Sales & Conversion
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Here's a confession: I spent years obsessing over Monthly Recurring Revenue (MRR) like it was the holy grail of SaaS metrics. Every conversation with founders revolved around predictable revenue, flat subscription tiers, and those beautiful linear growth charts that VCs love to see.
Then I worked with a client who completely changed my perspective on SaaS pricing. Their product was an AI-powered analytics tool that some customers used once a month, while others ran thousands of queries daily. The flat $99/month pricing was bleeding money from heavy users and overcharging light users who were churning faster than we could acquire them.
The conventional wisdom says "predictable pricing equals predictable revenue." But what if that predictability is actually limiting your growth? What if the fear of complexity is costing you millions in potential revenue?
After helping multiple SaaS companies transition from subscription to usage-based models, I've learned that the switch isn't just about pricing—it's about fundamentally understanding how your customers actually create value with your product. Here's what you'll discover:
The hidden signals that indicate your flat pricing model is broken
How to identify the "usage cliff" that's driving customer churn
The framework I use to evaluate pricing model transitions
Real implementation strategies that reduce pricing friction
Why some industries should never switch (and which ones absolutely should)
This isn't another theoretical pricing guide. This is a playbook built from real transitions, real mistakes, and real results from SaaS companies that dared to challenge the subscription orthodoxy.
Industry Reality
What the pricing gurus won't tell you
Walk into any SaaS pricing workshop and you'll hear the same gospel: "Subscription models create predictable revenue." The pricing consultants love to show those beautiful hockey stick charts where MRR grows month over month, creating a foundation for valuations and investor confidence.
The traditional advice follows a predictable pattern:
Start with simple tiers - Basic, Pro, Enterprise with clear feature differentiation
Focus on seat-based pricing - Scale revenue by adding more users to accounts
Annual contracts reduce churn - Lock customers in for predictable cash flow
Usage-based is too complex - Customers want predictable bills, not variable costs
MRR is king - Monthly recurring revenue is the only metric that matters for SaaS
This conventional wisdom exists because it worked incredibly well in the early SaaS era. When software was primarily about replacing desktop applications, the seat-based model made perfect sense. You bought licenses for your team, everyone used the software roughly the same amount, and predictable pricing aligned with predictable usage.
But here's where this approach falls apart in today's reality: modern SaaS products aren't just software licenses—they're value creation engines. The customer who processes 10,000 API calls per month is creating vastly more value than the customer who processes 100 calls, yet traditional pricing treats them almost identically.
The result? You're either undercharging your power users (leaving money on the table) or overcharging light users (driving unnecessary churn). Both scenarios limit your growth potential and create misaligned incentives between you and your customers.
The pricing orthodoxy works until it doesn't. And increasingly, it doesn't.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
The wake-up call came when I was working with a client whose SaaS had hit a revenue plateau at around $2M ARR. On paper, everything looked healthy—decent conversion rates, manageable churn, steady acquisition. But the growth had flatlined, and no amount of feature development or marketing optimization was moving the needle.
Their product was a data visualization platform with three tiers: Starter ($49/month), Professional ($149/month), and Enterprise ($499/month). The pricing was clean, predictable, and followed every best practice I'd learned about SaaS pricing.
But when we dug into the usage data, a disturbing pattern emerged. About 15% of their Professional plan customers were processing more data in a month than some Enterprise customers. Meanwhile, 40% of Starter plan users were barely touching the platform after their first week.
The heavy users were getting incredible value—some were saving their companies hundreds of thousands of dollars in analyst time. Yet they were paying the same $149 as someone who created one dashboard and never came back. On the flip side, light users felt like they were overpaying for features they'd never use.
We tried the conventional fixes first: adding more tiers, implementing seat-based pricing, offering annual discounts. Nothing worked. The fundamental mismatch between how customers used the product and how we charged for it remained.
That's when I started questioning whether the problem wasn't our execution—it was our entire pricing philosophy. Maybe the predictability we were chasing was actually creating unpredictable value delivery. Maybe the simplicity we prioritized was oversimplifying a complex value equation.
The client was skeptical. "Our customers want predictable bills," they insisted. "Usage-based pricing will create budgeting nightmares." But the plateau was real, and the status quo wasn't working.
That's when we decided to run an experiment that would completely change how I think about SaaS pricing.
Here's my playbook
What I ended up doing and the results.
The transition to usage-based pricing isn't a decision you make lightly. After working through multiple pricing model shifts, I've developed a framework that helps identify when the switch makes sense and how to implement it without destroying your existing business.
