Linkedin’s endorsement feature analysis

1. Proposal for a revised Linkedin endorsement feature
Kash Khaleghi

2. Agenda
 Problems with the current design (In my humble opinion)
 Proposal for a new design
 Different approach
 Different presentation (1)
 Different presentation (2)
 An Illustrative example
 Proposal for revision of metrics for the new design
 Disclaimer

3. Problem with the current design (In my humble opinion)
 Scope for abuse
 Since no contextual support is required for endorsements (unlike
recommendations), it can easily lend itself to abuse. Connections who
have had little/no professional interaction may endorse each other
either to facilitate each other’s success and/or in hopes that the “favor”
will be reciprocated
 All endorsements have the same weight/value
 One’s manager’s endorsement is worth the same as her aunt’s (who has
had no common professional/academic ties to her)

4. Proposal for a new design - different approach
 Assign an “endorsement value” factor (e.g. 0 to 10) based on 3
factors:
1. Prior business/academic interactions
2. Endorser’s level of expertise in the domain
3. Endorser’s level of seniority/affiliation with reputable institutions
 Define an “endorsement score” as the weighted average of the
endorsement values

5. My proposal for a new design – different presentation (1)
 Instead of a linear approach, use a graphic approach (e.g. cloud) that
reflects endorsement score and volume
 Define color intensity as a function of endorsement score (the higher
the opacity, the higher the endorsement score)
 This function (color intensity) will kick in once the number of endorsements
has surpassed a certain threshold (e.g. 10) in order to conceal individual
endorsement scores.
High endorsement score Low endorsement score

6. My proposal for a new design – different presentation (2)
 Define shape size as a function of endorsement volume (The more
endorsements one receives the larger the cloud)
High endorsement volume Low endorsement volume
Relatively speaking

7. Proposal for a new design – an illustrative example

8. Proposal for revision of metrics for the new design
 Successful A/B testing (control group A gets the new feature and B doesn’t)
 Increase in the number of endorsements with high credibility in group A versus B
 Decrease in the number of endorsements with low credibility in group A versus B
 Increase in overall “endorsement scores” in group A versus B – as per our definition on slide 4
 Increased relevance and effectiveness in targeted ads for group A versus group B
 Successful regression test on all other feature metrics (as a result of the new release) on group A

9. Disclaimer
 This is an attempt to address some of the current issues and propose an alternative
approach, which begs for more experiments and iterations to solidify and prove to be
valuable
 In particular, more science needs to go into my proposal for the endorsement value
function, since size of companies and titles/seniority levels differ massively in different
countries