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Cash collection forecasting requires detailed AR aging data with customer payment history and credit terms that NetSuite’s standard AR aging reports don’t provide in the granular format needed for accurate collection modeling.
Here’s how to set up automated AR aging data extraction that provides the detailed customer and invoice information needed for accurate cash collection modeling and working capital management.
Automate AR aging data extraction using Coefficient
Coefficient enables automated AR aging data extraction optimized for cash collection forecasting with customer payment history integration and automated daily updates. You can extract aging data, payment patterns, and credit information that NetSuite standard reports can’t provide for NetSuite collection forecasting.
How to make it work
Step 1. Extract detailed AR aging data with configurable periods.
Import AR aging data through standard Reports with configurable aging periods and subsidiary selection. This provides the foundation for collection forecasting models with automated daily updates that capture new invoices and payments as they’re processed.
Step 2. Integrate customer payment history for behavior analysis.
Use Records & Lists imports to extract Customer Payment records with payment dates, amounts, and methods. This enables collection forecasting models to incorporate historical payment patterns and customer behavior analysis for more accurate collection timing predictions.
Step 3. Import invoice-level detail for granular forecasting.
Extract Invoice records with due dates, payment terms, and customer credit information, supporting granular collection forecasting by customer risk profile and payment terms. This invoice-level detail enables precise collection timing models.
Step 4. Include custom collection fields for qualitative factors.
Import custom fields related to collection activities like collection notes, payment plans, and credit holds. This enhances forecasting accuracy with qualitative collection factors that pure aging data can’t capture.
Step 5. Handle multi-currency collections for international operations.
Extract AR data with both transaction and base currency amounts, supporting collection forecasting for international operations with currency risk considerations. This ensures accurate cash collection modeling across multiple currencies.
Step 6. Combine AR data with customer credit analysis.
Integrate AR aging data with customer master data including credit limits, payment terms, and credit ratings. This combination enables collection probability and timing scenario modeling based on customer creditworthiness.
Step 7. Analyze collection patterns with SuiteQL.
Write custom queries to analyze collection patterns by customer segment, payment terms, or geographic region. This provides data-driven inputs for collection forecasting assumptions and helps optimize collection strategies.
Optimize cash collection with predictive insights
This automated AR data stream eliminates manual collection forecasting data preparation while providing detailed customer and invoice information needed for accurate cash collection modeling and working capital management. Start building predictive collection models with automated AR aging data.
