Best Practices to Improve Recurring Revenue

Revenue forecasting is one of the most critical elements in managing business growth, particularly for companies relying on subscription-based models. With all the complexities surrounding booking dates, start dates, and potential churn, it’s essential to have a comprehensive and structured approach to predict revenue accurately. Let’s break down some of the key elements of how to improve forecasting and ensure consistent growth.

The Importance of Contract Data in Forecasting

One of the first aspects to consider is the duration of a contract and how that impacts your revenue projections. Even something as seemingly minor as whether a contract starts in December or January can significantly affect whether the revenue gets credited to the current or upcoming fiscal year. Understanding these nuances is vital to accurately forecast growth, particularly when accounting for bookings that may close near the end of the year.

If you miss your targets in one period, it compounds and becomes increasingly difficult to catch up. For subscription-based businesses, missing months is especially difficult to recover from. If bookings are concentrated in January, February, and March instead of the last quarter of the previous year, it can distort the overall revenue flow. A clear understanding of close dates and their impact on when revenue is realized is essential.

Churn’s Impact on Revenue

Churn is a big concern for businesses, particularly in SaaS, where losing customers can cause revenue forecasts to collapse. One challenge lies in the “real churn” versus “accounting churn.” While you might lose a customer from an accounting perspective (due to delays in provision or renewal), you might still have opportunities to bring them back.

When forecasting, many companies now account for churn as a “negative opportunity” in their models. Doing so provides a realistic outlook, enabling teams to better prepare for future revenue impacts. This leads to a more proactive approach to revenue forecasting and churn management, especially when paired with real-time customer health insights from customer success teams.

The Need for Granularity

Monthly recurring revenue (MRR) is often the best metric for understanding the health of a subscription-based business. By focusing on MRR rather than just annualized numbers, companies can identify and respond to small dips or contractions before they evolve into larger problems.

Moreover, having visibility into performance on an itemized level—not just by customer or product family—allows teams to dig deeper into why revenue is changing. Granular data helps businesses monitor shifts in consumption patterns, address issues quickly, and adjust forecasts to reflect the most current reality.

Empowering Customer Success Teams

Customer success teams play a crucial role in managing and forecasting revenue. They need visibility into the revenue forecast, not just customer activity. For example, if a customer dips in usage for a quarter but plans to resume normal operations, that doesn’t necessarily indicate churn. Providing customer success teams with insights into these revenue fluctuations can help the broader organization make informed decisions, improve forecasting accuracy, and avoid overreacting to temporary dips.

With consumption-based models, customer engagement becomes the ultimate indicator of a customer’s likelihood to churn or renew. For that reason, customer success should be tightly integrated into the revenue forecasting process, helping to identify trends early and adjust projections accordingly.

Concrete Takeaways for Better Forecasting

1. Automate and Analyze: Automating processes where possible and closely monitoring the resulting data can lead to actionable insights. Understand why forecasts are falling short and address the root causes to improve future forecasts.
2. Develop a Data Strategy: Particularly as businesses grow and acquire other companies, a well-thought-out data strategy is essential. Having a roadmap for organizing and consuming data at a granular level ensures more accurate forecasting and effective decision-making.
3. Item-Level Forecasting: Don’t rely solely on aggregated customer or product family data. Starting with an item-code level approach enables detailed analysis and empowers companies to perform more precise cohort and industry-specific analyses.

Watch the Full Webinar for More Insights

Revenue forecasting can be complicated, but with the right approach and tools, it can become a powerful driver for growth. If you’re looking for more in-depth strategies to fine-tune your revenue forecasts and improve your business outcomes, be sure to check out our webinar on this topic. You’ll gain valuable insights into how leading companies tackle forecasting challenges and set themselves up for success year after year.