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#Measurecamp : 18 Simple Ways to F*** up Your AB Testing

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An expanded deck of the top 18 blockers to getting successful AB or Multivariate test results. In this deck, you get a complete checklist of the stuff you need to prepare, watch, launch and monitor your testing, so it gets you the *right* conclusions.

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#Measurecamp : 18 Simple Ways to F*** up Your AB Testing

  1. 1. 18 simple ways to fuck up your AB testing 28th March 2014 @OptimiseOrDie
  2. 2. @OptimiseOrDie • UX and Analytics (1999) • User Centred Design (2001) • Agile, Startups, No budget (2003) • Funnel optimisation (2004) • Multivariate & A/B (2005) • Conversion Optimisation (2005) • Persuasive Copywriting (2006) • Joined Twitter (2007) • Lean UX (2008) • Holistic Optimisation (2009) Was : Group eBusiness Manager, Belron Now : Spareroom.co.uk
  3. 3. @OptimiseOrDie Timeline Tested stupid ideas, lots Most AB or MVT tests are bullshit Discovered AB testing Triage, Triangulation, Prioritisation, Maths Zen Plumbing AB Test Hype Cycle
  4. 4. Craig’s Cynical Quadrant Improves revenue Improves UX YesNo No Yes Client delighted (and fires you for another UX agency) Client fucking delighted Client absolutely fucking furious Client fires you (then wins an award for your work)
  5. 5. #1 : You’re doing it in the wrong place @OptimiseOrDie
  6. 6. #1 : You’re doing it in the wrong place There are 4 areas a CRO expert always looks at: 1. Inbound attrition (medium, source, landing page, keyword, intent and many more…) 2. Key conversion points (product, basket, registration) 3. Processes and steps (forms, logins, registration, checkout) 4. Layers of engagement (search, category, product, add) 1. Use visitor flow reports for attrition – very useful. 2. For key conversion points, look at loss rates & interactions 3. Processes and steps – look at funnels or make your own 4. Layers and engagement – make a ring model @OptimiseOrDie
  7. 7. Examples – Concept Bounce Engage Outcome @OptimiseOrDie
  8. 8. Examples – 16-25Railcard.co.uk Bounce Login to Account Content Engage Start Application Type and Details Eligibility Photo Complete @OptimiseOrDie
  9. 9. Examples – Guide Dogs Bounce Content Engage Donation Pathway Donation Page Starts process Funnel steps Complete @OptimiseOrDie
  10. 10. Within a layer Page 1 Page 2 Page 3 Page 4 Page 5 Exit Deeper Layer Email LikeContact Wishlist Micro Conversions @OptimiseOrDie
  11. 11. #1 : You’re doing it in the wrong place • Get to know the flow and loss (leaks) inbound, inside and through key processes or conversion points. • Once you know the key steps you’re losing people at and how much traffic you have – make a money model. • Let’s say 1,000 people see the page a month. Of those, 20% (200) convert to checkout. • Estimate the influence your test can bring. How much money or KPI improvement would a 10% lift in the checkouts deliver? • Congratulations – you’ve now built the worlds first IT plan with a return on investment estimate attached! • I’ll talk more about prioritising later – but a good real world analogy for you to use: @OptimiseOrDie
  12. 12. Think like a store owner! If you can’t refurbish the entire store, which floors or departments will you invest in optimising? Wherever there is: • Footfall • Low return • Opportunity @OptimiseOrDie
  13. 13. Insight - Inputs #FAIL Competitor copying Guessing Dice rolling An article the CEO read Competitor change Panic Ego Opinion Cherished notions Marketing whims Cosmic rays Not ‘on brand’ enough IT inflexibility Internal company needs Some dumbass consultant Shiny feature blindness Knee jerk reactons #2 : Your hypothesis is crap! @OptimiseOrDie
  14. 14. Insight - Inputs Insight Segmentation Surveys Sales and Call Centre Session Replay Social analytics Customer contact Eye tracking Usability testing Forms analytics Search analytics Voice of Customer Market research A/B and MVT testing Big & unstructured data Web analytics Competitor evalsCustomer services #2 : These are the inputs you need… @OptimiseOrDie
