Export Shopify Customer Data By Tags, Segments And Purchase History Into One Report

Most Shopify merchants export store data for a few obvious reasons. Create a backup of your inventory, migrate products to a new Shopify store, or edit large lists of items in a spreadsheet rather than updating them one by one in the admin.

But in a growing e-commerce business, exporting data becomes useful for much more than basic maintenance. If you want to improve inventory planning, adjust pricing, manage your product assortment, or strengthen customer relationships, you need quick access to the right data. This is where Shopify exports come in. When used well, exported data can help you make smarter decisions across your store.

For example, merchants often export Shopify data to improve:

• Customer engagement and retention
• Inventory planning
• Assortment and merchandising decisions

Here, we will focus on one of the most common reporting needs. Many merchants want to export customer data together with tags, segments, and purchase history into a single report.

At first, it sounds simple, but Shopify stores this information in different places, so you need to understand how the export system works before you can build a clean report.

Why exporting “customer data + purchase history” is harder than it sounds

Many merchants expect that customer information and purchase history can be exported with a single click. In reality, Shopify separates these datasets because they serve different purposes. Customer records store profile information, while order records track orders. Because of this separation, creating one report that includes both often requires multiple exports.

Customer exports in Shopify usually include the basic profile details that help you understand who your customers are. These files are often used by marketing, CRM, and customer support teams because they focus on customer-level information rather than orders.

A typical customer export may include details such as:

• Customer name
• Email address
• Phone number
• Customer tags
• Marketing preferences
• Customer creation date
• Default address
• Supported customer metafields
  
  

Because this information focuses on the customer profile, these exports are commonly used for tasks like building marketing lists, managing CRM records, or analyzing customer engagement.

Order exports work a little differently. Instead of focusing on the customer profile, they focus on the sales side of the business. These files are typically used by operations and finance teams who need to understand what was purchased and how orders were processed.

What you can export natively from Shopify customers

Shopify allows merchants to export customer data directly from the admin panel. This export generates a structured file containing customer profiles along with several useful attributes. While it does not include detailed order line items, it provides a clear overview of your customer base and their general purchasing activity.

To export customer data, Shopify offers a few options depending on the group of customers you want to download. The process starts from the customer section of the Shopify admin.

Follow these steps to export customer data:

• Go to Customers in your Shopify admin
• Click Export
• Choose one of the export options available

Attributes like Customer name, email address, phone number, customer tags, and marketing consent status make it easier to analyze customer engagement, build marketing lists, and track overall customer value. You can use customer metafields to track details such as loyalty program levels, internal account identifiers, membership status, or B2B customer information. Including these fields in exports can make reports more useful and aligned with your store’s internal workflows.

Shopify exports also include additional attributes depending on how your store is configured. Many merchants add custom data fields to capture more detailed customer information beyond the default profile data. In many cases, this includes customer tags, which are used to group and classify customers internally, and supported customer metafields, which allow stores to store custom attributes such as loyalty status, membership details, or other business-specific customer information.

Note: Shopify explicitly states that customer tags and supported customer metafields can be included in the customer export flow, depending on how these fields are configured in your store admin.

How to export customers by segment in Shopify (the scalable method)

Customer segments are one of the most useful tools in Shopify when you want to group customers based on behavior instead of manually applying labels. Unlike tags, which are added manually, segments are rule-based groups that update automatically as customer data changes.

This makes segments a scalable way to organize customers. For example, instead of tagging customers every time they make a purchase, you can create a segment that automatically includes customers who have placed multiple orders or spent above a certain amount.

Segments are managed inside the Customers section of the Shopify admin. Merchants can create and manage them using the built-in segmentation tools.

Steps to create a customer segment

• Go to Customers in your Shopify admin
• Click Segments
• Select Create segment
• Define your filtering rules
• Save the segment

Tips for using segments effectively

• Use behavioral rules instead of manual tagging. Segments automatically update when customers place new orders or meet new conditions.
• Create segments for customer lifecycle stages, such as you can include new customers, repeat buyers, and high-value customers.
• Combine multiple filters for better targeting. For example, customers who placed three orders and spent more than $300.
• As your store grows, review segments periodically, segmentation rules may need adjustments to remain useful.
  

