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A/B TESTING
INTRODUCTION
Ahmed Khaled
AGENDA
Definitions
Process
Examples
Tips and tricks
​Summary​
DEFINITION
A/B testing introduction 3
allows you to test one variable at a time. It’s used to pick the
better out of two variables. for instance, if you are testing the
background color of your landing pages to determine which one
helps you generate the most conversions, you would use an A/b
test. You can also test on the page level, which means that you
are looking at the entire page as the variable.
A/B testing can tell you whether or not where are you standing now is better
or worse than where you were. Even if, you’ve redesigned everything.
A/B testing 4
Not like that
How it works?
A/B testing 5
To prevent this
We A/B test
A/B testing 6
Why do we need A/B testing ?
- Testing and research historically has been difficult because:
*Expensive.
*Time consuming.
*Unreliable.
*Competitively complicated.
- Experimentation program success needs speed and infrastructure.
- Old approach wasn’t efficient:
*Mailing 20% offers.
*That’s what users love.
HOW IT WORKS?
A/B testing 7
A/B testing 8
A/B testing 9
This is not an A/B testing:
- Too many variants which means that we aren’t sure about what affected
the metrics.
- Bike Size changed.
- Font changed.
- Text changed.
- Order of features is different too.
A/B testing 10
A/B testing 11
What to experiment? (Experimentation 101)
- We don’t work directly on North Star metrics.
For example: we don’t go to MRR but we go to conversion which
affects MRR directly.
-Primary Metrics: When you create a primary metric, it has to be
conclusive in order to accept that there a statistical significance in you
assumption.
Primary Metric = Conclusive Positive or Conclusive Negative.
- Supporting Metrics: Not as conclusive as primary metrics but gives
you an indication about the direction whether it’s positive or negative.
(Customer service tickets or product returns).
- Health Metrics: Related to the performance of your platform to make
sure that any changes doesn’t impact unexpected metrics (Backend
errors- Queries).
A/B testing 12
Binominal Goals: Binominal goals are all the little breadcrumbs users
are leaving while navigating your platform.
Like: Bounce Rate from page- Category page visits.
A/B testing 13
OUR SUCCESS AT AMAZON IS
A FUNCTION OF HOW MANY
EXPERIMENTS WE DO PER
YEAR, PER MONTH, PER
WEEK, PER DAY
14
“
Jeff Bezos ”
A/B TESTING IN ACTION
15
A/B testing 16
- Ideate:
Building your backlog of A/B tests.
How to prioritize?
RICE framework
-Reach
-Impact
-Confidence
-Ease
A/B testing 17
A/B testing 18
A/B testing 19
A/B testing 20
A/B testing 21
- Design:
Hypothesis If (…..) Then (……)
Based on [evidence] we believe that if we change
[your change] for [customer segment] it will help them
[impact].
Success Metrics Primary => Conversion
Secondary => Bounce Rate
We will know this is true if we see [your expected change]
in [primary metric]. This is good for our business because
an increase in [primary metric] is an increase in [business
KPI].
Launch Decision Variant A VS Variant B
A/B testing 22
- Setup:
1- Correct UX.
2- Correct Tracking.
3- Free of data quality
Issues.
A/B testing 23
- Run:
1- Reach minimum sample size.
Sample size calculator (https://cxl.com/ab-test-calculator/)
2- No mid-test changes to avoid attribution issues.
A/B testing 24
- Analyze:
1- Ship, iterate or abandon.
2- Statistical significance and power.
3- Confidence level.
A/B testing 25
- Learn:
1- Learn from both success and failure.
2- Uncover segment-specific insights and discoveries for future
iterations of ideas.
A/B testing 26
REAL-LIFE
EXAMPLES
A/B testing introduction 28
A/B testing introduction 29
A/B testing 30
A/B testing 31
Hypothesis: If we added
the sense of joining
people of joining others in
achieving success would
increase conversion
Success Metric:
Conversion from different
touch points.