  15. 15. #2 : Solutions • You need multiple tool inputs – Tool decks are here : www.slideshare.net/sullivac • Usability testing and User facing teams – If you’re not doing these properly, you’re hosed • Session replay tools provide vital input – Get vital additional customer evidence • Simple page Analytics don’t cut it – Invest in your analytics, especially event tracking • Ego, Opinion, Cherished notions – fill gaps – Fill these vacuums with insights and data • Champion the user – Give them a chair at every meeting @OptimiseOrDie
  16. 16. We believe that doing [A] for People [B] will make outcome [C] happen. We’ll know this when we observe data [D] and obtain feedback [E]. (reverse) @OptimiseOrDie
  17. 17. #3 : No analytics integration • Investigating problems with tests • Segmentation of results • Tests that fail, flip or move around • Tests that don’t make sense • Broken test setups • What drives the averages you see? @OptimiseOrDie
  18. 18. 18 A B B A
  19. 19. These Danish porn sites are so hardcore! We’re still waiting for our AB tests to finish! • Use a test length calculator like this one: • visualwebsiteoptimizer.com/ab-split-test-duration/#4 : The test will finish after you die
  20. 20. #5 : You don’t test for long enough • The minimum length – 2 business cycles (cross check) – Usually a week, 2 weeks, Month – Always test ‘whole’ not partial cycles – Be aware of multiple cycles – Don’t self stop! – PURCHASE CYCLES – KNOW THEM • How long after that – I aim for a minimum 250 outcomes, ideally 350+ for each ‘creative’ – If you test 4 recipes, that’s 1400 outcomes needed – You should have worked out how long each batch of 350 needs before you start! – 95% confidence or higher is my aim BUT BIG SECRET -> (p values are unreliable) – If you segment, you’ll need more data – It may need a bigger sample if the response rates are similar* – Use a test length calculator but be aware of BARE MINIMUM TO EXPECT – Important insider tip – watch the error bars! The +/- stuff – let’s explain * Stats geeks know I’m glossing over something here. That test time depends on how the two experiments separate in terms of relative performance as well as how volatile the test response is. I’ll talk about this when I record this one! This is why testing similar stuff sux. 20
  21. 21. #5 : The tennis court – Let’s say we want to estimate, on average, what height Roger Federer and Nadal hit the ball over the net at. So, let’s start the match: @OptimiseOrDie
  22. 22. First Set Federer 6-4 – We start to collect values 62cm +/- 2cm 63.5cm +/- 2cm @OptimiseOrDie
  23. 23. Second Set – Nadal 7-6 – Nadal starts sending them low over the net 62cm +/- 1cm 62.5cm +/- 1cm @OptimiseOrDie
  24. 24. Final Set Nadal 7-6 – We start to collect values 61.8cm +/- .3cm 62cm +/- .3cm
  25. 25. Let’s look at this a different way 62.5cm +/- 1cm @OptimiseOrDie 9.1% ± 0.3 9.3% ± 0.3
  26. 26. 62.5cm +/- 1cm @OptimiseOrDie 9.1% ± 0.5 9.3% ± 0.5 9.1% ± 0.2 9.3% ± 0.2 9.1% ± 0.1 9.3% ± 0.1
  27. 27. Graph is a range, not a line: 9.1 ± 0.3%9.1 ± 0.9%9.1 ± 1.9%
  28. 28. #5 : Summary • The minimum length: – 2 business cycles and > purchase cycle as a minimum, regardless of outcomes. Test for less and you’re cutting. – 250+, prefer 350+ outcomes in each – Error bar separation between creatives – 95%+ confidence (unreliable) • Pay attention to: – Time it will take for the number of ‘recipes’ in the test – The actual footfall to the test – not sitewide numbers – Test results that don’t separate – makes the test longer – This is why you need brave tests – to drive difference – The error bars – the numbers in your AB testing tool are not precise – they’re fuzzy regions that depend on response and sample size. – Sudden changes in test performance or response – Monitor early tests like a chef! @OptimiseOrDie
  29. 29. #6 : You peek and jump to conclusions! • Ignore the graphs. Don’t draw conclusions. Don’t dance. Calm down. • Get a feel for the test but don’t do anything yet! • Remember – in A/B - 50% of returning visitors will see a new shiny website! • Until your test has had at least 1 business cycle and 250-350 outcomes, don’t bother even getting excited! • Watching regularly is good though. You’re looking for anything that looks really odd – your analytics person should be checking all the figures until you’re satisfied • All tests move around or show big swings early in the testing cycle. Here is a very high traffic site – it still takes 10 days to start settling. Lower traffic sites will stretch this period further. 29
  30. 30. #7 : No QA testing for the AB test?
