In the online world, sell-through is known as conversion rate optimization. For almost every online business, conversion rate is the single highest leverage point for increasing sales and return on marketing spend. In this presentation, Zac Aghion goes through the basics of conversion rate optimization. From A/B to multivariate testing and beyond, the presentation reviews the theory, case studies and best practices in this critical field of online marketing. The presentation begins with an overview of the basic web analytics terms and concepts needed to grasp the value of conversion rate optimization. It continues with an example of

1. Conversion
Optimization
Drive More Sales from Existing Traffic
and Increase Marketing ROI
Presented by Zac Aghion for Barcamp Shanghai
March 23, 2013

2. Some Basic Web Analytics
 Visitors – Number of people that visit your
website
 Conversions – Number of visitors that complete a
goal (purchase, sign up, download, click, etc.)
 Conversion Rate = Conversions ÷ Visitors
 Example:
 200 Visitors and 5 Purchases
 Conversion Rate = 5 ÷ 200 = 2.5%

3. A/B Testing Overview
Test two versions of your website to see which converts at a
higher rate.

4. Which Version Won?
Source: WhichTestWon.com

5. Marketer’s Surprise: ‘FREE’ Loses
Source: WhichTestWon.com
Version B led to a +13.48% increase in conversion rate.
✔✗

6. What to Test?
Source: TechWyse.com

7. Red Distraction & Clutter
Source: TechWyse.com

8. Remove Clutter, Promote Signup
Source: TechWyse.com

9. Red Truck & Logos Distraction
Source: TechWyse.com

10. Remove Logos, Yellow Button
Source: TechWyse.com

11. That’s Much Better!
Source: TechWyse.com

12. Multivariate Testing Overview
 Every landing page has X elements (MM’s hair)
 For each element, we test Y variations (Neon)
 In total, we have (Y1× Y2 × Yx) LP combinations

13. How Obama Raised +$60 MM
Source: Optimizely Blog

14. Four Button Variations

15. Six Media Variations

16. 24 Landing Page Combinations!

17. And the Winner is…

18. +40.6% CR = +$60 MM
 Original CR  8.26%
 New CR  11.6% (+40.6%)
 10 million signups from New (would be closer to
7,120,000 signups with Original)
 +2,880,000 additional signups
 $21 average donation / signup
 +$60,000,000 in additional donations

19. Testing With Confidence
Source: Optimizely Blog

20. Confidence Intervals
 Confidence intervals indicate the statistical
significance of an observed conversion rate
 95% CI = Observed CR +/- (1.96 × Standard Error)
Standard Error = √ (p × (1-p) ÷ n)
 Common Pitfalls
 Low # trials = non-significant results
 High # trials = significant results
 General balance between speed and reliability
 My website has less traffic than Obama
 But I still want +40% CR
 How??

21. Enter Genichi Taguchi
 Japanese statistician
(1924 – 2012)
 Taguchi Methods – fractional
factorial experimental designs
 Basic Concept – data collected
for a specific subset of
combinations can be
extrapolated to determine the
best performer of a full factorial
set

22. Orthogonal Arrays
 Taguchi arrays are used to determine what
subset of combinations need to be tested to
extrapolate results
 Most Basic Taguchi Array, L4:

23. L8 Array: Testing Efficiency +32x
 7 page elements, 2 variations for each
 2 × 2 × 2 × 2 × 2 × 2 × 2 = 256 LP combinations
 Data extrapolated from specific subset of 8
combinations determines best of 256

24. In-House or Agency?
In-House Agency
Pros
Lower initial costs
More control over the
testing process
More relevant
advanced goal tests
No need for internal resources
Faster results as agency
provides specialized expertise
Learn best practices and gain
experience
Cons
Long time to build
expertise from scratch
Longer time to start
achieving great test
results
Higher initial costs
Less understanding of
complex details of your
business goals and industry
Less control over testing

25. Thank You!
For More Information:
Zac Aghion
Email: zac.aghion@hotmail.com
China: +86 15921631924 | US: +1 6177506684
Twitter: @zacaghion | Skype: zac.aghion | Blog: www.ChinaAppMarketing.com