tags: - Funnel Stage/Awareness - Marketing - Content Marketing - Social - Leading hide: - toc - tags

Likes, Shares, Comments¶

Definition¶

Likes: A basic interaction indicating approval, enjoyment, or agreement with a post or piece of content. Shares: When users repost content to their own network, amplifying its reach and demonstrating strong resonance. Comments: User-generated responses to content, reflecting deeper engagement and encouraging conversation.

Description¶

Likes, Shares, Comments are key indicators of audience resonance and content performance, reflecting how well your content connects, inspires, and spreads across social platforms and communities.

These signals vary in depth:

Likes show surface interest
Comments reflect deeper engagement or feedback
Shares amplify your reach through advocacy and endorsement

A rising volume typically means high content relevance and emotional resonance, while stagnation may indicate message fatigue, poor format fit, or audience mismatch. By segmenting by channel, content type, or audience, you can identify what drives engagement, virality, and conversation.

Likes, Shares, Comments inform:

Strategic decisions, like content mix, brand narrative, and platform focus
Tactical actions, such as repurposing high-performing posts
Operational improvements, including content calendars and social workflows
Cross-functional alignment, helping brand, content, and community teams sync on audience voice and market pulse

Key Drivers¶

These are the main factors that directly impact the metric. Understanding these lets you know what levers you can pull to improve the outcome

Content Relevance and Emotion: Posts that resonate on a personal or professional level get shared. Purely promotional content flops.
Channel–Audience Match: Different platforms serve different behaviors — the same post on Twitter vs. LinkedIn performs differently.
Timing and Frequency: Posting when your audience is active (not just “when content is ready”) boosts interaction.

Improvement Tactics & Quick Wins¶

Actionable ideas to optimize this KPI, from fast, low-effort wins to strategic initiatives that drive measurable impact.

If engagement is low, test 3 content formats: thought leadership, tactical tips, and behind-the-scenes.
Add a CTA that invites response (“What’s your take?” or “Tag a teammate”).
Run a test with user-generated content (e.g., customer wins, team shoutouts) vs. branded content.
Refine your post scheduling based on engagement windows per platform.
Partner with community or brand to build weekly content themes and expand reach with employee advocates.

Required Datapoints to calculate the metric
- Likes: Number of users who liked the post.
- Shares: Number of users who shared the post.
- Comments: Number of user responses to the post.
- Reach: The total audience exposed to the content.
- Engagement Rate: The percentage of users who interacted with the content (calculated below).
Example to show how the metric is derived

A fitness app posts a motivational video on Instagram and tracks 2,000 likes, 500 shares, and 300 comments from an audience of 50,000 followers:
- Engagement Rate = [(2,000 + 500 + 300) / 50,000] × 100 = 5.6%

Formula¶

Formula

\[ \mathrm{Engagement\ Rate} = \left( \frac{\mathrm{Likes} + \mathrm{Shares} + \mathrm{Comments}}{\mathrm{Total\ Reach}} \right) \times 100 \]

Data Model Definition¶

How this KPI is structured in Cube.js, including its key measures, dimensions, and calculation logic for consistent reporting.

SocialInteractions

cube('SocialInteractions', {
  sql: `SELECT * FROM social_interactions`,

  measures: {
    likes: {
      sql: `likes`,
      type: 'sum',
      title: 'Likes',
      description: 'Total number of likes received on posts.'
    },
    shares: {
      sql: `shares`,
      type: 'sum',
      title: 'Shares',
      description: 'Total number of shares of posts.'
    },
    comments: {
      sql: `comments`,
      type: 'sum',
      title: 'Comments',
      description: 'Total number of comments on posts.'
    },
    engagementRate: {
      sql: `engagement_rate`,
      type: 'number',
      title: 'Engagement Rate',
      description: 'Percentage of users who interacted with the content.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true,
      title: 'ID',
      description: 'Unique identifier for each interaction record.'
    },
    postId: {
      sql: `post_id`,
      type: 'string',
      title: 'Post ID',
      description: 'Identifier for the post.'
    },
    userId: {
      sql: `user_id`,
      type: 'string',
      title: 'User ID',
      description: 'Identifier for the user.'
    },
    createdAt: {
      sql: `created_at`,
      type: 'time',
      title: 'Created At',
      description: 'Timestamp of when the interaction occurred.'
    }
  }
});

