Understanding the full landscape of Shopify Analytics dimensions and metrics is essential for anyone running an e-commerce business on Shopify. Whether you're building custom reports in Shopify Analytics, pulling data through the Shopify API, or analyzing store performance in external dashboards, knowing exactly what data is available — and what each field means — is the foundation of effective e-commerce optimization.
This guide provides a complete reference of every dimension and metric available in Shopify Analytics as of 2026. We've organized them by category — orders, customers, products, traffic, sales, marketing, and inventory — and included practical context on when and how to use each one.
What Are Shopify Dimensions vs Metrics?
Before diving into the full reference, it's important to understand the difference between dimensions and metrics — two concepts that serve fundamentally different purposes in e-commerce data.
Dimensions are descriptive attributes that define what you're looking at. They are the labels, categories, and identifiers that let you organize and filter your data. Examples include order status, customer location, product type, sales channel, and UTM source. Dimensions answer the question: "How do I want to slice this data?"
Metrics are quantitative measurements that tell you how things performed. They are the numbers: total sales, orders, average order value, sessions, conversion rate. Metrics answer the question: "How is my store performing?"
Breakdowns let you segment any metric by a dimension. For example, breaking down total sales by product type shows which categories generate the most revenue. Breaking down conversion rate by traffic source reveals which channels convert best.
How Is Shopify Data Structured?
Shopify data is organized around several core entities: Orders (the transactions), Customers (who placed them), Products (what was purchased), Sessions (how users arrived and browsed), and Inventory (what's in stock). Unlike advertising platforms that follow a strict hierarchy, Shopify data is relational — orders connect to customers, products, and sessions through shared identifiers.
Shopify Analytics provides built-in reports through the admin dashboard, while the Shopify API (GraphQL and REST) gives programmatic access to raw data. Many merchants also use Shopify's integration with Google Analytics for deeper traffic analysis and cross-platform attribution.
Order Dimensions
Order dimensions describe the core transactional data in your Shopify store. Every purchase creates an order with its own identifiers, statuses, and metadata. These dimensions are essential for understanding purchase patterns, managing fulfillment, and analyzing financial performance.
| Dimension | Description |
|---|---|
| Order ID | Internal unique identifier for the order in Shopify's system |
| Order Name | Customer-facing order reference (e.g., #1001, #1002) — sequential and human-readable |
| Order Number | Sequential numeric order number — distinct from the internal ID |
| Order Status | Overall order state: Open, Archived, or Cancelled |
| Financial Status | Payment state: Pending, Authorized, Paid, Partially Paid, Partially Refunded, Refunded, Voided |
| Fulfillment Status | Shipping state: Unfulfilled, Partially Fulfilled, Fulfilled, Restocked |
| Order Date | Date and time the order was placed by the customer |
| Order Tags | Custom labels applied to orders for organization and filtering (e.g., "VIP", "wholesale", "gift") |
| Discount Code | The discount code applied to the order, if any (e.g., "SUMMER20", "WELCOME10") |
| Payment Gateway | The payment processor used: Shopify Payments, PayPal, Stripe, Amazon Pay, etc. |
| Risk Level | Fraud risk assessment: Low, Medium, or High — based on Shopify's fraud analysis |
Customer Dimensions
Customer dimensions describe the people who purchase from your store. These attributes are critical for customer segmentation, retention analysis, and understanding the demographics and behavior patterns of your buyer base.
| Dimension | Description |
|---|---|
| Customer ID | Unique internal identifier for the customer in Shopify |
| Customer Name | Full name of the customer (first and last name) |
| Customer Email | Email address used for the customer account |
| Country | Customer's country based on billing or shipping address |
| Region / Province | State, province, or region within the customer's country |
| City | Customer's city based on billing or shipping address |
| Customer Tags | Custom labels applied to customer profiles (e.g., "loyal", "wholesale", "influencer") |
| Accepts Marketing | Whether the customer opted in to receive marketing emails and SMS |
| Order Count | Total number of orders the customer has placed — used to segment by purchase frequency |
| Total Spent | Cumulative amount the customer has spent across all orders |
| Customer Created Date | When the customer account was first created |
| Customer Segment | Shopify segment the customer belongs to (created via Shopify's segmentation tools using filters like order count, spend, location) |
Product Dimensions
Product dimensions describe the items in your catalog. These are essential for understanding which products drive revenue, which collections perform best, and how inventory levels relate to sales velocity. Product-level analysis is the foundation of merchandising and assortment optimization.
