How to Build a Usage-Based Revenue Forecast

Consumption pricing breaks the assumption that most forecast models rest on. A subscription forecast asks when a contract will be signed. A usage forecast has to ask what customers will actually do after signing, which is a behavioral question rather than a contractual one. The build works, but the components are different.

What makes usage revenue harder to forecast?

Revenue is decided after the sale rather than at it.

With a subscription, the contract sets the number and the question is timing. With usage pricing, the contract sets a floor and a rate, and the customer decides everything above the floor. Two accounts that sign the same deal on the same day can bring in very different revenue over the next year.

That changes the data you need. Contracts still set the floor, and usage data drives everything else. A forecast built only on contract values misses the part of revenue that actually moves.

What are the components of the model?

Five lines, each with a different method. The minimum line is the only certain one, and treating it as the forecast is the most common mistake. Companies with heavy consumption exposure can find that half their revenue sits above the floor, which means half the forecast comes from behavior rather than from contracts.

Keep the components separate in the output. When the number moves, you need to know whether existing accounts consumed less or fewer new accounts signed, because those failures belong to different teams.

How do you forecast overage?

Per account, from the trailing three-month consumption trend, never in aggregate.

Overage bunches up. A few fast-growing accounts produce most of it, and an average across the base hides them. A single smooth line misses both ways: it waters down the accounts that are surging and props up the flat ones.

Work account by account. Take the last three months of usage, fit a simple trend, project it forward and compare it with the commitment. Anything above the commitment is that account's overage. Add them up only at the end.

Cap the projections. An account that tripled its usage in two months will rarely triple again, and an uncapped trend will embarrass you at the forecast call. Set a ceiling from the fastest steady growth your mature accounts have shown.

How do you model new accounts ramping?

Build an average ramp curve from historical accounts, indexed to month one consumption.

New usage customers rarely start at full speed. They onboard, move work over and level off months later. Measure that curve from accounts that signed a year or more ago, scaled so accounts of different sizes compare.

The ramp curve turns a new bookings forecast into a revenue forecast. A deal that closes in the first month of a quarter adds very different revenue from one that closes in the third. Subscription models can ignore that. Usage models cannot.

Segment the curve if your data supports it. Enterprise migrations ramp slowly and land high. Self-serve accounts often reach steady state within weeks. One blended curve applied to both will misstate the timing of most of your revenue. The cohort mechanics behind this are the same ones covered in the net revenue retention glossary entry.

Which leading indicators predict consumption changes?

Trailing usage trend first, then workload or active user counts, then support engagement.

The usage trend is the main and most direct signal. Active user or workload counts often move before usage does, which gives you time to act before revenue moves.

Support engagement is the third signal and the least intuitive, because the relationship is not linear. In ORM customer data, accounts with no support cases at all are at risk, since nobody is using the product deeply enough to hit a problem. Accounts with seven or more cases in a year are also at risk. Accounts with three to five cases, usually lower severity, churn least often, because they are engaged and getting help.

Feed those flags into the contraction line as named accounts rather than as a blended risk percentage. A contraction forecast that lists twelve specific accounts can be acted on. One that applies a 4 percent risk haircut to the base cannot.

How do you forecast what drives consumption?

Model what drives usage, then turn the usage forecast into revenue. A seat-based forecast runs on the contract, from seats and price to renewals and expansion. Usage revenue follows what customers do, so the forecast has to learn what makes them use more or less.

In ORM's experience, statistical models do this job well. Start with your own account data and find the factors that best predict future usage. Past usage is one of them, along with customer growth and seasonal patterns. Transaction volume and product adoption can matter too. So can the wider economy and the operating drivers specific to your product. Test each one against past quarters and keep the ones that held up.

Forecast each driver across the period, and let the model predict usage from those forecasts. The last step turns that usage forecast into revenue.

Carrying last month's usage forward breaks the moment a driver changes. A model of the drivers shows where usage is heading, and the revenue line follows it.

Here is a hypothetical account, with illustrative figures. Its contract carries a $10,000 monthly minimum covering 5,000,000 units, with overage at $0.0025 per unit. Its usage follows its own order volume, and its busy season is the fourth quarter. Last month it used 5,500,000 units.

The flat line understates this account's quarter by $6,500, about 19%, because it ignores the one driver that matters most. Across a portfolio of consumption accounts, errors like that decide whether the quarter is called right.

How do you report a usage forecast?

As a monthly reconciling waterfall, with each component visible.

Build the output as beginning revenue, churned customer revenue, churned product revenue, product decreases, new customer revenue, new product revenue, product increases, and ending revenue. Beginning revenue for each month equals ending revenue from the prior month, which forces the model to reconcile against itself.

That structure answers the question executives actually ask, which is what changed and why. A single total hides whether a strong month came from new logos or from three existing accounts expanding, and those two outcomes carry entirely different implications for the next quarter. Compare the output against your booked forecast using the discipline in how to forecast revenue, and grade it with the measures in the forecast accuracy glossary entry.