Web analytics 101: Optimization
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Transcript of Web analytics 101: Optimization
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Web Analytics 101 OPTIMIZATION
The power of A/B and Multivariate testing.
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Optimization is the practice of using hypothesis testing and experimentation to continuouslyimprove site and campaign performance.
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A/B TestingRandomized experiment with two variants.
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Multivariate TestingRandomized experiment with multiple variants.
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Benefits
Sustained performance improvement.
Predictable, controlled routine.
Lower risk from changes.
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Optimization TestingExamples
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Mobile vs. OnlinePre-header clicks
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A
B
“Mobile”
“Online”
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A
“Mobile”
Version A received a 173% increase in unique clicks.
Test was conducted after learning 25% of members read email on their
mobile devices.
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Sub-NavigationLeads
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A BWithout Sub-Navigation With Sub-Navigation
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B With Sub-Navigation
Version B increased leads by
39%.
Noticed a correlation between
leads and visitors seeing 5
specific pages on the site.
Good site analytics leads to
strong hypotheses!
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ImagesAccount sign-ups
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A
B
Sarah
Blair
A
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ASarah
A
Sarah drove 36% more account signups than Blair.
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TipsTo keep in mind
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Get agreement up front on the
Evaluation Criteria with Business
and IT decision-makers.
Targeting increases the power of
optimization.
Be aware of confounding factors
such as site changes that occur
independently of your experiments.
They can pollute your metrics and
lead to incorrect conclusions.
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TricksTo remember
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Optimization should be an ongoing
program – you will continue to get
benefit as long as you do it.
As you get more sophisticated, you
can run many experiments at once.
But don’t get sloppy with the
samples.
Be patient – some tests (especially
UX changes) take many months to
show results.
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