Percent of Accounts with Multi-Role Engagement¶

Definition¶

Percent of Accounts with Multi-Role Engagement measures the share of accounts where users from two or more distinct roles are actively using the product. It helps identify cross-functional adoption and account maturity.

Description¶

Percent of Accounts with Multi-Role Engagement is a key indicator of organizational adoption and expansion potential, reflecting whether different user types within the same account are actively using your product — signaling broad utility across workflows.

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

In design tools, it could mean designers, developers, and project managers are collaborating in one space
In sales platforms, it might reflect rep-level usage for pipeline updates and manager logins for reporting
In cross-functional SaaS, it surfaces workflow alignment across teams — a signal of deep product embedment

A rising trend signals stickiness and value across functions, increasing the odds of retention, advocacy, and account expansion. A low rate may suggest siloed usage, narrow value perception, or poor role-specific onboarding. Segment by industry, company size, or plan tier to prioritize multi-role engagement plays or discover gaps.

Percent of Accounts with Multi-Role Engagement informs:

Strategic decisions, like roadmap planning for role-based features or packaging by function
Tactical actions, such as triggering invites or role-specific prompts
Operational improvements, including customizing onboarding by role
Cross-functional alignment, by helping CS, product, and marketing teams focus on multi-user activation for long-term success

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

Ease of Inviting and Onboarding Additional Roles: If it’s hard to invite others or unclear why multiple roles are needed, accounts will stay single-threaded. Smooth, incentivized invites increase multi-role participation.
Role-Based Value Messaging: Different roles care about different things — admins want control, end users want efficiency, execs want outcomes. Generic messaging fails to engage all personas.
In-Product Prompts for Collaboration: Without triggers encouraging collaboration (e.g., “Share this dashboard with your manager”), users stay siloed. Prompting early collaboration drives broader adoption.

Improvement Tactics & Quick Wins¶

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

If accounts stay single-user, add prompts during key workflows encouraging them to invite specific roles (e.g., “Need help from an admin to finish setup?”).
Add role-specific guides or onboarding tracks to help each persona see their value quickly.
Run an A/B test on incentivizing team invites (e.g., unlock premium features or extend trial) and track the impact on multi-role engagement.
Refine lifecycle emails with persona-specific CTAs, like “Are you the team lead? Here’s how to track usage across your org.”
Partner with CS and sales to identify expansion-ready accounts with limited role diversity, and prompt targeted outreach to bring in missing personas.

Required Datapoints to calculate the metric
- Account User Roster: User IDs and roles per account.
- Engagement Logs: Activity tracking per user.
- Role Classification System: Standardized tags (admin, contributor, analyst, etc).
Example to show how the metric is derived

A SaaS platform has 400 active accounts:
- Accounts with Multi-Role Engagement: 160
- Formula: (160 ÷ 400) × 100
- Result: 40%

Formula¶

Formula

\[ \mathrm{Percent\ of\ Accounts\ with\ Multi\text{-}Role\ Engagement} = \left( \frac{\mathrm{Accounts\ with\ \geq 2\ Roles\ Engaged}}{\mathrm{Total\ Accounts}} \right) \times 100 \]

Data Model Definition¶

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

AccountUserRosterEngagementLogsRoleClassificationSystem

cube('AccountUserRoster', {
  sql: `SELECT * FROM account_user_roster`,

  joins: {
    EngagementLogs: {
      relationship: 'hasMany',
      sql: `${CUBE}.user_id = ${EngagementLogs}.user_id`
    },
    RoleClassificationSystem: {
      relationship: 'belongsTo',
      sql: `${CUBE}.role_id = ${RoleClassificationSystem}.id`
    }
  },

  measures: {
    accountCount: {
      sql: 'account_id',
      type: 'countDistinct',
      title: 'Total Accounts'
    },
    multiRoleAccountCount: {
      sql: `account_id`,
      type: 'countDistinct',
      filters: [{
        sql: `${CUBE}.role_id IN (
          SELECT role_id FROM account_user_roster
          GROUP BY account_id, role_id
          HAVING COUNT(DISTINCT role_id) > 1
        )`
      }],
      title: 'Accounts with Multi-Role Engagement'
    },
    percentMultiRoleEngagement: {
      sql: `100.0 * ${multiRoleAccountCount} / NULLIF(${accountCount}, 0)`,
      type: 'number',
      title: 'Percent of Accounts with Multi-Role Engagement'
    }
  },

  dimensions: {
    accountId: {
      sql: 'account_id',
      type: 'string',
      primaryKey: true,
      title: 'Account ID'
    },
    userId: {
      sql: 'user_id',
      type: 'string',
      title: 'User ID'
    },
    roleId: {
      sql: 'role_id',
      type: 'string',
      title: 'Role ID'
    }
  }
});