One important detail to remember is that segmentation capabilities can vary depending on your Shopify plan. All Shopify plans support these features, though some advanced filtering and segmentation capabilities may vary depending on the store setup.

How to include purchase history in the same report (what “one report” really means)

When merchants say they want one report that includes customers and purchase history, they often mean different things.  

The approach you choose depends on how detailed the purchase data needs to be. Some merchants only need a customer-level summary, while others require detailed product-level purchase history. Understanding this difference helps determine the right workflow.

If you only need customer level purchase summary

If your goal is to understand overall customer value and activity, Shopify already provides useful summary metrics in the customer dataset for each customer. These fields show how often customers purchase and how much they spend over time.

One simple approach is to create customer segments based on purchase behavior, then export those customers.

If you need line item purchase history (SKU, product, quantity)

Sometimes a summary is not enough. Finance teams, operations teams, and product analysts often need to see exactly what customers purchased.

This level of detail is stored in the orders dataset, not the customer dataset. To build a report that includes both customers and product purchases, you will need to export both datasets and combine them.

The usual process involves:

• Exporting the customer dataset
• Exporting the orders dataset
• Joining the datasets using a shared identifier

A practical “one report” layout that finance teams use

Many teams organize combined exports into a structured spreadsheet. Instead of forcing everything into one table, the data is often separated into logical tabs that make analysis easier.

A common report layout used by finance and operations teams includes:

Tab 1: Customer summary

• Customer email
• Customer tags
• Total orders
• Total amount spent
• Account creation date
  

Tab 2: Orders and items

• Order ID
• Customer email
• Product name
• SKU
• Quantity
• Order value
  
  

Tab 3: Rollups by tag or segment

• Revenue by customer tag
• Revenue by segment
• Order counts by segment

Native workflow: Build the export in under 10 minutes

Even though Shopify stores customer data and order data separately, merchants can still generate useful exports fairly quickly. With the right workflow, most customer and purchase reports can be prepared in just a few minutes using Shopify’s native export options.

The key is choosing the right workflow depending on the type of data you need. Some reports only require a filtered customer list, while others require purchase-level information from the orders dataset.

Workflow A: Tag filter for Customer export (customer list use cases)

This is the simplest workflow and works best when you already organize customers using tags. Since tags are stored directly on the customer profile, you can filter the customer list and export the results without needing additional datasets.

Typical use cases for this workflow include marketing lists, loyalty programs, or internal customer classifications.

Steps to follow:

• Go to Customers in your Shopify admin
• Apply a tag filter to the customer list
• Click Export
• Select Customers matching your filters
  

This will generate a file that includes only the customers with the selected tag.

This method is commonly used for exporting groups such as:

• VIP customers
• Wholesale buyers
• Loyalty program members
• Customers assigned to specific internal workflows
  

Because the data comes directly from the customer profile, the export will include fields such as customer name, email, tags, and purchase summary metrics.

Workflow B: Segment for Export customers (behavior and purchase history at the customer level)

This workflow is useful when you want to export customers based on behavior or purchase activity rather than manual labels. Instead of filtering by tags, you create a customer segment using rules and then export the customers who match those conditions.

Segments automatically update as customer behavior changes, which makes them a scalable way to analyze purchasing patterns.

Steps to follow:

• Go to Customers
• Click Segments
• Create or open an existing segment
• Apply rules based on purchase behavior or customer attributes
• Click More actions → Export

Some common segmentation filters include:

• Total number of orders
• Total amount spent
• Products purchased
• Time since last purchase
• Customer location
  

Workflow C: Orders export for line items, then join back to customers

When you need detailed purchase data such as products, SKUs, and quantities, the customer export alone will not be enough. In this case, you will need to export order data and connect it with the customer dataset.

This workflow is commonly used for finance reporting, product analysis, and inventory planning.

Steps to follow:

• Export the customer dataset from Shopify
• Export the orders dataset that includes line item details
• Combine the datasets using a shared identifier

When Shopify exports hit a wall (and what merchants do next)

Shopify’s native export tools work well for occasional reporting and quick data downloads. Many merchants use them to generate customer lists, export orders for accounting, or review basic store activity. However, as a store grows and reporting becomes more frequent, managing multiple exports manually can start to feel inefficient.

Over time, merchants often need more structured reporting workflows. When reports need to be generated regularly or combined across multiple datasets, the manual export process can become difficult to maintain.

