Average Revenue Per User (ARPU)¶

Definition¶

Average Revenue Per User (ARPU) is a metric that represents the average amount of revenue generated per user or customer over a specific time period, typically calculated on a monthly or yearly basis.

Description¶

Average Revenue Per User (ARPU) is a foundational monetization metric that tracks how much revenue is earned from each active user, giving a snapshot of pricing efficiency, customer value, and monetization health.

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

In subscription-based SaaS, it highlights revenue distribution across user segments
In consumer apps, it reflects freemium-to-premium dynamics and upsell impact
In telecom or media, it often informs bundling and data-driven pricing strategies

A rising ARPU trend signals successful value delivery, cross-sells, or tier adoption. A drop may suggest discounting, user churn, or feature underutilization. Segment by plan type, cohort, or acquisition channel to detect patterns and refine your pricing and packaging strategy.

Average Revenue Per User (ARPU) informs:

Strategic decisions, like launching new tiers or optimizing monetization models
Tactical actions, such as testing upsell campaigns or feature gates
Operational improvements, including LTV forecasting and retention planning
Cross-functional alignment, by giving product, growth, and finance teams a shared lens on revenue per user

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

User-to-Account Ratio: In team-based tools, more users per account = higher ARPU. Flat user count = flat growth.
Pricing Model Structure: Per-seat pricing increases ARPU as usage grows, but flat-rate models cap potential unless feature-based upsells exist.
Feature Access and Tier Distribution: Users on higher-tier plans with more advanced features contribute more to ARPU. Too many users on base plans? ARPU stays low.

Improvement Tactics & Quick Wins¶

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

If ARPU is flat, explore plan-based feature gating that nudges users toward upgrades without killing usability.
Add usage indicators in-app (e.g., “Using 90% of your available integrations — unlock more with Pro”), prompting upsell.
Run a cohort analysis comparing ARPU between new and existing users, and adjust pricing nudges accordingly.
Refine packaging so teams must upgrade to unlock collaborative workflows, boosting per-user revenue naturally.
Partner with finance and pricing to model hybrid plans (seat + feature tiers) and test revenue impact.

Required Datapoints to calculate the metric
- Total Revenue: The revenue earned over the period.
- Number of Active Users: The total number of users or customers that generated the revenue.
Example to show how the metric is derived

A streaming platform calculates ARPU for Q1:
- Total Revenue: $5,000,000
- Number of Users: 1,000,000
- ARPU = $5,000,000 / 1,000,000 = $5 per user

Formula¶

Formula

\[ \mathrm{Average\ Revenue\ Per\ User} = \frac{\mathrm{Total\ Revenue}}{\mathrm{Number\ of\ Active\ Users}} \]

Data Model Definition¶

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

RevenueUsersAverageRevenuePerUser

cube('Revenue', {
  sql: `SELECT * FROM revenue`,
  measures: {
    totalRevenue: {
      sql: `total_revenue`,
      type: 'sum',
      title: 'Total Revenue',
      description: 'The total revenue earned over the period.'
    }
  },
  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true
    },
    revenueDate: {
      sql: `revenue_date`,
      type: 'time',
      title: 'Revenue Date',
      description: 'The date when the revenue was recorded.'
    }
  }
});

cube('Users', {
  sql: `SELECT * FROM users`,
  measures: {
    activeUsers: {
      sql: `active_users`,
      type: 'count',
      title: 'Number of Active Users',
      description: 'The total number of users or customers that generated the revenue.'
    }
  },
  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true
    },
    userDate: {
      sql: `user_date`,
      type: 'time',
      title: 'User Date',
      description: 'The date when the user was active.'
    }
  }
});

