How Predictive Analytics Supports NRR
Net revenue retention (NRR) is the revenue retained during a period from customers, taking churned customers into account. It’s a lesser-known metric compared to MRR and ARR. All three metrics are essential for measuring recurring revenue. However, NRR takes churned customers into account.
Although NRR is a revenue metric, it’s becoming an essential measure of success for customer experience and engagement teams. Customer experience (CX) professionals have started using this metric alongside other critical metrics like CSAT and FCR. NRR is an excellent indicator of profitable customer relationships that will drive repeat purchases and secure high customer retention rates.
Overall, NRR is a reliable metric for analyzing customer interaction and gauging the “stickiness” of a product or service. So how is this metric calculated?
How Do Businesses Calculate NRR?
Why Is NRR Important for Companies?
Net revenue retention rate is a powerful metric to monitor. It’s a leading indicator of business expansion and contraction trends. When this metric is above 100%, it means the percentage of recurring revenue from existing customers is growing. On the other hand, NRR below 100% indicates the business is shrinking and losing revenue from churned customers.
- NRR > 100% = Higher Recurring Revenue Retained From Existing Customers
- NRR < 100% = Lower Recurring Revenue From Customer Churn
The higher the NRR, the more stable and positioned the company is for growth. In addition, high net revenue retention indicates a robust product-market fit. It also reflects a loyal customer base that obtains value from its service provider.
As brands turn to this metric to measure growth, AI and predictive analytics will become critical tools for forecasting revenue trends.
How Does Predictive Analytics Support NRR Goals?
A baseline goal for net revenue retention is 100%, with many SaaS businesses benchmarking themselves in the 120% range. Predictive analytics supports NRR goals in SaaS companies and other subscription businesses by predicting:
- Customer Lifetime Value: AI and machine learning make it possible to predict customer lifetime value for new customer relationships. With predictive LTV software, you can deepen customer relationships. Identify your most valuable customers for exclusive offers and special incentives, then retain the customers you want for the long term.
- Customer Churn: Predicting churn helps you stay ahead of your NRR metrics and reduce your churn rate. Predictive churn software can help detect 85% of customer churn from downgrades and cancellations. You can improve retention rates by as much as 35% and lower revenue lost due to churn.
- Cross-Sell/Upsell: Predictive analytics for cross-sell/upsell is one of the best models for NRR. Predicting when a customer wants to increase their service will help you reach your revenue goals with expansion revenue. It also shows the stickiness of your product.
- Customer Winback: Bringing back past customers is a great way to improve your NRR. Predicting winback opportunities allow you to identify and segment audiences most likely to re-engage with your brand. Predictive analytics enhances winback campaigns by as much as 260%.
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, contact us or schedule a demo.