Practical Ways to Unlock Revenue Growth with AI

If you’re relying on a general health score to run your business, you’re leaving substantial retention and growth revenue on the table. In the world of Customer Success, Account Management, Support, and Services, traditional health scores have long been the go-to metric for assessing customer relationships. We’ve all used them. Sometimes successfully, and other times, with mixed results. These conventional metrics often fall short. Not due to lack of intent, but because their broad strokes fail to paint the detailed picture needed to drive specific actions and outcomes. Why are Traditional Health Scores Dead? As a seasoned CS leader, I've grappled with the shortcomings of traditional health scores more times than I can count. While they seem promising on paper, they often struggle with: Accuracy: Are "green" customers *truly* never churning? Are "red" customers *always* at risk? Actionability: A general health score of 62 doesn't inherently tell you what actions to take. Impact Measurement: How do we know if our interventions *actually* made a difference? While traditional health scores may have a time and place, the industry is quickly evolving to ML-powered Outcome-Based Scoring. What is Reef’s Outcome-Based Scoring? At Reef, we've pioneered a more nuanced approach that aligns closely with driving specific revenue outcomes: Outcome-Based Scoring (OBS) . This method represents a shift from a generalized score to multiple, highly tuned ML-powered scores tailored to specific challenges and opportunities within customer management from churn mitigation to growth. Instead of relying on one holistic score that attempts to do too much, we're developing targeted scores backed by data science and tied to specific revenue outcomes. It's like having a suite of specialized tools instead of a one-size-fits-all approach. What are Some Outcome-Based Scores in Action? Our data scientists are constantly experimenting with different machine learning models to optimize for each revenue outcome. Let's dive deeper into three examples: Big Deal Finder: Reef analyzes your best customer expansions over the recent past to identify common traits among customers who expanded. Based on these learnings, Reef isolates current customers who share these traits and are likely to produce more growth pipeline that converts at a higher rate and closes at a higher rate for a higher sales price. Reef feeds targeted leads to Sales and CSMs and delivers ABM targets to Marketing. Churn Predictor: While many companies have moved towards churn prediction models, Reef automates recommendation delivery, keeps the model up-to-date, and continuously improves the model over time. It provides CSMs and Renewal Managers early warnings about potential churn, allowing for preemptive action. Product Downgrade Blocker: Some companies don’t have a full churn problem, but struggle with partial churn or downgrades of specific products. Reef can optimize a model to predict these downgrades and alert the right team member 6-9 months in advance of renewal, while there’s still time to change the outcome.