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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 Looker integration typically requires complex ETL processes and custom LookML modeling for cohort analysis, especially when dealing with customer lifecycle data and time-based segmentation. Looker’s native NetSuite connector struggles with custom fields and complex saved searches.
Here’s how to set up automated cohort analysis without LookML expertise or complex ETL infrastructure.
Build cohort analysis directly from NetSuite data using Coefficient
Coefficient provides a direct approach to NetSuite cohort reporting by enabling automated data sync into spreadsheets where cohort calculations can be performed using familiar formulas. This eliminates the need for LookML expertise while providing the analytical power needed for NetSuite cohort analysis.
How to make it work
Step 1. Set up SuiteQL queries for cohort data extraction.
Use the SuiteQL Query Builder to join customer and transaction data with proper date filtering. Create queries that pull customer acquisition dates, transaction history, and relevant segmentation fields needed for cohort analysis.
Step 2. Configure automated refresh scheduling for current cohort data.
Set up daily or weekly refresh schedules to capture new cohort data automatically. The 100,000 row limit per SuiteQL query handles most cohort analysis datasets while maintaining data freshness without manual intervention.
Step 3. Apply advanced filtering for customer segmentation.
Use built-in filtering with date ranges and customer segmentation criteria. The AND/OR logic supports complex cohort definitions, allowing you to segment customers by acquisition channel, product type, or geographic region.
Step 4. Build cohort calculations using spreadsheet formulas.
Create cohort retention calculations using familiar Excel or Google Sheets formulas. Build pivot tables to analyze customer behavior over time, calculating metrics like monthly retention rates, lifetime value progression, and churn patterns.
Step 5. Create trend analysis charts with automatic data updates.
Build cohort visualization charts that update automatically with each scheduled refresh. Track cohort performance over time with dynamic charts that reflect new customer acquisitions and behavioral changes.
Start analyzing customer cohorts without the technical overhead
This approach provides cohort analysis capabilities without Looker’s modeling complexity while maintaining automated data freshness through intelligent scheduling. Begin building your NetSuite cohort analysis today.
