How to Use SKU-Level Performance Analysis in Squarespace: Step-by-Step Tutorial
Identify Your Top-Selling Products and Discontinue Underperformers with Data-Driven Insights
Introduction to SKU-Level Performance Analysis
Running a successful Squarespace store requires more than just adding products and hoping for the best. You need to know exactly which Stock Keeping Units (SKUs) are driving revenue, which are gathering dust in your inventory, and which products deserve more marketing attention.
SKU-level performance analysis gives you granular insights into every product variant in your catalog. Unlike store-wide metrics that only show aggregate sales, SKU analysis breaks down performance by individual items, colors, sizes, and variations. This allows you to answer critical business questions:
- Which specific product variants generate the most revenue?
- Which SKUs have the highest conversion rates?
- What's the average order value when customers buy specific products?
- Which products should I discontinue to free up inventory capital?
- Where should I focus my marketing budget for maximum ROI?
This tutorial will walk you through the complete process of performing SKU-level performance analysis on your Squarespace commerce data using MCP Analytics' specialized SKU performance tool. By the end, you'll have actionable insights to optimize your product catalog and increase profitability.
Prerequisites and Data Requirements
Before diving into SKU-level analysis, ensure you have the following requirements in place:
Account Access
- Squarespace Commerce Account: You need an active Squarespace Commerce (Basic or Advanced) plan with order history
- Administrator Permissions: Access to export order data from your Squarespace dashboard
- Minimum Order History: At least 30 days of order data (90+ days recommended for statistical significance)
Data Quality Checklist
For accurate SKU-level analysis, your Squarespace data should include:
- Unique SKU identifiers for each product variant
- Complete order information (dates, quantities, prices)
- Product names and variant details (size, color, etc.)
- Revenue and tax data
Technical Requirements
- Modern web browser (Chrome, Firefox, Safari, Edge)
- Spreadsheet software for data review (Excel, Google Sheets, or Numbers)
- Basic understanding of CSV file formats
Time Commitment: Plan for 20-30 minutes to complete this tutorial, including data export, upload, and initial analysis.
Step-by-Step: Analyzing SKU Performance
Step 1: Export Your Squarespace Order Data
The first step is extracting your order data from Squarespace in the correct format.
- Log into your Squarespace dashboard
- Navigate to Commerce → Orders
- Click the Export button in the top-right corner
- Select your date range (recommended: last 90 days for meaningful insights)
- Choose CSV format for the export
- Click Download and save the file to your computer
Expected Output: You should receive a CSV file named something like squarespace-orders-2024-01-01-2024-12-31.csv
Verify Your Data Export
Open the CSV file in your spreadsheet software and verify it contains these essential columns:
Order Number, Order Date, Product Name, SKU, Variant, Quantity, Item Price, Item Subtotal, Order Total
If any columns are missing, check your Squarespace export settings. Your products must have SKUs assigned for this analysis to work effectively.
Step 2: Review and Clean Your Data (Optional but Recommended)
While MCP Analytics handles most data formatting automatically, reviewing your export can prevent issues:
- Check for blank SKUs: If products don't have SKUs assigned, they'll be grouped as "Unknown" in analysis
- Verify date formats: Ensure dates are consistent (YYYY-MM-DD or MM/DD/YYYY)
- Review pricing data: Confirm prices are numerical values without currency symbols in some exports
- Check for test orders: Remove any test transactions you placed while setting up your store
Understanding AI-first data analysis pipelines can help you appreciate how modern analytics platforms automatically handle data cleaning and preparation tasks that traditionally required manual intervention.
Step 3: Upload Data to MCP Analytics
Now you're ready to analyze your SKU performance using the specialized analysis tool.
- Navigate to the SKU Performance Analysis tool
- Click the Upload Data button
- Select your Squarespace orders CSV file
- Wait for the upload progress bar to complete (typically 5-15 seconds for most datasets)
- Review the data preview to confirm columns are mapped correctly
- Click Analyze to start the SKU-level analysis
What Happens During Analysis: The platform aggregates your order data by SKU, calculating key performance metrics including total revenue, unit sales, average order value, conversion contribution, and performance trends over time.
