Understand Your Shopify Numbers

Shopify gives you data. But data does not give you decisions. That is where Claude comes in. You ask plain-English questions and get plain-English answers. No pivot tables. No formulas. No analyst needed.

There are two ways to work with your Shopify data. Start with the CSV method and move to the live connector when you are ready.

The modern approach: Shopify MCP connector

The Shopify MCP connector (Model Context Protocol) connects Claude directly to your live store. Once it is set up — which takes about 20 minutes via Composio — you never export a file again. You just ask Claude questions and it checks your Shopify store in real time:

  • “How many orders do we have today?”
  • “What is our revenue this week vs last week?”
  • “Which bikes sold most in October?”
  • “Is the Gazelle Ultimate T10 still in stock?”

Why this matters: Instead of a monthly data review, you get a live business dashboard you can query any time. And you can combine it with Claude Schedule (see below) for automated weekly reports delivered without lifting a finger.

Setting it up via Composio:

  1. Go to composio.dev and create a free account
  2. Find the Shopify connector
  3. Connect your Shopify store (OAuth, no coding needed)
  4. In Claude, select the Composio MCP server
  5. Test it: “What were our top 5 products by revenue last month?”

See the tools page for more on MCP connectors.

The getting-started approach: CSV export

Tip
Export your Shopify data as CSV every Monday morning. Upload it to your Adam Bike HQ project. In 3 minutes you have insights that would take an hour in a spreadsheet.

If you have not set up the MCP connector yet, this workflow gets you the same insights with a small amount of manual export work. It is a great place to start.

Step 1: Export from Shopify

In your Shopify admin:

  • Go to Orders
  • Click Export (top right)
  • Choose the time range (last month, last quarter)
  • Select “All columns” in the export options
  • Click Export orders
  • Download the CSV file

Step 2: Upload to Claude

Open your Adam Bike HQ project in Claude. Start a new conversation. Click the paperclip icon and upload the CSV. Then start with this analysis prompt — adjust the categories to match your actual product range:

Try this promptI have uploaded our Shopify sales data. Please analyze it and tell me: 1. Total revenue and order count for the period 2. Which product categories sold best (bikes, accessories, services)? 3. Our average order value 4. Best and worst performing days or weeks 5. Any trends you notice (seasonal patterns, popular price ranges, repeat customers) 6. Two or three specific actions we could take based on this data Context: We are Adam Bike, a Dubai bike shop. Cycling season runs October to April. We sell kids bikes (AED 350 to AED 1,400), adult Dutch bikes (AED 1,200 to AED 5,500), electric bikes (AED 3,500 to AED 11,000), and accessories.

Claude reads the file and returns a summary in 30 to 60 seconds.

Step 3: Ask follow-up questions

After the initial analysis, drill down into what matters most for your next decision:

Try thisWhich specific products had the highest revenue? List the top 10.
Try thisWhat percentage of our orders were for bikes vs accessories vs services?
Try thisBased on this data, what should we stock more of going into the next cycling season?

Automate weekly reports with Claude Schedule

The best data habit is consistent: the same analysis every Monday morning. Set up Claude Schedule once and it runs automatically. With the Shopify MCP connector active, Claude reads live data. Without it, it reads the most recent CSV in your sales folder:

Set this up once in Claude Desktop/schedule weekly on Monday at 8am: Read the Shopify data for the past 7 days. Summarize: weekly revenue, top 3 products, average order value, and one pattern worth noting. Keep it to 5 bullet points. Save the summary to my sales-reports folder.

Every Monday morning you open your folder and find a fresh summary waiting. No manual export, no prompt to write, no time spent.

Weekly questions to build into your routine

Even without automation, these questions are worth asking regularly. Build them into a Monday morning habit:

Sales performance:

Try thisCompare this week's sales to the same week last year. What changed?

Customer analysis:

Try thisHow many new customers did we have this month vs returning customers? What did returning customers buy?

Inventory planning:

Try thisBased on the last 3 months of sales, which products should I reorder soon? Which are moving slowly?

Pricing insights:

Try thisWhat is our most popular price range? How many orders fall below AED 1,000, between AED 1,000 and AED 3,000, and above AED 3,000?

Google Sheets: ongoing AI tracking

If you track business data in Google Sheets, you can use the =AI() function (available in Google Workspace) to add AI-powered columns directly in your spreadsheet.

Examples:

  • Customer feedback column: =AI("Is this review positive, negative, or neutral? " & A2)
  • Product tagging: =AI("Which category does this product belong to: bikes, accessories, or services? " & B2)
  • Trend flags: =AI("Is this week's revenue higher or lower than last week? Week 1: " & C2 & " Week 2: " & D2)

This is useful for ongoing monitoring without exporting data each time.

Where to start:

If you are new to AI data analysis, export last month’s Shopify orders and upload to Claude in your Adam Bike HQ project. Start with this:

Try this promptAnalyze this month's sales data for Adam Bike. Give me: total revenue, top 5 products by revenue, average order value, and the single most interesting pattern you notice. Keep the summary short and actionable.

Run this every month. Over time you will see patterns that help you buy better, sell more, and waste less money on slow inventory. When you are comfortable with the results, set up the Shopify MCP connector and automate the whole thing.