)
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.”
QuickBooks tracks subscription billing transactions but doesn’t provide the granular payment behavior analysis needed to predict which customers are likely to churn based on billing patterns and payment changes.
Here’s how to build sophisticated churn prediction models using live QuickBooks subscription data with automated updates for continuous model accuracy.
Extract comprehensive billing data for predictive modeling using Coefficient
Coefficient connects QuickBooks subscription billing data directly to Excel, providing the transaction-level detail needed for churn prediction. You get automated data refresh and advanced filtering to focus on subscription-specific billing patterns.
How to make it work
Step 1. Import subscription billing objects from QuickBooks.
Use Coefficient’s “From Objects & Fields” method to pull Invoice objects with recurring billing indicators, Payment patterns, and Customer details. Include Item-level subscription information to track service changes and billing amount variations over time.
Step 2. Create churn indicator calculated fields.
Build formulas to identify leading churn signals like payment delays using `=DAYS(Invoice_Date,Payment_Date)`, failed payment attempts, and subscription downgrades. Track billing frequency changes with `=COUNTIFS(Customer,customer_name,Date,”>=”&start_date)` to count billing events per period.
Step 3. Set up historical cohort datasets.
Use Coefficient’s date filtering to pull complete billing history for training your prediction model. Create cohort groups based on subscription start dates, billing amounts, or customer segments to identify patterns specific to different customer types.
Step 4. Apply Excel’s statistical functions for prediction modeling.
Use Excel’s FORECAST.LINEAR or TREND functions with your churn indicators to predict customer behavior. For more advanced modeling, apply Excel’s Analysis ToolPak regression analysis or machine learning add-ins to the live QuickBooks data.
Step 5. Configure automated model updates.
Set up daily or weekly automated refresh schedules to continuously update model inputs with new billing activity. This ensures your churn predictions reflect current customer behavior rather than outdated historical patterns.
Predict churn before it happens
Live QuickBooks billing data enables sophisticated churn prediction that updates automatically and catches behavioral changes early. Start building predictive models that help you retain customers before they decide to leave.
