Migrating an e-commerce platform is a complex project, and one of the most critical assets to get right is your customer data. Before you launch a new site or switch to a platform like Shopware, you need to ensure that all customer information is accurate, complete, and properly formatted for the move. Taking the time to prepare customer data before migration can save you from major headaches later. It helps prevent lost accounts, broken logins, compliance issues, and unhappy customers. In short, preparing customer data is critical for a successful migration because it ensures your shoppers experience a seamless transition to the new platform without needing to re-register or fix their account details.
Having clean and well-structured customer data means your new e-commerce site can hit the ground running. All the features – from personalized marketing to account order history – will work properly if the data is migrated correctly. On the other hand, poor data quality can lead to login problems, missing past orders, or even privacy compliance violations. In the sections below, we’ll cover exactly how to get your customer data ready for an e-commerce platform migration. We’ll define the types of customer data you should gather, walk through step-by-step preparation, and highlight Shopware-specific tips (though the principles apply to any platform). By the end, you’ll have a comprehensive plan to migrate customer accounts smoothly and confidently.
Before diving into the migration process, it’s important to understand what customer data you need to gather and prepare. Customer information isn’t just a list of names and emails – it spans several categories, each of which must be handled carefully:
By clearly identifying these categories, you can ensure you don’t miss anything during export. Make a checklist of all the customer-related data you need to move. Now, let’s move on to the step-by-step guide for preparing and migrating this data.
Migrating customer data involves more than simply copying files from one system to another. You need to extract it properly, clean and organize it, and then import it in a way the new platform understands. Below is a step-by-step guide to preparing your customer data for migration:
The first step is to get all your customer data out of the current system. Use your e-commerce platform’s export tools or database queries to export all customer information. Typically, platforms can export customers in a CSV or Excel file with all their details. Make sure to include all relevant fields: names, emails, addresses, phone numbers, company names (if B2B), and any metadata like customer group or tags. If possible, also export related data such as consent statuses (marketing opt-in) and any unique identifiers used for orders (for linking later).
Once you have the data exported, create a secure backup of this raw data file before making any changes. Store the backup in a safe location. This backup is your safety net – if anything goes wrong or if you need to reference original information later, you can always look at the untouched data. It’s wise to keep multiple backup copies (for example, one on your local computer and one in cloud storage) given how valuable customer data is. Also, ensure the backup is protected (customer data is sensitive – consider encryption or secure access control when storing these files).
In addition to the main customer list, back up related assets. For example, if you have scans of documents, signed agreements, or any data not in the main export (perhaps stored in a different system like a CRM), gather those too. The goal is to have everything about your customers preserved before you start transforming it for the new platform. Only work on copies of the data – never the sole copy – so that the original remains intact.
Lastly, review the exported data to confirm completeness. Check the number of customer records exported versus the count you expected from your old platform. If something seems off (for instance, you expected 10,000 customers but only see 9,500 in the file), investigate and re-export if needed. It’s easier to solve such issues now than after you’ve started migration.
With your data in hand, the next crucial step is data cleaning. Raw customer data often contains errors or inconsistencies that have accumulated over the years. Cleaning the data means fixing those issues so that the information is accurate and standardized. This will prevent problems during import and ensure a professional, error-free customer database on the new platform.
An example of raw customer data in a spreadsheet, with errors highlighted in red (such as typos, inconsistent country names, and missing information). Cleaning involves correcting these issues.
Start by scanning for obvious errors: spelling mistakes in names, invalid email addresses, phone numbers in different formats, etc. Correct typos and use consistent formatting. For instance, if countries are spelled out in some records (“United States”) but abbreviated in others (“USA” or lower-case “us”), decide on one format (perhaps the two-letter country code like “US”) and change all entries to match. Standardizing fields like country, state, and phone number format (with country codes, no extraneous characters) will help the new system recognize and use this information properly.
Next, deal with incomplete or missing data. Identify any records with critical missing fields (e.g., customer account with no email or no last name). If an email is missing or obviously wrong (“bob at example.com” instead of “bob@example.com”), try to correct it if possible (maybe you have the correct email elsewhere) or decide if that record should be excluded from migration. It’s usually not useful to migrate accounts that don’t have essential contact info, since the user wouldn’t be able to log in anyway. However, if you cannot obtain missing information for important customers, have a plan – you might have to contact the customer to update their info, or mark the account to handle specially.
