Sales & Conversion
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
Last year, I watched a SaaS client burn through their runway because they couldn't predict how customers would actually use their platform. They had all the traditional metrics—MRR, CAC, LTV—but none of it told them that their biggest customers were about to hit usage walls that would trigger massive churn.
Sound familiar? You're tracking everything the industry tells you to track, but you're still getting blindsided by customers who either blow past your usage limits or barely touch your product. The problem isn't your tracking—it's that most SaaS companies are still thinking in subscription terms when they should be thinking in consumption terms.
Here's what I learned after helping multiple SaaS companies implement consumption forecasting: the businesses that understand how their customers will consume their product over time don't just retain customers better—they build more predictable, scalable revenue models.
In this playbook, you'll discover:
Why traditional SaaS metrics miss 80% of the consumption story
The consumption patterns that predict churn 3 months early
How to build usage-based pricing that customers actually love
The forecasting model that turned our client's biggest cost center into their biggest revenue driver
When consumption-based billing makes sense (and when it's a terrible idea)
This isn't about jumping on the latest pricing trend. It's about building a business model that scales with actual customer value. Usage-based pricing is growing 50% faster than traditional SaaS models for a reason—but only if you can forecast consumption correctly.
Industry Reality
What every SaaS founder thinks they know about pricing
Walk into any SaaS conference and you'll hear the same gospel repeated: "Focus on MRR growth, optimize for LTV:CAC ratios, and nail your cohort retention curves." The industry has built an entire ecosystem around these subscription-first metrics.
Here's what the typical SaaS founder gets told about pricing and forecasting:
Predictable revenue is king - Lock customers into annual contracts and focus on expansion revenue through seat-based growth
Usage-based pricing is too complex - Stick to simple per-seat or tiered pricing to avoid billing headaches and customer confusion
Churn is binary - Customers either renew or they don't; focus on reducing gross revenue churn
Freemium or free trials rule - Get users in the door and convert them to paid plans through feature limitations
ARR is the north star - Everything should be optimized around growing annual recurring revenue
This advice isn't wrong—it's just incomplete. It works perfectly for companies selling productivity software or collaboration tools where usage is predictable and value scales with team size. But what happens when your product's value directly correlates with how much customers actually use it?
The subscription-first approach breaks down when customers have wildly different consumption patterns. A customer processing 100 API calls per month and another processing 100,000 calls are fundamentally different businesses with different needs, different price sensitivities, and different churn risks. Yet traditional SaaS metrics treat them almost identically.
The real problem? Most SaaS companies discover too late that their biggest revenue opportunities—and their biggest churn risks—are hidden in consumption patterns that traditional metrics completely miss. By the time your cohort analysis shows declining retention, you've already lost months of optimization opportunities.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Six months ago, I started working with a B2B SaaS platform that helps e-commerce companies automate their inventory management. On paper, they looked healthy: $2M ARR, 85% gross revenue retention, solid LTV:CAC ratios. But underneath, they were bleeding.
Their biggest problem? Customers were hitting usage limits and churning without warning. The platform processed inventory updates, and customers paid based on "SKUs managed"—a classic per-seat model adapted for e-commerce. Small stores with 500 SKUs paid $99/month, medium stores with 2,000 SKUs paid $299/month, and enterprise customers paid custom rates.
Sounds reasonable, right? The issue was that SKU count had almost no correlation with actual platform usage. A small store running flash sales might process 50,000 inventory updates per month, while an enterprise customer with seasonal products might only need 5,000 updates.
Here's where it got messy: customers in the $299/month tier were either massively over-consuming (and hitting rate limits that killed their experience) or barely using the platform at all (and feeling ripped off). The client couldn't predict which new customers would become their most valuable users or which existing customers were about to churn.
We tried the standard approaches first. We optimized their onboarding to drive more engagement. We built better usage dashboards. We even experimented with annual discounts to lock customers in longer. Nothing moved the needle on the core problem: they couldn't predict or plan around how customers would actually consume their service.
The breakthrough came when we stopped thinking about their platform as a subscription service and started thinking about it as a consumption-based utility. Instead of asking "How many SKUs do you manage?" we asked "How often does your inventory change, and how critical is real-time accuracy?"
That's when we discovered the hidden patterns that traditional SaaS metrics completely missed.
Here's my playbook
What I ended up doing and the results.
The first thing we did was audit six months of usage data across their entire customer base. What we found changed everything.
