How to Choose the Right Metrics for Your Usage-Based Pricing Model

When implementing usage-based pricing, perhaps no decision is more critical than selecting the right usage metric. This choice determines how well your pricing aligns with the value customers receive, how predictable their bills will be, and ultimately, how successful your pricing model becomes.

The Foundation: Value Metrics vs. Cost Metrics

Before diving into specific metrics, it's important to understand the difference between value metrics and cost metrics:

• Value metrics directly correlate with the value customers derive from your product. When usage increases, it's because customers are getting more value.
• Cost metrics track what costs you money to provide the service. While important for your margins, these don't necessarily align with customer value.

The most successful usage-based pricing models primarily use value metrics, with cost metrics playing a secondary role. Let's explore how to identify the right value metrics for your business.

Key Criteria for Selecting Usage Metrics

1. Alignment with Customer Value

Your primary usage metric should have a direct correlation with the value customers receive. Ask yourself:

• When customers get more value from your product, does this metric increase?
• Do customers intuitively understand why this metric matters to them?

Examples:

• Stripe: Charges based on payment volume - as businesses process more payments, they're making more money
• Twilio: Charges per message or minute of voice - as customers communicate more, they're driving more value
• Snowflake: Charges for compute and storage - as customers analyze more data, they're gaining more insights

2. Predictability for Customers

Customers need to be able to reasonably forecast their costs:

• Can customers estimate how much of this metric they'll use?
• Is usage of this metric within their control?
• Does usage grow gradually rather than unexpectedly spiking?

Examples:

• Good: Number of API calls for specific operations customers actively trigger
• Problematic: Background system operations that customers can't anticipate or control

3. Simplicity and Understandability

Complex metrics create friction in the sales process and confusion post-sale:

• Can you explain the metric in one sentence?
• Will a non-technical person understand what drives this metric up or down?
• Can customers easily see and track their usage of this metric?

Examples:

• Clear: Number of users, documents processed, or transactions completed
• Confusing: Composite metrics that combine multiple factors with weights

4. Growth Alignment with Your Business

The ideal metric should also scale with your own costs and business model:

• As usage of this metric increases, does your cost to serve increase proportionally?
• Does this metric grow as your customers' businesses grow?
• Does this metric encourage healthy product usage rather than abuse?

Common Usage Metrics by Industry

Different industries have gravitated toward metrics that work well for their specific value propositions:

SaaS & API Services

• API calls/requests
• Compute time
• Data processed/stored
• Active users
• Transactions processed

Data & Analytics

• Data volume stored
• Query compute time
• Number of dashboards/reports
• Data transfer
• Concurrent queries

Communication Tools

• Messages sent/received
• Minutes of voice/video
• Number of participants
• Bandwidth used
• Channels/rooms created

Developer Tools

• Build minutes
• Deployment frequency
• Seats/active developers
• Code lines analyzed
• CI/CD pipeline executions

Case Study: Finding the Right Metric

Let's look at an example of how one company navigated the process of selecting usage metrics.

Company: A B2B email marketing platform
Initial Metric Considered: Number of emails sent

While "emails sent" seemed like an obvious choice, the company discovered several issues during customer interviews:

• Customers were concerned about being penalized for testing and optimization (sending more variations)
• Email volume could spike unexpectedly based on customer actions
• The metric didn't differentiate between valuable targeted emails and mass blasts

After testing several alternatives, they landed on a hybrid approach:

• Base tier with included email volume
• Usage pricing based on "active recipients" (unique email addresses that engaged with emails)
• Small surcharge for total email volume beyond the base tier

This approach rewarded customers for sending effective, targeted emails rather than just sending high volumes, aligning pricing with the actual value of successful email marketing.

Testing Your Metric Before Full Implementation

Before fully committing to a usage metric, consider these validation approaches:

1. Shadow billing: Calculate what customers would pay under the new model while still charging them under the old one, then compare
2. Customer interviews: Present the proposed metric to customers and gauge their reaction and understanding
3. Sales team feedback: Have your sales team pitch the pricing to see how prospects respond
4. Pilot program: Roll out the new pricing to a small subset of customers
5. Competitive analysis: Examine how competitors in your space measure and price usage

When to Use Multiple Metrics

While simplicity is valuable, sometimes a single metric doesn't capture all dimensions of value. Consider using multiple metrics when:

• Your product provides distinctly different types of value
• Different customer segments value different aspects of your product
• A single metric would create unfair pricing for certain use cases

However, keep these guidelines in mind:

• Limit to 2-3 primary metrics at most
• Make sure each metric is independently understandable
• Consider using one primary metric with others as modifiers or tiers

Conclusion: The Iterative Approach

Finding the perfect usage metric is rarely a one-time decision. The most successful companies:

1. Start with the metric that most directly connects to customer value
2. Implement robust usage tracking and analytics
3. Regularly analyze how customer behavior and value realization correlate with the chosen metric
4. Refine the pricing model as they learn more about customer usage patterns

Remember that your pricing model is a product in itself, one that requires ongoing optimization based on customer feedback and business results. By focusing on metrics that truly reflect the value you deliver, you can create a pricing model that grows your revenue while ensuring customer success.