5.7 Evaluate reactions on Linkedin

Measure the success of the LinkedIn reactions feature


As human emotions, aren't always binary; LinkedIn just like Facebook launched the reaction feature on its platform. I will take this question as a Product Management Interview question as I was asked recently in a PM interview, and I will evaluate the impact of this feature and the impact it puts on the entire LinkedIn ecosystem separately.

I would divide to divide of the goal of using this feature into two verticals.

1. Customer Goal 🍮

  • Providing customers with more options in addition to “Like” to express themselves – Human emotions have a wide range and variety. It isn’t binary. The Like button limits the range of emotions that someone can express while engaging with content on their news feed.

  • Increasing social validationGiving users more ways to quickly and constructively communicate with one another – When users post on LinkedIn, they want more ways to feel heard and understand why someone has liked what they have said.

2. Business goal 🧑‍💼

  • Increase engagement of users on the news feed- By engagement, I mean – the average number of shares, posts, likes, reactions, comments per visitor during a 30-day period.

  • Retention – For the test group that is exposed to reactions. – Do we see higher repeat visits (to see how their friends have reacted to their posts/shares or if others have liked/reacted to posts that they have liked/reacted)

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Testing the reaction feature 🖥️

I would use the following three metrics to test the feature of reactions.

  • 🏸 Discoverability/Acquisition: Are people able to discover “Reactions”? It is currently hidden under the Like button- is it easily discoverable?

  • 🎢 Usage: For the visitors that discover Reactions – How many people are clicking on the reactions? What is the total number of clicks? I would measure this for at least a week. I would also measure the % of users who are using likes compared to reactions?

  • 🤘🏼Usage by reaction type: How many clicks per reaction type? This would help me understand if I even have the right set of reactions in there or do, I need different reactions?

Testing the impact of reactions on the entire ecosystem 😻

  • 🎮 Usage/Engagement/Content Creation – For the user group that is exposed to reactions (Test Group)– does their average activity during a 30 day period increase due to reactions – Are they liking/reacting/commenting/sharing more as compared to people (Control group) who do not see reactions? Are they creating more content than before the test (pre-post analysis) and as compared to the control group (A/B)?

  • 🦋 Retention – For the test group that is exposed to reactions – do we see higher repeat visits (to see how their friends have reacted to their posts/shares or if others have liked/reacted to posts that they have liked/reacted)

  • 🐧 Engagement – Does it increase the time spent and length of the scroll on the news feed? Test A/B and see if there is a statistically significant difference?

  • 💲Monetization – Is there a higher click-thru rate (CTR) on ads for visitors who were exposed to reactions?

Interviewer (I): You mentioned A/B testing many a time. How would you prevent the control group from seeing reactions in their feed if their friends are in the test group?


  • I would ensure that the control and test groups are far apart in the LinkedIn social graph.
  • If they are in the same social graph and reactions are encountered by the control group, I would ask the engineering team to convert the reactions to like for the test groups.

Interviewer (I): How would you differentiate that whether passive users have become active or active users or highly socially engaged people” also had a marked improvement in their social activity due to the launch of this feature.

Response: To measure incremental improvement in engagement, I would take people in test and control groups with similar levels of prior engagement and then compare both.

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