.png)
Frameworks, core principles and top case studies for SaaS pricing, learnt and refined over 28+ years of SaaS-monetization experience.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form.
You can spend weeks arguing about whether to charge $99 or $149 per month.
But the question that will actually make or break your SaaS business isn’t “how much?”—it’s “what are we charging for in the first place?”
Your SaaS pricing metrics (the units you bill on) control how revenue scales, how customers perceive fairness, and how easy it is to expand accounts. Get them wrong, and even a “perfect” price point will underperform.
Quick Answer
SaaS pricing metrics fall into three categories:
Value Metrics (what you charge for):
The unit tied to value—per-seat, per-usage, per-transaction, per-outcome, etc.Pricing Models (how you charge):
The structure and timing of payment—subscription, usage-based, hybrid, freemium, etc.Performance Metrics (measuring pricing effectiveness):
KPIs like ARPU, expansion rate, and price realization that tell you if pricing is working.
The right metric:
- Aligns with customer value perception
- Scales with customer usage and success
- Supports your revenue model and cash flow
Most successful B2B SaaS companies now use hybrid approaches—a base subscription plus usage-based or outcome-based components.
Introduction: Why Your Pricing Metric Choice Matters More Than Your Price Point
Imagine two startups:
- Both sell a workflow automation tool.
- Both target mid-market companies.
- Both aim for about $1,000/month per customer.
Startup A prices at $1,000/month per company—unlimited users, unlimited automations.
Startup B prices at $25/month per active user.
A prospect with 80 employees trials both. They love the tool.
- For Startup A, the decision is easy: $1,000/month for company-wide automation.
- For Startup B, the CFO does the math: 80 users × $25 = $2,000/month. Adoption gets limited to 10 “power users” to control cost. The team never fully onboards. The deal stalls. They churn during pilot.
Startup B had a reasonable price point per user—but the wrong pricing metric for how value was perceived (company-wide automation, not individual users).
The pricing metric decision is strategic, not tactical
Your pricing metric is the core of your monetization strategy. It determines:
- Who you win and who you lose
- How big your average customer can become
- How naturally expansion and upsells happen
- How your revenue grows relative to your customers’ success
Price points ($99 vs $149) are tactics.
Choosing what you charge for is strategy.
How the wrong metric quietly caps your growth
A misaligned metric will:
- Create friction at every step—trial, rollout, expansion, renewal
- Encourage customers to limit usage (fewer users, fewer events, fewer messages)
- Trigger “this feels unfair” reactions in procurement and finance
- Make revenue growth decouple from customer success
A few patterns:
- Per-seat pricing for a product whose value scales with volume processed, not team size → expansion stalls at a handful of power users.
- Per-GB storage pricing for a tool whose real value comes from insights and decision support → you undercharge power users, and customers think of you as “cheap storage.”
- Pure usage-based pricing in a category where CFOs expect predictable subscriptions → deals lost on “budget uncertainty,” even if your total cost is competitive.
How to use this guide
This encyclopedia covers 25+ SaaS pricing metrics across three categories:
- Value Metrics – what you charge for
- Pricing Models – how you charge
- Performance Metrics – how you measure success
For each metric you’ll get:
- Clear definition
- Formula
- Pros and cons
- Real-world examples
- When to use it (and when not to)
Use it in three ways:
- Designing a new product’s pricing: Explore value metrics, then choose a model.
- Re-evaluating existing pricing: Diagnose issues using performance metrics.
- Educating your team and board: Use the definitions and examples as a shared language.
The Three Categories of SaaS Pricing Metrics
Before diving into individual metrics, it helps to understand the big picture:
- Value Metrics – What You Charge For
- Pricing Models – How You Charge
- Performance Metrics – How You Measure Success
Think of it as a simple stack:
- Top (Value Metric): What shows up on the invoice (users, API calls, GB, transactions, revenue share).
- Middle (Pricing Model): The billing structure (monthly, annual, usage, hybrid).
- Bottom (Performance Metrics): The KPIs you track to see if the top two are working.
1. Value Metrics (What You Charge For)
Definition:
The unit of consumption that determines how much a customer pays.
Examples:
- Per user / per seat
- Per active user
- Per API call
- Per transaction
- Per GB stored
- Per document, message, or lead
- Percentage of GMV or revenue
Why it matters:
- Directly shapes customer perception of fairness
- Controls how revenue scales with adoption and usage
- Influences how customers deploy your product internally
Subcategories:
- User-based metrics: per-seat, per-active-user, per-role
- Usage-based metrics: per API call, per GB, per compute hour, per document, per message
- Transaction-based metrics: per payment, per order, per event
- Outcome-based metrics: percentage of revenue, per hire, per qualified lead
2. Pricing Models (How You Charge)
Definition:
The structure and timing of when and how customers pay you.
Examples:
- Monthly subscription
- Annual contract
- Pay-as-you-go usage
- Credits/prepaid
- Freemium, free trial, reverse trial
- Hybrid base + usage
Why it matters:
- Affects cash flow and revenue predictability
- Shapes customer commitment and churn
- Aligns (or misaligns) with how customers buy similar tools
Subcategories:
- Subscription models (monthly/annual ARR pricing model)
- Usage/consumption models
- Hybrid models (base + usage)
- Alternative models (freemium, reverse trial, credits)
3. Performance Metrics (Measuring Pricing Success)
Definition:
Pricing KPIs for SaaS that show whether your pricing and packaging are performing.