| Dimension | Description |
|---|---|
| Product ID | Unique internal identifier for the product in Shopify |
| Product Title | The name of the product as displayed in the store |
| Product Type | Category classification (e.g., "T-Shirt", "Running Shoes", "Moisturizer") |
| Vendor | The manufacturer or supplier of the product |
| Collection | The collection(s) the product belongs to (e.g., "Summer 2026", "Bestsellers", "Sale") |
| SKU | Stock Keeping Unit — unique code for inventory tracking at the variant level |
| Variant Title | The specific variant (e.g., "Medium / Blue", "32oz", "US 10") |
| Variant ID | Unique internal identifier for the product variant |
| Barcode | The product barcode (UPC, EAN, ISBN) for inventory and POS identification |
| Product Status | Current state: Active (visible in store), Draft (not published), or Archived |
| Inventory Quantity | Current stock on hand across all locations |
| Product Tags | Custom labels applied to products for filtering and organization |
Channel & Traffic Dimensions
Channel and traffic dimensions tell you where your store visitors come from and how they arrived. These dimensions are critical for understanding which marketing channels drive traffic and sales, optimizing your marketing spend allocation, and identifying high-value traffic sources.
| Dimension | Description |
|---|---|
| Sales Channel | Shopify's built-in channel: Online Store, Shop App, Facebook/Instagram, Google, POS, Draft Orders, Buy Button, Wholesale |
| Referring Site | The full URL of the page that linked to your store |
| Referring Domain | The domain of the referring site (e.g., google.com, facebook.com, instagram.com) |
| UTM Source | The utm_source parameter value identifying the traffic source (e.g., facebook, google, newsletter) |
| UTM Medium | The utm_medium parameter value identifying the channel type (e.g., cpc, email, social, organic) |
| UTM Campaign | The utm_campaign parameter value identifying the specific campaign |
| UTM Content | The utm_content parameter for differentiating ad creatives or links within a campaign |
| UTM Term | The utm_term parameter for identifying paid search keywords |
| Landing Page | The first page a visitor sees when arriving at your store |
| Device Type | The device used to browse: Desktop, Mobile, or Tablet |
| Browser | The browser used: Chrome, Safari, Firefox, Edge, Samsung Internet, etc. |
Sales & Revenue Metrics
Sales and revenue metrics are the financial backbone of your Shopify Analytics. Understanding the specific hierarchy of Shopify's revenue metrics — gross sales, discounts, returns, net sales, shipping, taxes, and total sales — is critical for accurate financial reporting and performance analysis.
| Metric | Description | Formula / Notes |
|---|---|---|
| Gross Sales | Total price of all items sold before any deductions | Quantity × Price per unit — no discounts, returns, or taxes subtracted |
| Discounts | Total value of discounts applied to orders | Includes percentage discounts, fixed amount discounts, and automatic discounts |
| Returns | Total value of returned items | Refunded product amounts (not including shipping refunds) |
| Net Sales | Revenue after discounts and returns | Gross Sales - Discounts - Returns |
| Shipping Charges | Total shipping fees collected from customers | Revenue from shipping — not the actual cost of shipping |
| Taxes | Total taxes collected on orders | Sales tax, VAT, or GST depending on store configuration and customer location |
| Total Sales | Complete revenue including product sales, shipping, and taxes | Net Sales + Shipping + Taxes |
| Orders | Total number of orders placed | Counts all orders regardless of fulfillment or financial status |
| Average Order Value (AOV) | Average revenue per order | Total Sales ÷ Orders |
| Tips | Total tips collected through Shopify checkout | Available when tips are enabled in checkout settings |
Which revenue metric should you use? For product revenue performance, use net sales — it reflects what customers actually paid for products after accounting for discounts and refunds. For total revenue reporting, use total sales. For marketing ROI calculations (ROAS), use net sales to avoid inflating returns by including shipping and tax.
