1. Mobile/Desktop User Analysis on
Email Interaction Patterns
Bo Ma
boma@linkedin.com

2. Outline
1. Data and Preliminary Observations
2. Link Click Distribution Analysis
3. Section Click Distribution Analysis
4. Analysis on different section type, size and position
5. Analysis on the same section format
6. Summary

3. Outline
1. Data and Preliminary Observations
2. Link Click Distribution Analysis
3. Section Click Distribution Analysis
4. Analysis on different section type, size and position
5. Analysis on the same section format
6. Summary

4. What kind of data
Event Type Data Source Notes
Email Send Event
/data/tracking/Email
SendEvent
Get the detailed structure
send Emails like link, type,
position.
Email Click Event
/data/tracking/Email
ClickEvent
Get Email click details like
userid, device
Email View Event
/data/tracking/Email
ViewEvent
Get Email view details like
userid, device

5. Preliminary Observations
• From previous data, we can know:
• 1.There are at most 34 links in the one email.
• 2.These 34 links can be grouped as at most 7
sections.
• 3.which user click on which email and which
specific links in the email.
• 4.I count the click distribution on different links and
link position for both mobile and desktop
• 5.We only focus on “digest email” in this analysis.

6. Outline
1. Data and Preliminary Observations
2. Link Click Distribution Analysis
3. Section Click Distribution Analysis
4. Analysis on different section type, size and position
5. Analysis on the same section format
6. Summary

7. 2.Link Click Distribution

8. Group the links by Section
• We actually interested in the section in the email.
• I group the 34 links.
• There are at most 7 sections in one email.
SectionNo SectionName
0 positions
1 milestones
2 shares
3 profile
4 endorsements
5 connections
6 pymk

9. Outline
1. Data and Preliminary Observations
2. Link Click Distribution Analysis
3. Section Click Distribution Analysis
4. Analysis on different section type, size and position
5. Analysis on the same section format
6. Summary

10. 3.Section Click Distribution

11. Some section can be missing
SectionPos SectionName
0 positions
1 milestones
2 shares
3 profile
4 endorsements
5 connections
6 pymk
Original 7 sections
SectionPos SectionName
0 positions
1 milestones
2 shares
4 sections
are
missiong

12. Section Click Distribution

13. Bias on previous analysis
• 1. On same position, section name is different
• 2. One section name can be in different position
SectionPos SectionName
0 positions
1 milestones
2 shares
3 profile
4 endorsements
5 connections
6 pymk
Original 7 sections
SectionPos SectionName
0 positions
1 milestones
2 shares
SectionPos SectionName
0 positions
1 shares
2 profile
SectionPos SectionName
0 profile
1 endorsements
2 connections
4 sections is
missiong
4 sections is
missiong
4 sections is
missiong

14. Bias on previous analysis
• 3. Section size is also different.
SectionPos SectionName
0 positions
1 milestones
2 shares
3 profile
4 endorsements
5 connections
6 pymk
Original 7 sections SectionPos SectionName
0 positions
1 milestones
2 shares
3 profile
4 endorsements
SectionPos SectionName
0 positions
1 milestones
2 shares
3 profile
SectionPos SectionName
0 profile
1 endorsements
2 connections
2 sections
are
missiong
3 sections
are
missiong
4 sections
are
missiong

15. Outline
1. Data and Preliminary Observations
2. Link Click Distribution Analysis
3. Section Click Distribution Analysis
4. Analysis on different section type, size and position
5. Analysis on the same section format
6. Summary

16. Bias
• So we should consider differences:
1. Section name
2. Section size
3. Section position

17. • Original order is not optimal
• 1.As the section type increases the Uctr
decreases.
• 2. 3Profile’s Uctr is higher than 2shares with
section size 3 on pos 0.
• 3. The Desktop’s Uctr is higher than the
mobile’s Uctr.

18. • Original order is not optimal
• With same section size and
same section pos. Pymk
and connection’s Uctr are
higher than the
endorsement

19. For endorsement:
1. As the section
position increases
the Uctr drops.
2. As the section size
increases the Uctr
drops.
3. Top position is
important

20. Bias
• Bias: For section endorsements with section size 3,
and section pos 1. we don’t know what is in the pos
0 and pos 2.
• For example:
• We can have two different format:
1, shares;endorsements;pymk
2, shares;endorsements;connections

21. Outline
1. Data and Preliminary Observations
2. Link Click Distribution Analysis
3. Section Click Distribution Analysis
4. Analysis on different section type, size and position
5. Analysis on the different section format
6. Summary

22. Uctr Difference between setion name on Same Format
Format section name Uctr
shares;endorsements shares 0.070833333
shares;endorsements endorsements 0.027083333
shares;endorsements;connections shares 0.067493113
shares;endorsement;connections endorsements 0.022956841
shares;endorsements;connections connections 0.02892562
shares;endorsements;pymk shares 0.073609732
shares;endorsements;pymk endorsements 0.025819265
shares;endorsements;pymk pymk 0.02599861
Pymk and connection’s Uctr are higher than edorsement.
Pymk increases the Uctr for the first section ‘shares”.

23. Maybe A better order
SectionNo SectionName
0 positions
1 milestones
2 profile
3 Shares
4 Pymk
5 connections
6 Endorsements
SectionNo SectionName
0 positions
1 milestones
2 shares
3 profile
4 endorsements
5 connections
6 pymk

24. Summary
• 1. First link is very important, since it actually contains
more than 50% of all the clicks.
• 2. The section order that we have now is not
optimal.
• 3. On the Mobile data, the click distribution for the
first position is higher than the Desktop data, but the
click distribution on mobile drops faster than the
desktop data from the first position to the second
position.

25. •Thank you!
• You can find more detailed analysis on Email User Analysis Wiki
• Go/bomaEmailAnalysis

27. Pymk increases Uctr on first section
Format section name Uctr Format section name Uctr increase rate
endorsements;co
nnections endorsements 0.069423175
endorsements;con
nections;pymk endorsements 0.071578619 3.01%
connections 0.032272702 connections 0.030748472 -4.96%
pymk 0.021006685
profile;endorsem
ents profile 0.140718563
profile;endorseme
nts;pymk profile 0.174781765 19.49%
endorsements 0.041916168 endorsements 0.027158099 -54.34%
pymk 0.025800194

28. • 1.As the section position increases
the Uctr decreases.
• 2.6Pymk and 5connection’s Uctr is
higher than the 4endorsements.
• This shows us the original order is
not optimized

29. Example of email

30. module = endorsement
module = connections
module = pymk