Revenue forecasting

Revenue forecasting is the process of estimating a company's future revenue over a defined period, using historical performance, current pipeline or subscriber data, and assumptions about growth, churn, and pricing. For a subscription business, a forecast usually starts from the recurring revenue already under contract and layers on assumptions about new bookings, expansion, contraction, and churn to project where revenue will land in future months or quarters.

Because subscription revenue compounds over time, forecasting a recurring revenue business looks different from forecasting one-time sales. A subscription platform such as Recurly holds the underlying billing and subscriber data, active plans, upgrade and downgrade history, and cancellations, that a forecast model needs as its starting point. Getting the inputs right, current committed recurring revenue plus realistic assumptions about future change, is what separates a useful forecast from a guess.

Why revenue forecasting matters for subscription businesses

A forecast is the basis for nearly every forward-looking business decision: budgeting, hiring plans, fundraising conversations, and board reporting all depend on a credible view of future revenue. When the forecast is wrong, the consequences compound. A forecast that is too optimistic can lead to overhiring or overspending against revenue that never materializes. A forecast that is too conservative can leave a company under-resourced to capture growth it could have captured.

Recurring revenue businesses have to forecast two distinct things at once: the new revenue expected from sales activity, and the retention and expansion behavior of the customers already on the books. Because existing subscribers can churn, downgrade, or expand their plans, a forecast built only on new bookings will consistently miss. Accurate forecasting requires visibility into monthly recurring revenue (MRR) or annual recurring revenue (ARR) components, including new, expansion, contraction, and churned revenue, so that the base and the pipeline are modeled together rather than separately.

How to build a revenue forecast

Most subscription businesses use a mix of the following approaches, often blending several for greater accuracy:

• Historical trend forecasting: projecting revenue forward based on past growth rates and seasonality patterns.

• Pipeline-based forecasting: weighting open sales opportunities by stage and win probability to estimate incremental new revenue.

• Cohort-based forecasting: modeling how groups of customers acquired in the same period behave over time, including retention curves, expansion, and churn, then rolling those cohorts up into a company-level forecast.

• Bottom-up forecasting: building the forecast from granular units, such as by sales rep, product line, or customer segment, and aggregating the pieces.

• Top-down forecasting: starting from a market-level or company-wide growth target and allocating it down to segments or periods.

For a recurring revenue business, the process typically starts with committed MRR or ARR already under contract, then adds projected new MRR and expansion MRR, and subtracts projected contraction and churned MRR to arrive at a forward projection.

Revenue forecasting vs revenue recognition

Revenue forecasting and revenue recognition are often confused but serve different purposes. Forecasting is forward-looking and predictive: it estimates what revenue is likely to be, based on assumptions the business chooses and can adjust. Revenue recognition is backward-looking and rules-based: under accounting standards such as ASC 606 or IFRS 15, it determines when and how revenue that has already been earned must be recorded in the financial statements, regardless of what any forecast assumed. A forecast can be revised at will as new information arrives; recognized revenue must follow the applicable accounting rules once a transaction has occurred.

Benefits and examples

A disciplined revenue forecasting process delivers value well beyond the finance function:

• Better resourcing decisions: hiring, marketing spend, and product investment can be pegged to a credible revenue trajectory rather than a hopeful one.

• Clearer investor and board communication: consistent forecast versus actuals tracking builds credibility with stakeholders over time.

• Early warning on churn or expansion trends: because subscription forecasts require modeling the existing base, a forecast that starts missing on the downside often surfaces a retention problem before it shows up in the topline numbers.

• Scenario planning: modeling best case, worst case, and most likely outcomes helps leadership prepare contingency plans rather than reacting after the fact.

