Pricing workshops often begin with a spreadsheet and opinions about “Basic, Pro and Enterprise.” The harder question comes first: can the company observe what different customer segments use, what they value, what it costs to serve them and where expansion naturally occurs?
“Higher-growth software companies are 1.7 times more likely to integrate advanced analytics into pricing guidance.”McKinsey · The art of software pricing ↗
The market signal
McKinsey’s software-pricing research highlights both the growing use of advanced pricing analytics among higher-growth companies and a shift toward pricing metrics more closely aligned with value. Stripe similarly documents the range of subscription, per-seat, usage-based and hybrid approaches available to SaaS businesses.
The common requirement underneath those models is instrumentation. Usage-based or value-aligned pricing is impossible to operate well if the product cannot reliably identify, meter and explain the unit being monetized.
Why pricing gets stuck
Many SaaS companies have three disconnected views of the customer: the application knows activity, the cloud bill knows infrastructure cost, and the CRM/billing stack knows contract value. None of them can answer a simple question such as: “Which workflows create the most value for this account, what do they cost us, and is the current package aligned?”
AI makes the gap more visible because token, model, retrieval and runtime costs can vary by feature and customer. A flat subscription can hide dramatically different economics.
How stronger product companies handle it
| Disconnected state | Monetization-ready state |
|---|---|
| Page-view analytics | Tenant- and workflow-level product events |
| Cloud bill by service | Cost-to-serve by tenant, feature and AI workflow |
| Static feature flags | Entitlements tied to packages and contracts |
| Invoice totals only | Metering, rating, billing and revenue linked to usage |
| Annual pricing debate | Continuous cohort, value and packaging analysis |
The CodePravaha perspective
Create a Monetization Data Plane that links Customer → Tenant → Persona → Feature → Workflow → Usage → Outcome → Cost → Entitlement → Contract → Revenue. This does not mean one giant database. It means consistent identifiers, events and governed joins across product, finance and commercial systems.
Once that foundation exists, packaging becomes an evidence-based product discipline rather than an annual price-list exercise.
What to change now
Metrics worth watching
Track activation and retention by package, feature adoption by cohort, usage intensity by tenant, expansion-conversion triggers, gross margin by customer segment, cost-to-serve by high-cost workflow, overage/entitlement events, discount leakage and the percentage of revenue tied to a pricing metric that actually correlates with customer value.
Build a monetization data plane before endlessly debating tiers: customer → tenant → persona → feature/workflow → usage → outcome → cost → contract → revenue.