Data cleaning also includes verifying data integrity. Ensure fields are in the correct format (e.g., dates of birth are actual dates, ZIP codes are numeric where expected, emails have an “@” and valid domain). If your current system didn’t enforce strong validation, you might find junk data that slipped in (like “123” entered in a name field). Filter those out and fix them. In some cases, you might choose to purge clearly bogus or test accounts to avoid cluttering the new system.
Another part of cleaning is normalizing values. That means making the data consistent – for example, using the same units or formats. If some phone numbers have “(555) 123-4567” and others “+1 555 1234567”, pick a standard (like international format +15551234567 without spaces) and apply it across the board. If some names are all uppercase or lowercase, you could format them in proper case (e.g., “JOHN DOE” to “John Doe”) for professionalism. While these might seem cosmetic, a clean, consistent database looks much more professional when the data surfaces on your new site (such as in user profiles or address labels).
Finally, consider using tools to help with cleaning. Spreadsheet software (Excel or Google Sheets) has functions and sorting that can help find anomalies (for instance, sort the “Email” column to quickly spot those missing “@” symbols). There are also data cleaning tools or scripts that can validate emails or addresses. If you have a very large dataset, a script to automatically fix common issues (like adding “@” in a known domain or converting country names to codes) might save time – just be cautious and verify the changes it makes.
Duplicate customer records can cause major issues, especially because most platforms (including Shopware) require unique email addresses for each account. De-duplicating means finding and merging or removing any duplicate customer entries in your data. Duplicates might have occurred over time due to imports, system glitches, or customers accidentally creating multiple accounts with the same email. Before migration, it’s the perfect time to eliminate them.
How do you identify duplicates? The most common identifier is the email address, since it should be unique per customer. Sort or filter your customer list by email and look for repeats. You might find exact duplicates (same email, name, etc.) or partial duplicates (same email but slightly different name spellings, or same name with different emails). For exact email duplicates, decide which record to keep – usually the one with the most complete or latest information – and mark the others for removal. You’ll also want to consolidate any data from duplicates: for example, if one record has an old address and the other a new address, you might combine them to ensure no information is lost (perhaps keep the new address as primary and the old as a secondary address if your new platform allows multiple addresses).
In cases of partial duplicates (like John Doe with email john@example.com and Jon Doe with email jon@example.com – which might actually be the same person who made two accounts), you need to use judgment. Compare details like shipping address or phone number to see if they match. If you determine they are the same person, consider merging their data into one account (choosing one email as the primary). This can be tricky – you might prefer not to merge unless you’re fairly sure, to avoid incorrectly combining two separate individuals. If unsure, you could still migrate both and deal with it later, but that means the duplicate persists. Better is to reach out to the customer (if practical) to clarify which account is active. For a large customer base, manual outreach isn’t feasible, so you rely on the data clues you have.
While de-duplicating, also pay attention to case sensitivity or formatting issues that might mask duplicates. For example,
JOHN@EXAMPLE.COM
vs
john@example.com
– many systems treat those the same (emails case-insensitive), but in raw data they appear different. Convert all emails to lowercase to check for duplicates. Similarly, watch out for trailing spaces or typos (
alice@example.co
missing the “m” might actually be intended to be the same person as
alice@example.com
). Use address and name similarities as secondary indicators for duplicates.
After identifying duplicates, remove or merge them in your data file. Ensure that only one unique email remains for each customer. If you merge data, make sure the final record is complete. For instance, if one duplicate had no phone number and the other did, keep the phone number. If both had different information (like two different phone numbers), decide which to keep or keep both if possible by adding one as a secondary contact in notes.
By the end of this step, your customer list should have one entry per customer, with no duplicates. This will make the import to the new platform much smoother – you won’t hit errors about duplicate emails, and customers won’t complain about accounts not working because their email was tied to multiple profiles.
