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Analyze your Shopify data in plain English.

Analyze your Shopify data — ask about sales, SKUs, margin, customers, and refunds, and keep the answers running.

Shopify data analysis questions

→ "What was revenue last week, and how does it compare to the week before?"
→ "Which SKUs lost the most margin last week after factoring in returns?"
→ "Show me my top 10 products by revenue this month, with units sold."
→ "What's my repeat-purchase rate, and which customers are on their third order?"
→ "Which discount codes actually drove profitable orders vs. just cannibalized full-price sales?"
→ "Break down average order value by first-time vs. returning customers."
→ "Show cohort retention by signup month for the last 12 months."
→ "Which products get refunded most often, and what reasons do customers give?"
→ "Forecast next week's revenue from the last 90 days' trend and seasonality."
→ "Build me a dashboard for top movers, slow movers, and inventory at risk."

How to connect

Sign in to Tablize and open the Integrations page in your workspace.
Pick Shopify and follow the OAuth flow (or paste an API key, depending on the connector).
Run your first sync. Tablize pulls historical data and sets up an incremental cursor so future syncs stay fresh.
Open a new chat and ask your first question. Tablize already knows the schema.

What lands in your workspace

orders All orders with totals, status, customer, discounts, and timestamps.

order_line_items Per-order line items: SKU, quantity, unit price, discount applied.

customers Shopify customer records with name, email, order count, and total spend.

products Product catalog with variants, prices, and inventory levels.

refunds Refund events with amount, reason, and the line items refunded.

Common workflows

Weekly Shopify brief

Every Monday: revenue vs. the prior week, top movers, refund callouts, and a short list of what to act on — delivered to Slack or email.

Margin-after-returns report

Joins orders, line items, and refunds so you see true margin per SKU — not gross sales that quietly evaporate at the returns desk.

Refund spike watcher

Triggers when any SKU's refund rate crosses 5%. Tablize investigates the orders behind it and suggests likely causes (variant, batch, or listing issue).

Repeat-customer cohort tracking

Tracks first-order cohorts month over month, so you can see whether recent acquisition actually comes back and buys again.

Reorder & stockout alert

Watches sell-through against on-hand units and pings you when a top seller drops below its reorder point — before it goes out of stock.

Shopify analytics usually means exporting a CSV, cleaning it, and rebuilding the same pivot every week. Connect your store to Tablize instead and the questions get answered directly: “which SKUs actually lost margin after returns” becomes one sentence, not an afternoon in spreadsheets.

Once Shopify is authorized, tables like orders and order_line_items sync into a dedicated schema in your workspace’s PostgreSQL — incrementally, so after the first backfill only new and changed rows move. Ask something like “Which SKUs lost the most margin last week after factoring in returns?” and Tablize writes the SQL, runs it, draws the chart, and offers to keep the answer as a report, a scheduled script, or a live dashboard.

For a DTC operator, the setup teams reach for first is the Weekly Shopify brief: every Monday, revenue vs. the prior week, top movers, refund callouts, and a short list of what to act on — delivered to Slack or email. From there it’s a short step to a margin-after-returns report and a refund spike watcher that tells you the moment a SKU starts coming back at the returns desk.

Try Shopify with Tablize.

Free to try on your own data. Your first answer in under 60 seconds.

Try free with your data