cube('AverageRevenuePerUser', {
  sql: `SELECT * FROM revenue JOIN users ON revenue.user_id = users.id`,
  measures: {
    averageRevenuePerUser: {
      sql: `${Revenue.totalRevenue} / NULLIF(${Users.activeUsers}, 0)`,
      type: 'number',
      title: 'Average Revenue Per User',
      description: 'The average amount of revenue generated per user over a specific time period.'
    }
  },
  dimensions: {
    id: {
      sql: `id`,
      type: 'number',
      primaryKey: true
    },
    calculationDate: {
      sql: `calculation_date`,
      type: 'time',
      title: 'Calculation Date',
      description: 'The date when the ARPU was calculated.'
    }
  },
  joins: {
    Revenue: {
      relationship: 'belongsTo',
      sql: `${CUBE}.revenue_id = ${Revenue}.id`
    },
    Users: {
      relationship: 'belongsTo',
      sql: `${CUBE}.user_id = ${Users}.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.
- High Base Plan Usage: A large proportion of users on base plans negatively impacts ARPU as these plans generate less revenue compared to higher-tier plans.
- Customer Churn Rate: High churn rates negatively affect ARPU as losing customers reduces the overall revenue generated per user.
- Discounting Strategies: Frequent or deep discounting can negatively impact ARPU by reducing the average revenue generated per user.
- Market Saturation: In saturated markets, the potential for acquiring new users diminishes, negatively impacting ARPU growth.
- Economic Downturns: Economic downturns can lead to reduced spending by users, negatively affecting ARPU as users may downgrade or cancel services.
Positive influences

Factors that push the metric in a favorable direction, supporting growth or improvement.
- User-to-Account Ratio: A higher user-to-account ratio in team-based tools leads to increased ARPU as more users per account typically result in higher revenue generation.
- Pricing Model Structure: Per-seat pricing models positively influence ARPU as they allow revenue to scale with increased usage, unlike flat-rate models which limit revenue growth unless supplemented by feature-based upsells.
- Feature Access and Tier Distribution: Users on higher-tier plans with access to advanced features contribute more to ARPU, as these plans are priced higher and offer more value.
- Upselling and Cross-selling: Effective upselling and cross-selling strategies increase ARPU by encouraging existing users to purchase additional features or services.
- Customer Retention Rate: Higher customer retention rates positively impact ARPU as retained customers continue to generate revenue over time, often increasing their spend as they grow more engaged.

Involved Roles & Activities¶

Involved Roles

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

Finance
Growth
Product Management (PM)
Product Marketing (PMM)
Activities

Common initiatives or actions associated with this KPI:

Monetization Models
Usage-Based Pricing
ARPU Optimization

Funnel Stage & Type¶

AAARRR Funnel Stage

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

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: Increases or decreases in Monthly Active Users (MAU) often precede changes in ARPU, since more active users can drive higher aggregate revenue or signal engagement trends that impact monetization per user.
- Activation Rate: A higher Activation Rate suggests that more users are experiencing product value early, which often leads to improved retention and a higher ARPU as these users are more likely to upgrade or purchase.
- Product Qualified Leads: Growth in Product Qualified Leads (PQLs) indicates a pipeline of high-intent users likely to convert to paying users, forecasting future ARPU increases as these users generate more revenue per user.
- Customer Loyalty: Strong Customer Loyalty signals higher retention and likelihood of repeat or expanded purchases, which uplifts ARPU by increasing the average spend per user over time.
- Trial-to-Paid Conversion Rate: An increase in the rate at which trial users convert to paying plans directly influences ARPU by adding more revenue-generating users and reducing the denominator of non-paying users.
Lagging

These lagging indicators confirm, quantify, or amplify this KPI and help explain the broader business impact on this KPI after the fact.
- Expansion Revenue Growth Rate: Growth in expansion revenue (through upsell/cross-sell) directly raises ARPU, as existing users contribute more revenue without increasing the user base.
- Customer Downgrade Rate: A high downgrade rate negatively impacts ARPU by reducing per-user revenue as customers move to lower-priced plans or features.
- Revenue Churn Rate: Revenue lost from churned or downgraded customers reduces ARPU, quantifying the impact of retention challenges on average user monetization.
- Net Revenue Retention: Higher NRR reflects successful retention and expansion among current users, which is often mirrored in higher ARPU as more revenue is maintained or grown per user.
- Average Order Value: Increases in Average Order Value (AOV) generally boost ARPU, especially in transactional or e-commerce models where user-level revenue is linked to order sizes.