Examples:
- ARPU / ARPA
- Net Dollar Retention (NDR)
- Price realization
- Average discount rate
- Packaging penetration
- Pricing objection rate
- Value-to-price ratio
Why it matters:
- Turns pricing from “opinion-based” to data-driven
- Highlights whether your pricing metrics and models support growth
- Provides early signals for churn, underpricing, and expansion potential
Subcategories:
- Revenue metrics: ARPU, NDR, expansion revenue
- Customer metrics: pricing objection rate, discount rate
- Efficiency metrics: price realization, packaging penetration
Category 1: Value Metrics – What You Charge For
This is the most important decision in SaaS pricing:
What unit of value do you charge on?
Your value metric should:
- Track closely with customer value
- Increase as customers succeed and grow
- Be easy to understand and measure
Below, you’ll find major value metrics grouped by type.
User-Based Metrics
Charging based on how many people use the product.
Best when:
- Value scales with team adoption
- Each incremental user gains similar value
- Customers are used to per-seat pricing in your category
1. Per Seat / Per User
Definition
A fixed price per user account, regardless of usage level.
Formula Price = Number of Users × Price Per Seat
Example
Slack charges a per-user price for its Pro and Business+ plans (e.g., ~$7–$15/user/month depending on plan and contract).
Pros
- Very simple for customers to understand
- Predictable revenue and easy forecasting
- Aligns with team growth (more employees → more users)
- Category standard in many B2B tools (CRM, collaboration)
Cons
- Discourages broad adoption (teams avoid adding seats)
- Doesn’t differentiate between power users and light users
- Encourages password sharing or limited rollouts
- May not capture value in high-usage scenarios
Best for
- Collaboration tools
- Productivity software
- Team communication platforms
- CRM and sales tools
Real examples
- Salesforce – CRM per-user licensing
- Zoom – per meeting host/user
- Asana – task management per user
2. Per Active User
Definition
You charge only for users who actually use the product in a defined period (e.g., last 30 days).
Formula Price = Number of Active Users × Price Per Active User
Example
Some analytics and dashboard products bill only for users who logged in or viewed a dashboard in the last 30 days.
Pros
- Perceived as “fair” (pay only for actual usage)
- Reduces friction in adding users
- Aligns more closely with real engagement and value delivered
Cons
- Less predictable revenue (active user counts fluctuate)
- More complex to track and explain in contracts
- Can lead to “gaming” behavior (teams limiting logins to control spend)
Best for
- Analytics platforms
- Reporting and dashboard tools
- Seasonal-use software (e.g., planning or budgeting tools)
Key consideration
- Define “active” clearly:
- Is it a login?
- A specific action taken?
- A usage threshold over time?
3. Per Role / Per User Type
Definition
Different pricing for distinct user types (e.g., admin, contributor, viewer).
Formula Price = (Admins × Admin Price) + (Contributors × Contributor Price) + (Viewers × Viewer Price)
Example
Figma charges for “editors” while offering free or cheaper “viewers.”
Pros
- Captures more value from power users
- Lets you keep low-value roles free or inexpensive to encourage adoption
- Better matches cost/value differences between user types
Cons
- More complex to explain, sell, and bill
- Requires clear and enforced role definitions
- Can create friction when users want additional capabilities (upgrade conversations)
Best for
- Design and creative tools
- Project management and collaboration tools
- Content platforms where not everyone needs full editing rights
Real examples
- Figma – editors vs viewers
- Notion – members vs guests
- Airtable – creators vs commenters
Usage-Based Metrics
Charging based on how much of a resource the customer consumes.
Best when:
- Your costs scale with usage
- The value to the customer scales with volume or throughput
- Customers are comfortable with consumption-based pricing metrics
4. Per API Call
Definition
You charge a fee per API request made to your service.
Formula Price = Number of API Calls × Price Per Call
(often with tiered pricing: volume discounts, free tier, etc.)
Example
Twilio charges per API-based SMS or voice minute. Other APIs charge per 1,000 requests, etc.
Pros
- Strong alignment between usage and infrastructure cost
- Revenue scales naturally with customer’s product usage
- Fair to low-usage customers (they pay less)
Cons
- Harder for customers to predict their bill
- Requires robust metering and billing infrastructure
- Can discourage heavy usage if unit prices are high
Best for
- API platforms
- Payment gateways
- Communications APIs (SMS, voice, email)
- Developer tools consumed via API
Real examples
- Twilio – per SMS/call/voice minute
- SendGrid – per email sent (tiered)
- Google Maps API – per request
5. Per Gigabyte (Storage)
Definition
You charge based on how much data customers store (or sometimes transfer).
Formula Price = GB Stored × Price Per GB
Example
Cloud storage providers like AWS S3 charge per GB stored per month, often with tiers.
Pros
- Direct cost correlation (storage, backup, bandwidth)
- Easy to measure and explain
- Scales with data growth over time
Cons
- Storage is relatively cheap—may not reflect actual business value
- Ignores the value of access, collaboration, insights
- Can encourage customers to delete data they might otherwise keep
Best for
- Cloud storage and backup
- Data warehouses and data lakes
- Archival and compliance storage
Real examples
- AWS S3
- Backblaze
- Box and Dropbox (often per-user with underlying storage tiers)
6. Per Compute Hour / Per Compute Unit
Definition
Price is based on the compute resources consumed (CPU/GPU time, instance hours, vCPU, etc.).
Formula Price = Compute Hours × Instance Rate
Example
AWS EC2 charges by instance type (e.g., t3.medium, c6g.large) per hour or per second of use.
