
For Magento extension developers and vendors on Adobe Commerce Marketplace (formerly Magento Marketplace), access to structured customer data can be a persistent challenge. Adobe Commerce Marketplace does not provide vendors with a tool to extract customer information directly, leaving customer details locked inside individual purchase confirmation emails. This presents a cumbersome task of manual processing—a clear candidate for automation.
In this post, I’ll share a practical automation I’ve implemented using N8N and a large language model (LLM) to extract structured customer data from emails and integrate it with Mailchimp. While my implementation uses Mailchimp, the workflow can be adapted to other platforms or data sinks, depending on your business needs.
The Problem: Customer Data Stuck in Emails
When a customer purchases your extension on Adobe Commerce Marketplace, the only information about who they are is provided via email. The data is unstructured and buried in the message body, making it difficult to use at scale for marketing or support purposes. Manual data entry is error-prone and time-consuming, which is far from ideal for modern eCommerce businesses. I personally experience this headache, Magenable in addition to professional services developed several Magento/Adobe Commerce extensions and made them available on Adobe Commerce Marketplace. So I decided that it will be a great case to use LLM and automate this.
The Solution: A Workflow Powered by N8N and LLMs
To address this, I built an automation workflow in N8N, leveraging the power of a large language model for data extraction. If you never used N8N it is similar with a more widespread tools like Zapier and Make. I prefer it because you can self-host N8N for a fraction of competing tools cost and forget about going off your current plan limits. N8N also has number of pre-built components that makes work with AI and specifically LLM models from OpenAI and Anthropic easier.

Here’s how the workflow works step-by-step:
1. Trigger: Monitor Your Email Inbox
The workflow starts by monitoring an IMAP inbox for new emails from Adobe Commerce Marketplace. In my case, I use a dedicated email address (e.g., [email protected]) to ensure only relevant emails are processed. We take the text body from the email for further processing.
2. Data Extraction Using LLM
The unstructured email content is passed to an Information Extractor node that uses a large language model to extract structured data. This node is configured with specific instructions to:
- Identify and extract fields like First Name, Last Name, Email, and Opt-in status from the “Order Summary” section of the email.
- Skip irrelevant content or fields with missing values.
This ensures high accuracy and minimal manual intervention. I use OpenAI API with GPT-4O-mini model.
Below is task specific prompt I added to the Open AI node system prompt:
You are an expert extraction algorithm.
Only extract relevant information from the text.
If you do not know the value of an attribute asked to extract, you may omit the attribute's value.
Extract the data from Order Summary section.
Example below:
--input--
Order Summary
MageIDMAG0012121212
Order ID401212121
Name Joe Doe
Company Email [email protected]
The buyer agreed to receive marketing information: Yes
---output---
FirstName: Joe
LastName: Doe
Email: [email protected]
Opt-in: True
3. Conditional Processing: Check Marketing Consent
Before taking further action, the workflow evaluates whether the customer has agreed to receive marketing communications (Opt-in status is “true”). This ensures compliance with privacy regulations like GDPR and maintains trust with customers.
4. Integration with Mailchimp (or Your Preferred Tool)
If the customer has opted in, their details are added to a Mailchimp mailing list. Tags like “marketplace” are applied to help segment the audience for targeted campaigns. The workflow is configured to handle errors gracefully, so a single failure doesn’t interrupt the entire process. That is an important step, because the call we are making is to create a new subscriber and with possible repeat purchase adding already existing email cause an error.
5. Adaptability and further improvements
While I use Mailchimp, the endpoint can easily be swapped out for another tool such as HubSpot, a custom CRM, or even a Google Sheet, depending on your business requirements. Another improvement, especially if the email you use for processing is used for other purposes is to pre-filter emails (on subject and sender for example) to only process those that contain marketplace orders. It is important to save $$$ you pay to OpenAI.
Why Use LLMs for This Task?
Traditional parsing tools struggle with the variability and complexity of email content. By using a large language model, the workflow adapts to minor changes in email structure and provides robust data extraction without the need for constant updates. This makes the solution scalable and future-proof.
Results and Benefits
- Time Savings: The process now runs automatically, eliminating hours of manual work.
- $$$ saving: Time is money, you pay few cents for LLM use rather than hundreds dollars (on a time scale) for your team time
- Data Accuracy: With structured extraction, errors in data entry are virtually eliminated.
- Improved Marketing Efficiency: Customer data flows directly into Mailchimp, enabling faster and more targeted email campaigns.
- Happier team: copy-pasting data is a very boring job, one that your marketing team will be happy to get reid of
Final Thoughts
This workflow demonstrates how LLMs can transform everyday business tasks by automating processes that previously required manual effort. For vendors on Adobe Commerce Marketplace, this solution unlocks the potential of customer data hidden in emails and helps streamline marketing efforts.
If you’re struggling with similar challenges, consider implementing an LLM-powered workflow. The tools and techniques are accessible, and the return on investment in time and accuracy is immediate.
Have questions or want to discuss how automation can help your business? Let me know in the comments or reach out directly.