Prevent Customer Churn Before It Happens
Most organizations don’t lack customer data. They’re drowning in it.
Billing systems track renewals and revenue. Product platforms capture usage and engagement. CRMs store account details and lifecycle stages. Support logs mark another way customers interact with your organization. But when these signals live in silos, churn risk signals can stay hidden until it’s too late.
Even when churn is analyzed, efforts often fall short operationally, driven by factors such as unclear ownership, a focus on prediction without intervention, or the absence of repeatable processes to act on insights.
Brooklyn Data’s Churn Intelligence Console, a native Domo app, transforms raw data into dynamic, explainable, and actionable churn risk so your teams can intervene before revenue is lost.
A Practical Approach to Churn Prediction
At a high level, this solution is designed to do three things:
- Identify customers at high risk of churn within a defined post-renewal window
- Provide actionable, explainable churn risk scores
- Provide an easy way to deploy console supports for model performance monitoring and evaluation
Beyond simply building a model, this solution enables businesses to take action. Teams get dynamic churn risk scores, clear signals of what is driving higher and lower risk scores, and embedded workflows. Retention becomes proactive instead of reactive.
Turning Data into Subscription and Renewal Churn Intelligence
Churn prediction starts with bringing together the right data. Our solution integrates key sources across the customer lifecycle:
- Subscription and billing systems (renewals, pricing, tenure, discounts)
- Product usage and engagement (logins, feature usage, activity trends)
- Consumption data (how customers actually use your product over time)
- CRM and customer attributes (segment, industry, lifecycle stage)
- Product and customer support data
From there, the raw data is transformed into structured inputs for modeling. This includes:
- Feature engineering for RFM (recency, frequency, monetary value)
- Engagement trends and changes in behavior over time
- Consumption patterns and tenure-based features
The solution allows for versioned feature sets to ensure reproducibility, model governance, and further development
Modeling Churn Risk
Once the data foundations are in place, we leverage multiple machine learning models. The modeling approach is designed for performance, usability, and reliability. Key components include:
- Segmentation-aware modeling: Separate models for new and renewing customers
- Ensemble machine learning
- Evaluation metrics aligned to business goals: Including AUC, precision, recall, and confusion matrix analysis
Model Outputs and Monitoring
Once the model is trained and scored, the outputs are fully contextualized results that can be tracked and evaluated over time. Each model run captures churn risk scores and supporting metadata such as model parameters, performance metrics, and feature importances. This ensures that every prediction can be understood, audited, and compared across time.
To make this usable, the solution includes a dashboard that shows how the model is performing and what it’s learning. Teams can explore key metrics like AUC, precision, recall, and overall accuracy, along with visual diagnostics such as ROC curves and the model confusion matrix. The dashboard also highlights the most influential drivers of churn and compares predicted churn rates against historical trends.
Users can also define what “at risk” means for their business by setting a configurable threshold. From there, they can identify the specific accounts that fall into that category.
Activating Insights in Domo
Churn insights are delivered through a configurable experience in Domo, where teams can interact with the data in a way that fits their workflows. Within the platform, users can adjust churn thresholds, filter results by key segments, and compare different model runs. The interface is designed to make insights accessible, with purpose-built dashboards, account-level risk scores, and narrative explanations that highlight the main drivers behind each prediction.
Because these insights are delivered through Domo, they can be embedded directly into business workflows. Teams can use churn risk outputs to support customer success outreach, marketing efforts, and revenue operations planning, ensuring that identified risks lead to action.
High-Impact Use Cases
Organizations using this approach unlock value quickly across multiple areas:
- Proactive Retention: Identify high-risk customers within renewal windows and intervene early—before churn occurs.
- Intervention Hypothesis Generation: Leverage look-alike churners to build out A/B tests for different kinds of interventions (e.g. email versus another channel, email content, discount levels, etc.).
- Revenue Protection: Focus time and resources on accounts with the most revenue at risk.
- Customer 360 Intelligence: Unify data into a single, actionable view of customer health.
- Product & CX Insights: Understand the behavioral drivers behind churn to inform product and experience improvements.
How Teams Use the Solution
The solution is designed to support the teams who need to act on it –marketing, customer success, and product – without the additional overhead of a dedicated data science team.
Analysts and business users can use the framework to tune, and monitor models over time. They can evaluate performance, iterate on features, and make sure the model continues to reflect evolving customer behavior.
On the other hand, marketing, commercial, customer success, and product teams can use the outputs to guide their decision-making. By regularly reviewing churn risk at both the segment and account level (typically through scheduled updates), these teams can use those insights to prioritize outreach and interventions.
High-risk accounts can also be pushed into downstream workflows such as customer success playbooks or marketing campaigns, ensuring that insights translate into targeted action.
AI-Powered Extensions
In addition to prediction, the solution creates opportunities to streamline how teams engage with churn insights through AI.
Teams can explore model outputs using natural language, generate automated summaries of churn trends, and even create content to support retention efforts. AI can also help surface emerging risks by alerting internal teams when new patterns appear in the data.
These extensions build on the core churn console solution, which helps to reduce the effort required to interpret insights. In turn, this accelerates the path from signal to response.
Why Brooklyn Data
Churn prediction isn’t new. The difference is in how it’s operationalized.
We connect model development, transparency, and downstream action in a way that teams can actually run-- bridging the gap between data science and real business impact. This is done through:
- Proven, scalable ML frameworks embedded in Domo
- Deep expertise in Domo and the modern data stack
- A strong emphasis on explainability and usability
- Solutions tailored to your business model—not generic templates
Churn prediction is powerful, but only if it drives actions.