How Predictive Analytics Supports MRR
Monthly recurring revenue, or MRR, is the total predictable revenue generated monthly by all of a business’s active subscriptions. It is a recurring monthly amount that includes all revenue recurring throughout a given month.
MRR is not all the revenue a company can generate monthly but the regular monthly payments it generates. Monthly recurring revenue will include revenue generated from recurring add-ons but will not include one-time customizations.
How Do Companies Calculate MRR?
Calculating this metric is pretty straightforward, but you’ll choose an MRR formula based on the subscription type.
Monthly Subscription Type: Multiply the average revenue per user (ARPU) by the number of monthly subscribers.
Annual Subscription Type: For subscribers with annual contracts, divide the yearly contract cost by 12. Then, multiply the result by the number of annual customers.
Why is MRR important for businesses?
Subscription businesses usually see monthly recurring revenue as their most important metric. For example, SaaS companies, cable providers, cell phone carriers, streaming services, and DTC companies use this metric to report monthly trends.
MRR allows companies with this business model to track the underlying increase or decrease in monthly customers.
Increase in MRR: Tracking an increase in monthly recurring revenue is typically reported in two ways:
- New MRR: This type of MRR is new monthly recurring revenue added to the monthly cash flow and driven by new customer acquisition. It’s essential to remove any churned customers when calculating net new MRR.
- Recurring Add-on MRR: This monthly recurring revenue results from upselling or cross-selling products and services to existing customers. For example, in a SaaS business, this might mean customers pay for a new add-on feature. Selling additional products and expanding monthly services improves CAC payback periods and creates long-term customer lifetime value.
Decrease in MRR: Tracking decreases in this metric is tied to customer churn. Customer churn happens when customers either decrease their monthly service level or cancel their monthly services.
How Can Predictive Analytics Support MRR?
Predictive analytics is an excellent method to help meet and exceed your monthly recurring revenue goals. With predictive analytics, companies can predict key factors influencing monthly recurring revenue. A few of the most common predictive models that support MRR are:
- Demand forecasting: Leveraging predictive analytics for demand forecasting allows you to set healthy and obtainable monthly recurring revenue goals. Predicting demand can also help align resources and ensure you are adequately staffing.
- Cross-sell/upsell: Predictive analytics for cross-sell/upsell is one of the best models for boosting MRR. Predicting when customers will likely buy a complementary product or upgrade their service has multiple positive effects. It will improve your monthly recurring revenue, CAC payback periods, and loyalty initiatives.
- Predictive churn solutions: Predicting customer churn is a great way to get ahead of declining MRR and increasing churn rates. Predictive churn software can detect 85% of customer churn and improve retention rates among your customer base by 35%.
- Customer winback: Reactivating past customers is an ideal way to improve your monthly recurring revenue. Predictive analytics improves winback campaigns by as much as 260%. This technology also allows you to identify and segment the audiences most likely to re-engage with your brand.
If you’re looking to get started in predictive analytics, we created a helpful guide. The guide leads you and other stakeholders through gathering information and making decisions about your data-driven strategy.
If you are interested in how Pecan AI can support your business, feel free to contact us or schedule a demo.