Presentation emailteam

Mobile/Desktop User Analysis on Email Interaction Patterns

Bo [email protected]

Outline1. Data and Preliminary Observations2. Link Click Distribution Analysis3. Section Click Distribution Analysis4. Analysis on different section type, size and

position5. Analysis on the same section format6. Summary

What kind of dataEvent Type Data Source Notes

Email Send Event/data/tracking/EmailSendEvent

Get the detailed structure send Emails like link, type, position.

Email Click Event/data/tracking/EmailClickEvent

Get Email click details like userid, device

Email View Event/data/tracking/EmailViewEvent

Get Email view details like userid, device

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.

2.Link Click Distribution

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 SectionName0 positions1 milestones2 shares3 profile4 endorsements

5 connections6 pymk

3.Section Click Distribution

Some section can be missing

SectionPos SectionName0 positions1 milestones2 shares3 profile4 endorsements

5 connections6 pymk

Original 7 sections

SectionPos SectionName

0 positions1 milestones2 shares

4 sections

are missiong

Section Click Distribution

Bias on previous analysis

• 1. On same position, section name is different• 2. One section name can be in different position


5 connections6 pymk

Original 7 sections SectionPos SectionName

0 positions1 milestones2 shares


0 positions1 shares2 profile


0 profile1 endorsements2 connections

4 sections is

missiong

4 sections is

missiong

4 sections is

missiong

Bias on previous analysis

• 3. Section size is also different.


5 connections6 pymk

Original 7 sections


0 positions1 milestones2 shares3 profile4 endorsements


0 positions1 milestones2 shares3 profile


0 profile1 endorsements2 connections

2 sections are

missiong

3 sections

are missiong

4 sections are

missiong

Bias• So we should consider differences:

1. Section name2. Section size3. Section position

• 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.

• Original order is not optimal

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

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

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;pymk2, shares;endorsements;connections


position5. Analysis on the different section format6. Summary

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”.

Maybe A better orderSectionNo 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

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.

• Thank you!• You can find more detailed analysis on

Email User Analysis Wiki• Go/bomaEmailAnalysis

https://iwww.corp.linkedin.com/wiki/cf/pages/viewpage.action?pageId=105329246

Pymk increases Uctr on first section

Format section name Uctr Format section name Uctr increase rate

endorsements;connections endorsements 0.069423175

endorsements;connections;pymk endorsements 0.071578619 3.01%

connections 0.032272702 connections 0.030748472 -4.96%

pymk 0.021006685

profile;endorsements profile 0.140718563

profile;endorsements;pymk profile 0.174781765 19.49%

endorsements 0.041916168 endorsements 0.027158099 -54.34%

pymk 0.025800194

• 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

Example of email

module = endorsement

module = connections

module = pymk

Presentation emailteam

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