Pros
- Aligns directly with infrastructure cost
- Fair to customers with varied, bursty workloads
- Encourages efficient usage and optimization
Cons
- Complex for non-technical buyers to understand
- Highly unpredictable bills if workloads spike
- Requires strong monitoring and governance
Best for
- Cloud infrastructure and hosting
- Data processing and ETL
- Machine learning training and inference
Real examples
- AWS EC2 / Fargate / Lambda (per compute unit/time)
- Google Cloud Compute Engine
- Azure Virtual Machines
7. Per Transaction / Per Event
Definition
You charge per discrete business transaction processed (payment, order, booking, etc.).
Formula Price = Number of Transactions × Price Per Transaction
(often combined with a % of the transaction amount)
Example
Stripe charges a percentage of transaction value (e.g., 2.9%) + a fixed fee per successful charge (e.g., $0.30).
Pros
- Direct alignment with the customer’s revenue and success
- Low barrier to entry (no transactions = no fees)
- Very easy for merchants and operators to model
Cons
- Vendor revenue can be volatile (depends on customer sales)
- Perceived as a “tax” on revenue at higher scales
- Requires access to transaction data (trust and integration)
Best for
- Payment processing
- E-commerce platforms
- Marketplaces and booking platforms
Real examples
- Stripe – % + fixed fee per transaction
- Shopify – transaction fees on certain plans
- Square – per card transaction
8. Per Document / Per Object
Definition
You charge per discrete item processed or managed (document, file, issue, workflow object).
Formula Price = Number of Documents (or Objects) × Price Per Document
Example
DocuSign charges based on envelopes/documents sent for signature.
Pros
- Clear and tangible value unit
- Easy for customers to budget (“we sign ~200 contracts/month”)
- Aligns with specific workflows
Cons
- May encourage batching or limiting usage
- Doesn’t capture complexity (a 50-page MSA vs 2-page NDA)
- Can limit adoption if customers are cost-sensitive per object
Best for
- E-signature and contract management
- Document automation
- Workflow automation where “units” are clear artifacts
Real examples
- DocuSign – per envelope/document
- PandaDoc – per document/seat hybrid
- Adobe Sign – document/transaction counts
9. Per Message / Per Communication Unit
Definition
You charge per message, email, SMS, notification, or call minute.
Formula Price = Messages (or Units) Sent × Price Per Message
Example
SendGrid charges per email sent, with volume-based tiers. Twilio charges per SMS or voice minute.
Pros
- Strong correlation with provider costs
- Revenue scales with customer growth and activity
- Simple to understand (“we send 500k emails/month”)
Cons
- Can discourage communication volume (especially in marketing)
- Harder to predict for campaigns or seasonality
- Does not reflect message effectiveness or ROI
Best for
- Email service providers (ESPs)
- SMS and push notification services
- Contact center and communications platforms
Real examples
- Twilio – per SMS/voice/minute
- SendGrid – per email
- Mailchimp – per contact plus send/feature tiers
Outcome-Based Metrics
Charging based on results achieved rather than pure usage.
Best when:
- You can measure outcomes reliably
- Your product directly influences revenue, hires, leads, or cost savings
- Customers want risk-sharing and strong ROI alignment
10. Percentage of Revenue / GMV
Definition
You charge a percentage of the customer’s revenue or gross merchandise volume processed through your platform.
Formula Price = Customer Revenue (or GMV) × Percentage Rate
Example
Payments and marketplaces often charge a percentage fee on each transaction.
Pros
- Perfect alignment: you earn more only if the customer earns more
- Low initial barrier for small or early-stage customers
- Automatically scales with customer size and success
Cons
- Perceived as expensive at higher scales
- Requires trust and visibility into revenue
- Vendor revenue subject to macro factors (customer’s sales cycles)
Best for
- Payment processors
- E-commerce and marketplace platforms
- Revenue-driving SaaS (booking engines, monetization tools)
Real examples
- Stripe – % of transaction value
- Shopify – % of GMV on top of subscription (for some plans)
- Faire, Airbnb, other marketplaces – take rate on GMV
11. Per Lead / Per Conversion
Definition
You charge for each qualified lead or conversion you generate for the customer.
Formula Price = Number of Qualified Leads (or Conversions) × Price Per Lead
Example
Some ad networks and lead-gen platforms use cost-per-lead (CPL) or cost-per-acquisition (CPA) models.
Pros
- Easy to tie to marketing ROI
- Customers pay only for results, not impressions or clicks
- Clear unit that marketing and sales teams understand
Cons
- Requires strong definitions of “qualified” and tracking
- Shifts risk heavily to vendor (if leads don’t convert)
- May ignore full platform value (brand, insights, workflows)
Best for
- Lead-generation platforms
- Advertising networks and performance marketing tools
- Marketplaces connecting buyers and sellers
Considerations
- You need tight SLAs on lead quality and clear attribution.
12. Per Job Filled / Per Hire
Definition
You charge for each successful hire, placement, or other high-value outcome.
Formula Price = Number of Hires (or Successes) × Price Per Hire
Example
Recruiting agencies typically charge 20–30% of first-year salary per successful hire. Some SaaS recruiting platforms mimic this.
Pros
- Very low risk to customer (no hire, no payment)
- Perfect alignment with outcome that matters most
- Can command premium pricing per successful outcome
Cons
- Revenue recognition is delayed and lumpy
- Attribution challenges (“did your platform cause this hire?”)
- Harder to forecast revenue reliably
Best for
- Recruiting platforms and applicant tracking systems (ATS)
- Talent marketplaces
- High-value professional services/SaaS hybrids
Real examples
- Traditional recruiting agencies
- Some job boards and talent marketplaces with success-based pricing
Hybrid / Compound Value Metrics
Many modern SaaS companies combine multiple value metrics to balance fairness, predictability, and upside.