Limitations merchants commonly run into

As reporting requirements increase, several common challenges tend to appear. These limitations usually arise when merchants try to combine different types of data or repeat the same reporting process regularly.

Some common challenges include:

• Combining customers and line items: Customer exports and order exports come from different datasets. This means merchants often need to manually merge files in spreadsheets to connect customers with their purchase history.
  
  
• Exporting consistent custom fields: Customer metafields and other custom attributes may not always appear consistently across different export workflows, especially when multiple datasets are involved.
  
  
• Repeating the process regularly: When reports need to be generated weekly or monthly, manual exports can become time-consuming and increase the risk of human error.
  
  

These limitations do not usually affect occasional reporting, but they can become noticeable when teams rely heavily on data for marketing analysis, operations, or finance reporting.

What “better than CSV” looks like

As reporting needs grow, many merchants begin looking for workflows that reduce manual effort and improve data consistency. Instead of exporting multiple CSV files and merging them manually, they often prefer solutions that automate and standardize the reporting process.

Some of the capabilities merchants typically look for include:

• Saved filters that allow frequently used reports to be generated quickly
• Scheduled exports that automatically generate reports on a daily, weekly, or monthly basis
• Consolidated outputs that combine customer and order data into a single structured report
• Consistent field structures so that reports remain reliable across different exports

How a reporting tool can consolidate customers, segments, and purchase history

As reporting needs grow, many Shopify merchants find that exporting data manually from multiple sections of the admin can become time-consuming. Customer data, segments, and order history often need to be combined to answer common business questions such as who your most valuable customers are, which segments generate the most revenue, or what products repeat buyers tend to purchase.

A reporting tool helps simplify this process by bringing together different Shopify datasets into a single structured report. Instead of exporting customer data, order data, and segment lists separately and merging them in spreadsheets, a reporting tool can consolidate these datasets automatically. This allows merchants to view customer attributes and purchasing behavior in one place, making the data easier to analyze.

Consolidated customer export with filters (tags, segments, date ranges)

One of the main advantages of a reporting tool is the ability to apply multiple filters at the same time when generating a report. In Shopify’s native exports, merchants often have to apply filters in different places and then combine the results manually. A reporting tool allows these filters to be applied within the same report configuration.

For example, merchants can generate a customer report based on a combination of attributes and behavioral conditions.

Common filters that can be combined include:

• Customer tags
• Segment rules
• Customer creation date ranges
• Order date ranges
  
  

Using these filters together allows merchants to create highly targeted exports. For example, you could generate a report that includes customers tagged as VIP who placed at least one order within the last six months. Because the filters are applied within the same report, the output is already consolidated and does not require additional spreadsheet work.

Purchase history as a customer-level rollup plus drill-down table

Another benefit of reporting tools is the ability to present both summary metrics and detailed transaction data in the same report. Instead of exporting separate files for customers and orders, the report can be structured in layers.

At the top level, the report can show customer-level data that summarizes purchasing behavior. These metrics provide a quick overview of how valuable each customer is to the business.

Typical customer-level rollup metrics include:

• Total number of orders
• Total revenue generated
• Average order value
• First order date
• Most recent order date
  

In addition to these summaries, reporting tools often provide drill-down order tables that display the individual transactions linked to each customer. These tables allow teams to explore purchase activity in more detail.

Drill-down tables usually include fields such as:

• Order ID
• Product name
• SKU
• Quantity purchased
• Order value
  

This structure makes it easier to analyze both high-level customer performance and detailed purchasing patterns within the same report.

Custom fields and customer metafields 

Many Shopify stores store additional information about customers using custom fields or customer metafields. These attributes can represent business-specific data that is now included in Shopify’s standard customer profile fields.

A reporting tool can often include these fields alongside the standard customer and order attributes, making reports more aligned with the store’s internal data structure.

Examples of custom fields that may appear in reports include:

• Loyalty program tiers
• B2B account identifiers
• Internal segmentation attributes
• Membership or subscription status
  

Including these fields in reports allows teams to analyze customer behavior using the same attributes they rely on internally for marketing, sales, or account management.

Automation and scheduling

Another important advantage of reporting tools is automation. Instead of generating exports manually every time a report is needed, merchants can schedule reports to run automatically.

