Upload a Shopify orders export to visualize sales trends, order values, product and variant performance, discounts, refunds, customers, and regions.
The workflow helps merchants and analysts move from a raw Shopify CSV to a clear ecommerce report without manually rebuilding pivot tables and charts.
Supports structured .csv and .xlsx files. Public examples use synthetic data. Review VizMint’s current privacy and retention policy before uploading sensitive information.
Use the fixed preview below to switch between the source spreadsheet and a dashboard sample. Open the full demo in a new tab for every KPI and chart.
Shopify CSV Analyzer
Interactive preview from the sample Shopify orders export
Switch preview
Total Records
50
rows in sample
Total Discount
383.7
sum of Discount
Total Refund
139.4
sum of Refund
Grouped by Order Date
Full dashboard with all KPI details and 6 charts is on the demo page.
Open full demoThe sample contains 50 line items across 25 orders. Charts and KPI cards are generated from the same JSON dataset.
Sample totals: $5,857 gross sales, $384 discounts, $139 refunds, $5,334 net sales, and $213 average order value.
Turn a Shopify order export into a reliable view of net sales, orders, products, discounts, refunds, and customers.
Ecommerce exports often mix orders, line items, discounts, refunds, customers, and fulfilment records. Reliable analysis makes the aggregation level explicit so orders, units, customers, and line items are not confused.
The sample contains 50 line items and 25 orders, producing $5,857 gross sales, $384 discounts, $139 refunds, $5,334 net sales, and a $213 average order value.
The workflow is designed around visible aggregation rules and source-level evidence for every headline metric.
Shopify exports contain valuable order, product, discount, refund, customer, and location data, but line-item structures make totals easy to misread. A dedicated analyzer applies order-level and line-level logic so KPIs are not accidentally duplicated.
Use the output to review sales trends, average order value, product performance, discounts, refunds, regions, and customer counts.
Follow the same dataset from source rows to the generated dashboard preview. The spreadsheet table, KPI cards, and charts all come from one JSON sample, so every visible result can be checked against the source.
The source preview highlights the exact columns and representative rows used in the analysis.
| Order Date | Quarter | Variant | Country / Region | Gross Sales |
|---|---|---|---|---|
| 2025-07-05 | Q3 2025 | Black | United States | 79 |
| 2025-07-05 | Q3 2025 | Navy | United States | 258 |
| 2025-08-08 | Q3 2025 | Navy | Canada | 78 |
| 2025-08-08 | Q3 2025 | Stone | Canada | 165 |
| 2025-09-11 | Q3 2025 | Stone | United Kingdom | 87 |
The finished example is generated from the sample dataset rather than an unrelated mockup or stock image.
Grouped by Order Date
VizMint works best when every column has one clear heading and each row represents one consistent record.
| Column | Requirement | Example | Purpose |
|---|---|---|---|
| Order ID | Required | 1001 | Counts unique orders and joins line items |
| Order Date | Required | 2026-06-12 | Creates sales trends |
| Product | Required for product analysis | Classic Backpack | Creates product rankings |
| Variant | Recommended | Black / Large | Creates variant analysis |
| SKU | Recommended | BAG-BLK-L | Provides stable product identifier |
| Quantity | Required for unit analysis | 2 | Calculates units sold |
| Gross Sales | Recommended | 160.00 | Stores pre-discount sales |
| Discount | Optional | 10.00 | Calculates discount impact |
| Refund | Optional | 20.00 | Calculates refund impact |
| Net Sales | Recommended | 130.00 | Stores post-discount and return sales |
| Financial Status | Optional | Paid | Creates payment-status analysis |
| Fulfilment Status | Optional | Fulfilled | Creates fulfilment analysis |
| Customer ID or Email Hash | Conditional | CUST-100 | Supports customer counting |
| Country or Region | Optional | Canada | Creates geographic analysis |
Sales after supported discounts and returns.
Count of unique order IDs.
Sum of product quantities.
Net sales divided by unique orders.
Total discount value.
Total refunded value.
Number of distinct products or SKUs.
Number of distinct customer identifiers.
Customers with more than one valid order, when identifiers exist.
| Chart or Output | Required Fields | Business Question |
|---|---|---|
| Revenue Trend | Order date, net sales | How are sales changing over time? |
| Orders Trend | Order date, unique order ID | How is order volume changing? |
| Average Order Value Trend | Order date, net sales, order ID | Are customers spending more per order? |
| Product Ranking | Product, net sales, quantity | Which products drive sales? |
| Variant Performance | Variant, net sales, quantity | Which variants perform best? |
| Discount and Refund Trend | Date, discount, refund | How are promotions and returns affecting results? |
| Sales by Region | Country or region, net sales | Where are customers located? |
Use clear headers, one record per row, consistent dates, and numeric values stored as numbers.
Select the CSV or Excel file using the page’s upload control.
For multi-sheet workbooks, choose the sheet containing the relevant data table.
Review the detected fields and map any column VizMint could not identify confidently.
Check dates, numbers, categories, missing values, and record counts before generation.
Create the supported dashboard, report, presentation, chart, or answer.
Review calculations and limitations, then save, share, or export using the options available in the active plan.
| Metric | Formula or Method | Required Fields | Important Rule |
|---|---|---|---|
| Average Order Value | Net sales ÷ unique orders | Net sales and order ID | Line-item exports must be aggregated to unique orders. |
| Net Sales | Gross sales − discounts − returns | Gross sales, discount, refund | Use the export’s native net sales when available and verified. |
| Units per Order | Units sold ÷ unique orders | Quantity and order ID | Requires line-item quantity. |
| Repeat Customer Count | Customers with more than one valid order | Stable customer identifier and order ID | Unavailable when customer identity is missing or inconsistent. |
| Problem | Recommended Fix |
|---|---|
| Order totals are duplicated across line items | Aggregate at order level before counting or summing order totals. |
| Refund fields use different structures | Map refund amount and returned quantity carefully. |
| Customer identifiers are missing | Hide repeat-customer metrics. |
| Product names change over time | Use SKU where possible. |
| Cancelled or test orders are included | Filter by valid financial status. |
| Multiple currencies are mixed | Separate or convert before combining totals. |
Available options depend on the live VizMint plan. The final page should list only verified capabilities, such as saving the analysis, downloading chart images, exporting PDF or PowerPoint files, creating a shareable link, refreshing with updated data, or removing branding on eligible plans.
Do not publish a plan comparison until product and billing owners confirm every feature.
VizMint analyzes exports; it does not replace the accounting, CRM, help-desk, HR, warehouse, advertising, or project-management system that produced them.
Upload a structured CSV or Excel file, review the detected fields, and generate the workflow-specific output described on this page.