Shopify Repeat Customer Analysis: Upload Export, Get List

By MCP Analytics Team | Published January 15, 2024

Why Shopify Doesn't Show You This (And Why Store Owners Ask For It Weekly)

If you've ever logged into your Shopify admin looking for a simple list of customers who've purchased from you more than once, you've probably been frustrated. It's one of the most common questions in Shopify forums: "How do I see which customers have ordered 2+ times?"

The answer? Shopify doesn't give you this out of the box. Not on the Basic plan, not on Advanced. You can see individual customer purchase histories one by one, but there's no "sort by repeat customers" button. No filter that says "show me everyone who bought twice." And that's a problem when you're trying to build a loyalty program, send targeted emails, or just understand who your best customers actually are.

Before we build a complex customer segmentation model, let's just look at the data. This tutorial will walk you through getting exactly what you need: a named list of repeat customers with their purchase counts and total spend. No apps, no monthly fees, no complicated setup.

What You're Actually Getting: A CSV With Customer Names, Purchase Counts, Total Spend

Let me be clear about what you'll have at the end of this process. You'll download a CSV file that looks something like this:

Customer Name,Email,Purchase Count,Total Spend,First Order,Last Order
Sarah Johnson,[email protected],5,$487.32,2023-03-15,2024-01-08
Michael Chen,[email protected],3,$234.50,2023-06-22,2023-12-01
Emma Rodriguez,[email protected],2,$156.00,2023-08-10,2023-11-25
...

This is actionable data. You can sort by purchase count to find your most loyal customers. You can filter by total spend to identify high-value buyers. You can look at date ranges to see who's bought recently versus who might need a re-engagement email.

The simplest explanation is often the most useful: this list tells you who comes back, how often they come back, and how much they spend when they do.

Prerequisites: What You Need Before Starting

Before we start, here's what you'll need:

That's it. You don't need any technical skills, you don't need to install apps, and this works whether you're on Shopify Basic or Shopify Plus.

Step 1: Export Your Orders From Shopify Admin

Let's start by getting your order data out of Shopify. Let me walk you through this step by step.

Navigate to Orders

Log into your Shopify admin panel. On the left sidebar, click Orders. You'll see your list of recent orders.

Click the Export Button

In the top right corner of the Orders page, you'll see an Export button. Click it, and a modal window will pop up with export options.

Configure Your Export Settings

Here's exactly what to select:

Why "All orders"? Because repeat customers might have their first purchase months or years ago. You need the complete history to get an accurate count.

Why "Plain CSV file"? This format preserves all the column data we need for analysis without Excel-specific formatting that can cause issues.

Download Your File

Click the Export orders button. Shopify will prepare your file and either download it immediately or send you an email with a download link if you have a large order volume.

The file will be named something like orders_export_1.csv and will land in your Downloads folder.

Understanding Your Shopify Order Export CSV Columns

When you open this CSV in a text editor or spreadsheet, you'll see dozens of columns. Here are the key ones that matter for repeat customer analysis:

You might notice columns like "Lineitem compare at price" or "Lineitem compare-at price" in your export. These show the pre-discount prices for products and are useful if you want to analyze how discounts affect repeat purchases, but they're not required for basic repeat customer analysis.

Verification: What Your Export Should Look Like

Open your CSV file in Excel, Google Sheets, or even a text editor. You should see:

If you see data that looks reasonable and you have at least a few dozen rows, you're ready for the next step.

Step 2: Upload to MCP Analytics and Select 'Customer Repeat Purchase Analysis'

Now that you have your order export, let's turn it into the repeat customer list you actually need.

Go to the MCP Analytics Tool

Open your browser and navigate to the MCP Analytics tool. You don't need to create an account or log in for basic analysis.

Upload Your Shopify Export

You'll see an upload area on the page. Either drag and drop your orders_export_1.csv file onto the upload area, or click to browse and select it from your Downloads folder.

The tool will automatically detect that this is a Shopify order export based on the column structure.

Select Analysis Type

Once your file uploads, you'll see a dropdown menu or list of analysis options. Look for and select "Customer repeat purchase analysis" or "Repeat customer identification".

Configure Your Settings (Optional)

Depending on the tool interface, you might see optional settings like:

The default settings work well for most stores. If you're not sure what to choose, just leave the defaults.

Run the Analysis

Click the "Analyze" or "Generate Report" button. The tool will process your order history, typically in just a few seconds.

What's happening behind the scenes? The tool is:

  1. Reading your order export line by line
  2. Grouping orders by customer email address
  3. Counting how many unique orders each customer has placed
  4. Summing up the total spend per customer
  5. Filtering to show only customers with 2+ purchases
  6. Sorting by purchase count or total spend

There's no such thing as a dumb question in analytics, so let me address a common one: "Why group by email instead of name?" Because email addresses are unique identifiers. You might have three customers named "John Smith," but each one has a different email address.

Step 3: Reading Your Results - Who Bought 2x, 3x, 5x+ and What They Spent

Your analysis is complete. Now let's look at what the data is telling us.