Customer Metrics
Customer metrics help you understand your buyer base — how many customers you're acquiring, how many return, and how valuable they are over time. These metrics are essential for retention strategy, customer lifetime value analysis, and forecasting future revenue.
| Metric | Description | Formula / Notes |
|---|---|---|
| Total Customers | Total number of unique customers who placed at least one order | Deduplicated by customer account — guest checkouts create separate entries |
| New Customers | Customers placing their first-ever order in the selected period | First order date falls within the reporting window |
| Returning Customers | Customers who have placed more than one order and ordered again in the period | Had at least one prior order before the reporting window |
| Returning Customer Rate | Percentage of customers who have made repeat purchases | (Returning Customers ÷ Total Customers) × 100 |
| Customer Lifetime Value (CLV) | Average total revenue a customer generates over their entire relationship | Average Order Value × Average Orders Per Customer — Shopify uses predictive models for forward-looking CLV |
| Average Orders Per Customer | Average number of orders per customer | Total Orders ÷ Total Customers |
Product Performance Metrics
Product performance metrics tell you how individual products perform across the full customer journey — from initial view to add-to-cart to purchase. These metrics are essential for merchandising decisions, collection optimization, and identifying products that convert well versus those that attract views but don't sell.
| Metric | Description | Formula / Notes |
|---|---|---|
| Units Sold | Total quantity of a product's variants sold | Counts each unit — an order with 3 of the same item counts as 3 units |
| Net Quantity | Units sold minus units returned | Units Sold - Returned Units — reflects actual units delivered to customers |
| Product Views | Number of times the product page was viewed | Counts each page view — includes repeat views from the same session |
| Add-to-Carts | Number of times the product was added to a shopping cart | Intent signal — compare to product views for add-to-cart rate |
| Reached Checkout | Number of sessions where the product was in the cart at checkout initiation | Tracks progression from cart to checkout — drop-off here indicates checkout friction |
| Purchases | Number of orders that included this product | Order count, not unit count — one order with 3 units counts as 1 purchase |
| Product Conversion Rate | Percentage of product views that resulted in a purchase | (Purchases ÷ Product Views) × 100 |
| Average Units Per Order | Average number of units of this product per order that includes it | Units Sold ÷ Purchases |
Traffic & Conversion Metrics
Traffic and conversion metrics measure how visitors discover, browse, and convert on your online store. These metrics are the e-commerce equivalent of a sales funnel — tracking each step from initial visit to completed purchase and revealing where potential customers drop off.
| Metric | Description | Formula / Notes |
|---|---|---|
| Sessions | Total number of visits to your store | A session is a continuous period of activity — 30+ minutes of inactivity starts a new session |
| Visitors | Total number of unique people who visited your store | Cookie-based identification — one visitor can have multiple sessions |
| Unique Visitors | Deduplicated count of individual visitors | Same as visitors — deduplicated by first-party cookie |
| Page Views | Total number of pages viewed across all sessions | Counts each page load — a session with 5 page loads counts as 5 page views |
| Pages Per Session | Average number of pages viewed per session | Page Views ÷ Sessions — higher values indicate more engaged browsing |
| Average Session Duration | Average time spent per session | Total time on site ÷ Sessions — measured in minutes and seconds |
| Online Store Conversion Rate | Percentage of sessions that resulted in a purchase | (Orders ÷ Sessions) × 100 — typical range is 1-3% |
| Added to Cart Rate | Percentage of sessions where a product was added to cart | (Sessions with Add-to-Cart ÷ Total Sessions) × 100 |
| Reached Checkout Rate | Percentage of sessions that reached the checkout page | (Sessions Reaching Checkout ÷ Total Sessions) × 100 |
| Cart Abandonment Rate | Percentage of sessions with add-to-cart that did not complete a purchase | ((Add-to-Cart Sessions - Purchase Sessions) ÷ Add-to-Cart Sessions) × 100 — typical range 60-80% |
Reading the conversion funnel: Track the progression from sessions → product views → add-to-cart → reached checkout → purchase. Each step reveals a different optimization opportunity. High traffic but low product views suggests poor navigation or irrelevant traffic. Low add-to-cart from product views indicates pricing, imagery, or copywriting issues. High cart abandonment points to checkout friction, shipping costs, or trust issues.
