This is the accompanying presentation to a webinar on doing research for A/B testing. It describes how online marketers can create better A/B tests by investing in rigorous, useful research to create great testing hypotheses. The topics covered are - Analytics (digging into analytics with an eye on useful insights) - Usability Testing - On-site surveys - Visual Analysis - And finally, pulling all of these together

1. How to Create Winning A/B
Tests through Stronger
Research

2. View the video of this webinar here

3. About Siddharth Deswal
● Senior Marketer at VWO
● Passionate about analytics,
optimization and storytelling
● Connect on Twitter
@SiddharthDeswal

4. Research - The Scientific Method
Problems are usually well defined: Increase sales,
signups, leads generated, etc.
Neglected part of conversion rate optimization (or
just lip-service paid)
HiPPOs rule over here (Highest Paid Person’s
Opinion)
Most users focus on these steps

5. Why research
1. Only one in seven tests results in a winner
2. Many people don’t know what to test after the initial experiments
3. Tests with results that don’t move the needle

6. Analytics

7. Analytics - Top sources

8. Analytics - Understand entry points

9. Analytics - Mapping exit pages

10. Analytics - Key pages

11. Segment for better insights
1) New visitors who don’t bounce

12. Segment for better insights
1) New visitors who don’t bounce (continued)

13. Segment for better insights
2) Visitors who behave similar to those who convert

14. Segment for better insights
2) Visitors who behave similar to those who convert (continued)

15. Segment for better insights
3) Finally, analyze the visitors who behaved like converters but did not convert
by looking at their top sources, entry points and exit pages

16. Usability Testing

17. Usability Testing
Putting 5 to 10 users in front of your website or landing pages and observing
them can teach you far more than most other forms of research.
Caveat: these users have to be representative of your target customers.

18. Usability Testing - Asking questions
“You received a damaged product, try and return it”
1) User searches for ‘Returns Policy’ page and then proceeds to follow the steps listed
vs.
“You received a damaged product, what do you do now?”
1) User opens ‘Live Chat’ and asks rep what to do, or
2) Takes to Twitter to complain to the brand, or
3) Looks for the ‘Returns’ page, or
4) Something that none of us were able to anticipate

19. Usability Testing - Asking questions
“You received a damaged product, try and return it”
vs.
Useful when trying to understand roadblocks in a
specific process but the question has biased the
user towards a particular action
“You received a damaged product, what do you do now?”
Better when you want to
understand how users react to
problems without any bias

20. Usability Testing - Measurement
Quantitative measurement
1. Number of clicks to solve the problem
2. Time taken to solve the problem
3. Number of page loads to solve the problem
4. Errors while attempting to solve the problem
5. Number of goals completed while solving the problem

21. Usability Testing - Measurement
Qualitative insights
1. What catches the participant’s attention in the first 5/10/15 seconds after the landing page has
loaded
2. How quickly is the headline able to communicate the business’s primary offering
3. How does the participant search for information
4. How convinced is the user with and without trust signs/badges
5. Questions that the user generates while attempting to solve the problem
6. How pleasant the user finds the website
7. Distractions that make participants leave the conversion funnel
8. Points where participants fumble, are confused or have to turn to you for further direction

22. User Testing - Attensee.com

23. On-site surveys

24. On-site surveys

25. On-site surveys - SaaS
Page Question Insights drawn
Pricing / Features / Trial-Signup What’s stopping you from signing
up with us?
Common concerns, objections,
anxieties
Top exit pages Is there something you were
looking for that you couldn’t find
on the website?
Reasons that users leave,
Information that they needed to
make a decision but couldn’t find
Top landing pages / Homepage How familiar are you with
[business offering]?
Understand how much
convincing/explaining do your
visitors need
Top landing pages / Homepage Where did you come to know of
us?
Top marketing channels

26. On-site surveys - SaaS

27. On-site surveys - eCommerce
Page Question Insights drawn
Product page What’s stopping you from adding
this product to cart?
Common concerns, objections,
anxieties or ‘just looking’
Top exit pages (product or
category)
Is there something you were
looking for that you couldn’t find
on the website?
Reasons that users leave,
Information that they needed to
make a decision but couldn’t find
Top landing pages / Homepage Where did you come to know of
us?
Top marketing channels
Checkout / Cart What’s stopping you from buying
this product?
Why are visitors not completing
purchases in spite of adding to
cart

28. On-site surveys - Exit intent

29. On-site surveys - Exit intent
WAIT
Tell Us Why You’re Leaving And Get A 10% Discount Coupon On Your Next
Purchase
I’m leaving because
● I don’t want to create an account before buying
● I found a better price elsewhere
● I can’t find a coupon code
● I don’t see free-shipping
● I was just browsing
● Other

30. Visual Analysis

31. Visual Analysis - Heatmaps

32. Heatmaps case study - original

33. Heatmaps case study - Heatmap

34. Heatmaps case study

35. Heatmaps case study - Result
25% increase in clicks on app store buttons!

36. Visual Analysis - Scrollmaps

37. Hypothesis

38. Hypothesis
Research
Hypothesis
Image credit
http://www.pasosadelantepv.com/evaluation-treatment.html

39. Hypothesis Framework
The Change:Effect model
Image credit http://www.marketingexperiments.com/blog/analytics-testing/creating-good-hypothesis.
html

40. Hypothesis Framework
The Change: Effect model depends on
1. Presumed problem
2. Proposed solution
3. Anticipated result

41. Hypothesis Framework
The Change: Effect model
Image credit http://www.marketingexperiments.com/blog/analytics-testing/creating-good-hypothesis.
html

42. Putting it all together
1. Start by creating a segment that shows only those visitors who came from your largest or most
important traffic source
2. Get a list of the top landing and exit pages for this traffic source
3. Run user tests where representative participants are asked to solve a problem that takes them
through these pages
4. Make note of the key concerns, objections and difficulties expressed by test participants
5. Run on-site surveys on the key pages

43. Putting it all together
6. Again, make a note of the concerns, objections and reasons given by respondents
7. Run heatmaps and scrollmaps on your key pages to understand visitor behavior in their natural
environment
8. In our experience, once you’ve completed the analysis of all the previous steps, the problems on
your landing pages and websites automatically jump out, i.e. they become so obvious that
everyone is easily able to identify them
9. Create hypotheses that attempt to fix these problems
10. Run your A/B tests!

44. Thank You!