Order ID
- Requirement
- Core
- Example
- ID-10042
- Purpose
- Identifies and groups records accurately
Upload a structured spreadsheet or platform export to create a focused analytics view with verified metrics, comparisons, and charts. Start with the broad analysis below, then move into a dedicated workflow when you need deeper calculations.
Supports structured CSV and Excel files. Display only file limits, privacy wording, and export options confirmed in the live product.
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.
Ecommerce Analytics Tool
Interactive preview from the sample ecommerce orders dataset
Switch preview
Total Records
48
rows in sample
Total Gross Sales
2,418,971
sum of Gross Sales
Total Net Sales
2,260,269
sum of Net Sales
Bin width 10,000 · 48 observations
Full dashboard with all KPI details and 6 charts is on the demo page.
Open full demoThe sample aggregates 5,961 orders and $2,260,269 net sales across 4 products, 4 categories, and 3 channels.
Sample results: AOV $379, margin 61.6%, and top net sales from Implementation and Analytics Pro. Aggregation level stays visible beside every headline metric.
Organize ecommerce workflows around orders, profitability, returns, inventory, cohorts, and product performance.
Ecommerce exports often mix orders, line items, discounts, refunds, customers, and fulfillment records. Reliable analysis makes the aggregation level explicit so orders, units, customers, and line items are not confused.
The hub explains the order-versus-line-item distinction before the visitor chooses a workflow. This sample reports 5,961 orders and $2,260,269 net sales at a consistent aggregation level.
The workflow is designed around visible aggregation rules and source-level evidence for every headline metric.
Net sales, order count, units sold, average order value, and period trends.
Revenue, quantity, discounts, refunds, and ranking by product, SKU, variant, or category.
New and returning customers, customer value, order frequency, and segment comparisons when identifiers exist.
Gross profit and margin when reliable product cost and refund fields are available.
Return count, refunded value, reasons, and true return rates when sales denominators are present.
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 by the workflow.
| Date | Quarter | Category | Product | Gross Sales |
|---|---|---|---|---|
| 2025-07-01 | Q3 2025 | Software | Analytics Pro | 55,692 |
| 2025-07-01 | Q3 2025 | Services | Implementation | 74,638 |
| 2025-07-01 | Q3 2025 | Add-ons | Connector Pack | 12,654 |
| 2025-07-01 | Q3 2025 | Training | Training Package | 17,585 |
| 2025-08-01 | Q3 2025 | Software | Analytics Pro | 67,077 |
The finished output includes the KPI cards, charts, tables, and report views described below.
Grouped by Date
Use one clear header row and one consistent record per row. The exact required fields depend on the selected analysis. VizMint should detect available fields, show what can be calculated, and hide metrics that require missing data.
| Column | Requirement | Example | Purpose |
|---|---|---|---|
| Order ID | Core | ID-10042 | Identifies and groups records accurately |
| Order date | Core | 2026-06-01 | Creates trends and period comparisons |
| Customer ID | Core | CUST-2048 | Identifies and groups records accurately |
| Product | Core | Analytics Pro | Identifies and groups records accurately |
| Variant | Core | Annual | Adds context and improves analysis |
| SKU | Recommended | SKU-1042 | Identifies and groups records accurately |
| Quantity | Recommended | 12 | Supports volume and unit-based metrics |
| Gross sales | Recommended | 12500.00 | Supports totals, comparisons, and financial calculations |
| Discount | Recommended | 250.00 | Supports volume and unit-based metrics |
| Refund | Recommended | 250.00 | Adds context and improves analysis |
| Net sales | Recommended | 12500.00 | Supports totals, comparisons, and financial calculations |
| Unit cost | Recommended | 42.50 | Supports totals, comparisons, and financial calculations |
| Channel | Recommended | Online | Adds a useful breakdown or comparison |
| Region | Recommended | North | Adds a useful breakdown or comparison |
Upload a CSV, Excel file, or supported export containing the records you want to analyze.
Choose the worksheet that contains the main structured table.
Review identifiers, dates, numeric values, categories, statuses, and target fields.
Check record counts, missing values, duplicates, date coverage, and any excluded rows.
Create the supported KPI cards, charts, and comparison tables.
Move to a dedicated workflow when you need deeper calculations, a specialized sample, or a purpose-built output.
Keep this ecommerce page as the broad parent hub. Use one complete orders example here, then route users to focused Shopify, profitability, returns, and inventory workflows rather than duplicating child-page content.
Use the Shopify CSV analyzer when you need a more specific analysis than the broad hub provides.
Use the product profitability dashboard when you need deeper margin and cost calculations.
Use the ecommerce returns dashboard when you need a dedicated returns and refunds workflow.
Use the inventory dashboard when stock levels, coverage, and replenishment matter more than order mix.
| Problem | Recommended Fix |
|---|---|
| Order-level and line-item data mixed | Document the rule, standardize the source data, and validate the corrected result before generation. |
| Duplicate orders | Use a stable identifier, review repeated rows, and remove or reconcile duplicates before calculation. |
| Missing customer IDs | Add the missing field when available; otherwise hide dependent metrics and explain the limitation. |
| Refunds not linked to products | Document the rule, standardize the source data, and validate the corrected result before generation. |
| Gross and net sales confused | Document the rule, standardize the source data, and validate the corrected result before generation. |
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 file to review the available metrics, comparisons, and charts, then open the focused workflow that matches your reporting goal.