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Customer Engagement Score

Definition

Customer Engagement Score measures how actively and consistently a customer is interacting with your product, content, or brand. It helps assess product adoption, value realization, and retention potential.

Description

Customer Engagement Score is a behavioral composite metric that tracks how deeply and frequently customers interact with your product, giving a quantifiable view of retention, upsell potential, and product fit.

The relevance and interpretation of this metric shift depending on the model or product:

  • In PLG models, it tracks usage frequency, feature depth, and login habits
  • In community-led platforms, it reflects content views, post creation, and event attendance
  • In support-heavy tools, it may factor in ticket volume and response quality

A high engagement score signals strong product value. A declining trend may flag drift, friction, or disengagement before churn. Segment by persona, usage tier, or lifecycle stage to activate the right plays.

Customer Engagement Score informs:

  • Strategic decisions, like CSM prioritization or upsell sequencing
  • Tactical actions, such as in-app nudges or playbook-driven follow-ups
  • Operational improvements, including score recalibration based on feature changes
  • Cross-functional alignment, by aligning product, marketing, and CS on real usage signals

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

  • Feature Adoption and Workflow Integration: When users make your product part of their daily or weekly routine, engagement sticks.
  • Onboarding Completion and Time-to-First-Value: Customers who activate fully and fast tend to explore more.
  • In-App Education and Reinforcement: Contextual nudges and prompts help users discover new ways to use the product — boosting score.

Improvement Tactics & Quick Wins

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

  • If engagement scores are flat, run onboarding experiments that surface more features early.
  • Add in-app checklists or milestone celebrations to increase momentum through core workflows.
  • Run cohort analysis to compare high-score vs. low-score behaviors — and replicate what’s working.
  • Refine lifecycle marketing to drive inactive users back into key features or actions.
  • Partner with CS to flag low-engagement accounts and prioritize outreach before churn risk escalates.

  • Required Datapoints to calculate the metric

    • Product Usage Logs (e.g., sessions, time spent, features used)
    • Login Frequency or Recency
    • Customer Segment or Tier
    • Weights or Scoring Rules based on desired behaviors
    • Optional Add-Ons: Support interactions, NPS, CSAT, community activity

  • Example to show how the metric is derived

    • Weekly logins: 5
    • Core feature use: 8 actions
    • In-product support searches: 3
    • Custom formula: (5 × 2) + (8 × 3) + (3 × 1) = 31 Engagement Score

Formula

Formula

\[ \mathrm{Customer\ Engagement\ Score} = (\mathrm{Login\ Frequency} \times 0.3) + (\mathrm{Feature\ Use} \times 0.4) + (\mathrm{Support\ Activity} \times 0.3) \]

Data Model Definition

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

cube('CustomerEngagement', {
  sql: `SELECT * FROM customer_engagement`,

  measures: {
    engagementScore: {
      sql: `engagement_score`,
      type: 'number',
      title: 'Engagement Score',
      description: 'Calculated score representing customer engagement based on various interactions and activities.'
    },
    sessionCount: {
      sql: `session_id`,
      type: 'count',
      title: 'Session Count',
      description: 'Number of sessions a customer has engaged in.'
    },
    featureUsageCount: {
      sql: `feature_id`,
      type: 'countDistinct',
      title: 'Feature Usage Count',
      description: 'Number of distinct features used by the customer.'
    },
    loginFrequency: {
      sql: `login_count`,
      type: 'number',
      title: 'Login Frequency',
      description: 'Frequency of customer logins over a specified period.'
    }
  },

  dimensions: {
    customerId: {
      sql: `customer_id`,
      type: 'string',
      primaryKey: true,
      title: 'Customer ID',
      description: 'Unique identifier for each customer.'
    },
    customerSegment: {
      sql: `customer_segment`,
      type: 'string',
      title: 'Customer Segment',
      description: 'Segment or tier of the customer based on business rules.'
    },
    engagementDate: {
      sql: `engagement_date`,
      type: 'time',
      title: 'Engagement Date',
      description: 'Date of the customer engagement activity.'
    }
  },

  joins: {
    ProductUsage: {
      relationship: 'belongsTo',
      sql: `${CUBE}.customer_id = ${ProductUsage}.customer_id`
    },
    CustomerSupport: {
      relationship: 'belongsTo',
      sql: `${CUBE}.customer_id = ${CustomerSupport}.customer_id`
    }
  }
});

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.

