Tag Archives | Recurring Revenue Management

How Can You Tell If Your Customer Health Score Is Working?

Segmenting Customers by HealthScore

Most of us are familiar with the idea of a customer health score, especially in the subscription economy. But what’s the payback from that score? And how do you determine it? The predictive lift of a health score—i.e., its predictive performance measured against random choice—is the key to understanding payback. If the predictive lift is high and actionable, the score has good economic value—because you’ll use it to apply resources to priorities that impact revenue retention and growth. But if the predictive lift of a health score is low, its economic value is low—because acting on the score is only slightly better than random or “gut-based” customer interaction. So how do you measure economic value of your customer health scoring? […]

Why One-Size-Fits-All Annual Price Increases Don’t Work

Value-based Pricing Best for Renewals

In the subscription economy, cost of delivery matters in setting prices, but the factor that’s quickly becoming dominant is value delivered. As described in the book B4B: How Technology and Big Data Are Reinventing the Customer-Supplier Relationship, customers increasingly want to pay for outcomes, or “realized value.”

How a Simple Three-question Survey Tripled the Activation Rate of “No-Show” Users

Reactivation Through User Nurturing

User nurturing can have a big positive impact on both adoption and customer success, as we’ve discussed in earlier research alerts. But user nurturing can also be critical to retention. Like so many customer dynamics, churn starts gradually—and if left unnoticed, it can develop into issues that are impossible to resolve. Assuming you’ve been able to achieve your implementation and adoption milestones, any customer and revenue churn usually starts with user churn. User churn (i.e., loss of users) is when individuals start to drift away from using your solution. At renewal time, this drop in usage results in seat churn (i.e., loss of quantity on renewal), which is the first loss of revenue. Ultimately, further usage drops can result in […]

The Data Behind Adoption and Retention in the Customer Journey

In the last research alert, we talked about the “90/10 Rule of Adoption.” That was an observation based on our own real-world data which shows that after 90 days, a non-loyal user has only a ten percent chance of becoming a loyal user. Like the time-honored 80/20 rule, our 90/10 rule is meant as a guide for setting priorities—and the key takeaway is that adoption is critical to the customer journey. The 90/10 rule of adoption encourages companies to better understand the dynamics of customer adoption, because adoption is often highly correlated with customer retention and renewal revenue. This correlation between adoption and retention makes intuitive sense: Customers only want to pay for what they actually use, so if adoption […]