Download Your Results

Click the "Download Results" button to get your repeat customer list as a CSV file. Save it somewhere you'll remember—you'll be coming back to this file.

Open and Review

Open the file in Excel or Google Sheets. Let's walk through what each column means:

Understanding the Numbers

Let's start with the basics and build from there. Sort your spreadsheet by "Purchase Count" from highest to lowest. Now you can see:

Example Interpretation

Let's say you run a skincare store and you see this customer:

Lisa Martinez, [email protected], 4 purchases, $312.00 total,
First: 2023-02-10, Last: 2023-11-30, Avg order: $78.00, Avg days: 90

What does this tell us? Let's look together:

If today is February 15th, Lisa is overdue for her next purchase. She's a perfect candidate for a "We miss you" email or a special offer.

Statistical Significance Note

If you're interested in whether patterns in your repeat customer data are statistically meaningful or just random variation, check out our guide on A/B testing and statistical significance. The same principles apply when analyzing customer segments.

Step 4: What to Do With This List - Email Campaigns, Loyalty Tiers, VIP Outreach

Now you have the data. Let's talk about what to actually do with it.

Email Campaign Segmentation

Your email marketing platform (Klaviyo, Mailchimp, etc.) can import this CSV. Here are proven segments to create:

Loyalty Tier Creation

Use purchase counts to create tiered loyalty levels:

Email each segment with their tier status and benefits. This creates a sense of achievement and gives customers a reason to reach the next level.

VIP Personal Outreach

Take your top 10-20 customers by either purchase count or total spend and reach out personally. Not an automated email—a real message from the founder or store manager:

"Hi Sarah, I noticed you've ordered from us five times in the past year, and I wanted to personally thank you for your support. Your loyalty means everything to a small business like ours. I'd love to send you [special gift] as a token of appreciation. What's your current shipping address?"

This kind of personal touch turns repeat customers into brand advocates.

Product Development Insights

Go back to your original Shopify order export. Filter it to show only orders from your repeat customers (match on email addresses). Now look at what products these loyal customers buy most often.

These are your winning products—the ones that bring people back. Double down on similar product development and make sure these items are always well-stocked.

Going Deeper: Which Products Drive Repeat Purchases?

Once you know who your repeat customers are, the natural next question is: what are they buying?

Product-Level Repeat Analysis

Here's a simple way to understand this without any fancy tools:

  1. Open your original Shopify order export CSV
  2. Filter to show only orders from customers in your repeat customer list (match by email)
  3. Create a pivot table with "Product Title" as rows and "Count" as values
  4. Sort by count to see which products appear most in repeat customer orders

What does this visualization tell us? Let's look together.

If you see that 60% of your repeat customers have purchased your "Hydrating Face Serum," that's a hero product. That's what brings people back. Feature it prominently on your homepage, include samples with first-time orders, and never let it go out of stock.

First Purchase vs. Repeat Purchase Products

This is where it gets really interesting. You can split your analysis:

Often these are different. Maybe customers discover you through a trendy product, but they come back for staple items. Or maybe a low-priced "gateway" product gets them in the door, and they return for premium items.

Understanding this pattern helps you optimize your acquisition strategy (promote the products that attract first-timers) and retention strategy (ensure repeat-driving products are always available).

Repurchase Rate by Product Category

If your Shopify store has product categories or collections, you can calculate which categories have the highest repeat purchase rates:

Category Repeat Rate = (Customers who bought from category 2+ times) / (Total customers who bought from category)

Categories with high repeat rates are your retention drivers. Categories with low repeat rates might be one-time purchase items (like gifts) or might need product improvements.

For a more comprehensive approach to prioritizing which products or categories to focus on, you might find our guide on ABC analysis and the Pareto principle helpful—it's the same framework that helps you identify which 20% of your products drive 80% of your results.

Shopify App Alternatives (And Why They Cost $29/Month for the Same Thing)

Let's be honest: there are Shopify apps that will give you this repeat customer analysis. Apps like "Customer Loyalty Tracker," "Repeat Customer Insights," and "Loyal Customer Analytics" all do versions of this.

They typically cost $29-49 per month. Here's what you're paying for:

Are these worth $29/month? It depends on your business.

When a Shopify App Makes Sense

When the Manual Export Method Works Better

The method I've walked you through in this tutorial gives you the raw data. Apps give you convenience and automation. Both are valid choices depending on your needs and budget.

Shopify Plus Advanced Reports

If you're on Shopify Plus (starting at $2,000/month), you get access to advanced reporting that includes some repeat customer metrics. But even Shopify Plus doesn't give you a simple exportable list of repeat customers—you still need to build custom reports or use the export method described here.

Analyze Your Shopify Data in Minutes

Ready to see who your repeat customers really are? Upload your Shopify order export to our free analysis tool and get your repeat customer list in under 5 minutes.

No installation, no monthly fees, no credit card required. Just upload your CSV and download your results.