  31. 31. #7 - QA Test or Die! • Over 40% of tests have had QA issues. • It’s very easy to break or bias the testing Browser testing www.crossbrowsertesting.com www.browserstack.com www.spoon.net www.cloudtesting.com www.multibrowserviewer.com www.saucelabs.com Mobile devices www.perfectomobile.com www.deviceanywhere.com www.mobilexweb.com/emulators www.opendevicelab.com @OptimiseOrDie
  32. 32. #7 : What other QA testing should I do? • Cross Browser Testing • Testing from several locations (office, home, elsewhere) • Testing the IP filtering is set up • Test tags are firing correctly (analytics and the test tool) • Test as a repeat visitor and check session timeouts • Cross check figures from 2+ sources • Monitor closely from launch, recheck, watch • WATCH FOR BIAS! @OptimiseOrDie
  33. 33. #8 : Opportunities are not prioritised Once you have a list of potential test areas, rank them by opportunity vs. effort. The common ranking metrics that I use include: •Opportunity (revenue, impact) •Dev resource •Time to market •Risk / Complexity Make yourself a quadrant diagram and plot them
  34. 34. #9 : Your cycles are too slow 0 6 12 18 Months Conversion @OptimiseOrDie
  35. 35. #9 : Solutions • Give Priority Boarding for opportunities – The best seats reserved for metric shifters • Release more often to close the gap – More testing resource helps, analytics ‘hawk eye’ • Kaizen – continuous improvement – Others call it JFDI (just f***ing do it) • Make changes AS WELL as tests, basically! – These small things add up • RUSH Hair booking – Over 100 changes – No functional changes at all – 37% improvement • Inbetween product lifecycles? – The added lift for 10 days work, worth 360k @OptimiseOrDie
  36. 36. #9 : Make your own cycles @OptimiseOrDie
  37. 37. #10 : How do I know when it’s ready? • The hallmarks of a cooked test are: – It’s done at least 1 or preferably 2+ business and at least one if not two purchase cycles – You have at least 250-350 outcomes for each recipe – It’s not moving around hugely at creative or segment level performance – The test results are clear – even if the precise values are not – The intervals are not overlapping (much) – If a test is still moving around, you need to investigate – Always declare on a business cycle boundary – not the middle of a period (this introduces bias) – Don’t declare in the middle of a limited time period advertising campaign (e.g. TV, print, online) – Always test before and after large marketing campaigns (one week on, one week off) 37
  38. 38. 38 #11 : Your test fails @OptimiseOrDie
  39. 39. #11: Your test fails • Learn from the failure! If you can’t learn from the failure, you’ve designed a crap test. • Next time you design, imagine all your stuff failing. What would you do? If you don’t know or you’re not sure, get it changed so that a negative becomes insightful. • So : failure itself at a creative or variable level should tell you something. • On a failed test, always analyse the segmentation and analytics • One or more segments will be over and under • Check for varied performance • Now add the failure info to your Knowledge Base: • Look at it carefully – what does the failure tell you? Which element do you think drove the failure? • If you know what failed (e.g. making the price bigger) then you have very useful information • You turned the handle the wrong way • Now brainstorm a new test @OptimiseOrDie
  40. 40. #12 : The test is ‘about the same’ • Analyse the segmentation • Check the analytics and instrumentation • One or more segments may be over and under • They may be cancelling out – the average is a lie • The segment level performance will help you (beware of small sample sizes) • If you genuinely have a test which failed to move any segments, it’s a crap test – be bolder • This usually happens when it isn’t bold or brave enough in shifting away from the original design, particularly on lower traffic sites • Get testing again! @OptimiseOrDie
  41. 41. • There are three reasons it is moving around – Your sample size (outcomes) is still too small – The external traffic mix, customers or reaction has suddenly changed or – Your inbound marketing driven traffic mix is completely volatile (very rare) • Check the sample size • Check all your marketing activity • Check the instrumentation • If no reason, check segmentation #13 : The test keeps moving around @OptimiseOrDie