13. Base + Usage
Definition
A fixed platform or subscription fee plus variable usage charges.
Formula Price = Base Subscription Fee + (Usage × Usage Rate)
Example
Snowflake charges separately for compute (usage-based) and storage, often with committed spend.
Pros
- Predictable base revenue for the vendor
- Captures upside from heavy usage
- Fair for light vs heavy customers; they don’t all pay the same
Cons
- More complex to explain and sell
- Requires two pricing decisions (base + usage)
- Can feel like “double charging” if not framed well
Best for
- Data platforms and warehouses
- Infrastructure and communications platforms
- Products with distinct “platform” value plus variable consumption
Real examples
- Snowflake – storage + compute (credits)
- MongoDB Atlas – cluster (base) + usage
- Twilio – minimum commitments + usage
14. User + Feature Tiers
Definition
Per-user pricing, with different feature bundles at different tiers (Starter, Pro, Enterprise).
Formula Price = (Number of Users × Tier Price)
where Tier Price increases with more features, limits, or SLAs.
Example
HubSpot charges per user (in some products) and per contact tier, with Starter/Pro/Enterprise feature sets.
Pros
- Maximizes revenue across segments (SMB vs mid-market vs enterprise)
- Clear upsell path as customers need more functionality
- Easy to align with sales and marketing messaging
Cons
- Requires careful feature gating (what goes in what tier)
- Can confuse customers if tiers are too numerous or subtle
- Risk of “middle tier trap” where one tier dominates and others underperform
Best for
- CRM, marketing automation, and sales tools
- Productivity suites
- Customer support and success platforms
Real examples
- HubSpot – multiple hubs with tiered features + contacts
- Salesforce – Essentials/Pro/Enterprise tiers
- Zendesk – Support Suite tiers plus add-ons
15. Platform Fee + Transaction
Definition
Customers pay a base platform fee plus transaction-based fees on activity.
Formula Price = Platform Subscription Fee + (Number of Transactions × Transaction Fee)
Example
Some e-commerce platforms charge $29/month plus a percentage of GMV or per-transaction fee.
Pros
- Secures predictable base ARR
- Keeps incentives aligned with customer revenue (via transaction fees)
- Works well with freemium or lower-cost plans
Cons
- Two-part pricing can confuse buyers
- High take rates may discourage volume over time
- Tricky to optimize both platform price and transaction fee simultaneously
Best for
- E-commerce and retail platforms
- Payment systems
- Marketplaces and booking tools
Real examples
- Shopify – subscription + processing fees (via Shopify Payments)
- BigCommerce – subscription + revenue limits and transaction terms
Get Expert Help Choosing Your Pricing Metric
Choosing the right SaaS pricing metrics is strategic, not tactical.
Monetizely has helped companies from $1M–$300M ARR design value metrics that drive expansion, reduce churn, and align with customer value.
- Validate your metric with customer research
- Benchmark against competitors
- Model revenue impact and risk
Get Expert Help Choosing Your Pricing Metric → Book Pricing Strategy Session
Category 2: Pricing Models – How You Charge
Once you’ve chosen what to charge for (value metric), the next question is how to structure payment.
Below are the major SaaS pricing models and where they fit.
1. Monthly Subscription
Definition
A fixed recurring payment each month for access to the product (often per user or per account).
Key characteristics
- Predictable monthly spend for customers
- Lower commitment than annual; easier to get started
- Higher churn risk compared to annual
- Works well with product-led growth (PLG) and self-serve onboarding
When to use
- Self-serve or low-ACV products
- SMB customers
- New product launches where you want adoption and low friction
Revenue impact
- Typically lower LTV vs annual
- Easier sign-up, but more susceptible to monthly cancellations
Example companies
- Most B2B SaaS starter plans (e.g., Slack, Notion, Trello monthly options)
2. Annual Subscription
Definition
Fixed payment for a full year, paid upfront or in scheduled installments.
Key characteristics
- Strong cash flow and clearer ARR visibility
- Lower effective churn (harder for customers to “bounce” monthly)
- Higher commitment; requires more robust sales and onboarding
- Often includes discount vs monthly pricing
When to use
- Enterprise and mid-market sales
- Mature products with proven value
- When cash flow and predictability are priorities
Revenue impact
- Typically 10–20% discount vs monthly
- Higher LTV and better net retention
Example companies
- Most enterprise SaaS: Salesforce, Workday, ServiceNow
3. Pure Usage-Based / Consumption Pricing
Definition
Customers pay only for what they use—no or minimal base fee.
Key characteristics
- Very low barrier to entry
- Perfect cost alignment for customers
- Revenue can be volatile; difficult forecasts
- Requires sophisticated metering and billing
When to use
- Infrastructure and API products
- Developer tools and platforms
- Commodity services where usage maps to cost (bandwidth, compute)
Revenue impact
- Can scale dramatically with heavy users
- More sensitive to macro changes and changes in customer volumes
Example companies
- AWS (on-demand services)
- Snowflake (compute, when fully usage-based)
- Twilio (per message/call)
4. Freemium
Definition
A permanent free tier with limited features or capacity; customers pay for upgraded tiers.
Key characteristics
- Maximizes adoption and top-of-funnel growth
- Conversion from free to paid can be challenging
- Requires a clear line between free and paid value
- Very efficient CAC when working (free users refer others)
When to use
- PLG-friendly products
- Viral or collaborative tools (network effects)
- Use cases with strong “aha moment” and clear upgrade triggers
Conversion benchmarks
- Typical free → paid conversion: 2–5%
(varies widely by product, segment, and tiering)
Example companies
- Slack, Dropbox, Notion, Figma, Airtable
5. Free Trial → Paid
Definition
Time-limited full access (or nearly full) before requiring payment.