Analyze Your Shopify Orders Now →

Next Steps: Where to Go From Here

You now have your repeat customer list. Here's what to do next:

  1. This week: Import your list into your email platform and send a thank-you campaign to all repeat customers. A simple "We appreciate your loyalty" message with a small discount code.
  2. This month: Create your loyalty tier structure and email customers about their status and benefits. Set up automated welcome sequences for when customers hit each tier.
  3. Next quarter: Re-run this analysis to track growth. Are you converting more one-time buyers into repeat customers? Are your repeat customers increasing their purchase frequency?
  4. Ongoing: Export and analyze monthly. Build a simple spreadsheet to track key metrics over time: total repeat customers, average purchase count, average total spend.

Metrics to Track Over Time

Create a simple dashboard (even just a Google Sheet) to track these monthly:

Watching these numbers improve over time tells you your retention efforts are working.

Advanced Analysis Ideas

Once you're comfortable with basic repeat customer analysis, consider exploring:

If you're interested in how to identify and investigate issues that might be preventing repeat purchases—like problems revealed in customer feedback—our article on analyzing 1-star reviews to find real problems can help you uncover friction points in the customer experience.

Troubleshooting: Common Issues and Solutions

There's no such thing as a dumb question in analytics. Here are the most common issues people run into with this process and how to fix them.

Problem: "My export has way too many rows"

Why this happens: Shopify exports one row per line item, not per order. If an order has 3 products, it creates 3 rows.

Solution: This is normal and expected. The MCP Analytics tool handles this automatically by grouping line items back into orders before counting. You don't need to clean this up manually.

Problem: "Some customers appear twice with different email addresses"

Why this happens: The customer used different emails for different orders (e.g., personal email and work email).

Solution: Unfortunately, there's no automatic way to merge these. If you notice obvious duplicates (same name, similar email), you can manually combine them in your downloaded results. For small stores, this is manageable. For larger stores, focus on the overall trends rather than perfect accuracy.

Problem: "My repeat customer count seems too low"

Why this happens: Depending on your industry, a 20-30% repeat customer rate is actually normal. Not every business has high repeat rates.

Solution: Compare your rate to industry benchmarks. For fashion and electronics, 20-25% is typical. For consumables like beauty products or supplements, you'd expect 40-60%. If you're significantly below benchmark, that's a signal to focus on retention strategies.

Problem: "The analysis tool doesn't recognize my file"

Why this happens: You might have exported "CSV for Excel" instead of "Plain CSV," or your file might be corrupted.

Solution: Go back to Shopify and export again, making sure to select "Plain CSV file." Save it directly to your computer without opening it in Excel first (Excel sometimes reformats dates and can corrupt the file).

Problem: "Guest checkout orders don't have email addresses"

Why this happens: Some customers checkout as guests without creating accounts.

Solution: Guest orders should still have email addresses in Shopify exports (they're required for order confirmation). If you're seeing blank emails, those might be test orders or old imported data. Filter them out when reviewing your results.

Problem: "Results include refunded/cancelled orders"

Why this happens: The analysis might include all orders regardless of financial status.

Solution: Before uploading to the analysis tool, open your Shopify export in Excel and delete rows where "Financial Status" is "refunded" or "cancelled." Or check if the tool has a filter option to exclude these statuses.

Problem: "I want to see Shopify orders CSV columns with lineitem compare at price"

Why you're asking: You want to understand the original pricing before discounts to analyze how promotions affect repeat purchases.

Solution: Your Shopify order export already includes this! Look for columns named either "Lineitem compare at price" or "Lineitem compare-at price" (Shopify uses both formats). This shows the original price before any discounts. You can add this column to your pivot table analysis to see average discount rates for first-time vs. repeat customers.

Problem: "The dates in my export look weird after opening in Excel"

Why this happens: Excel auto-formats dates when you open CSV files, sometimes incorrectly.

Solution: Upload the original CSV file to the analysis tool without opening it in Excel first. If you need to view it in Excel, use the "Import Data" feature instead of double-clicking the file, and specify that the date column should be treated as text.

Final Thoughts: The Learning Journey Continues

You've just completed a fundamental e-commerce analysis that many stores pay apps $29/month to do for them. More importantly, you now understand how to extract meaningful insights from your Shopify data without depending on third-party tools.

This is exploratory data analysis at its core: taking raw transactional data and transforming it into actionable customer intelligence. You didn't need complex statistical models or expensive software. You just needed to look at your data with fresh eyes and ask the right question: "Who comes back?"

The list you've created is more than just names and numbers. It's a map of your most valuable relationships. These are the customers who believed in your product enough to buy again. They're the foundation of sustainable e-commerce growth.

Let me walk you through this step by step one more time: Export your orders, upload to the analysis tool, download your repeat customer list, and take action. Run this analysis monthly, watch your metrics improve, and celebrate the learning journey, not just the destination.

Questions about your results? Unsure how to interpret a pattern you're seeing? Remember: there's no such thing as a dumb question in analytics. Every question is an opportunity to understand your business better.