How would you measure the success of Meta (Facebook) Likes?

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Like is the fundamental feature of Facebook. I would start this question by first confirming that the interviewer only wants to measure success of Like feature used in Facebook application(mobile, web etc.) and not the ability to include Facebook Like button in social components in websites. That is one of the big ways in which Facebook Likes is used in collecting data throughout the web.

Let us assume that this question is only for the Facebook Like feature in the Facebook applications.

What is Facebook Like? – It is the most important way of user expression in Facebook to denote the user’s engagement with a post/entity on Facebook

Success of Facebook Likes is based on the user adoption of using this feature and continuing to express in the platform. Therefore, engagement metrics are the cornerstone for this features success. Some of them are as follows –

Direct Feature Engagement Metrics due to Like

a. Number of Likes/User or Brand/Day

b. Number of comments, shares per post

Indirect User Engagement Metrics to track

c. Number of 1:1 communications as a result of a Like

d. Number of new connections or recommendations generated as a result of a Like

e. Number of  followers as a result of a Like

f. Number of group conversations and Increase in engagement due to Like

Platform Metrics – Likes increases Posts , Articles, User and Brand popularity which in turn drives recommendations and better engagement for customers. Likes also helps Facebook with data point about user interest and therefore better ads to be served. Some example metrics are as follows –

g. Time Spent by the user in the application

h. Number of Ads/user

i.  Ads Click through rate

Metrics Prioritization – The metrics prioritization is based on metrics which drive direct engagement, indirect engagement and finally business and platform metric improvement. Therefore the order would be – a, b, c, d, i, g, e, f, h


@Marie Hamilton,On the success metrics of the “Like” feature you mentioned above, I would directly provide feedback.

1. Adoption: Adoption is very obviously not the metric that Facebook’s Like feature aims to shift. It is moved, as you mentioned, and is only a result of a feature’s indirect impacts. To better understand, let’s pose the question backwards: Would you implement the “Like” function if I were asked to “formulate the strategy to increase the number of subscribers to the business page”?

2. Engagement: Yes, the Like feature will move the engagement metrics; however, it will not move like the way you said –  If I have liked a post, I have spent more time than the person who has not liked the post. The “average time spent” metric, not the engagement meter, is used to measure the amount of time spent using the app.
A feature like this will boost user engagement since businesses gauge user involvement by looking at their varied actions. A “like” option would make interacting with the content effortless for users. It will boost engagement.

3. Monetization: This is beyond my understanding. Someday, it might have characteristics similar to monetization, but it isn’t the main or dominant mover.

4. Retention: You have the wrong impression about what retention is. When users return to the app more quickly than they did previously, retention does rise. Let’s comprehend this: You will log in to Facebook on Monday and Thursday, twice a day. Would you check into Facebook more than twice a day now that the Like option has been introduced? It, in my opinion, implies that a characteristic has no direct bearing on retention. Facebook’s notification system, however, has the ability to change the retention measure.

Hope you might agree to these.

Framework i’ll use to answer this:

  1. Define feature
  2. Define goal of feature
  3. User journey
  4. Metrics

Define feature: Ability to ‘thumbs up’ a post from another user – including original text/images, as well as responses. AND to thumbs up ads/ company pages on Facebook

Goal of feature: Reduce the barrier to user interaction on FB by supporting ‘one-click’ interaction. Thereby drive more interactions and overall more engagement and retention/stickiness + ideally more ad revenue longer-term

User journey:

Adding a like:

  1. User on their timeline
    1. Sees a post – thumbs up
    2. Sees an ad – thumbs up
  2. User on a 3rd party facebook site: Sees company they like – thumbs up
Receiving a like:
  1. User on their timeline: sees a response
  2. User not on FB: gets notified of a like

Metrics:

  1. Adoption
  2. Engagement
  3. Monetisation
  4. Retention

Since primary goal of feature is to increase engagement, will start here.

2. Engagement:

We want to understand uplift in engagement coming from this feature. Engagement levers:

  • # users
  • # sessions
  • length of a session [less important, will ignore]
  • # ‘Interactions’
    • Primary: users who give/receive likes
    • Secondary: increase in other interactions following these likes

Uplift metrics:

  • # users: % users who have only interacted through likes and not otherwise
  • # sessions:
    • % users /sessions which have / only have likes
    • % users /sessions triggered by viewing what someone has liked
  • interactions – primary:
    • % interacted/all posts which have only received a like
    • Likes as % of all interactions with a given post (track this over time: if decreasing, then examine the other features that are replacing it)
  • interactions – secondary:
    • likes of individuals: % interactions generated following a like => ‘network/re-engagement effect’ of likes
    • likes of ad/company: uplift in traffic from user’s network following a like => upsell potential

Other metrics to consider:

1. Adoption:

Two dimensions to adoption – measuring both (absolute and as % total) is a baseline:

  • Like: Users who have ‘given’ a like
  • Liked: Users/posts which have been liked

3. Monetisation:

Relevant for likes on ads/companies.

  • Levers: ad views, click-through, revenue
  • Metrics:
    • Uplift in all levers for ads which have received a ‘like’ vs not
    • Uplift in ad views & revenue from users who receive likes vs not?

4. Retention: Ideally feature keeps more users on FB over time, which will eventually translate into more monetisation. Levers are similar to engagement – frequency of sessions and interactions. Want to look at cohorts of users who receive/give likes and see how this affects their session and interaction frequency.

Here’s a simple way to approach this Facebook metrics question in a PM interview:

Describe

  • Like button offers low friction way to signal interest
  • Inside FB, provides validation back to content creator
  • Across web allows user to bookmark content they are interested in
  • Allows FB to build expanding interest graph for each user
  • Creates virtuous engagement cycle, both liker and like-ee

Feature goals

  • Engagement
    • More consumption and production of content
  • Richer user insights
    • Surface more personalized content
    • Offer advertisers better targeting

Journey

  • I’m scrolling through newsfeed
  • I see friend’s video
  • No time to comment
  • Click Like
  • I feel good about sending positive signal
  • My friend gets endorphin hit
  • More like-able content shows up in my feed
  • My friend is motivated to post more content

Success metrics

 

  • Is the feature discoverable?
    • Focus groups
    • Likes per session
  • Are users using the feature as intended?
    • % Un-likes
    • % of Likes followed by a comment
  • Is usage of the feature growing?
    • average likes per user
    • Likes per session
  • What is driving usage of the feature?
    • Likes by content type (video, photos, articles, webpages, groups, etc)
    • Likes by user segment (age, geography, time on platform etc)
  • Does the feature increase engagement?
    • Average session length by Like volume by user
    • Post frequency by like volume
    • Content interactions by like volume
    • Session frequency by like volume
  • Does the feature increase user value to advertiser?
    • Ad clicks by like volume by user
    • Length of ad video viewed by like volume