Mobile/desktop user analysis with the
effects on email interaction patterns
Bo Ma
boma@linkedin.com
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
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.
Click Distribution
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
Section Click Distribution
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
Section Click Distribution
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
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
Bias
• So we should consider different:
• 1. Section type
• 2. Section size
• 3. Section posistion
• 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.
• 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
• 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
• With same section
size and same
section pos.
• Pymk and
connection’s Uctr
are higher than the
endorsement
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
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.
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.
•Thank you!
• You can find more detailed analysis on Email User Analysis Wiki
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

Presentation emailteam copy

  • 1.
    Mobile/desktop user analysiswith the effects on email interaction patterns Bo Ma boma@linkedin.com
  • 2.
    Data Event Type DataSource 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 • Fromprevious 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.
  • 5.
    Group the clickby 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.
  • 7.
    Some section canbe 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.
  • 9.
    Bias on previousanalysis • 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 previousanalysis • 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 weshould consider different: • 1. Section type • 2. Section size • 3. Section posistion
  • 12.
    • 1.As thesection 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 thesection 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 samesection 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 betweensetion 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. Firstlink 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! • Youcan find more detailed analysis on Email User Analysis Wiki
  • 21.
    Pymk increases Uctron 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

Editor's Notes

  • #5 1.explain Title, x-axis, y-axis 2.Clicks on First link contains 50% all the clicks. 3.Click on First link Mobile > desktop
  • #6 34 links -> 7 Section
  • #7 1. Explain Title, x-axis, y-axis, 2. The Click on different pos is different . 3. first click and first drop on mobile Vs desktop . 4. Pos 2 & 5 have higher Uctr than previous pos
  • #9 Explain Title, x-axis, y-axis Big difference mobile vs desktop
  • #13 Explain more Title, x-axis, y-axis 2 Then show the full chart
  • #14 Explain more 1. 6Pymk 5connection > 4endorsements. 2. Show pos
  • #15 Explain more Title, x-axis, y-axis
  • #18 Do I need to show 5 dimension data
  • #20 Summary and 1mobile and desktop difference, 2, order is not good.
  • #22 DELTE THIS SLIDE , you can see after I add pymk, how the click rate changes.