This is particularly useful for teams that rely on regular reporting cycles for operations, marketing, or finance.

Common automated reporting workflows include:

• Daily customer activity reports
• Weekly segment exports
• Monthly purchase summaries

Troubleshooting and gotchas

Shopify customer exports are generally easy to generate, but sometimes the results may look a little different from what you expected. This usually happens because of how Shopify stores customer and order data behind the scenes. A few small details in how customers are created, how segments work, or how orders are recorded can affect what appears in your exported file.

Knowing about these common situations can save time when reviewing reports and help you understand why certain numbers or records may look slightly different.

Tags vs segments confusion

One of the most common points of confusion for merchants is the difference between tags and segments. Both are used to group customers, but they work in different ways.

• Tags are labels that merchants manually add to customer profiles
• Segments are groups created using rules that update automatically
  

For example, you might tag someone as VIP, but a segment could automatically include customers who have spent more than a certain amount. Because segments update as new orders come in, the number of customers in a segment may change over time.

Customer merges and duplicate emails

Sometimes, customer data may look inconsistent because multiple profiles exist for the same person. This often happens when customers place orders using slightly different email addresses or when guest checkouts are later converted into accounts.

A few common situations that can affect exports include:

• Customers are placing orders with different email variations
• Guest checkout orders are being linked to an account later
• Customer profiles are being merged after several purchases
  

In these cases, you may see duplicate entries or purchase history spread across different profiles.

Guest checkout customers vs accounts

Not every customer creates an account before buying from your store. Many customers simply complete their purchase using guest checkout.

When this happens, Shopify still creates a customer record, but it may not behave exactly like a registered account in some reports.

Exports can therefore include a mix of:

• Customers with full accounts
• Customers created through guest checkout

Yes, Shopify supports guest checkout, allowing customers to complete their purchase without creating an account, which can help reduce cart abandonment. You can manage this setting in the Shopify admin under Settings > Customer Accounts by ensuring that the option to require customers to log in before checkout is turned off.

This is normal, but it can sometimes affect how customers appear in filtered lists or segments.

Multi-currency order totals vs reporting currency

If your store sells internationally and accepts multiple currencies, you may notice differences in order totals when exporting reports.

This happens because orders can be recorded in:

• The currency used by the customer during checkout
• The store’s reporting currency
  
  

Depending on how the export is generated, totals may appear slightly different when compared with what you see in the Shopify admin dashboard.

Test orders are excluded from segmentation calculations

Many merchants create test orders when setting up payment gateways or testing the checkout process. Some users may want these test orders included in their reports, while others may prefer to exclude them depending on their reporting needs.

FAQ

How do I export Shopify customers by tag?
You can export customers by tag by filtering your customer list in the Customers section and selecting the desired tag. Then click Export and choose Customers matching your filters.

Can I export a Shopify customer segment? 

Yes. Open the segment in Customers → Segments, then use the Export option to download the list of customers currently included in that segment.

Can Shopify export customer purchase history?
Yes. Shopify exports include purchase metrics like total orders and total spent. For detailed purchase history with products and quantities. To access this information, you need to export the orders dataset and combine it with customer data for a complete view.

How do I export line items by customer?

To view products purchased by each customer, export the orders report, which includes line items along with the customer ID. You can then match it with the customer export using the customer email or ID.

Can I export customer metafields?

Yes, if your store uses customer metafields and they are supported in report exports. These fields appear as additional columns in the exported customer file.

Why does my segment count not match the expected customers?

Segment counts may differ because segments update dynamically based on rules. Changes in orders, filters, or merged customer profiles can affect the final number.

Conclusion

Exporting Shopify customer data becomes truly valuable when it goes beyond basic lists and brings together tags, segments, and purchase history into one clear view. While Shopify provides strong native export options, the real challenge lies in combining customer and order data in the right way.

By understanding how these datasets are structured and using the right workflows, merchants can build reports that reveal meaningful insights about customer behavior, purchasing patterns, and overall value. As your store grows, moving from manual exports to more structured reporting can save time and improve accuracy. Whether you rely on native exports or advanced reporting tools, the goal remains the same. Create reports that are easy to use, consistent, and aligned with your business needs.

With the right approach, customer data becomes more than just information. It becomes a powerful tool for growth.