1. Mobile/desktop user analysis with the
effects on email interaction patterns
Bo Ma
boma@linkedin.com

2. Data
Event Type Data Source Notes
Email Send Event
/data/tracking/Email
SendEvent
Get the Email send details
like click type, section
name, type, order,
position, size.
Email Click Event
/data/tracking/Email
ClickEvent Get Email click number
Email View Event
/data/tracking/Email
ViewEvent Get Email view number

3. Preliminary Observations
• From previous data, we can know:
• 1.There are at most 34 links in the 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 distribution on different clicks and click
position for both mobile and desktop
• 5. We only focus on “digest email” now.

4. Click Distribution

5. Group the click by Section
• We actually interested in the section
order and click in the email.
• I group the click by SectionNo.
• 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

6. Section Click Distribution

7. 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 is
missiong

8. Section Click Distribution

9. Bias on previous analysis
• 1. Section type is different on even same position.
• 2. one section type 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

10. 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 is
missiong
3 sections is
missiong
4 sections is
missiong

11. Bias
• So we should consider different:
• 1. Section type
• 2. Section size
• 3. Section posistion

12. • 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.

13. • 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

14. • For endorsement:
• As the section pos
increases the Uctr
drops.
• As the section size
increases the Uctr
drops.
• But it is not the same
trend for some
section

15. • With same section
size and same
section pos.
• Pymk and
connection’s Uctr
are higher than the
endorsement

16. Bias
• For example:
• For section endorsements，with section size 2, and
section pos 2.
• We can have two different format:
• 1, shares;endorsements;pymk
• 2, shares;endorsements;connections

17. 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’ Uctr are higher than edorsement.
Pymk increase the Uctr for the first section shares.

18. 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 quicker than the desktop
data from the first position to the second position.

19. •Thank you!
• You can find more detailed analysis on Email User Analysis Wiki

21. 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