Key characteristics
- Lower adoption volume than freemium
- Higher trial-to-paid conversion (you’ve pre-qualified interest)
- Creates natural urgency (trial expiration)
- Requires excellent onboarding and in-trial value demonstration
When to use
- More complex products that need hands-on evaluation
- Higher ACV products where you want committed evaluators
- Clear value within 14–30 days of use
Conversion benchmarks
- Typical trial → paid conversion: 15–25%
Example companies
- Many B2B SaaS apps, especially in analytics, security, and operations
6. Reverse Trial
Definition
Customers start on a premium experience (often with payment details) and can downgrade or get a refund if not satisfied.
Key characteristics
- Higher initial revenue capture per user
- Strong signal of user commitment
- Lower trial volume but more serious buyers
- Demands excellent onboarding and success motion
When to use
- High-touch, high-ROI products
- When you have strong CS/sales support
- When your product’s value is clear within a short time frame
Example companies
- Some enterprise and premium SaaS
- Coaching/consulting SaaS hybrids
7. Credits / Prepaid
Definition
Customers buy a bucket of credits upfront, then consume them as they use the service.
Key characteristics
- Great for vendor cash flow
- Low usage risk for customers (they know max exposure)
- Credits may expire, adding complexity to revenue recognition
- Requires clear credit definition and usage mapping
When to use
- Products with lumpy or seasonal usage
- Agencies or resellers managing usage across clients
- Usage-based pricing where customers want budget caps
Example companies
- SMS/email providers offering prepaid bundles
- Ad platforms selling prepaid campaigns
- Some infrastructure services with credit packs
Category 3: Performance Metrics – Measuring Pricing Success
Once you’ve chosen your SaaS pricing metrics and models, you need KPIs to know if they’re working.
Here are the core pricing KPIs for SaaS, plus how to interpret them.
1. ARPU (Average Revenue Per User / Account)
Definition
Average recurring revenue per customer in a given period.
Formula ARPU (Monthly) = Total MRR / Number of Customers ARPU (Annual) = Total ARR / Number of Customers
Why it matters
- Indicates whether you’re moving upmarket or staying low-ACV
- Helps track the impact of upsells, cross-sells, and expansion
- Critical input to LTV and payback models
Good benchmark (very rough, by segment)
- SMB-focused SaaS: $50–$500/month per account
- Mid-market: $500–$5K/month
- Enterprise: $5K+/month
What to watch
- ARPU should generally increase over time due to expansion
- If ARPU is flat or declining, pricing or packaging might be limiting growth
2. Price Realization Rate
Definition
The percentage of your list price you actually capture after discounts.
Formula Price Realization = Average Selling Price / List Price
Why it matters
- Shows effectiveness of discounting policies and sales discipline
- Helps detect whether your list pricing is realistic
- Impacts gross margin and perceived value
Good benchmark
- Healthy B2B SaaS: 80–90% price realization
What to watch
- Declining realization = increasing discount pressure
Possible drivers: - Overly aggressive list pricing
- Stronger competition
- Sales team using discounting to close value-gap issues
3. Expansion Revenue Rate / Net Dollar Retention (NDR)
Definition
Measures revenue growth from existing customers after accounting for churn and contraction.
Formula NDR = (Starting ARR + Expansion – Churn – Contraction) / Starting ARR
Why it matters
- Captures how well your pricing metric and packaging support land-and-expand
- High NDR means you can grow without heavy reliance on new logo acquisition
- Power law: public SaaS leaders often have NDR of 120%+
Good benchmark
- 100%: neutral (no net expansion)
- 110–120%: strong for B2B SaaS
- 130%+: exceptional (often usage-based infrastructure and dev tools)
What to watch
- Low NDR suggests:
- Wrong value metric (no natural expansion)
- Weak upsell/cross-sell paths
- Misaligned pricing tiers
4. Packaging Penetration
Definition
Distribution of customers across your pricing tiers.
Formula % of Customers in Tier X = Customers in Tier X / Total Customers
Why it matters
- Shows if your tiers are well-balanced and differentiated
- Indicates whether you’re leaving money on the table or overcomplicating
Good patterns
- For a 3-tier structure, a common healthy distribution is:
- 40–50% in middle tier
- 30–40% in entry tier
- 10–20% in top tier
- Or a “barbell”:
- 50% in lowest + 30% in highest (if you focus on two core segments)
What to watch
- If almost everyone is in the cheapest tier:
- Poor feature differentiation
- Underpriced higher tiers
- If almost everyone is in the highest tier:
- Base plans likely underpriced
- You may need an additional premium tier
5. Average Discount Rate
Definition
Average discount off your published list price across deals.
Formula Average Discount = (List Price – Actual Price) / List Price
Why it matters
- Reflects pricing power and sales behavior
- High discounts can erode brand, margins, and future pricing moves
Good benchmark (rough guidelines)
- SMB deals: 10–20%
- Enterprise deals: 15–30% (depending on deal size and competition)
What to watch
- Rising discount rates indicate:
- Stronger competition
- Weak differentiation
- Over-ambitious list pricing
6. Pricing Objection Rate
Definition
Share of lost deals where price is cited as the primary reason.