We discovered that customers fell into four distinct consumption patterns that had nothing to do with their subscription tier:
Steady Processors - Consistent daily usage, predictable growth patterns
Burst Users - Massive spikes during sales events, minimal usage otherwise
Seasonal Heavies - High consumption during specific seasons, lower baseline usage
Growth Rockets - Exponential usage growth, typically fast-growing e-commerce companies
Each pattern required completely different pricing approaches and had different churn risk profiles. But here's the key insight: we could predict which pattern a customer would follow based on their first 30 days of usage, not their company size or SKU count.
Next, we built a consumption forecasting model with three core components:
1. Baseline Consumption Tracking
Instead of tracking just "monthly API calls," we tracked granular usage patterns: time of day distributions, request type ratios, batch vs. real-time processing preferences, and error rates. We discovered that customers who processed requests consistently throughout business hours had 3x higher retention than customers who batch-processed everything at midnight.
2. Growth Pattern Recognition
We created algorithms to identify each customer's consumption growth trajectory within their first month. Customers whose usage accelerated week-over-week were flagged for proactive expansion conversations. Customers whose usage plateaued quickly were flagged for retention interventions.
3. Value Correlation Analysis
Most importantly, we mapped consumption patterns to business outcomes for each customer segment. High-frequency processors during business hours were typically growing e-commerce companies seeing real ROI. Batch processors were often using the platform for compliance, not growth—making them higher churn risks regardless of their subscription level.
The implementation required rebuilding their entire billing system around consumption units rather than subscription tiers. We partnered with a usage-based billing platform and created five pricing buckets based on monthly "processing credits" rather than SKU counts.
But the real magic happened in the forecasting layer. We could now predict with 87% accuracy which customers would expand their usage (and revenue) over the next quarter, and which customers were at risk of churning based on declining consumption patterns.
Pattern Recognition
Understanding the four customer consumption archetypes and their different pricing sensitivities
Early Warning System
Building alerts for customers whose usage patterns indicate expansion opportunities or churn risks
Billing Transformation
Moving from subscription tiers to consumption-based credits while maintaining predictable revenue streams
Value Alignment
Connecting consumption patterns to actual customer business outcomes and ROI metrics
The results were dramatic and immediate. Within 90 days of implementing consumption forecasting:
Revenue Growth: Monthly revenue increased 34% without acquiring a single new customer. Existing customers who moved to consumption-based pricing expanded their usage significantly when they weren't constrained by artificial tier limits.
Churn Reduction: Gross revenue churn dropped from 8% to 3.2% monthly. More importantly, we could predict churn 3 months earlier and intervene with pricing adjustments or feature recommendations.
Customer Satisfaction: NPS scores increased by 28 points. Customers loved paying for what they actually used rather than being locked into tiers that didn't match their business.
Expansion Revenue: Expansion revenue grew 156% as customers naturally increased consumption when pricing aligned with value. We eliminated the awkward "upgrade sales" conversations entirely.
But the most valuable outcome was operational: the forecasting model gave them 3-month visibility into revenue trends that traditional SaaS metrics never captured. They could predict busy seasons, plan infrastructure scaling, and make hiring decisions based on actual consumption trends rather than hoping subscription growth would continue.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the seven critical lessons I learned from implementing consumption forecasting:
Start with usage patterns, not pricing - Don't jump straight to usage-based pricing. First, understand how customers actually consume your product and what drives value for them.
30 days reveals everything - You can predict long-term consumption patterns from the first month of usage. Build your forecasting model around early behavioral indicators.
Consumption ≠ engagement - High usage doesn't always mean high satisfaction. Map consumption to business outcomes, not just product engagement.
Hybrid models win - Pure consumption pricing can be unpredictable for customers. The best models combine predictable base fees with consumption-based expansion.
Infrastructure costs matter - Make sure your consumption pricing covers actual delivery costs with healthy margins. It's easy to subsidize usage growth accidentally.
Billing complexity is real - Usage-based billing requires better customer communication, detailed usage reporting, and flexible payment processing. Budget for this.
Not every SaaS should do this - Consumption forecasting works best for products where usage directly correlates with customer business outcomes. Productivity tools and collaboration software should probably stick to seats.
The biggest lesson? Consumption forecasting isn't just about pricing—it's about building a business model that scales with customer success rather than fighting against it.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies considering consumption forecasting:
Audit your current usage data to identify distinct consumption patterns
Map consumption to customer business outcomes and ROI
Start with hybrid pricing before going full consumption-based
Build early warning systems for expansion and churn prediction
For your Ecommerce store
For e-commerce platforms implementing consumption models:
Track transaction volume and seasonal patterns, not just product catalog size
Align pricing with peak usage periods and business critical operations
Build flexible billing that accommodates seasonal e-commerce cycles
Focus on consumption that drives direct revenue impact for clients