It doesn’t take a machine learning model to understand that retention is critical to sustainable growth—but building smart, scalable strategies to reduce churn is a much harder problem to solve.
For a leading SaaS-based observability platform that helps IT teams maintain full visibility across complex tech stacks, efforts to address churn had historically been reactive. Despite rich operational and usage data across their systems, Customer Success and Product teams had limited data-driven guidance on when and how to intervene effectively.
That’s when they turned to 7Rivers. Our shared goal: shift from reactive response to proactive retention using an AI-powered churn prediction model built for real-world impact. Together, we set out to identify at-risk customers, understand the behavioral and contextual drivers behind churn and downgrades, and enable targeted interventions where they would have the greatest impact.
Foundation: Aligning Strategy and Data for Predictive Success
Every Data Native™ journey begins with a solid foundation. For this organization, this meant aligning cross-functional teams and preparing the data ecosystem for intelligent decision-making.
- Cross-functional collaboration: We partnered with Customer Success, Product, and Data stakeholders to align on objectives, define KPIs, and ensure the solution addressed real business needs.
- Data readiness: We integrated product usage metrics, customer interaction data, and account metadata—then cleansed, validated, and structured it for model development.
- Business context: Using both intuition and historical analytics, we formed hypotheses about what was driving churn and set measurable success criteria for the model.
By investing in this foundational work, it ensured the churn model would be more than just accurate—it would be relevant, trusted, and ready for action.
Insights: Building Intelligent Signals with Cortex AI
With the foundation in place, the next phase of the Data Native™ model focuses on generating insights. Using the Cortex AI platform, 7Rivers applied advanced machine learning techniques to build a churn prediction model tailored to this company’s unique data and customer dynamics. Our process included:
- Exploratory Data Analysis (EDA): Identifying patterns, trends, and signals in customer behavior over time.
- Feature engineering: Crafting predictive variables from usage patterns, call sentiment, engagement scores, and more.
- Model experimentation: Testing and iterating on multiple algorithms to optimize performance and explainability.
Targeted engagement strategies were developed based on key behaviors, and a business impact score was established, enabling the team to focus on high-value customers where the ROI of intervention is highest.
Actions: Turning Predictions into Retention Strategy
The final pillar of the Data Native™ model is all about taking action. With predictive intelligence in hand, 7Rivers helped this company operationalize AI, making insights usable within the flow of work.
CSMs now had the tools to:
Prioritize accounts based on a combination of churn risk and business value
Understand the “why” behind churn and tailor outreach accordingly
Visualize trends and track intervention performance using dashboards built in PowerBI and Looker
With a roadmap for future productionization—including scoring engines, CRM integration, and MLOps best practices—this leading SaaS platform is equipped for continued AI-driven evolution.
Final Thoughts: Retention, Reinvented
By moving through the Data Native™ framework—Foundation, Insights, and Actions—this organization transformed its retention approach. With the support of 7Rivers and Snowflake’s Cortex AI, they evolved from reacting to churn to preventing it—using data not just to explain the past, but to shape the future.
Want to predict—and prevent—churn at your company?
Let’s talk about how 7Rivers and Cortex AI can help you turn data into action.
