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13 Easy split testing f***ups

@OptimiseOrDie

Top Fuckups for 20131. Testing in the wrong place2. Your hypothesis inputs are crap3. No analytics integration4. Your test will finish after you die5. Not testing for long enough6. No QA for your split test7. Opportunities are not prioritised8. Testing cycles are too slow9. Your test fails10. The result is ‘about the same’11. Test flips or moves around12. Nobody ‘feels’ the test13. You forgot you were responsive

@OptimiseOrDie

@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, BelronNow : Consulting

@OptimiseOrDie

Timeline

- 1998 1999 - 2004

2004-2008 2008-2012

#1 : You’re doing it in the wrong place

@OptimiseOrDie

#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)

5. Use visitor flow reports for attrition – very useful.6. For key conversion points, look at loss rates & interactions7. Processes and steps – look at funnels or make your own8. Layers and engagement – make a model

Let’s look at an example I’ve used recently@OptimiseOrD

ie

Examples – Concept

Bounce

Engage

Outcome

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Examples – Shoprush.com

Bounce

Search or Category

Product Page

Add to basket

View basket

Checkout

Complete

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Examples – 16-25Railcard.co.uk

Bounce

Login to Account

Content Engage

Start Application

Type and Details

Eligibility

Photo

Complete

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6.3 – Examples – Guide Dogs

Bounce

Content Engage

Donation Pathway

Donation Page

Starts process

Funnel steps

Complete

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6.3 – Within a layer

Page 1

Page 2

Page 3

Page 4 Page 5

Exit

Deeper Layer

Email

LikeContact

Wishlist

Micro Conversions

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#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

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

Insight - Inputs

#FAIL

Competitor copying

GuessingDice rolling

An article the CEO

read

Competitor change

Panic

Ego

OpinionCherished

notions Marketing whims Cosmic rays

Not ‘on brand’ enough

IT inflexibility

Internal company

needs

Some dumbass

consultant

Shiny feature

blindnessKnee jerk reactons

#2 : Your hypothesis inputs are all wrong

@OptimiseOrDie

Insight - Inputs

InsightSegmentation

Surveys

Sales and Call Centre

Session Replay

Social analytics

Customer contactEye tracking

Usability testingForms analyticsSearch analyticsVoice of Customer

Market research

A/B and MVT testing

Big & unstructured data

Web analytics

Competitor evals

Customer services

#2 : These are the inputs you need…

@OptimiseOrDie

#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 using 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

#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?

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These Danish porn sites are so hardcore!

We still keep watching our old AB tests in retirement

#4 : The test will finish after you die• Use a test length calculator like this one:• visualwebsiteoptimizer.com/ab-split-test-duration/

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#5 : You don’t test for long enough• The minimum length

– 2 business cycles (comparison)– Always test ‘whole’ not partial cycles– Don’t self stop!– Usually a week, 2 weeks, Month– Be aware of multiple cycles

• How long after that– 95% confidence or higher is my aim – and often hit higher than this– 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!– 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 minimums– 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.

#2 : 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:

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First Set Federer 6-4– We start to collect values

62cm+/- 2cm

63.5cm+/- 2cm

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Second Set – Nadal 7-6– Nadal starts sending them low over the net

62cm+/- 1cm

62.5cm+/- 1cm

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Final Set Nadal 7-6– We start to collect values

61.8cm+/- .3cm

62cm+/- .3cm

Let’s look at this a different way

62.5cm+/- 1cm

9.1 ± 0.3%

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Graph is a range, not a line:

9.1 ± 0.3%

#5 : Summary• The minimum length:

– 2 business cycles minimum, regardless of outcomes– 250+, prefer 350+ outcomes in each– 95%+ confidence– Error bar separation between creatives

• 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

#6 : No QA testing for the AB

test?

www.crossbrowsertesting.comwww.browserstack.com

www.spoon.netwww.cloudtesting.com

www.multibrowserviewer.comwww.saucelabs.com

#6 : What 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

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#7 : Opportunities are not prioritised

Once you have a list of potential test areas, rank them by opportunity vs. effort.

The common ranking metrics I use include:

• Opportunity (profit, revenue)

• Dev resource• Time to market• Risk / Complexity

Make yourself a quadrant diagram and plot them

#8 : Your cycles are too slow

0 6 12 18

Months

Conversion

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#8 : 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

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#8 : Make your own cycles“Rather than try and improve one thing by 10% - which would be very, very difficult to do,

We go and find 1,000 things and improve them all by a fraction of a per cent, which is totally do-able.”Chris Boardman

@OptimiseOrDie

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#9 : Your test fails

@OptimiseOrDie

#9 : 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

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#10 : 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

• 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

#11 : The test keeps moving around

@OptimiseOrDie

• 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.

#11 : The test has flipped on me

#12 : 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.

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#13 : You forgot you were responsive

• 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

@OptimiseOrDie

Top Fuckups for 20131. Testing in the wrong place2. Your hypothesis inputs are crap3. No analytics integration4. Your test will finish after you die5. Not testing for long enough6. No QA for your split test7. Opportunities are not prioritised8. Testing cycles are too slow9. Your test fails10. The result is ‘about the same’11. Test flips or moves around12. Nobody ‘feels’ the test13. You forgot you were responsive

@OptimiseOrDie

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BONUS : What is a good conversion rate?

Higher than the one you had last month!

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Is there a way to fix this then?Conversion Heroes!

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:@OptimiseOrDie

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