First Contact Engagement Rate¶

Definition¶

First Contact Engagement Rate measures the percentage of new users who engage meaningfully after their very first interaction with your brand or product. It helps assess how well your initial touchpoints drive further action.

Description¶

First Contact Engagement Rate is a key indicator of top-of-funnel strength and early user journey performance, reflecting how initial interactions (ads, demos, content) spark meaningful user action like sign-ups, product exploration, or onboarding.

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

In B2B, it shows how many demo attendees activate or follow up with sales
In consumer or app-based products, it measures conversion from ad click to signup or core action
In self-serve SaaS, it reflects trial starts or first-page depth after landing

A rising First Contact Engagement Rate indicates strong messaging and user fit, while a decline may reflect misaligned CTAs, poor targeting, or early friction. By segmenting by campaign, persona, or channel, you unlock insights to improve early conversion paths and traffic quality.

First Contact Engagement Rate informs:

Strategic decisions, like creative direction and landing page positioning
Tactical actions, such as campaign targeting and nurture sequencing
Operational improvements, including first-touch UX and onboarding readiness
Cross-functional alignment, connecting marketing, lifecycle, and product teams to optimize early activation momentum

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

Message Clarity and Relevance: If your first touch doesn’t connect to the user’s role, need, or pain point — it gets ignored.
Timing Relative to User Intent: Reaching out too early (before clear interest) or too late (after intent cools) kills engagement.
Channel–Persona Match: The right message on the wrong channel won’t land. Different personas respond better to email, LinkedIn, chat, etc.

Improvement Tactics & Quick Wins¶

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

If engagement rate is low, rewrite the first message to lead with benefit, not description (“How you can cut reporting time in half”).
Add persona segmentation to vary copy and CTA by role.
Run a test comparing email vs. in-app or chat outreach for first contact.
Refine first-touch timing to align with activity triggers (e.g., signup, page view, intent signal).
Partner with sales or growth to build a swipe file of best-performing first touches by segment.

Required Datapoints to calculate the metric
- Users Who Had First Contact (e.g., ad click, event attendee)
- Users Who Took Meaningful Action Afterward
- Action Definition (e.g., signup, session, conversion)
Example to show how the metric is derived
- 3,000 webinar registrants (first contact)
- 600 logged into the product within 48h
- Formula: 600 ÷ 3,000 = 20% First Contact Engagement Rate

Formula¶

Formula

\[ \mathrm{First\ Contact\ Engagement\ Rate} = \left( \frac{\mathrm{Users\ Who\ Engaged\ After\ First\ Contact}}{\mathrm{Total\ First\ Contacts}} \right) \times 100 \]

Data Model Definition¶

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

UserEngagement

cube('UserEngagement', {
  sql: `SELECT * FROM user_engagements`,

  joins: {
    Users: {
      relationship: 'belongsTo',
      sql: `${CUBE}.user_id = ${Users}.id`
    }
  },

  measures: {
    usersWhoHadFirstContact: {
      sql: `user_id`,
      type: 'countDistinct',
      title: 'Users Who Had First Contact',
      description: 'Count of unique users who had their first contact with the brand.'
    },
    usersWhoTookMeaningfulAction: {
      sql: `user_id`,
      type: 'countDistinct',
      filters: [{
        sql: `${CUBE}.action_type = 'meaningful'`
      }],
      title: 'Users Who Took Meaningful Action',
      description: 'Count of unique users who took a meaningful action after first contact.'
    },
    firstContactEngagementRate: {
      sql: `100.0 * ${usersWhoTookMeaningfulAction} / NULLIF(${usersWhoHadFirstContact}, 0)` ,
      type: 'number',
      title: 'First Contact Engagement Rate',
      description: 'Percentage of users who engaged meaningfully after their first contact.'
    }
  },

  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true
    },
    userId: {
      sql: `user_id`,
      type: 'number',
      title: 'User ID',
      description: 'Unique identifier for the user.'
    },
    actionType: {
      sql: `action_type`,
      type: 'string',
      title: 'Action Type',
      description: 'Type of action taken by the user.'
    },
    eventTime: {
      sql: `event_time`,
      type: 'time',
      title: 'Event Time',
      description: 'Timestamp of the user event.'
    }
  }
});

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.
- Message Ambiguity: Ambiguous or generic messaging that fails to connect with the user's specific needs or interests can reduce engagement rates.
- Poor Timing: Contacting users too early or too late relative to their intent can negatively impact engagement.
- Channel Mismatch: Using an inappropriate channel for the target persona can lead to lower engagement rates.
- Overwhelming Information: Providing too much information at once can overwhelm users and deter them from engaging further.
- Lack of Follow-up: Failing to follow up after the initial contact can result in lost engagement opportunities.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Message Clarity and Relevance: Clear and relevant messaging that addresses the user's role, needs, or pain points increases the likelihood of meaningful engagement after the first contact.
- Timing Relative to User Intent: Engaging with users at the right time, when their interest is high, enhances the chances of meaningful engagement.
- Channel–Persona Match: Using the appropriate communication channel for the target persona improves the effectiveness of the first contact, leading to higher engagement rates.
- Personalization: Tailoring the first contact to the individual user's preferences and past behavior can significantly boost engagement.
- Incentives and Offers: Providing attractive incentives or offers during the first contact can encourage users to engage more meaningfully.

Involved Roles & Activities¶

Involved Roles

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

Growth
Customer Lifecycle Management
Product Marketing (PMM)
Sales Development Representative (SDR)
Sales Enablement
Activities

Common initiatives or actions associated with this KPI:

Top-of-Funnel Strategy
Onboarding
Lead Follow-Up
Email Sequences

Funnel Stage & Type¶

AAARRR Funnel Stage

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

Acquisition
Activation
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.
- Activation Rate: A high Activation Rate indicates that a larger proportion of new users are reaching a key milestone in their early experience, which creates the conditions for meaningful engagement immediately after first contact and is often a prerequisite for driving up the First Contact Engagement Rate.
- Drop-Off Rate: A high Drop-Off Rate on initial pages or flows signals friction or lack of interest, which is likely to lead to a lower First Contact Engagement Rate. Monitoring this helps proactively identify and fix issues that reduce engagement after the first interaction.
- Unique Visitors: The number of Unique Visitors provides early context on the volume and quality of new users entering the funnel. A spike in unique, relevant traffic can forecast an increase in First Contact Engagement Rate if onboarding and messaging align.
- First-Time User Conversion Rate: A high First-Time User Conversion Rate suggests that new users are persuaded to take desired actions on their very first interaction, which is closely tied to improved First Contact Engagement Rate.
- Onboarding Completion Rate: The percentage of users completing onboarding strongly predicts future First Contact Engagement, as a seamless onboarding experience is often the first opportunity to drive meaningful user actions.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Signup Completion Rate: This metric quantifies how many users finish the initial signup process, directly impacting the denominator for First Contact Engagement Rate and helping to identify upstream drop-off points.
- Trial Sign-Up Rate: A higher Trial Sign-Up Rate means more users are entering the product trial funnel, thus increasing the pool of first contacts and affecting the opportunities for engagement after the first touch.
- Number of Monthly Sign-ups: This measures the volume of new users each month, which contextualizes overall First Contact Engagement Rate by revealing if changes are due to traffic shifts or engagement effectiveness.
- Onboarding Drop-off Rate: This metric highlights how many users exit before completing onboarding, explaining a low First Contact Engagement Rate and pointing to friction points that may need attention.
- First Feature Usage Rate: Measures how many new users engage with a core product feature in their initial sessions, helping to validate and quantify the First Contact Engagement Rate by tracking actual product interaction.