Formula Pricing Objection Rate = Lost Deals with Price as Primary Reason / Total Lost Deals
Why it matters
- Distinguishes between price problems and value/fit problems
- Informs whether you should lower prices, change packaging, or just improve value storytelling
Good benchmark
- Under 30% of lost deals citing price as primary objection
What to watch
- High pricing objection rate + low win rate:
- Your pricing may be misaligned with the market
- Low pricing objection rate + low win rate:
- Likely a positioning, feature, or fit issue—not price
7. Value-to-Price Ratio
Definition
Customer’s perceived value relative to the price they pay. Typically measured through surveys (e.g., “Estimate the dollar value you receive vs what you pay”).
Formula Value-to-Price Ratio = Perceived Value ($) / Price Paid ($)
Why it matters
- Indicates pricing power and headroom for increases
- Low ratios signal churn risk; high ratios suggest you’re underpricing
Good benchmark
- Healthy SaaS: 3:1 to 10:1 perceived value to price
What to watch
- <3:1: customers may question renewal; high churn risk
- >10:1: you are likely leaving money on the table and could increase prices gradually
How to Choose the Right Pricing Metric for Your Product
With so many SaaS pricing metrics explained, how do you pick the right one (or two) for your product?
Use this six-step decision framework.
Step 1: Identify Your Value Drivers
Ask:
- What actually makes our product more valuable to customers?
- Does value scale with team size, usage volume, revenue, or outcomes?
- What best correlates with customer success?
Action
- List 3–5 potential value metrics that correlate most closely with customer value.
Example
For a project management tool:
- Team size (users)
- Number of projects
- Number of tasks
- Storage used (attachments)
- Integrations used
Step 2: Test Against the Customer’s Mental Model
Ask:
- How do customers naturally think about your product’s value?
- What metrics do they already track and budget on?
- What would feel fair and intuitive to them?
Action
- Interview 10–15 target customers. Ask:
- “If you were paying for this today, what would feel fair to pay per…?”
- Explore user-based vs usage-based vs outcome-based mental models.
Example
For an email marketing tool:
- Customers typically think in “contacts” not “emails sent.”
- Charging per contact often feels more natural than per send—especially for recurring newsletters.
Step 3: Evaluate Scalability
Ask:
- Does this metric grow as customers grow and succeed?
- Can it support 10x–100x customer growth?
- Does it have a built-in expansion motion?
Action
- Model revenue for a typical customer over 3–5 years using each candidate metric.
Example
- Per-seat for a dev tool: customer headcount might grow 2–3x.
- Per-API-call for the same dev tool: usage could grow 10–50x as their user base scales.
Usage-based metrics often create more expansion potential than pure per-seat.
Step 4: Assess Predictability
Ask:
- Can customers reasonably predict their bill?
- Can you forecast revenue well enough to plan?
- How volatile will revenue be across your base?
Action
- Analyze historical or beta usage data.
- Calculate variability (e.g., coefficient of variation) for each candidate metric.
Example
- Pure usage-based billing for data processing can be highly spiky.
- A base + usage model balances predictability (base) with growth (usage).
Step 5: Check Competitive Alignment
Ask:
- What pricing metrics do the top 3–5 competitors use?
- Is there a category standard (e.g., CRM = per user)?
- Should you differentiate or conform—and why?
Action
- Audit competitor pricing pages and talk to customers who evaluated them.
- Decide consciously whether to match the standard or break it.
Example
In CRM:
- Per-user pricing is an entrenched standard.
- Switching to “per email sent” or “per API call” would require a strong narrative and heavy sales enablement.
Default to standards unless you have a compelling differentiation story.
Step 6: Validate Implementation Feasibility
Ask:
- Can we reliably measure this metric?
- What’s the cost and complexity of metering and billing?
- Do our existing billing systems support it?
Action
- Work with engineering and finance to run a technical feasibility review.
- Consider edge cases, refunds, and future-proofing.
Example
- Per-API-call requires real-time metering and robust logs.
- Per-user is simpler but may be less aligned with true value.
The Decision Matrix
Score each candidate metric (1–5) on:
- Alignment with customer value
- Ability to scale with customer growth
- Predictability for customers and for you
- Ease of explanation to buyers
- Implementation feasibility
Total the scores.
The best metric is usually the one with the highest total, weighted heavily toward value alignment (factor 2x).
Get Expert Help Choosing Your Pricing Metric
If you’re stuck between per-seat, per-usage, or outcome-based, you’re not alone.
Monetizely specializes in pricing metric selection:
- Customer interviews and willingness-to-pay research
- Competitive and category norm analysis
- Revenue modeling across alternative metrics
Get Expert Help Choosing Your Pricing Metric → Book Pricing Strategy Session
Common Pricing Metric Mistakes (And How to Avoid Them)
Even seasoned pricing teams get tripped up by value metrics. Here are the most common mistakes.
Mistake 1: Optimizing for Simplicity Over Value Alignment
What it looks like
- Choosing per-user pricing because “everyone understands it” even though your value scales with volume processed or revenue generated.
Why it’s bad
- Creates friction at upsell: customers feel overcharged for light usage.
- Heavy users may pay less than they should; light users pay more.
- Expansion becomes disconnected from actual success.
Real example
- A security vendor priced per user, but true value came from vulnerabilities scanned across assets. High-volume customers felt ripped off; low-volume customers overpaid. Churn increased.
How to fix
- Start with value drivers, not convenience.
- Choose the simplest metric that still tracks value (e.g., per asset, per domain, per scan).
Mistake 2: Choosing Metrics That Discourage Product Adoption
What it looks like
- Per-seat pricing that leads teams to limit user invites or share logins.
- Per-document pricing that leads customers to avoid sending documents.
