Connect NetSuite to Scoop to analyze your ERP data—financials, inventory, orders, and customer transactions. Go beyond NetSuite's built-in reporting with enhanced visualization, snapshot process analysis, and the ability to blend operational data with CRM, marketing, and product metrics.

What You Can Analyze

Domain	Analysis Types
Financial	Revenue trends, expense tracking, profitability
Orders	Order pipeline, fulfillment cycles, backlog aging
Inventory	Stock levels, turnover, demand patterns
Customers	Lifetime value, payment patterns, segmentation
Vendors	Spend analysis, performance, payment cycles

Connecting NetSuite to Scoop

Automated Email Reports

NetSuite saved searches can be scheduled to email to Scoop:

Create or select a Saved Search in NetSuite
Schedule the search for email delivery
Add your Scoop dataset email as recipient
Choose frequency (daily, weekly, monthly)

Report Format Support

Scoop handles multiple NetSuite export formats:

Format	Best For
CSV	Transaction data, detailed reports
Excel	Financial statements, formatted exports
PDF	Not recommended (use CSV/Excel instead)

Manual Upload

For one-time or ad-hoc analysis:

Export data from NetSuite
Upload to Scoop via file upload or Google Drive
Configure dataset settings
Process and analyze

Recommended Data Extracts

Financial Data

Report Type	Key Fields	Analysis Use
P&L	Account, Period, Amount	Profitability trends
Balance Sheet	Account, Date, Balance	Financial position
Revenue by Customer	Customer, Amount, Date	Revenue analysis
Expenses	Account, Department, Amount	Cost management

Order Data

Report Type	Key Fields	Analysis Use
Sales Orders	Order ID, Customer, Amount, Status	Pipeline tracking
Purchase Orders	PO ID, Vendor, Amount, Status	Procurement analysis
Invoices	Invoice ID, Customer, Amount, Due Date	AR management
Fulfillments	Order ID, Ship Date, Carrier	Delivery performance

Inventory Data

Report Type	Key Fields	Analysis Use
Stock Levels	Item, Location, Quantity	Inventory health
Item Receipts	Item, Date, Quantity	Replenishment tracking
Item Fulfillments	Item, Date, Quantity	Demand patterns

Use Cases: NetSuite Only

Financial Analysis

Enhanced reporting beyond NetSuite's capabilities:

Revenue trends: Track by customer, product, region
Margin analysis: Profitability by segment
Cash flow patterns: AR/AP aging and trends
Budget vs. actual: Variance with drill-down

Order Process Analysis

Snapshot orders to analyze fulfillment cycles:

Order aging: Time from order to fulfillment
Stage progression: How orders move through statuses
Bottleneck identification: Where orders get stuck
Backlog trends: Growing or shrinking pipeline

Inventory Intelligence

Track inventory patterns over time:

Stock trends: Levels by location and item
Turnover analysis: Fast vs. slow moving items
Reorder patterns: Timing and quantity trends

Use Cases: Blending with Other Sources

NetSuite + CRM

Goal: Connect financial outcomes to sales activities

NetSuite Data	CRM Data	Combined Insight
Revenue by customer	Account details	Revenue by segment/industry
Order history	Opportunity data	Win rate by segment
Collections	Customer health score	Credit risk analysis

NetSuite + Marketing

Goal: Calculate full-funnel ROI

Marketing spend per campaign
Revenue attributed to marketing
Customer acquisition cost
Marketing-influenced pipeline

NetSuite + Product Usage

Goal: Correlate usage with revenue outcomes

Revenue per active user
Feature adoption vs. expansion
Usage patterns of churned customers

NetSuite + Support

Goal: Calculate true customer profitability

Revenue per customer
Support tickets and costs
Net profitability ranking

Setting Up Snapshot Analysis

For order and inventory process analysis, configure as Snapshot dataset:

Order Snapshot

Include orders that are open or recently completed:

Status is not Closed OR Closed Date is within last 7 days

Inventory Snapshot

Daily snapshots of stock levels enable trend analysis:

Track inventory build-up or depletion
Identify seasonal patterns
Monitor days of supply

Best Practices

Saved Search Design

Include unique identifiers (Transaction ID, Item ID)
Add all date fields needed for analysis
Use consistent naming conventions
Include classification fields for segmentation

Update Frequency

Data Type	Recommended Frequency
Financial summaries	Weekly or monthly
Order data	Daily
Inventory levels	Daily
Customer data	Weekly

Data Quality

Ensure customer/item records are linked properly
Validate currency conversions if multi-currency
Check that classifications are consistently applied

Troubleshooting

Report Format Issues

Use CSV or Excel format (avoid PDF)
Check for special characters in data
Ensure column headers in first row

Missing Transactions

Verify Saved Search filters
Check subsidiary and date filters
Confirm user permissions

Data Not Syncing

Confirm scheduled search is running
Check email delivery to Scoop address
Verify dataset email configuration