going to win and it loses, it’s valuable (learning)  If you thought it was “meh” and it was a breakthrough, it’s HIGHLY valuable See http://bit.ly/expRulesOfThumb for some examples of breakthroughshttp://bit.ly/expRulesOfThumb  Experiments ran at Microsoft’s Bing with millions of users in each  For each experiment, we provide the OEC, the Overall Evaluation Criterion  Can you guess the winner correctly? Three choices are: o A wins (the difference is statistically significant) o Flat: A and B are approximately the same (no stat sig diff) o B wins 5

6. Example : Bing Ads with Site Links  Should Bing add “site links” to ads, which allow advertisers to offer several destinations on ads?  OEC: Revenue, ads constraint to same vertical pixels on avg  Pro adding: richer ads, users better informed where they land  Cons: Constraint means on average 4 “A” ads vs. 3 “B” ads Variant B is 5msc slower (compute + higher page weight) Ronny Kohavi 6 A B Raise your left hand if you think A Wins (left) Raise your right hand if you think B Wins (right) Don’t raise your hand if they are the about the same

7. Bing Ads with Site Links  If you raised your left hand, you were wrong  If you did not raise a hand, you were wrong  Site links generate incremental revenue on the order of tens of millions of dollars annually for Bing  The above change was costly to implement. We made two small changes to Bing, which took days to develop, each increased annual revenues by over $100 million Ronny Kohavi 7

8. Example: Underlining Links  Does underlining increase or decrease clickthrough-rate? Ronny Kohavi 8

9. Example 4: Underlining Links  Does underlining increase or decrease clickthrough-rate?  OEC: Clickthrough Rate on search engine result page (SERP) for a query Ronny Kohavi 9 A (with underlines) B (no underlines) Raise your left hand if you think A Wins (left, with underlines) Raise your right hand if you think B Wins (right, without underlines) Don’t raise your hand if they are the about the same

10. Underlines  If you raised your right hand, you were wrong  If you did not raise a hand, you were wrong  Underlines improve clickthrough-rate for both algorithmic results and ads (so more revenue) and improve time to successful click  Modern web designs do away with underlines, and most sites have adopted this design, despite data showing that users click less and take more time to click  For search engines (Google, Bing Yahoo), this is a very questionable industry direction Ronny Kohavi 10

11. Pitfall 1: Misinterpreting P-values  NHST = Null Hypothesis Statistical Testing, the “standard” model commonly used  P-value <= 0.05 is the “standard” for rejecting the Null hypothesis  P-value is often mis-interpreted. Here are some incorrect statements from Steve Goodman’s A Dirty Dozen 1. If P =.05, the null hypothesis has only a 5% chance of being true 2. A non-significant difference (e.g., P >.05) means there is no difference between groups 3. P =.05 means that we have observed data that would occur only 5% of the time under the null hypothesis 4. P =.05 means that if you reject the null hyp, the probability of a type I error (false positive) is only 5%  The problem is that p-value gives us Prob (X >= x | H_0), whereas what we want is Prob (H_0 | X = x) Ronny Kohavi 11

12. Pitfall 2: Expecting Breakthroughs  Breakthroughs are rare after initial optimizations.  At Bing (well optimized), 80% of ideas fail to show value  At other products across Microsoft, about 2/3 of ideas fail  Take Sessions/User, a key metric at Bing. Historically, it improves 0.02% of the time: that’s one in 5,000 treatments we try!  Most of the time, we invoke Twyman’s law (http://bit.ly/twymanLaw)http://bit.ly/twymanLaw  Note relationship to prior pitfall  With standard p-value computations, 5% of experiments will show stat-sig movement to Sessions/User when there is no real movement (i.e., if the Null Hypothesis is true), half of those positive  99.6% of the time, a stat-sig movement with p-value = 0.05 will be a false positive Ronny Kohavi 12 Any figure that looks interesting or different is usually wrong

13. Pitfall 3: Not Checking for SRM  SRM = Sample Ratio Mismatch  If you run an experiment with equal percentages assigned to Control/Treatment (A/B), you should have approximately the same number of users in each  Real example from an experiment alert I received this week:  Control: 821,588 users, Treatment: 815,482 users  Ratio: 50.2% (should have been 50%)  Should I be worried?  Absolutely  The p-value is 1.8e-6, so the probability of this split (or more extreme) happening by chance is less than 1 in 500,000  Note that the above statement is not a violation of the pitfall #1 because by the experiment design, there should be an equal number of users in control/treatment, so we want the conditional probability P(actual split=50.2% | designed split=50%) Ronny Kohavi 13