  42. 42. • Something like this can happen: • Check your sample size. If it’s still small, then expect this until the test settles. • If the test does genuinely flip – and quite severely – then something has changed with the traffic mix, the customer base or your advertising. Maybe the PPC budget ran out? Seriously! • To analyse a flipped test, you’ll need to check your segmented data. This is why you have a split testing package AND an analytics system. • The segmented data will help you to identify the source of the shift in response to your test. I rarely get a flipped one and it’s always something changing on me, without being told. The heartless bastards. #14 : The test has flipped on me
  43. 43. • No – and this is why: – It’s a waste of time – It’s easier to test and monitor instead – You are eating into test time – Also applies to A/A/B/B testing – A/B/A running at 25%/50%/25% is the best • Read my post here : http://bit.ly/WcI9EZ 43 #15 : Should I run an A/A test first
  44. 44. #16 : Nobody feels the test • You promised a 25% rise in checkouts - you only see 2% • Traffic, Advertising, Marketing may have changed • Check they’re using the same precise metrics • Run a calibration exercise • I often leave a 5 or 10% stub running in a test • This tracks old creative once new one goes live • If conversion is also down for that one, BINGO! • Remember – the AB test is an estimate – it doesn’t precisely record future performance • This is why infrequent testing is bad • Always be trying a new test instead of basking in the glory of one you ran 6 months ago. You’re only as good as your next test. @OptimiseOrDie
  45. 45. #17 : You forgot about Mobile & Tablet • If you’re AB testing a responsive site, pay attention • Content will break differently on many screens • Know thy users and their devices • Use bango or google analytics to define a test list • Make sure you test mobile devices & viewports • What looks good on your desk may not be for the user • Harder to design cross device tests • You’ll need to segment mobile, tablet & desktop response in the analytics or AB testing package • Your personal phone is not a device mix • Ask me about making your device list • Buy core devices, rent the rest from deviceanywhere.com @OptimiseOrDie
  46. 46. • Forget MVT or A/B/N tests – run your numbers • Test things with high impact – don’t be a wuss! • Use UX, Session Replay to aid insight • Run a task gap survey (4Q style) • Run a dropped basket survey (LF style) • Run a general survey + check social + other sites • Run sitewide tests that appear on all pages or large clusters of pages – • UVPs (“We are a cool brand”), USPs (“Free returns!”), UCPs (“10% off today”). • Headers, Footers, Nudge Bars, USP bars, footer changes, Navigation, Product pages, Delivery info etc. #18 : Oh shit – no traffic!
  47. 47. • If small volumes, contact customers – reach out. • If data volumes aren’t there, there are still customers! • Drive design from levers you can apply – game the system • Pick clean and simple clusters of change (hypothesis driven) • Use a goal at an earlier ring stage or funnel step • Beware of using clickthroughs when attrition is high on the other side • Try before and after testing on identical time periods (measure in analytics model) • Be careful about small sample sizes (<100 outcomes) • Are you working automated emails? • Fix JFDI, performance and UX issues too! #18 : Oh shit – no traffic
  48. 48. Top F***ups for 2014 1. Testing in the wrong place 2. Your hypothesis inputs are crap 3. No analytics integration 4. Your test will finish after you die 5. You don’t test for long enough 6. You peek before it’s ready 7. No QA for your split test 8. Opportunities are not prioritised 9. Testing cycles are too slow 10. You don’t know when tests are ready 11. Your test fails 12. The test is ‘about the same’ 13. Test flips behaviour 14. Test keeps moving around 15. You run an A/A test and waste time 16. Nobody ‘feels’ the test 17. You forgot you were responsive 18. You forgot you had no traffic @OptimiseOrDie
  49. 49. Is there a way to fix this then? 49 Conversion Heroes! @OptimiseOrDie
  50. 50. Email Twitter : sullivac@gmail.com : @OptimiseOrDie : linkd.in/pvrg14 Slides uploaded to SLIDESHARE.NETSULLIVAC 50