Highest banner was
converting 15% from its
clicks, the banner
converted 55% from its
clicks
Variant A
Base
TIPS AND TRICKS
A/B testing introduction 33
A/B testing introduction 34
A/B testing introduction 35
A/B testing introduction 36
A/B testing introduction 37
Presentation title 38
Presentation title 39
Presentation title 40
Presentation title 41
Presentation title 42
Presentation title 43
SOURCES
A/B testing 44
• https://www.slideshare.net/productschool/ab-testing-101-by-bookingcom-product-manager-
238685991
• https://www.slideshare.net/productschool/how-to-run-ab-tests-successfully-by-vice-media-product-
manager?from_search=3
• https://www.slideshare.net/productschool/how-to-use-ab-testing-to-create-growth-by-crometrics-pm
• https://www.youtube.com/watch?v=RRsN7VLot10
• https://www.youtube.com/watch?v=di2e1QpKYUY
• https://www.youtube.com/watch?v=NsiDwGntnpA
• http://iterative.club/
THANK YOU
Ahmed Khaled
ahmed.khalid201030@gmail.co
m
akhaled@agel.io

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A B testing introduction.pptx

  • 3. DEFINITION A/B testing introduction 3 allows you to test one variable at a time. It’s used to pick the better out of two variables. for instance, if you are testing the background color of your landing pages to determine which one helps you generate the most conversions, you would use an A/b test. You can also test on the page level, which means that you are looking at the entire page as the variable. A/B testing can tell you whether or not where are you standing now is better or worse than where you were. Even if, you’ve redesigned everything.
  • 4. A/B testing 4 Not like that How it works?
  • 5. A/B testing 5 To prevent this We A/B test
  • 6. A/B testing 6 Why do we need A/B testing ? - Testing and research historically has been difficult because: *Expensive. *Time consuming. *Unreliable. *Competitively complicated. - Experimentation program success needs speed and infrastructure. - Old approach wasn’t efficient: *Mailing 20% offers. *That’s what users love.
  • 7. HOW IT WORKS? A/B testing 7
  • 9. A/B testing 9 This is not an A/B testing: - Too many variants which means that we aren’t sure about what affected the metrics. - Bike Size changed. - Font changed. - Text changed. - Order of features is different too.
  • 11. A/B testing 11 What to experiment? (Experimentation 101) - We don’t work directly on North Star metrics. For example: we don’t go to MRR but we go to conversion which affects MRR directly. -Primary Metrics: When you create a primary metric, it has to be conclusive in order to accept that there a statistical significance in you assumption. Primary Metric = Conclusive Positive or Conclusive Negative. - Supporting Metrics: Not as conclusive as primary metrics but gives you an indication about the direction whether it’s positive or negative. (Customer service tickets or product returns). - Health Metrics: Related to the performance of your platform to make sure that any changes doesn’t impact unexpected metrics (Backend errors- Queries).
  • 12. A/B testing 12 Binominal Goals: Binominal goals are all the little breadcrumbs users are leaving while navigating your platform. Like: Bounce Rate from page- Category page visits.
  • 14. OUR SUCCESS AT AMAZON IS A FUNCTION OF HOW MANY EXPERIMENTS WE DO PER YEAR, PER MONTH, PER WEEK, PER DAY 14 “ Jeff Bezos ”
  • 15. A/B TESTING IN ACTION 15
  • 16. A/B testing 16 - Ideate: Building your backlog of A/B tests. How to prioritize? RICE framework -Reach -Impact -Confidence -Ease
  • 21. A/B testing 21 - Design: Hypothesis If (…..) Then (……) Based on [evidence] we believe that if we change [your change] for [customer segment] it will help them [impact]. Success Metrics Primary => Conversion Secondary => Bounce Rate We will know this is true if we see [your expected change] in [primary metric]. This is good for our business because an increase in [primary metric] is an increase in [business KPI]. Launch Decision Variant A VS Variant B
  • 22. A/B testing 22 - Setup: 1- Correct UX. 2- Correct Tracking. 3- Free of data quality Issues.
  • 23. A/B testing 23 - Run: 1- Reach minimum sample size. Sample size calculator (https://cxl.com/ab-test-calculator/) 2- No mid-test changes to avoid attribution issues.
  • 24. A/B testing 24 - Analyze: 1- Ship, iterate or abandon. 2- Statistical significance and power. 3- Confidence level.
  • 25. A/B testing 25 - Learn: 1- Learn from both success and failure. 2- Uncover segment-specific insights and discoveries for future iterations of ideas.
  • 31. A/B testing 31 Hypothesis: If we added the sense of joining people of joining others in achieving success would increase conversion Success Metric: Conversion from different touch points. Highest banner was converting 15% from its clicks, the banner converted 55% from its clicks Variant A Base
  • 44. SOURCES A/B testing 44 • https://www.slideshare.net/productschool/ab-testing-101-by-bookingcom-product-manager- 238685991 • https://www.slideshare.net/productschool/how-to-run-ab-tests-successfully-by-vice-media-product- manager?from_search=3 • https://www.slideshare.net/productschool/how-to-use-ab-testing-to-create-growth-by-crometrics-pm • https://www.youtube.com/watch?v=RRsN7VLot10 • https://www.youtube.com/watch?v=di2e1QpKYUY • https://www.youtube.com/watch?v=NsiDwGntnpA • http://iterative.club/