The market has undergone a fundamental shift. We have moved from a world that prioritized revenue growth above all else to one that demands durable, profitable growth.
This change has placed immense pressure on Net Revenue Retention (NRR), which has been on a consistent downward trajectory for the last two years. Since Q1 2023, average NRR has dropped from 110.5% to 107.1%. Today, 58% of companies report lower NRR than they did two years ago.
This isn't just a problem for underperformers. This decline is affecting "excellent companies" just as much as average ones. The question in the boardroom is no longer "how much can we sell?" but "what can we actually do to improve NRR?".
In this podcast, I spoke with Dan Harmeson of QuadSci about the reality of the NRR crisis and the data-driven solution that is reversing the trend.
Here is how to move from guesswork to predictable growth.
The solution to reversing NRR decline lies in leveraging the massive datasets modern businesses already generate. Product telemetry data—user logins, clicks, and feature usage—provides a direct link to the value customers perceive.
However, most teams are flying blind. This data is extremely large, volatile, and was not originally intended for go-to-market teams. Its raw form makes it difficult for commercial teams to translate into actionable insights10.
When you apply AI to this data, the picture changes. By merging product usage patterns with business outcomes, we can now achieve 90% accuracy in predicting churn and growth events up to 12 months in advance. As Dan states, "If you can't see it, you can't shape it."
Teams focusing just on usage levels will never achieve the predictive accuracy they need. Seeing the true signal requires looking at usage consistency.
When you plot usage level against consistency, customers naturally cluster into six distinct "cohorts". This framework simplifies the complexity of the data into an actionable segmentation model.
These cohorts tell you exactly where an account stands:
Map the "Zone of Contraction": Identify Strugglers and Disconnected accounts immediately. Focus retention plays here.
Map the "Zone of Expansion": Direct your best account managers to the Power Users and Enthusiastic Adopters.
Stop the "peanut butter" approach: Do not treat every customer the same. Allocate resources based on the cohort.
The power of this model isn't just observation; it is action. Best-in-class organizations use this framework to "engineer cohort shift".
The goal is to gamify growth. You want to move a customer from a lower-value cohort (e.g., Struggler) to a higher-value one (e.g., Convert). This makes business growth predictable.
We found that not all features are created equal. "Growth Features" directly correlate with positive cohort shifts, while "Features of Volatility" indicate risk.
Nick Toman and Dan Harmeson go deep on the "Engineering SaaS Account Growth" report and how leading CEOs are using this data to achieve a 5% boost in NRR.