Marketing Attribution Metrics
Marketing attribution metrics help you understand which channels and campaigns drive sales. Shopify provides both first-click and last-click attribution models, giving you two perspectives on how marketing touchpoints contribute to conversions.
| Metric | Description | Formula / Notes |
|---|---|---|
| Orders Attributed | Number of orders attributed to a specific marketing channel or campaign | Counted under both first-click and last-click models — same order may credit different channels |
| Sales Attributed | Revenue from orders attributed to a specific marketing channel or campaign | Monetary value of attributed orders — use for ROAS calculation |
| First-Click Attribution | Credits the first marketing touchpoint that brought the customer to your store | Favors awareness channels (social, display, content) that introduce new visitors |
| Last-Click Attribution | Credits the final marketing touchpoint before the purchase | Favors conversion channels (search, retargeting, email) that close the sale |
| Sessions from Marketing | Number of sessions generated by marketing activities (paid and organic) | Includes sessions from tracked UTM parameters and recognized referring sources |
Attribution model differences: The same order will often credit different channels under first-click vs. last-click. A customer who discovered your store via a Facebook ad, returned via an email campaign, and purchased after a Google search would credit Facebook under first-click and Google under last-click. Use both models together to understand the full customer journey — first-click shows discovery channels, last-click shows conversion channels.
Inventory Metrics
Inventory metrics help you manage stock levels, prevent stockouts, and identify slow-moving products. These metrics connect your sales performance to your supply chain, enabling data-driven restocking decisions and assortment planning.
| Metric | Description | Formula / Notes |
|---|---|---|
| Inventory Quantity | Current units in stock across all locations | Real-time snapshot — updated as orders are placed and stock is received |
| Sold Quantity | Total units sold in the selected time period | Counts units sold — not orders (one order can include multiple units) |
| Returned Quantity | Total units returned in the selected time period | Returned items are restocked when the refund is processed with restock enabled |
| Days of Inventory Remaining | Estimated number of days until the product is out of stock | Current Inventory ÷ Average Daily Sales Rate — based on recent sales velocity |
| Percent of Inventory Sold | Percentage of total inventory that has been sold in the period | (Sold Quantity ÷ (Sold Quantity + Current Inventory)) × 100 — also called sell-through rate |
Using inventory metrics for decisions: Set reorder alerts when days of inventory remaining drops below your supplier lead time (plus safety stock). Products with high sell-through rates (>80%) may be understocked — consider increasing order quantities. Products with low sell-through (<20%) over 90 days are candidates for markdowns or discontinuation. Cross-reference inventory metrics with product performance metrics to identify items that sell fast and need more stock versus items that sit on shelves.
Shopify Analytics Benchmarks by Industry
Understanding what "good" looks like for Shopify metrics depends on your industry, average order value, and business model. Here are typical benchmarks to help you evaluate whether your store is performing within normal ranges.