Sample Analysis Configuration
If you need to manually configure column mappings, use these settings:
{
"sku_column": "SKU",
"product_name_column": "Product Name",
"quantity_column": "Quantity",
"revenue_column": "Item Subtotal",
"order_date_column": "Order Date",
"order_id_column": "Order Number"
}
Step 4: Interpret Your SKU Performance Results
Once analysis completes, you'll see a comprehensive dashboard with several key sections:
Top Performing SKUs Table
This table shows your highest-revenue products ranked by total sales:
- SKU: The unique product identifier
- Product Name: Human-readable product description
- Total Revenue: Cumulative sales for this SKU
- Units Sold: Total quantity ordered
- Average Item Price: Mean price per unit (useful for identifying discount patterns)
- Revenue Contribution %: Percentage of total store revenue from this SKU
Interpretation Tip: Apply the 80/20 rule (Pareto Principle). Typically, 20% of your SKUs generate 80% of revenue. Identify which products fall into this top tier and prioritize them for inventory, marketing, and customer experience optimization.
Underperforming SKUs
The bottom section highlights SKUs that may be candidates for discontinuation:
- Low Revenue SKUs: Products contributing less than 1% of total revenue
- Slow Movers: Items with fewer than X sales per month (threshold varies by business model)
- Declining Trend: SKUs showing month-over-month sales decreases
Decision Framework: Before discontinuing a SKU, consider:
- Is it a seasonal product currently out-of-season?
- Is it a new product that hasn't had time to gain traction?
- Does it serve as a gateway product leading to higher-value purchases?
- What's the inventory cost of keeping it versus the opportunity cost of shelf space?
Performance Trends Visualization
The time-series chart shows how SKU performance evolves over your selected date range. Look for:
- Growth trajectories: SKUs with upward trends deserve investment
- Seasonal patterns: Monthly or quarterly spikes indicating seasonality
- Sudden drops: May indicate stockouts, negative reviews, or competitive pressure
Step 5: Make Data-Driven Decisions
Transform your SKU analysis insights into concrete business actions:
For Top Performers
- Inventory Management: Increase stock levels to prevent stockouts on best-sellers
- Marketing Investment: Allocate more ad spend to proven winners
- Pricing Strategy: Test small price increases on high-demand, low-elasticity products
- Product Development: Create similar products or variations based on winning attributes
For Underperformers
- Clearance Sales: Discount slow movers to free up capital
- Bundle Strategies: Pair underperformers with best-sellers in product bundles
- Discontinuation: Remove persistent non-performers from your catalog
- Repositioning: Improve product descriptions, images, or categorization
Advanced Analysis Opportunities
Once you've mastered basic SKU analysis, consider these advanced techniques:
- Conduct A/B testing with statistical significance to validate pricing or marketing changes
- Apply AdaBoost machine learning techniques to predict future SKU performance
- Use Accelerated Failure Time (AFT) models to forecast when slow-moving SKUs will likely become obsolete
Step 6: Schedule Regular Analysis
SKU performance isn't a one-time analysis—it's an ongoing process:
- Monthly Reviews: Export and analyze data monthly to catch trends early
- Quarterly Deep Dives: Conduct comprehensive analysis quarterly to inform buying decisions
- Seasonal Preparation: Analyze 90-120 days before seasonal peaks to optimize inventory
- New Product Monitoring: Check performance of new SKUs weekly for the first 30 days
Pro Tip: Set calendar reminders for your analysis schedule. Consistency is key to catching opportunities and problems before they significantly impact revenue.
Verification: How to Know Your Analysis is Accurate
Validate your SKU performance analysis with these quality checks:
Revenue Reconciliation
Your total analyzed revenue should match your Squarespace reporting:
Sum of all SKU revenues = Total Squarespace revenue for date range (±2% acceptable variance)
Unit Count Verification
Total units analyzed should equal your order quantities:
SELECT SUM(Quantity) FROM orders WHERE date BETWEEN start_date AND end_date
Top SKU Sanity Check
Manually verify your #1 SKU by filtering your Squarespace orders:
- In Squarespace, filter orders by the top SKU
- Sum the quantities manually or in a spreadsheet
- Compare to the analysis results—they should match exactly
Expected Outputs Summary
| Metric | Expected Range | What It Means |
|---|---|---|
| Total SKUs Analyzed | Matches your catalog size | All products included in analysis |
| Revenue Contribution (Top 10 SKUs) | 40-80% of total revenue | Healthy product concentration |
| SKUs with <1% revenue share | 20-40% of total SKUs | Normal long-tail distribution |
| Date range coverage | Matches export parameters | Complete data included |
Ready to Analyze Your Squarespace SKUs?
