Conversion rate optimisation. What&#x27;s realluy proved to matter? Viacheslav kravchuk

This document discusses conversion rate optimization experiments. It emphasizes that while experiments are easy to run, drawing the right conclusions is difficult. It recommends running experiments for 6 weeks or more to achieve statistical significance of at least 95%. Segmenting traffic can provide additional insights. Examples of experiments discussed include testing different checkout designs, adding secure seals, and removing unnecessary fields from checkout. The key takeaways are that different stores require different tests, results need verification, and conversion rate depends on many factors beyond just UI/UX.

Conversion rate optimisation. !
What’s really proved to matter?
Viacheslav Kravchuk!
Atwix

Background
CEO of Atwix (www.atwix.com), MCSS, Google
Analytics QI, CSM.

The story about conversion rate!
experiments

In 5 days…
OneStepChekout!
converted 2.5x better!
vs. Awesome Chekout

Lesson #1:!
!
Making experiments is
easy. Making the right
conclusions - is not

Let’s do it properly
You want to prove something?
Do it on your own dime!

Let’s do it properly
~ 6 weeks
OSC shows 7.5% improvement
Statistical significance: 80%

Statistical significance is the probability
that your result is not a false positive !
!
95% is industry standard!
!
Run experiments longer for higher
confidence
http://bit.ly/1qZZCku

~ 6 weeks
OSC shows 2.8% improvement
Statistical significance: 85%

Segment for extra insight
- Device type!
- Location of the customers!
- Browser type!
- Any data that Google Analytics offers!

How to segment
- Create segments including sessions
with each checkout URL!
- Exclude PayPal express!
- Exclude checkout variation that
customers are redirected to

Segmenting converted customers by visited pages
> 50% >
PP Express Orders: Visited PP Express confirmation
OSC Orders: Visited OSC and not visited PP Express
AC Orders: Not visited OSC and not visited PP Express

Interesting observation:!
!
Responsive checkout does
not help non-responsive
website.

Conclusion:!
!
Each case is different. Test
checkouts on your store
long enough. It will pay off.

What researches tell?
2510 US adults were asked in 2013.!
!
About half answered “Don’t know or no
preference”.

CarPartKings Experiment!
(by ConversionIQ)

! Variation 2 (Norton
Secure Seal) won by 15%.

Conclusion:!
!
It’s worth testing. Chances
are the seal will help. Even
if it does not mean much.

Hiding the Fax field helps 6%
so far, hiding Fax & Company
fields helps 0.7% (SS 84%).

Experiments in progress:
- Add to cart button color!
- Welcome to checkout text!
- Free shipping order total motivation!
- Secure Seal impact

Ideas for experiments
http://bit.ly/1h0Md2i

Some takeaways
1. Different stores = different cases. Make your
own tests.!
2. Be careful with conclusions. Verify the
results (for ex. with GA) and try to find out the
reasons.!
3. Conversion rate is not only about UI/UX
(traffic quality, site speed, etc.).

This document discusses conversion rate optimization experiments. It emphasizes that making the right conclusions from experiments requires care and verification. Several experiments are described, such as testing different checkout pages or removing unnecessary fields, with results ranging from 2.5-7.5% improvement. It concludes that each store is unique and testing checkout variations directly on one's own site for sufficient time, such as 6 weeks, is necessary to determine what really improves conversion rates.

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Product School

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Kissmetrics on SlideShare

Ton Wesseling gave a presentation at ConversionXL Live in Austin on March 31st 2016 about utilizing test capacity. He discussed optimizing conversions through the ROAR model of risk, optimization, automation and re-thinking. Wesseling emphasized fully using a company's test capacity for impactful A/B tests and separating that capacity for IT releases, campaigns and behavioral learning. He advised celebrating failures to encourage risk-taking and continuous learning.

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UseItBetter

This document outlines 10 principles for software development without estimates. It begins by discussing trusting or changing your process (Principle 1) and shortening feedback cycles (Principle 2). Data is presented showing estimates are often inaccurate, with 80% of projects being late or over budget. Principle 3 states to believe data over estimates. Alternatives to estimate-driven decision making are suggested in Principle 4. Principles 5-8 discuss testing for value, measuring progress with working software, and understanding predictable system outputs. Principle 9 advocates using methods with proven track records over hoping estimates will improve. The transformation begins with individuals, per Principle 10.

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VWO

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2010 10 28 the lean startup at ucsd

Kissmetrics on SlideShare

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