Conversion rate benchmarks
| Industry | Below Average | Average | Above Average |
|---|---|---|---|
| Fashion & Apparel | < 1.0% | 1.0% - 2.0% | > 2.0% |
| Health & Beauty | < 1.5% | 1.5% - 3.0% | > 3.0% |
| Electronics | < 0.8% | 0.8% - 1.8% | > 1.8% |
| Food & Beverage | < 1.5% | 1.5% - 3.5% | > 3.5% |
| Home & Garden | < 1.0% | 1.0% - 2.5% | > 2.5% |
Cart abandonment benchmarks
| Metric | Below Average | Average | Above Average |
|---|---|---|---|
| Cart Abandonment Rate | > 80% | 65% - 80% | < 65% |
| Add-to-Cart Rate | < 3% | 3% - 8% | > 8% |
| Reached Checkout Rate | < 1.5% | 1.5% - 4% | > 4% |
Customer metrics benchmarks
| Metric | Below Average | Average | Above Average |
|---|---|---|---|
| Returning Customer Rate | < 15% | 15% - 30% | > 30% |
| Average Order Value | < $50 | $50 - $120 | > $120 |
| Pages Per Session | < 2.5 | 2.5 - 5.0 | > 5.0 |
Benchmark context: These ranges are approximate averages across Shopify stores. Your specific benchmarks depend on your price point, target audience, and acquisition channels. Stores with higher AOV typically have lower conversion rates (customers take longer to decide on expensive purchases) but higher revenue per session. Stores with consumable products tend to have higher returning customer rates than fashion or electronics stores.
Shopify Data Access and Reporting Tools
Shopify provides multiple ways to access your analytics data, from the built-in dashboard to the GraphQL API. Understanding your options helps you build the right reporting infrastructure for your business.
Shopify Analytics dashboard
The built-in Analytics section in Shopify Admin provides pre-built reports for sales, customers, acquisition, behavior, marketing, and inventory. These reports support date range selection, comparison periods, and basic filtering. The dashboard is sufficient for daily monitoring and basic analysis but limited for custom segmentation and cross-dimensional queries.
Shopify Reports
Shopify's Reports feature (available on Shopify plan and above) provides more detailed pre-built reports and the ability to create custom reports. Custom reports let you select specific dimensions and metrics, apply filters, and save report configurations. Reports can be exported as CSV for external analysis. The Shopify Plus plan adds additional reporting capabilities including ShopifyQL for custom queries.
Shopify GraphQL Admin API
The Shopify Admin API provides programmatic access to all store data — orders, customers, products, inventory, and analytics. The GraphQL API is more efficient than the REST API for complex queries because it lets you request exactly the fields you need. Use the API for building custom dashboards, integrating with business intelligence tools, and automating reporting workflows.
Third-party integrations
Shopify integrates natively with Google Analytics 4 for deeper traffic analysis, Facebook/Meta for advertising attribution, and dozens of analytics apps from the Shopify App Store. For enterprise-level analysis, tools like Looker, Tableau, and Power BI can connect to Shopify data through API integrations or data warehouse exports. Consider a data warehouse approach (BigQuery, Snowflake) if you need to join Shopify data with advertising data from multiple platforms.
How to Use Shopify Metrics for Store Optimization
Having access to dozens of metrics is powerful, but knowing which ones matter for your specific goals is what separates successful e-commerce operators from those overwhelmed by dashboards. Here's a practical framework for different optimization goals.
For revenue growth
Focus on total sales, orders, average order value, and net sales. Break down by product type and collection to identify which categories drive the most revenue. Track returning customer rate and CLV to understand whether growth comes from new acquisition or repeat purchases.
For conversion optimization
Track the full funnel: sessions → product views → add-to-cart rate → reached checkout rate → conversion rate. Identify the biggest drop-off point and focus optimization there. Use device type and browser breakdowns to find technical issues that hurt conversion on specific platforms.
For marketing optimization
Compare orders attributed and sales attributed across channels using both first-click and last-click models. Calculate ROAS per channel (attributed sales ÷ channel spend). Use UTM parameters to track campaign-level performance and landing page analysis to optimize entry points.
For inventory management
Monitor days of inventory remaining for your top sellers. Track sell-through rate by product to identify overstocked items. Cross-reference sold quantity trends with product views to distinguish between products that have demand but lack inventory versus products that simply are not selling.
Common Mistakes When Analyzing Shopify Data
Even experienced e-commerce operators make these mistakes when working with Shopify Analytics. Avoiding them will lead to more accurate insights and better decisions.