Stop guessing which products deserve your attention. Get instant, data-driven insights into your SKU performance with MCP Analytics' specialized Squarespace analysis tool.
Start Your Free SKU Analysis Now →
Upload your Squarespace order data and receive comprehensive SKU performance metrics in seconds. No credit card required. No installation needed. Just clear answers to your product performance questions.
Next Steps with Squarespace SKU Analysis
Once you've completed your first SKU-level performance analysis, consider these next steps to maximize value:
1. Segment Analysis by Category
If you have multiple product categories, run separate analyses for each to understand category-specific performance patterns. This reveals whether underperformance is a product issue or a category issue.
2. Customer Lifetime Value by SKU
Connect SKU analysis to customer data to identify which products attract high-lifetime-value customers versus one-time buyers. This informs acquisition strategy.
3. Inventory Optimization
Use SKU performance data to calculate optimal reorder points and quantities. Focus capital on fast-movers and reduce investment in slow-movers.
4. Marketing Campaign Analysis
Before and after major marketing campaigns, run SKU analysis to measure which specific products benefited from the increased visibility and traffic.
5. Competitive Benchmarking
Compare your top-performing SKUs to competitor offerings to understand your competitive advantages and market positioning.
Learn More
Troubleshooting Common Issues
Encountering problems with your SKU analysis? Here are solutions to common issues:
Issue: "No SKUs Found in Uploaded Data"
Cause: Your Squarespace products don't have SKUs assigned, or the SKU column is empty in the export.
Solution:
- Go to Squarespace Commerce → Inventory
- Edit each product and add a unique SKU in the "SKU" field
- Wait 24 hours for new orders with SKUs, or manually add SKUs to your existing CSV export
- Re-export and re-analyze
Issue: Revenue Totals Don't Match Squarespace Reports
Cause: Tax, shipping, or discount handling differences between export and analysis.
Solution:
- Ensure you're using "Item Subtotal" or "Item Price × Quantity" columns, not "Order Total"
- Check that your date range in analysis matches your comparison report
- Verify you haven't filtered out any orders in the export
- Small variances (±2%) are acceptable due to refunds or cancellations
Issue: Some Products Show "Unknown" SKU
Cause: Products ordered before SKUs were assigned, or blank SKU fields.
Solution:
- Review the "Unknown" products in your results to identify them by name
- Add SKUs to these products in Squarespace
- For historical data, manually update your CSV export with SKUs based on product names
- Alternatively, focus analysis on recent data where all products have SKUs
Issue: Upload Fails or Times Out
Cause: Very large dataset (>100,000 orders) or file formatting issues.
Solution:
- Break your analysis into smaller date ranges (e.g., quarterly instead of annual)
- Remove unnecessary columns from your CSV to reduce file size
- Ensure file is saved as UTF-8 encoded CSV
- Try a different browser or clear browser cache
Issue: Metrics Seem Unusual or Unexpected
Cause: Data quality issues, test orders, or legitimate business changes.
Solution:
- Sort by unusual metrics (extremely high/low prices) to find anomalies
- Filter out test orders before export (look for specific customer emails or $0 orders)
- Check for bulk orders or wholesale transactions that skew averages
- Verify that product variants (size, color) are properly distinguished
- Compare month-over-month to identify if changes are recent or historical
Issue: Can't Find Certain Products in Results
Cause: Product name changes, merged SKUs, or zero sales in date range.
Solution:
- Search by SKU instead of product name (SKUs are more stable)
- Expand your date range to include when the product was sold
- Check if the product exists in your raw CSV export
- Verify the product was actually sold (not just listed)
Still Having Issues?
If you've tried these solutions and still encounter problems:
- Check that your CSV follows standard formatting (comma-delimited, quoted text fields)
- Verify your Squarespace export settings match the recommended configuration
- Review the complete SKU performance documentation
- Contact support with your error message and a sample of your data structure
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