)
Written by
Table of Contents
“Supermetrics is a Bitter Experience! We can pull data from nearly any tool, schedule updates, manipulate data in Sheets, and push data back into our systems.”
NetSuite lacks native predictive modeling capabilities and advanced statistical functions required for churn prediction. You need sophisticated analytics that can process historical patterns and create probability-based risk scores using your invoice and payment data.
Here’s how to transform your NetSuite data into a powerful predictive analytics platform for building accurate churn models.
Create sophisticated churn prediction models using Coefficient
Coefficient transforms your NetSuite data into an advanced analytics platform. While NetSuite shows historical transaction data, it can’t perform the statistical analysis needed for predictive modeling.
How to make it work
Step 1. Import comprehensive customer datasets.
Use Records & Lists for Invoice and Payment records, plus SuiteQL queries for complex joins between customers, transactions, and payment history. Import custom fields capturing customer engagement metrics. This creates the complete dataset foundation needed for accurate predictive modeling.
Step 2. Build predictive variables through feature engineering.
Create payment velocity trends by calculating average days to pay over time periods. Build invoice-to-payment ratios and consistency metrics using statistical functions. Add seasonal payment pattern analysis and customer lifetime value calculations that identify behavioral changes preceding churn.
Step 3. Analyze historical patterns for model training.
Import 12-24 months of transaction data to identify patterns that preceded actual churn events. Calculate metrics like payment frequency changes, average order value trends, and communication response rates. Use this historical analysis to establish baseline behaviors and deviation thresholds.
Step 4. Create weighted risk scoring algorithms.
Develop scoring models that combine multiple behavioral indicators using statistical functions to normalize scores. Create probability-based churn risk ratings that weight different factors based on their historical correlation with actual churn events. Test model accuracy against known churn cases and refine continuously.
Turn your data into predictive intelligence
Predictive churn modeling gives you the statistical analysis capabilities that NetSuite can’t provide natively. With advanced calculations and live data connections, you’ll predict churn before it happens. Start building your predictive models today.