1. Confusing gross sales with net sales
Gross sales is the total price before any deductions. If you sold $100,000 in products but gave $15,000 in discounts and processed $5,000 in returns, your net sales are $80,000 — not $100,000. Always use net sales for revenue performance analysis and ROAS calculations.
2. Mixing up sessions and visitors
Sessions count visits, visitors count people. If 1,000 people each visit your store twice in a month, you have 1,000 visitors but 2,000 sessions. Using sessions for audience size overstates your reach. Using visitors for engagement analysis understates activity. Use visitors for audience sizing and sessions for engagement and conversion analysis.
3. Ignoring the attribution window
Shopify's attribution window is 30 days. A customer who clicked a Facebook ad on April 1st and purchased on April 25th will be attributed to that Facebook campaign. But comparing this to Facebook's own reporting (which may use a 7-day click window) will show different numbers. Always align attribution windows when comparing cross-platform data.
4. Comparing sales channel with UTM source
Sales channel (Online Store, Shop App, Facebook) tells you which Shopify integration processed the order. UTM source tells you which marketing campaign drove the traffic. A Facebook ad click that leads to a purchase on the Online Store will show "Online Store" as the sales channel but "facebook" as the UTM source. These are different questions — don't conflate them.
5. Using orders when you should use sessions for conversion rate
Shopify's online store conversion rate is orders divided by sessions — not orders divided by visitors. Using visitors as the denominator produces a higher (inflated) conversion rate because each visitor may have multiple sessions. Stick with Shopify's standard formula (orders / sessions) for benchmarking accuracy.
6. Averaging AOV or conversion rate across time periods
Average order value and conversion rate are ratios. Averaging weekly AOV values gives mathematically incorrect results because each week has different order volumes. Instead, sum total sales and total orders for the full period, then calculate AOV from the totals: Total Sales ÷ Total Orders.
7. Ignoring guest checkout data gaps
Guest checkouts create separate customer records for each order — meaning the same person buying twice as a guest will appear as two different customers. This inflates your "new customer" count and understates your returning customer rate. Encourage account creation during checkout (without making it mandatory) to improve customer data accuracy. You can also use email matching to manually deduplicate guest checkout records.
What Changed in Shopify Analytics in 2024-2026
Shopify has made significant updates to its analytics and reporting capabilities over the past two years. Understanding these changes helps you take advantage of new features and adjust for any changes in metric definitions.
Enhanced customer segmentation
Shopify has expanded its customer segmentation capabilities with more granular filter options, predictive segments (likely to purchase again, at risk of churn), and segment-based marketing automation. New customer metrics include predicted CLV and predicted spend tier, which use machine learning to forecast future customer value based on purchase history, browsing behavior, and demographic signals.
Improved marketing attribution
Shopify's marketing attribution has been enhanced with longer attribution windows, more accurate cross-session tracking, and better integration with advertising platforms. The attribution model now accounts for view-through conversions from connected advertising accounts (Meta, Google) in addition to click-based attribution from UTM parameters. Shopify Audiences provides enhanced targeting data back to ad platforms for improved campaign performance.
ShopifyQL and custom reporting
ShopifyQL (Shopify's query language) has been expanded with more functions, data types, and join capabilities. Shopify Plus merchants can now write complex analytical queries that combine order, customer, product, and session data in ways that were previously only possible through the API. ShopifyQL Notebooks provide an interactive environment for exploratory data analysis directly within the Shopify Admin.
Real-time analytics
Shopify now provides real-time analytics for key metrics including active visitors, current cart value, and live order feed. These real-time metrics are particularly useful during flash sales, product launches, and promotional events when you need immediate visibility into how campaigns are performing. Real-time data is available in the Shopify Admin dashboard and through the Live View feature.
Inventory analytics improvements
Inventory reporting has been enhanced with multi-location analytics, automated restock recommendations, and demand forecasting. New metrics include sell-through rate by location, transfer efficiency, and stockout frequency. These improvements help merchants with multiple fulfillment locations optimize inventory distribution and reduce both stockouts and overstock situations.