Why it’s bad
- Artificially limits adoption, reducing the value customers get.
- Lower adoption → weaker stickiness → higher churn.
Real example
- A collaboration tool charged per user. Customers restricted access to a few “owners” and didn’t roll out broadly. Usage stayed shallow and churned when budgets tightened.
How to fix
- Choose metrics that encourage, not punish, adoption.
- Options:
- Make viewer/consumer roles free or cheap.
- Charge per team, per workspace, or per outcome instead of per user.
Mistake 3: Picking Metrics You Can’t Actually Measure
What it looks like
- Pricing on “active campaigns” when you can’t reliably define or track campaign activity.
- “Active contacts” when engagement data is fuzzy.
Why it’s bad
- Billing disputes and mistrust (“why did we get charged for this?”)
- Operational overhead for support and finance
- Difficulty in reconciling invoices and usage
Real example
- A marketing platform priced on “active contacts” but had inconsistent engagement tracking. Customers disputed bills; sales cycles slowed; they eventually replatformed the billing system.
How to fix
- Only price on metrics you can cleanly and consistently measure.
- If measurement is shaky, pick a simpler proxy (e.g., total contacts, total campaigns).
Mistake 4: Ignoring Competitive Norms Without a Strong Reason
What it looks like
- A CRM charging per API call when the entire category is per user.
- A helpdesk charging per active ticket when buyers expect per agent.
Why it’s bad
- Massive buyer friction. Sales must re-educate every prospect.
- Harder comparability vs competitors; procurement sees you as “weird” risk.
Real example
- A sales tool tried per-email-sent pricing. Prospects constantly asked “why not per-user like Salesforce?” Confusion slowed deals; they reverted to per-user.
How to fix
- Conform to category norms unless you:
- Have a fundamentally different product
- Can tell a compelling, clear story about why your metric is better
Mistake 5: Using Too Many Metrics That Confuse Rather Than Clarify
What it looks like
- Charging per user + per project + per GB + per API call all at once.
- Complex overage rules on multiple dimensions.
Why it’s bad
- Buyers can’t predict their bill; procurement rejects complexity.
- Sales reps struggle to explain; marketing can’t message clearly.
- Trial users drop off when they can’t understand pricing.
Real example
- A project management tool started with four metrics. More than half of trial users cited “pricing confusion” as a reason for not converting.
How to fix
- Aim for 1–2 primary metrics (e.g., base + usage).
- If you need more than two, you’re likely over-optimizing. Simplify.
Mistake 6: Not Planning for Metric Evolution
What it looks like
- Locking into per-user at launch without considering that value may later be driven by usage or outcomes.
- Contracts that make it painful to switch metrics.
Why it’s bad
- You get trapped in suboptimal pricing as the product matures.
- Competitors with better metrics start outgrowing you.
Real example
- A collaboration startup used pure per-seat. As usage exploded in certain customers, they had no way to capture upside. When they tried to change metrics later, legacy customers revolted.
How to fix
- Build flexibility into contracts and systems.
- Review pricing metrics every 2–3 years as product and market evolve.
- Introduce new metrics with careful communication and grandfathering.
Get Expert Help Choosing Your Pricing Metric
Avoiding these mistakes is far easier with an experienced partner.
Monetizely can:
- Audit your current value metrics and pricing KPIs
- Run customer research to understand perceived value
- Design a roadmap for metric evolution with minimal churn risk
Get Expert Help Choosing Your Pricing Metric → Book Pricing Strategy Session
Pricing Metric Examples by Industry / Product Type
Here’s how typical SaaS pricing metrics tend to map to product categories.
Collaboration & Communication
- Primary metric: Per user / per active user
- Secondary metric: Per message/minute (for telephony/voice)
- Examples:
- Slack – per user
- Zoom – per host/user
- Microsoft Teams – per user (often bundled in suites)
- Why:
Value scales with team-wide adoption and collaboration breadth.
Developer Tools & APIs
- Primary metric: Per API call / per request
- Secondary metric: Per GB transferred, per compute hour
- Examples:
- Stripe – per transaction (usage + % of GMV)
- Twilio – per message/call
- AWS – per resource/usage unit
- Why:
Direct correlation between consumption and value/costs, and usage scales with customers’ own product adoption.
Marketing & Sales Tools
- Primary metric: Per contact / per lead / per account
- Secondary metric: Per email sent, per user
- Examples:
- HubSpot – per contact tiers + per seat (varies by hub)
- Mailchimp – per contact + sending limits
- Salesforce – per user (plus add-ons)
- Why:
Value tracks either with database size (contacts, accounts) or team size (reps, marketers).
Analytics & BI
- Primary metric: Per user / per data source or workspace
- Secondary metric: Per query / per GB processed
- Examples:
- Tableau – per user (viewer/creator)
- Looker – per user (plus platform fees)
- Snowflake – per compute + storage (usage-based)
- Why:
Value is in insights distributed and data processed. Hybrid models are common (user + usage).
Security & Compliance
- Primary metric: Per employee / per asset (device, endpoint, domain)
- Secondary metric: Per scan, per incident
- Examples:
- Okta – per user/identity
- Vanta – per employee
- Endpoint security tools – per device
- Why:
Protection scales with organization size or number of assets secured.
E-commerce & Payments
- Primary metric: % of GMV / per transaction
- Secondary metric: Base platform fee + transaction fee
- Examples:
- Shopify – subscription + % of GMV via payments
- Stripe – % of payment volume + fixed fee
- Why:
Perfect alignment with merchant success and sales volume.
