Presentation emailteam copy
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Transcript of Presentation emailteam copy
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
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 positions1 milestones2 shares3 profile4 endorsements
5 connections6 pymk
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 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 SectionName0 positions1 milestones2 shares3 profile4 endorsements
5 connections6 pymk
Original 7 sections SectionPos SectionName
0 positions1 milestones2 shares
SectionPos SectionName
0 positions1 shares2 profile
SectionPos SectionName
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.
SectionPos SectionName0 positions1 milestones2 shares3 profile4 endorsements
5 connections6 pymk
Original 7 sections
SectionPos SectionName
0 positions1 milestones2 shares3 profile4 endorsements
SectionPos SectionName
0 positions1 milestones2 shares3 profile
SectionPos SectionName
0 profile1 endorsements2 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;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