14. Pitfall 4: Wrong Success Metric (OEC)  Office Online tested new design for homepage  Objective: increase sales of Office products  Overall Evaluation Criterion (OEC) was clicks to the Buy Button [shown in red boxes] Which one was better? Control Treatment

15. Pitfall: Wrong OEC  Treatment had a drop in the OEC (clicks on buy) of 64%!  Not having the price shown in the Control lead more people to click to determine the price  Lesson: measure what you really need to measure: actual sales (it is more difficult at times)  Lesson 2: Focus on long-term customer lifetime value  Peep in keynote here said (he was OK with me mentioning this):  What’s the goal? More money right now  Common pitfall: You want to optimize long-term money. NOT right now. Raising prices gets you short-term money, but long-term abandonment  Coming up with a good OEC using short-term metrics is REALLY hard

16. Example: OEC for Search  KDD 2012 Paper: Trustworthy Online Controlled Experiments: Five Puzzling Outcomes Explained KDD 2012  Search engines (Bing, Google) are evaluated on query share (distinct queries) and revenue as long-term goals  Puzzle  A ranking bug in an experiment resulted in very poor search results  Degraded (algorithmic) search results cause users to search more to complete their task, and ads appear more relevant  Distinct queries went up over 10%, and revenue went up over 30%  This problem is now in the book data science interviews exposed  What metrics should be in the OEC for a search engine? Ronny Kohavi 16

17. Puzzle Explained Ronny Kohavi 17

18. Bad OEC Example  Your data scientists makes an observation: 2% of queries end up with “No results.”  Manager: must reduce. Assigns a team to minimize “no results” metric  Metric improves, but results for query brochure paper are crap (or in this case, paper to clean crap)  Sometimes it *is* better to show “No Results.” Real example from my Amazon Prime now search 3/26/2016 https://twitter.com/ronnyk/status/713949552823263234 https://twitter.com/ronnyk/status/713949552823263234 Ronny Kohavi 18

19. Pitfall 5: Combining Data when Treatment Percent Varies with time  Simplified example: 1,000,000 users per day  For each individual day the Treatment is much better  However, cumulative result for Treatment is worse (Simpson’s paradox) Conversion Rate for two days FridaySaturday Total C/T split: 99/1C/T split: 50/50 Control 20,000 = 2.02% 5,000 = 1.00% 25,000 = 1.68% 990,000500,0001,490,000 Treatment 230 = 2.30% 6,000 = 1.20% 6,230 = 1.22% 10,000500,000510,000

20. Pitfall 6: Get the Stats Right  Two very good books on A/B testing (A/B Testing from Optimizely founders Dan Siroker and Peter Koomen; and You Should Test That by WiderFunnel’s CEO Chris Goward) get the stats wrong (see Amazon reviews).  Optimizely recently updated their stats in the product to correct for this  Best techniques to find issues: run A/A tests  Like an A/B test, but both variants are exactly the same  Are users split according to the planned percentages?  Is the data collected matching the system of record?  Are the results showing non-significant results 95% of the time? Ronny Kohavi 20

21. More Pitfalls  See KDD paper: Seven Pitfalls to Avoid when Running Controlled Experiments on the Web (http://bit.ly/expPitfalls)http://bit.ly/expPitfalls  Incorrectly computing confidence intervals for percent change  Using standard statistical formulas for computations of variance and power  Neglecting to filter robots/bots Lucrative business, as shown in photo I took ->  Instrumentation issues Ronny Kohavi 21

22. The HiPPO  HiPPO = Highest Paid Person’s Opinion  We made thousands toy HiPPOs and handed them at Microsoft to help change the culture  Grab one here at ConversionXL  Change the culture at your company  Fact: Hippos kill more humans than any other (non-human) mammal  Listen to the customers and don’t let the HiPPO kill good ideas Ronny Kohavi 22

23. Ronny Kohavi 23 Getting numbers is easy; getting numbers you can trust is hard Slides at http://bit.ly/ABPitfallshttp://bit.ly/ABPitfalls See http://exp-platform.com for papers. Plane reading booklets with selected papers available outside roomhttp://exp-platform.com Remember this