    • Complexity of User Interface: A complicated interface can frustrate users, reducing their engagement with the product.
    • Lack of Feature Updates: Stagnant product development can lead to decreased interest and engagement over time.
    • Poor Customer Support: Ineffective support can lead to unresolved issues, causing users to disengage.
    • High Learning Curve: If the product is difficult to learn, users may not fully engage with it.
    • Infrequent Communication: Lack of regular updates or communication can make users feel disconnected, reducing engagement.

  • Positive influences

    Factors that push the metric in a favorable direction, supporting growth or improvement.

    • Feature Adoption and Workflow Integration: When users integrate the product into their routine, it leads to higher engagement as they rely on it regularly.
    • Onboarding Completion and Time-to-First-Value: Quick and complete onboarding encourages users to explore more features, increasing engagement.
    • In-App Education and Reinforcement: Providing contextual nudges helps users discover new functionalities, enhancing their interaction with the product.
    • Customer Support Interactions: Effective support interactions can resolve issues quickly, leading to increased satisfaction and engagement.
    • Community Engagement: Active participation in user communities can foster a sense of belonging and encourage more frequent product use.

Involved Roles & Activities

Funnel Stage & Type

  • AAARRR Funnel Stage

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

    Retention
    Revenue

  • 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.

    • Monthly Active Users: Monthly Active Users (MAU) is a strong leading indicator for Customer Engagement Score. A higher MAU typically forecasts higher engagement by showing sustained usage and ongoing interest in the product, which translates into better future engagement scores.
    • Stickiness Ratio: Stickiness Ratio (DAU/MAU) signals habit formation and user retention. An increasing stickiness ratio often precedes improvements in Customer Engagement Score, as it indicates more frequent product interactions by existing users.
    • Product Qualified Leads: Product Qualified Leads (PQLs) represent users demonstrating significant engagement behaviors. A rise in PQLs suggests that more users are finding value and engaging deeply with the product, leading to higher future engagement scores.
    • Content Engagement: Content Engagement is a leading indicator of how users interact with your content. High engagement rates here often predict elevated Customer Engagement Scores, as content consumption drives deeper product and brand interaction.
    • Customer Loyalty: Customer Loyalty reflects the likelihood of repeat engagement and advocacy. Increases in loyalty metrics often precede improvements in Customer Engagement Score, as loyal customers tend to interact more frequently and consistently with your brand.

  • Lagging

    These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.

    • Customer Churn Rate: Customer Churn Rate is a lagging KPI that quantifies the percentage of customers lost over a period. It confirms the impact of low Customer Engagement Scores, as disengaged customers are more likely to churn.
    • Net Revenue Retention: Net Revenue Retention (NRR) confirms the monetary impact of customer engagement by showing how much recurring revenue is retained after accounting for churn, downgrades, and expansions. High engagement scores typically align with strong NRR.
    • Expansion Revenue Growth Rate: Expansion Revenue Growth Rate quantifies the additional revenue from upsells and cross-sells. Improved Customer Engagement Scores are often followed by increased expansion revenue, confirming the downstream business value.
    • Contract Renewal Rate: Contract Renewal Rate measures the percentage of customers renewing their contracts. It is a lagging validation of engagement, as high engagement scores typically result in more renewals and higher lifetime value.
    • Customer Downgrade Rate: Customer Downgrade Rate measures the percentage of customers reducing their subscription or usage. A spike in downgrades often follows declines in Customer Engagement Score, providing confirmation of deteriorating account health and value realization.