Architecting great experiments


@kylerush


Head of Optimization, Optimizely


means

write it down

or

tweet it






experiment concepts



Significance


The risk of encountering a false positive.

95% = out of 100 a/a tests, 5 will inaccurately report a difference.



Power


The risk of encountering a false negative.

80% = out of 100 a/b tests, 
20 winners will not be reported.



one-tail vs. two tail



One tail = is the variation better?

Two tail = is the variation 
better OR worse?


MDE


Minimum detectable effect



Successful testing strategies 
are based around the minimum 
detectable effect (MDE) variable.




Sample size


How many subjects 
are in your experiment.



Always use a sample size 
calculator to calculate sample 
size before starting an a/b test.

bit.ly/VUBti8




sample size calculator


bit.ly/SWR3YC



Example


Absolute lowest MDE

conversion rate: 4%

MDE: 1%

POWER: 80%

SIGNIFICANCE: 5%

TAILS: ONE


2,972,435

Visitors per branch


example


Focus on time

1 month = 170,000 unique visitors


CONVERSION RATE: 4%

MDE: 6%

POWER: 80%

SIGNIFICANCE: 5%

TAILS: ONE


83,230

Visitors per branch


example


Small startup

1 month = 3,000 unique visitors

CONVERSION RATE: 4%

MDE: 45%

POWER: 80%

SIGNIFICANCE: 5%

TAILS: ONE


1,567

Visitors per branch



Sample size calculators tell you 
how many subjects, but not which 
subjects should be in your experiment.


sampling


  • can be really hard
  • week day vs. weekend traffic
  • campaign vs. organic traffic
  • returning vs. new visitors



Where should i test?




example


E-commerce website

homepage assumptions


  1. Lots of traffic
  2. Relatively few conversions

Let's estimate:

  • 2.5% conversion rate
  • 100,000 monthly unique visitors

Run experiment for 1 month (100k visitors)

CONVERSION RATE: 2.5%

POWER: 80%

SIGNIFICANCE: 5%

TAILS: ONE


MDE: 10%


Checkout page assumptions


  1. Lower traffic
  2. Relatively high conversion rate

Let's assume:

  • 50% conversion rate
  • 10,000 monthly unique visitors

RUN EXPERIMENT FOR 1 MONTH (10k visitors)

CONVERSION RATE: 50%

POWER: 80%

SIGNIFICANCE: 5%

TAILS: ONE


MDE: 5%




just cut your MDE in half!




Start by focusing a/b tests on the 
last step in your conversion funnel.



what should i test?


Depends on MDE and time.

Landing page optimization


(mozcon 2013)


bit.ly/1wkpgye

going beyond the low hanging fruit


(conversion conference 2014)


bit.ly/1kU4sZ0




be fearless




experiment


Terse vs verbose




result


+31% leads

99.9% confidence




Before you test


goals

  • Measure as many goals as possible
    • micro: form field errors, time on page
    • macro: purchase, revenue
  • Choose a primary goal
  • Don't forget about down the funnel goals
    • Repeat purchase
    • Save payment information




example

Success vs. submit


13.72% conversion rate


17.15% conversion rate





25% difference



Measure as many goals 
as possible for every experiment.

standards document


For each page/funnel record:

  • Three month monthly average of unique visitors
  • Stopping conditions (sample  size)
  • Goals
    • baseline conversion rate
    • MDE
    • visits per branch
    • baseline conversion rate over time



testing standards template


bit.ly/1oVRf6i



Quality assurance


  • No bugs in the variation
  • No bugs in the control
  • Tracking works correctly



ELIMINATING bias


Double blind experiments


Experiment brief

  • Hypothesis
  • Audience description
  • Goals tracked
  • Stopping conditions
  • Screenshots
  • QA summary


experiment brief template


bit.ly/1nvNJjx





after you test



statistical tie


Not enough data to
conclude that there is a difference



The overwhelming majority of 
experiment results are a statistical tie.




Example

Statistical tie



result




retesting


Example


Retesting

first test


                                                         +36% revenue

second TEST


                                         statistical tie

third TEST


                                           Statistical tie





Share your results



Always, always record detailed 

experiment results in an archive.

EXPERIMENT ARCHIVE


  • experiment date
  • audience/url
  • screenshots
  • hypothesis
  • results
  • link to experiment
  • link to result csv


Experiment archive template


bit.ly/1q9tRWI



ARCHITECTING GREAT EXPERIMENTS


@KYLERUSH


Head of Optimization, Optimizely

Copy of Architecting great experiments (iSummit)

By Kyle Rush

Copy of Architecting great experiments (iSummit)

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