HR & Recruiting
- Primary metric: Per employee / per hire
- Secondary metric: Per job posting, per applicant
- Examples:
- BambooHR – per employee
- Greenhouse – per user/recruiter + tiers
- Job boards – per job posting or per applicant
- Why:
Value correlates with company size or hiring activity/success.
Storage & Backup
- Primary metric: Per GB stored / protected
- Secondary metric: Per user + storage tiers
- Examples:
- Dropbox – per user with storage caps
- Backblaze – per GB or per computer
- Why:
Direct link to infrastructure costs and amount of data under management.
The Future of SaaS Pricing Metrics
Pricing in SaaS is evolving fast. Here’s where metrics are headed.
Trend 1: Shift to Outcome-Based Pricing
- Moving away from pure usage to results achieved (revenue, qualified leads, hires, cost savings).
- Examples:
- Marketing platforms with ROI-based or per-qualified-lead pricing
- Revenue tools that charge based on pipeline influenced or sales closed
Drivers
- Customers demanding more risk-sharing
- Better attribution and measurement via AI/ML
Timeline
- Already common in marketing and lead-gen
- Expanding into HR, sales, and operations by 2026+
Trend 2: Hybrid Models Becoming Standard
- Pure subscription or pure usage giving way to base + usage structures.
- Examples:
- Databricks, Snowflake, MongoDB
Drivers
- Need to balance predictability with fair usage alignment
- Desire to capture upside from heavy users without scaring smaller customers
Timeline
- Rapid adoption; likely to be the default for infra and data tools by 2026
Trend 3: AI Consumption Units
- New pricing units for AI features:
- Per token
- Per inference
- Per “AI action” or “AI minute”
Drivers
- AI workloads have very different cost structures vs traditional SaaS
- Vendors need granular units to price fairly and profitably
Timeline
- Already emerging in 2024–2025
- Expect standardization around a few AI value metrics (tokens, inferences, actions)
Trend 4: Value-Based Tiers Over Feature-Based Tiers
- Moving from “you pay more for more features” to “you pay more based on segment and outcome.”
- Examples:
- Tiers based on company size, SLA, security, or governance—not just features.
Drivers
- Feature gating creates friction and confusion
- Value-based segmentation captures willingness to pay more accurately
Timeline
- Growing quickly in infrastructure/dev tools
- Expect more horizontal SaaS to follow with segment-based packaging
Trend 5: Dynamic / Personalized Pricing
- AI-driven recommendations for pricing and upgrades:
- Personalized plans based on usage patterns
- Proactive downgrade/upgrade suggestions to prevent churn
Drivers
- Better behavioral data
- ML models that can predict customer lifetime value and risk
Timeline
- Experimental now; likely mainstream in 3–5 years, especially in high-volume PLG SaaS
Your Pricing Metric Action Plan
Turn this encyclopedia into a concrete plan, depending on where you are today.
If You’re Launching a New Product
- Interview 15–20 target customers about value drivers and current tools.
- Identify 3–5 candidate value metrics (users, volume, outcomes, etc.).
- Use pricing research methods (Van Westendorp, conjoint) with the top 2 metrics.
- Start with the simplest metric that still captures value.
- Plan a metric evolution review in Year 2–3.
If You’re Evaluating an Existing Pricing Metric
- Compute your key pricing KPIs:
- Price realization
- NDR / expansion rate
- Discount rate
- Survey customers on pricing clarity and fairness.
- Analyze win/loss data: what’s your pricing objection rate?
- Model revenue impact of 1–2 alternative metrics.
- If changing metrics, plan:
- Grandfathering for existing customers
- Clear communication and upgrade paths
If You’re Considering Usage-Based Pricing
- Audit your metering and billing capabilities.
- Analyze usage variability across your customer base.
- Model revenue volatility vs growth upside.
- Consider a hybrid model (base + usage) to soften risk.
- Build customer dashboards to show consumption transparently.
If You’re Struggling with Pricing Complexity
- Inventory all the pricing metrics you currently use.
- Consolidate to 1–2 primary metrics wherever possible.
- Simplify tiers (3 main tiers is usually enough).
- Document clear definitions for metrics and limits.
- Train sales and CS on a simple story that ties metrics to value.
Conclusion: The Metric Matters More Than The Number
A few core truths from this SaaS pricing metrics encyclopedia:
- Your pricing metric (what you charge for) has more strategic impact than your price point (how much you charge).
- The right metric:
- Aligns with customer value perception
- Scales naturally with customer success
- Is simple enough to explain and implement
- Wrong metrics create friction at every customer interaction; right metrics make adoption and upsell feel natural.
- Most successful B2B SaaS products now use hybrid models (base + usage) instead of pure subscription.
- Different products call for different metrics:
- User-based for collaboration
- Usage-based for infrastructure and APIs
- Outcome-based for revenue- and hire-driven products
- Pricing metrics are not static. Review and refine them every 2–3 years as your product, market, and customers evolve.
Start with your value drivers, validate with customers, choose the simplest metric that captures value, and build in flexibility to evolve.
Get Expert Help Choosing Your Pricing Metric
Choosing and evolving your SaaS value metrics is one of the highest-leverage decisions you’ll make.
Monetizely has helped SaaS companies from $1M to $300M ARR:
- Design and test value metrics
- Implement usage-based and hybrid pricing
- Improve expansion and reduce churn with better pricing KPIs
Get Expert Help Choosing Your Pricing Metric
Monetizely has helped companies from $1M–$300M ARR design value metrics that drive expansion and reduce churn. Book a consultation to validate your metric choice with customer research and competitive analysis.
Book Pricing Strategy Session →