Now that the data is cleaned and de-duplicated, you need to structure it in a way that the new platform can import easily. This often means arranging data in a specific file format or schema such as CSV (Comma-Separated Values), JSON, or an XML format if required. The key is to normalize the data structure: each field in the right place, consistent columns, and using templates the new system expects.
For most e-commerce platforms (Shopware included), CSV is a common format for bulk import. It’s basically a table of data where each line is a customer and each column is a field (name, email, etc.). Make sure your CSV has a header row defining what each column is. The new platform’s documentation will usually list the required and optional fields for import. Align your columns to match those expected names if possible. For example, Shopware might expect
email
instead of
customer_email
– such details matter. You can usually rename column headers in your CSV to match what the import tool wants (or map them during import).
If the platform allows JSON import or has an API, you might structure the data as JSON objects. This is more technical – typically done by developers writing scripts to push data via an API. The principles are the same: ensure each field from the old system goes to the correct field in the new system’s JSON structure. Whether CSV or JSON, consistency is key. Use the same format for each record. If a field is not applicable for a particular customer, leave it blank or use a null value that the new system accepts (check how it handles blanks).
Normalization also involves making sure all values conform to expected formats. Dates should be in a uniform format (like YYYY-MM-DD if required). Boolean or yes/no fields (like newsletter subscription) should be in a format the new system expects – e.g., it might want
true/false
or
1/0
rather than “Yes/No”. Convert those accordingly. Country and state codes should match the new system’s database (for example, if the new platform requires ISO country codes like “US” instead of “United States”, ensure your data uses “US”). This kind of normalization can often be done in bulk with find-and-replace or formulas in a spreadsheet.
Take care to maintain data integrity during this structuring. Commas in CSV can be tricky – if a customer’s address has a comma (e.g., “Apartment 4, Building A”), it could break the CSV format unless properly quoted. Ensure fields that contain commas or special characters are quoted according to CSV standards. The safer approach is to wrap text fields in double quotes in the CSV. If you’re using spreadsheet software, it often handles this when saving as CSV, but double-check by opening the CSV in a text editor.
If using multiple files (for example, one file for customers and another for addresses), make sure you have a clear way to link them (like a customer ID). Many systems let you import addresses separately but need to know which customer each address belongs to. In such cases, include a unique identifier in both the customer record and address record (for instance, a customer number or email) to tie them together. Shopware’s import might use email to match addresses to customers if done separately, so verify that in documentation.
In summary, format your data clearly and exactly how the new system wants it. A well-structured data file is like a neatly packed box ready for shipping – it will get delivered into the new platform without spillage or confusion. This preparation sets the stage for a successful import in later steps.
One often overlooked aspect of customer data migration is passwords and security. It would be ideal to simply move customers’ passwords so they can log into the new platform with their same credentials – but in practice, this is complicated and sometimes not possible (at least not directly). Passwords in modern systems are stored encrypted (hashed), and different platforms may use different hashing algorithms or salting techniques. You typically cannot decrypt a password (which is good for security) just to re-encrypt it for the new platform. So how can you handle this?
First, check if your new platform (e.g., Shopware) has a mechanism for importing hashed passwords from another system. Sometimes, migration tools or plugins exist to migrate passwords, but they often require that you know the hashing method from the old platform and that the new platform can be configured to accept it temporarily. If such a path exists (for example, a plugin that lets you import Magento hashed passwords into Shopware), you’ll need to export not the plain passwords (you never had those) but the hashed password values and maybe the salt. Then during import, map these to the fields that Shopware uses for password hash and salt. When the customer tries to log in, Shopware can use the old hash once to verify and then replace it with its own hashing on password change. This is an advanced approach and may require developer help.
If migrating hashes isn’t feasible, the simplest approach is to reset customer passwords on the new platform. You can generate a random temporary password for each account or simply invalidate the passwords and force a reset. Many migrations handle it this way for security and simplicity: after migration, customers must do a “password reset” via the “Forgot password” link. You’ll need to communicate this to customers around the time of migration – for example, send an email informing them that for security, they’ll need to set a new password when logging into the new site for the first time. Customers may find it a minor inconvenience, but it’s quite common and generally accepted if explained as a security measure.