As an illustrative example, imagine a subscription company that begins a quarter with $1,000,000 in committed MRR. The forecasting team projects $150,000 in new MRR from the sales pipeline, $40,000 in expansion MRR from existing customers upgrading plans, and expects $60,000 in churned or contracted MRR based on historical churn rates. The forecast for end-of-quarter MRR would be calculated as follows: $1,000,000 (starting MRR) + $150,000 (new) + $40,000 (expansion) - $60,000 (churned/contracted) = $1,130,000 projected ending MRR, an increase of 13% for the quarter.

Common mistakes with revenue forecasting

• Forecasting only new bookings while ignoring churn and contraction from the existing customer base.

• Using a single point estimate instead of a range or scenario-based forecast that accounts for best case, worst case, and most likely outcomes.

• Failing to reconcile forecast assumptions against actual recognized revenue and update the model accordingly.

• Relying on stale historical data that does not reflect recent pricing changes, new product lines, or shifts in customer mix.

Recurly's subscription and billing data significantly contributes to forecasting and reporting workflows for merchants through a suite of integrated tools and features. The platform provides comprehensive data and analytics that enable businesses to make informed financial projections and gain deep insights into their subscription performance.

Key ways Recurly data feeds into these workflows include:

• Revenue forecasting: Recurly helps merchants accurately forecast revenue by analyzing undelivered performance obligations and providing real-time reporting. This allows for more precise projections of future revenue streams. Specific tools like the Subscriptions Export report can be used to manually forecast upcoming revenue based on active subscription data, filtering by current_period_ends_at to identify future renewal dates. Additionally, the Recurly API's Invoice Preview Endpoint allows for programmatic generation of draft invoices for projection purposes, which can reflect future dates and display expected line items and total amounts.

• Advanced analytics and reporting: The platform offers customizable, in-depth analytics and reporting capabilities. Merchants can monitor crucial metrics such as subscriber growth, retention rates, plan performance, churn analysis (voluntary and involuntary), and recovered revenue. Built-in dashboards provide a 360-degree view of business health, offering instant performance insights and the ability to customize reports for specific metrics.

• Key financial metrics: Recurly tracks and reports on essential subscription metrics, including Monthly Recurring Revenue (MRR), Annual Recurring Revenue (ARR), subscriber churn, revenue churn, invoice recovery rate, and the number of subscriptions and revenue saved through churn management efforts. These metrics are vital for evaluating financial performance and momentum.

• Data accessibility and integration: Recurly facilitates data accessibility through built-in dashboards, automated exports, and robust APIs. This allows merchants to connect their subscription data with external data warehousing tools, ERP, CRM, and other systems for deeper analysis and a unified view of their business. Data refreshes occur in near real-time, providing up-to-date insights.

• Compliance and automation: Recurly RevRec automates revenue recognition processes, ensuring compliance with accounting standards like ASC-606 and IFRS-15. This automation reduces manual effort, minimizes audit risk, and accelerates the monthly financial close, providing accurate data for financial reporting.

• AI-powered insights: Recurly Compass leverages AI to surface insights, predict cancellations, and help merchants make faster, more informed decisions regarding subscriber acquisition and retention.

Frequently asked questions

How often should a subscription business update its revenue forecast? Many subscription businesses update forecasts monthly, aligned to their billing and reporting cycles, with a deeper reforecast at the start of each quarter as new pipeline and churn data become available.

What is the difference between MRR and a revenue forecast? MRR is a point-in-time snapshot of the recurring revenue currently under contract. A revenue forecast is a forward projection that starts from MRR and applies assumptions about future new, expansion, contraction, and churned revenue to estimate where MRR or total revenue will land in future periods.

Can revenue forecasting help identify churn risk early? Yes. Because subscription forecasts require modeling the retention and expansion behavior of the existing customer base, a forecast that consistently underperforms on the retention side can be an early signal of a churn problem worth investigating.

Does revenue forecasting replace the need for revenue recognition processes? No. Forecasting estimates future revenue for planning purposes, while revenue recognition determines how and when already-earned revenue must be recorded under applicable accounting standards. Businesses need both.