How Did a Dental SaaS Company Use ML to Predict and Prevent Customer Churn?
This case study highlights how a Dental SaaS company leveraged machine learning to proactively address customer churn. With a growing client base of dental clinics, the company needed a data-backed approach to identify at-risk customers and uncover the factors influencing retention.
Our team developed a predictive ML model that analyzed key variables across CRM, billing, and user activity systems. By identifying the most impactful churn indicators, the company was able to focus its retention strategies more effectively, ultimately improving customer satisfaction and reducing attrition.
Key Results
The Challenge
The client had extensive customer data but lacked a systematic way to identify and act on churn risk. Key pain points included:
- No existing ML pipeline or churn prediction capability
- Siloed data across Salesforce, Zuora, Pendo, and Intercom
- Uncertainty about which customer attributes mattered most
- Difficulty translating technical outputs into business actions
- Reactive, rather than proactive, customer retention strategy
Our Solution
We designed and deployed a lightweight, interpretable machine learning solution focused on actionable business impact:
- Combined relevant data across Salesforce (CRM), Zuora (Billing), Pendo & Intercom (User Activity)
- Engineered features such as account age, payment frequency, active users, conversations, and more
- Preprocessed data by removing outliers and balancing the dataset to reduce bias
- Trained a logistic regression model with L2 regularization to predict churn with transparency
- Mapped feature coefficients to identify top drivers of churn and retention
- Enabled prediction for recent 3-month windows to track trend changes over time
- Added plain-language interpretations for each feature's impact, helping non-technical users understand churn risks
Implementation Process
Feature Engineering
Gathered, joined, and curated features from Salesforce, Zuora, Pendo, and Intercom
Data Cleaning & Balancing
Removed outliers and applied distribution-based balancing to eliminate bias
Model Development
Trained a logistic regression model with L2 regularization to prevent overfitting
Evaluation & Refinement
Evaluated precision, accuracy, and adjusted parameters for optimal balance and generalization
Insight Interpretation
Created feature importance summaries and interpreted model output for business stakeholders
Technologies Used
"This project gave us clear visibility into which customers are likely to churn and, more importantly, why they do. The ML model’s transparency helped both our data and customer success teams act quickly and confidently."
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