Note: This is a reference implementation and should be used as a starting point. You’ll need to adapt it to match your own data model and schema

Positive & Negative Influences¶

Negative influences

Factors that drive the metric in an undesirable direction, often signaling risk or decline.
- Content Saturation: Overposting or repetitive content can lead to audience fatigue, reducing Likes, Shares, and Comments as users become disengaged.
- Irrelevant Content: Content that does not align with audience interests or needs results in fewer Likes, Shares, and Comments due to lack of resonance.
- Platform Algorithm Changes: Changes in platform algorithms can negatively impact the visibility of posts, leading to decreased Likes, Shares, and Comments.
- Poor Timing: Posting at times when the audience is inactive results in lower Likes, Shares, and Comments due to reduced visibility.
- Negative Sentiment: Content that evokes negative emotions or controversy may deter Likes and Shares, although it might increase Comments due to debate.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Content Relevance and Emotion: Posts that resonate emotionally or professionally with the audience tend to receive more Likes, Shares, and Comments as they connect on a personal level.
- Channel–Audience Match: Aligning content with the platform's audience behavior increases Likes, Shares, and Comments due to better engagement with the right audience.
- Timing and Frequency: Posting during peak activity times for the audience results in higher Likes, Shares, and Comments due to increased visibility and interaction.
- User Engagement History: Users who have previously engaged with similar content are more likely to Like, Share, and Comment again, reinforcing positive interaction patterns.
- Content Quality: High-quality, well-produced content tends to attract more Likes, Shares, and Comments as it is perceived as more valuable and credible.

Involved Roles & Activities¶

Involved Roles

These roles are typically responsible for implementing or monitoring this KPI:

Content Marketing
Marketing
Social Media & Community Manager
Activities

Common initiatives or actions associated with this KPI:

Lead and Demand Generation
Community Building
Content Marketing
Social Engagement

Funnel Stage & Type¶

AAARRR Funnel Stage

This KPI is associated with the following stages in the AAARRR (Pirate Metrics) funnel:

Awareness
Type

This KPI is classified as a Lagging Indicator. It reflects the results of past actions or behaviors and is used to validate performance or assess the impact of previous strategies.

Supporting Leading & Lagging Metrics¶

Leading

These leading indicators influence this KPI and act as early signals that forecast future changes in this KPI.
- Engagement Rate: Engagement Rate captures the breadth and depth of user interactions across content, providing a holistic early signal for overall engagement. High engagement rate often forecasts higher Likes, Shares, and Comments by reflecting audience resonance.
- Content Engagement: Content Engagement measures direct user interactions such as time spent, clicks, and other actions on content, closely aligning with and often preceding spikes in Likes, Shares, and Comments. It helps spot which content types are likely to drive social actions.
- Monthly Active Users: Monthly Active Users quantifies the pool of users exposed to and able to interact with content. Growth in MAU increases the potential for Likes, Shares, and Comments, and trends in MAU provide early signals of upcoming engagement spikes.
- Brand Awareness: Brand Awareness reflects how many people recognize and are predisposed to interact with your content. A lift in brand awareness typically precedes and increases the likelihood of higher Likes, Shares, and Comments on posts.
- Unique Visitors: Unique Visitors measures the influx of new and returning users who may engage with content. Surges in unique visitors often correlate with and can predict increases in visible social interactions such as Likes, Shares, and Comments.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Social Shares: Social Shares, as a downstream outcome, can validate and recalibrate content strategies based on observed virality and advocacy. Analyzing which posts achieve high shares helps optimize content for future engagement, informing leading indicators.
- Customer Engagement Score: Customer Engagement Score aggregates post-engagement behaviors and can help refine models predicting which content types generate early signals (Likes, Shares, Comments), improving future engagement forecasts.
- Engagement Rate on Awareness Campaigns: Engagement Rate on Awareness Campaigns quantifies the downstream impact of campaigns and can highlight which leading indicators (early engagement signals) most accurately predict campaign success.
- Branded Search Volume: Branded Search Volume rises after strong engagement and sharing activity, providing feedback on how engagement translates to brand interest and enabling recalibration of social content and leading indicators.
- Referral Invitation Rate: Referral Invitation Rate, while a lagging outcome, shows the extent to which engagement (Likes, Shares, Comments) translates into advocacy. Insights from referral behavior help refine future engagement strategies and leading indicator thresholds.