Step 1: The Usage Audit
First, we needed to understand the usage distribution across our customer base. We tracked three key metrics over 90 days:
Data processing volume per customer
Feature utilization depth
Session frequency and duration
The results were eye-opening. We had a classic "power law" distribution—20% of customers were responsible for 70% of the platform usage, while 30% of customers used less than 10% of their plan's capacity.
Step 2: Value Correlation Analysis
Next, we needed to correlate usage with value creation. We surveyed customers about the business impact of our platform and cross-referenced it with their usage patterns. The correlation was strong: customers who used more of the platform consistently reported higher ROI and were more likely to expand their accounts.
Step 3: The Pricing Simulation
Before making any changes, we modeled different usage-based pricing scenarios using historical data. We tested various pricing structures:
Pure usage-based - Pay per data point processed
Hybrid model - Base subscription + usage overages
Tiered usage - Different rates for different usage levels
The hybrid model emerged as the winner. It preserved revenue predictability for light users while capturing more value from heavy users. Under this model, revenue would have increased by 35% over the previous year without losing any customers.
Step 4: The Gradual Rollout
We didn't flip a switch overnight. Instead, we implemented a three-phase rollout:
Phase 1: New customers only, with clear usage tracking and bill previews
Phase 2: Existing customers who opted in, with grandfathered pricing protection
Phase 3: Full migration with 6-month advance notice and optimization consultations
The key was transparency. Every customer could see their usage patterns, understand their bills, and predict future costs. We built a usage dashboard that became one of the most-used features in the platform.
Step 5: The Feedback Loop
Usage-based pricing creates a natural feedback loop between customer success and revenue growth. When customers use the platform more, they create more value for their business and generate more revenue for you. This alignment fundamentally changes how you approach customer success and product development.
The result wasn't just higher revenue—it was better customer relationships, clearer product roadmap priorities, and a business model that scaled with customer success rather than fighting against it.
Clear Signals
When the traditional model shows cracks: usage patterns reveal pricing misalignment
Transparency Tools
Building trust through usage dashboards and bill predictability
Migration Strategy
Phased rollout approach that protects existing relationships
Value Alignment
Creating pricing that scales with customer success and business impact
The results spoke for themselves, but not in the way we initially expected. Yes, revenue increased—by 42% in the first year after full implementation. But the more significant changes were in customer behavior and business health.
Revenue Impact: The average revenue per customer increased, but not uniformly. Light users actually paid less under the new model (improving retention), while heavy users paid significantly more (improving unit economics). The revenue distribution became much healthier.
Customer Success Alignment: For the first time, our success team had a direct financial incentive to help customers use the platform more effectively. Usage consultations became revenue-generating activities rather than cost centers.
Product Development Focus: Feature requests started aligning with actual usage patterns rather than survey responses. We built features that drove usage rather than just checking feature comparison boxes.
Sales Conversations: Selling became easier because prospects could start small and scale their investment with their results. The "try before you buy" mentality reduced sales cycle friction significantly.
The most surprising result was customer satisfaction. Despite initial fears about "unpredictable billing," satisfaction scores increased. Customers felt they were paying fairly for the value they received, and the transparency of usage tracking actually increased trust in the platform.
Within 18 months, the client broke through their plateau and reached $3.2M ARR, with much healthier unit economics and customer relationships than they'd had under the traditional model.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
The transition taught me that pricing isn't just about revenue—it's about aligning incentives between you and your customers. Here are the key insights that emerged:
Usage patterns reveal value creation - If usage and value don't correlate, you have either a product problem or a pricing problem
Transparency builds trust - Customers fear surprise bills, not variable bills. Clear usage tracking eliminates anxiety
Hybrid models reduce risk - Pure usage-based pricing can be too volatile. Base fees provide stability while usage fees capture value
Migration timing matters - Never force existing customers into new pricing without protection and consultation
Sales training is crucial - Your team needs to understand value-based selling, not just feature-based selling
Customer success becomes profitable - When usage drives revenue, helping customers succeed becomes a profit center
Product development gets focused - Features that don't drive usage become obviously wasteful
The biggest lesson: don't switch to usage-based pricing to solve a revenue problem. Switch when you want to align your business model with customer value creation. The revenue improvement is a result, not the goal.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS startups considering usage-based pricing:
Track usage patterns from day one, even with flat pricing
Build usage dashboards as core product features
Start with hybrid models to reduce transition risk
Train sales team on value-based selling methodologies
Implement usage caps to prevent bill shock
For your Ecommerce store
For ecommerce platforms evaluating usage pricing:
Consider transaction-based pricing for payment processing
Implement storage-based pricing for catalog management
Use bandwidth pricing for high-traffic stores
Offer predictable billing cycles with usage summaries
Build ROI calculators linking usage to revenue