3 Tips for Accurate SaaS Mrr Forecasting
Accurate SaaS MRR forecasting is crucial for business success, but it can be challenging to get right. This article presents expert-backed strategies to enhance your forecasting accuracy, including modeling downgrade probabilities and implementing segmented forecasting. By analyzing user segments and incorporating these advanced techniques, businesses can significantly improve their ability to predict and manage revenue streams.
- Model Downgrade Probabilities for Accurate Forecasts
- Implement Segmented Forecasting for Complex Marketplaces
- Analyze User Segments to Predict Churn
Model Downgrade Probabilities for Accurate Forecasts
One challenge we ran into was overestimating upgrades and ignoring downgrade patterns, which made our MRR projections look stronger than they really were. It was easy to track new signups but much harder to predict when existing customers would shift to lower plans, especially during seasonal slumps. We fixed it by building downgrade probabilities into our model using past data and identifying leading signals like decreased product usage or missed check-ins. That small shift gave us a more honest picture and helped us take action earlier. If you're struggling with MRR forecasting, stop focusing only on growth inputs and start modeling churn and contraction just as seriously.
Implement Segmented Forecasting for Complex Marketplaces
One of the biggest challenges we've faced at Fulfill.com in accurately forecasting our MRR has been accounting for the variable nature of our two-sided marketplace. When you're connecting eCommerce businesses with 3PL providers, your revenue forecasting becomes inherently complex because you're dependent on both sides of that equation.
Early on, we made the mistake of using a single growth metric across all customer segments. We'd project our numbers based on overall marketplace growth, but reality showed us that different customer segments behaved very differently. Enterprise clients had longer sales cycles but higher retention rates, while smaller businesses onboarded quickly but had more churn variability.
We overcame this by implementing a segmented forecasting approach. Rather than treating our customer base as one homogeneous group, we began forecasting MRR by customer segment, accounting for the unique acquisition costs, retention rates, and expansion revenue for each. This segmentation revealed patterns we couldn't see before, like how seasonal spikes in eCommerce shipping affected our revenue cycle differently across segments.
For those struggling with MRR projections, my advice is threefold: First, get granular with your data. In the 3PL world, I've seen how much forecasting improves when you track metrics beyond top-line numbers. Second, build scenario planning into your models. During the supply chain disruptions of recent years, having multiple forecast scenarios helped us navigate uncertainty. Finally, regularly reassess your assumptions. The metrics that drove our business in year one were quite different from what drives it today.
Remember that forecasting isn't just about hitting a number—it's about understanding the underlying drivers of your business. When we aligned our forecasting with the actual customer journey through our marketplace, not only did our projections become more accurate, but we also gained insights that improved our product and operations.
Analyze User Segments to Predict Churn
One of the barriers I have been trying to solve for forecasting monthly recurring revenue (MRR), which is the most important metric when dealing with subscription-based businesses, for EVhype, is forecasting churn per user. As we've scaled, we've faced periods of high churn that we were unable to fully foresee, namely users switching between platforms or pausing services due to external factors (e.g., bear markets).
To overcome this, we proposed a user segment approach that would be less dependent on erratic thresholds. By analyzing user behavioral patterns, we found which segments had the most repeat customers and made the most accurate predictions. We also started using predictive analytics and tracking user data at a much closer level so that we could predict churn and alter our estimates.
Numbers for average SaaS churn rates fluctuate, but based on more recent data, the average annual rate stands at around 5.2%, depending on specifics such as company size and user engagement. Furthermore, companies with a higher average revenue per user (ARPU) show lower churn rates.
For those having issues with MRR forecasts, I would say invest heavily in building up a profile of an account where all relevant data touchpoints are captured, while also being enthusiastic about behavioral data, form fills, and time in product, etc. Do this for all users, and this helps make the forecasting less art and more science (albeit still a probabilistic one), but also proactive so you can head off churn.