Prepare your new platform to handle this. For instance, in Shopware you might import customers with a dummy password or a known default that you plan to invalidate. Or better, use the platform’s built-in password reset function: some migration processes allow you to mark accounts to require password change on first login. If not, a workaround is to email out password reset links to all users post-migration. This can be done via a script or an email campaign – carefully, so it doesn’t look like spam.
Also consider account security questions beyond passwords. If your old platform used features like security questions, two-factor authentication, or account lockouts, figure out how those will carry over. Maybe the new platform has different 2FA methods – you might have to ask users to set that up again. If the new platform requires stronger passwords (e.g., at least 8 characters with a number and symbol) and your old one didn’t, then during migration you might find some existing passwords don’t meet the criteria. Another reason to force a reset in that case.
In any scenario, do not transfer passwords in plain text (never email customers their old passwords or anything insecure like that). Treat this process securely: the less you actually handle real password values, the better. Focus on either migrating hashes or doing resets. And of course, ensure the rest of the customer data (like their personal info you’re migrating) is handled securely too – use secure channels or encryption if transferring files, and restrict access to those files on a need-to-know basis.
Compliance with privacy laws is non-negotiable during a migration. Make sure to carry over all GDPR and other consent-related records for each customer. This includes whether the customer has consented to email marketing, accepted your terms and privacy policy, or requested data removal. Losing this information could not only annoy customers (if they start getting emails they unsubscribed from) but also land you in legal trouble.
Start by identifying all the consent flags and privacy-related fields in your old system. Common ones are: newsletter subscription (yes/no), SMS or promotional messages consent, acceptance of terms and conditions date, and perhaps GDPR consent timestamp or similar. In some systems, every user might have a record like “Agreed to Privacy Policy on 2023-01-05” or a simple boolean flag “AgreedToPrivacy=true”. Extract all of these along with your customer data.
When structuring your data for import, ensure these consent fields are included. The new platform needs to have equivalent fields to store them. For example, Shopware and other platforms usually have a “newsletter” opt-in field for customers. Map the old “subscribed_to_newsletter” or similar field to the new platform’s field so that a “yes” stays a “yes” and a “no” stays a “no”. If the new system doesn’t have an exact field (say your old system stored a timestamp of consent), you might store that in a custom field or at least retain it offline. The key is not to lose the information.
If some customers have requested that their data not be transferred or have exercised a right to be forgotten, you must honor that. That might mean excluding certain customers entirely from the migration if legally you should not carry their data into a new system. Double-check if any deletion requests are pending or recently processed, to ensure you don’t accidentally revive a deleted profile in the new platform. If you’re not sure, it might be safer to leave out extremely old or deleted accounts rather than risk a breach of policy.
Another aspect is privacy policy updates. Migrating to a new platform might coincide with an update to your terms or privacy policy (especially if data will be handled differently). If so, consider how you’ll log customers’ agreement to the new terms. You might require them to accept new terms upon first login on the new platform. This can often be configured in the new system’s settings (e.g., force users to accept updated terms). While this isn’t strictly part of the data migration file, it is part of migration planning.
Don’t forget regional compliance beyond GDPR if applicable: for instance, the California Consumer Privacy Act (CCPA) in the US, or other local laws. Generally, the principles are similar – keep track of who opted out of data sale/sharing (in case of CCPA), etc. Ensure that any flags or statuses related to those are migrated or re-established.
Finally, once you import the data, do a test for compliance: pick a couple of customers who had unsubscribed from marketing in the old system and verify that in the new system they are also marked unsubscribed (and thus won’t get marketing emails). This verification step can save you from an accidental email blast to the wrong group. Aligning consent records perfectly means post-migration, you maintain the trust of your user base and stay on the right side of privacy regulations.
Migration time is actually a great opportunity to segment your customers and enrich your marketing data. Rather than treating all customers the same, you can categorize them based on behavior, purchase patterns, demographics, or region. This step isn’t strictly required to complete a migration, but doing it now can pay dividends once you launch on the new platform. Many modern e-commerce systems (Shopware included) have robust customer grouping and rule features, so providing segments upfront can help you leverage those features from day one.