cube('EngagementLogs', {
  sql: `SELECT * FROM engagement_logs`,

  dimensions: {
    userId: {
      sql: 'user_id',
      type: 'string',
      title: 'User ID'
    },
    activityDate: {
      sql: 'activity_date',
      type: 'time',
      title: 'Activity Date'
    }
  }
});

cube('RoleClassificationSystem', {
  sql: `SELECT * FROM role_classification_system`,

  dimensions: {
    id: {
      sql: 'id',
      type: 'string',
      primaryKey: true,
      title: 'Role ID'
    },
    roleName: {
      sql: 'role_name',
      type: 'string',
      title: 'Role Name'
    }
  }
});

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.
- Ease of Inviting and Onboarding Additional Roles: If the process of inviting and onboarding additional roles is difficult or unclear, it discourages users from involving others, leading to lower multi-role engagement.
- Role-Based Value Messaging: Generic messaging that does not cater to the specific needs of different roles can result in disengagement, reducing the likelihood of multi-role participation.
- In-Product Prompts for Collaboration: Lack of in-product prompts that encourage collaboration can lead to users working in silos, negatively impacting multi-role engagement.
- User Interface Complexity: A complex user interface can deter users from exploring features that facilitate multi-role engagement, thus reducing its occurrence.
- Lack of Training Resources: Insufficient training resources can prevent users from understanding the benefits of multi-role engagement, leading to lower adoption rates.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- Ease of Inviting and Onboarding Additional Roles: A smooth and incentivized process for inviting and onboarding additional roles encourages users to involve others, increasing multi-role engagement.
- Role-Based Value Messaging: Tailored messaging that addresses the specific needs and interests of different roles can enhance engagement and drive multi-role participation.
- In-Product Prompts for Collaboration: Effective in-product prompts that encourage users to collaborate with others can lead to increased multi-role engagement.
- Cross-Functional Training Programs: Training programs that emphasize the benefits of cross-functional collaboration can promote multi-role engagement.
- Integration with Other Tools: Seamless integration with other tools used by different roles can facilitate collaboration and increase multi-role engagement.

Involved Roles & Activities¶

Involved Roles

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

Customer Success
Growth
Product Management (PM)
Product Marketing (PMM)
Sales Manager
Activities

Common initiatives or actions associated with this KPI:

Expansion Campaigns
Sales Enablement
Product-Led Growth
Persona-Based Onboarding

Funnel Stage & Type¶

AAARRR Funnel Stage

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

Activation
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.
- Product Qualified Accounts: Product Qualified Accounts (PQAs) indicate accounts where multiple users have demonstrated significant engagement, often across different roles. A rising PQA rate typically precedes and forecasts an increase in the Percent of Accounts with Multi-Role Engagement as more accounts reach the engagement threshold.
- Activation Rate: High Activation Rate signals that accounts are successfully reaching key engagement milestones, which often leads to broader adoption by multiple roles within an account. This metric can act as an early warning for future multi-role engagement growth.
- Customer Loyalty: Elevated Customer Loyalty indicates strong satisfaction and advocacy, which frequently encourages adoption by additional roles within an account, thus increasing the percent of accounts with multi-role engagement.
- Monthly Active Users: Growth in Monthly Active Users, especially when distributed across multiple roles, often forecasts a subsequent rise in cross-functional adoption at the account level.
- Breadth of Use: While technically lagging, Breadth of Use is a direct precursor to multi-role engagement—accounts using more features are often involving more roles, so a rise here signals imminent growth in the target KPI.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Percent of Accounts Completing Key Activation Milestones: This metric quantifies the proportion of accounts reaching important activation checkpoints, which often aligns with or follows broader cross-role engagement, helping to confirm and explain increases in the percent of accounts with multi-role engagement.
- Customer Engagement Score: A higher Customer Engagement Score typically results from, and further validates, high multi-role engagement within accounts, as more users and roles actively interact with the product.
- Expansion Readiness Index: Accounts with multi-role engagement are more likely to score highly on expansion readiness, so this index confirms and amplifies the business impact of increased cross-functional adoption.
- Expansion Revenue Growth Rate: Growth in expansion revenue is often a downstream effect of robust multi-role engagement, as cross-functional adoption increases upsell/cross-sell opportunities and revenue per account.
- Net Revenue Retention: Improvements in net revenue retention are frequently observed after increases in multi-role engagement, as such engagement drives higher retention, expansion, and reduced churn within accounts.