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/EmailSendEvent
Get the Email send details like click
type, section name, type, order,
position, size.
Email Click Event /data/tracking/EmailClickEvent Get Email click number
Email View Event /data/tracking/EmailViewEvent Get Email view number

3. Preliminary Observations
• From above data, we can know which user click on
which email and which specific clicks in the email.
• So I count the distribution on different clicks and click
position for both mobile and desktop

4. Click Distribution

5. Group the click by Section
• We actually interested in the module
section order and click in the email.
• So I group the click by SectionNo.
• There are at most 7 sections in
‘nu_digest’ emailKey
SectionSeq SectionName
0 positions
1 milestones
2 shares
3 profile
4 endorsements
5 connections
6 pymk

6. Section Click Distribution

7. Section Click Distribution

8. Analysis
Interesting things:
• some section in position 5 , but it has more click
distribution than section in position 4.
Bias:
• what is the order for 7 section?
• Is there difference between the section type.
• What is the section size?
• It includes all the order and all the size.
• Does Mobile has more Uctr than Desktop?

9. Uctr in this question

10. • As the position increases
the Uctr decreases.
• 3Profile’s Uctr is higher
than 2shares with section
size 3 on pos 0.
• 6Pymk and 5connection’s
Uctr is higher than the
4endorsements.
• This shows us the original
order is not optimized
• The Desktop’s Uctr is
higher than the mobile’s
Uctr.

11. • 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

12. • Big difference for
section shares.
• As the section size
increases, the Uctr does
not drops.
• For pos >=1 , the Uctr
drops a little. Position is
not as
• This shows that
shares’s performance is
different from others.

13. • With same section
size and same
section pos.
• Pymk and
connections Uctr is
higher than the
endorsement

14. Bias
• But above chart still have bias.
• For example:
• For section endorsements，with section size 2, and
section pos 2.
• We can have format:
• 1, shares;endorsements;pymk
• 2, shares;endorsements;connections

15. 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.

16. 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

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