What kinds of segments should you consider? Common useful ones include:
How do you implement segmentation during migration? One way is to add a column in your customer data file (e.g., “Segment” or “Group”) and fill it with appropriate labels. A high-value customer might get “VIP”, while others could get “New” or “Dormant”. If the platform supports custom tags or groups, import this data directly. Shopware, for example, supports customer groups, so you can use this field to automatically place customers into segments during import. Even if you don’t import it right away, keeping segmentation data in a side file can be useful later for campaigns or reporting.
Make sure your segmentation logic is consistent. Using clear thresholds (e.g., total orders > 5 or last order within 6 months) and applying formulas or scripts helps reduce errors. And remember, segments can overlap – a “VIP” customer can also be in “EU Region” or “Women’s Apparel”. Flexibility is key. The goal is to go into the new platform with a well-defined customer base you can act on right away.
Field mapping is a fundamental step in migration – it’s like translating your data from the “language” of the old platform to that of the new platform. Field mapping means you create a clear map or list that shows which field in the legacy system corresponds to which field in the new system. This ensures, for example, that what was stored as “FirstName” in the old database goes into the “first_name” (or perhaps “firstname”) field in the new database correctly.
Start by listing all the customer-related fields you have from the old system. Then get a list of all fields that the new system can accept for customers (and which are required vs optional). The new platform’s import documentation is a great resource here, as it often lists field names and formats. Go through and match them up:
Document your field mappings in a spreadsheet or document. This acts as a blueprint for whoever executes the migration. It’s also a reference if you discover after import that something went wrong – you can check your mapping document to quickly spot if a field was mis-mapped (e.g., customer “State” ended up in the “City” field by mistake).
Before doing the full import, it’s highly recommended to test with a small subset of the data to verify that your mapping is correct. Take perhaps one or two customers, ideally ones with complex data (like multiple addresses or a name with special characters) and run them through the import on a staging environment. Then inspect the customer in the new system: Is all the info in the right place? Are the accents in names preserved correctly (testing encoding)? Is the address correctly split among fields? This dry run will either give you confidence that the mapping works or highlight adjustments needed.
In the previous sections, we discussed steps that apply broadly to any e-commerce platform migration. Now, let’s focus on Shopware, which is our reference example. Shopware is a modern e-commerce platform with its own tools and requirements for data import. Whether you’re moving to Shopware or something similar, understanding these specifics can help ensure nothing is missed on the big day.
Consider doing a combination for best results: for example, maybe use the Migration Assistant for core data, and then a CSV import for any custom fields or segments not handled by it. Always refer to Shopware’s documentation for specifics of each method.
Remember that migrations rarely go 100% perfectly on the first try. That’s why testing and backups are your safety net. If you encounter issues, don’t panic – analyze, adjust the data or method, and try again. With each iteration you’ll iron out the problems until all customer data sits nicely in Shopware (or your target platform). The effort spent in meticulous preparation greatly reduces the number of issues you’ll face at this stage.
Even seasoned professionals can overlook some details during migration. Here are some extra tips to ensure you avoid common mistakes when preparing and migrating your customer data:
By following these tips, you minimize the risk of errors and oversights. In essence, be thorough, be cautious, and think ahead. Most mistakes in migration come from assumptions (e.g., assuming a field isn’t needed when it actually was, or assuming data was clean when it wasn’t). Question those assumptions and verify everything.
Before you finally switch over to the new platform, run through a final checklist to ensure all customer data is ready and nothing has been missed. This checklist serves as a quick summary of key tasks:
Once everything on the checklist is ticked off, you’re ready to go live with confidence. Flip the switch, and monitor things closely. With well-prepared data, your customers should be able to log in and find their information intact. Keep the team on standby for the first day or two to quickly address any unexpected issues (maybe a handful of accounts didn’t migrate properly and you need to fix them manually – it happens).
By following this guide and the checklist, you’ve dramatically increased the chances of a successful e-commerce platform migration. Your customer data is clean, structured, and safely moved into Shopware (or the platform of choice). This means you can focus on serving your customers and leveraging the new features of your platform, rather than firefighting data problems. Good luck with your migration, and enjoy the benefits of a fresh, well-organized system!
