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“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 transaction history contains rich behavioral data that can transform basic email marketing into sophisticated, behavior-driven campaigns that significantly outperform demographic targeting.
Here’s how to import and analyze transaction data to create powerful behavioral segmentation that drives higher engagement and conversion rates.
Build behavior-based email campaigns using NetSuite transaction data with Coefficient
Coefficient provides comprehensive transaction record access from NetSuite with data transformation capabilities that enable sophisticated behavioral analysis for NetSuite Mailchimp integration.
How to make it work
Step 1. Import multiple transaction record types.
Use Coefficient’s Records & Lists method to import Sales Orders, Invoices, Cash Sales, and Estimates. Each provides different behavioral insights – Sales Orders show purchase intent, Invoices reveal completed purchases, and Estimates indicate consideration patterns.
Step 2. Apply date-based filtering for relevant behavioral data.
Use Coefficient’s date filtering to focus on recent transaction activity (last 30/60/90 days) for timely behavioral segmentation. This manages data volume while ensuring your segments reflect current customer behavior patterns.
Step 3. Calculate behavioral metrics using spreadsheet formulas.
Create purchase frequency calculations using COUNTIFS() functions, identify high-value customers through SUMIFS() for transaction amounts, and track product category preferences from item-level transaction details. For example: =COUNTIFS(CustomerID,A2,TransactionDate,”>”&TODAY()-90) for 90-day purchase frequency.
Step 4. Build RFM scoring for advanced segmentation.
Calculate Recency (days since last purchase), Frequency (purchase count), and Monetary (total spend) scores using spreadsheet functions. Create scoring formulas like =IF(DaysSinceLastPurchase<=30,5,IF(DaysSinceLastPurchase<=60,4,3)) to rank customer engagement levels.
Step 5. Use SuiteQL for complex transaction analysis.
For advanced analytics, use Coefficient’s SuiteQL Query method to create joins between customers, items, and transaction records. This enables customer lifetime value calculations and sophisticated product recommendation analysis.
Transform transaction data into marketing intelligence
Behavior-based segmentation using NetSuite transaction history creates highly targeted email campaigns that drive measurable business results. Start building your behavioral segmentation strategy today.
