We send and receive a large amount of emails every day. From order confirmations to lead inquiries and marketing campaigns, the amount of business information trapped in emails is staggering. And all that data can’t stay there; you need to have it in your databases and business applications. However, sifting through countless emails to locate, type, and organize data is overwhelming, to say the least. This is where email data extraction comes in: instead of inputting information manually, you can just extract it from your inbox and move it where it should be.
Maybe you have heard of it, or you may even have tried it. Either way, you’re at the right place. In this 101 guide, we will discuss what email data extraction is and why it’s needed more than ever. We will also examine common use cases and explain the challenges you might encounter. Lastly, we will show you how you can easily get started with email data extraction using Mailparser.
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What Is Email Data Extraction?
Email data extraction is the process of identifying, extracting, and structuring important data from emails. This includes the information found in an email’s recipient, sender, subject line, body, and attachments.
Think of all the information you receive on a regular basis: invoice confirmations, customer inquiries, shipping notifications, supplier quotes… with an email data extractor, you can pull data from emails within seconds and send it anywhere you want: a spreadsheet, a database, a cloud application, an API, etc. You no longer need to spend hours inputting data manually — a tedious task that no one will miss!
Why is email data extraction important?
The truth is, the amount of information that circulates via email is simply unfathomable. According to the Email Statistics Report 2021-2025 by The Radicati Group, more than 361 billion emails are sent and received every day. You read that right: every day, not every month or year. And a large portion of these emails come from businesses. Chances are the information your company receives via email is already too much for your team to handle.
With so much information moving around, manual data entry can’t catch up. It is necessary to have automation tools in place to reliably capture information from emails and move it to business systems. This is where email data extraction comes in handy. In fact, countless organizations are already using it and reaping massive benefits.
To paint a clear picture of how businesses use email data extraction to great effect, let’s take a look at some common use cases.
Capture Data from Emails Effortlessly
Save countless hours of tedious data entry and streamline your workflows.
No credit card required.
3 Common Use Cases of Email Data Extraction
1. eCommerce
Let’s say you run an online store and receive a lot of order notification emails. Instead of recording the details of each order (customer name, production information, total amount, etc.) by yourself, you use an application that pulls all that data from those emails and transfers it to your order management system. What may take hours only takes a few minutes now. Once payment is confirmed, the fulfillment team will prepare the order and ship it to the customer.
As a result, you fulfill orders faster, avoid embarrassing data errors, and deliver a stellar customer experience.
Pro tip: do you run an online store? Take a look at our eCommerce templates and create your email data extractor in minutes.
2. Real estate lead management
In real estate, every new lead counts. Leads may reach out to you on a platform like Zillow, directly on your website, on social media, or via other channels. When you receive inquiries in your inbox, your extraction tool extracts the details and routes them into your CRM. This allows you to quickly:
- Respond to inquiries;
- Organize leads by urgency;
- Create follow-up schedules without missing a beat.
The end result is a streamlined lead management process that helps you respond faster, ultimately increasing your chances of converting leads into clients.
Actually, this is one of the most popular use cases of Mailparser: real estate professionals use it to extract lead data from incoming emails and send it to their systems where they can assign leads, follow up with them, and so on.
3. SaaS customer support
SaaS providers receive customer support emails that contain essential details needed to address client issues. When a customer emails the support team with an issue (like “Can’t access my account” or “Billing error”), the email data extractor extracts the key details: the customer’s email, user ID, account type, issue description, and priority level.
Once extracted, this data is transferred to a support ticketing system (like Zendesk). New tickets are automatically created and organized by priority. Each ticket is then routed to the right support agent based on factors like complexity, area of expertise, or the customer’s account level.
As a result, assigned agents receive real-time notifications and can start addressing client issues promptly, ensuring fast response times and a more satisfying customer experience.
So these are three common use cases of email data extraction. However, the assumption here is that this automated data extraction system runs flawlessly, which is not always the case.
Challenges of Email Data Extraction
Email data extraction is not always easy to implement. In fact, a lot of businesses stumble upon a number of hurdles when trying to automate their data capture processes. Below are the most frequent and frustrating challenges that you should be aware of.
High volumes of emails to process
As your business grows, you will undoubtedly receive more emails that must be processed. So the solution you choose should be able to scale along with your data extraction needs, otherwise you might face slow processing times which then will lead to delays. So you want a solution with powerful processing capabilities to handle a growing volume of emails without sacrificing speed or accuracy.
Inconsistent email formats
Since you receive recurring emails from different sources, these emails have different layouts and structures. For instance, an order notification from Amazon won’t have the exact same layout and wording as a notification from a Shopify store. This can make it difficult for an automated system to locate data, extract it, and put it in a database where every data point is exactly where it’s supposed to be.
Data accuracy
Inaccurate data can undermine the entire workflow, leading to issues such as incorrect invoicing, miscommunication with customers, or inventory errors. To help maintain data accuracy, we recommend that all the emails you want to process for a specific use case have a consistent layout and formatting.
Integrating data with existing business systems
Integration is the last piece of the automation puzzle. You need to get it right in order to streamline your workflows. After extracting data, you want it to go where you need it to be, whether it’s a spreadsheet, new records in a CRM, an ERP, etc. However, sometimes there will be compatibility issues between apps, or discrepancies after transferring data. Effective data extraction requires middleware or APIs to export and map data fields accurately between the email source and the business systems, ensuring a smooth and cohesive workflow without additional manual intervention.
So, with these challenges in mind, you want a solution that can handle high volumes of emails in various formats and give you accurate data that lands on your business app seamlessly. We believe Mailparser fits the bill — allow us to explain why.
Why Use Mailparser to Extract Data from Emails?
Mailparser is an email parser, a web-based application that allows you to extract data from emails and export it to your business systems seamlessly and without inaccuracies.
Easy to use
Mailparser runs directly on your browser — just sign in and set up your email parser within minutes. You don’t need technical knowledge or extensive training. In a nutshell, the setup process consists of this:
- Create an inbox.
- Send a test email to your inbox.
- Set up parsing rules to extract relevant data fields.
- Add an integration with a cloud app or API and specify the desired action: create a new record, update a database, etc.
Once set up, the email parsing workflow will look like this:
- You forward emails to your Mailparser inbox.
- Mailparser processes the emails and extracts data.
- Mailparser then sends the parsed data to your chosen application.
Want to learn more about how Mailparser works? Feel free to explore Mailparser Academy and our Knowledge Base.
Customizable parsing rules
One of the standout features of Mailparser is how customizable it is — all while being user-friendly and entirely code-free.
For each data field, you want to have a parsing rule that will recognize and extract it from your emails. Luckily, Mailparser has an Automatic Setup that will identify simple data fields within seconds:
Then, you can add custom parsing rules to extract more data fields. Plus, for each parsing rule, you can add filters to refine the parsing results. For instance, you can set the start and end positions of a data point.
Mailparser’s filters are particularly effective when you want to extract tabular data. You can merge columns, split them, filter rows, remove unwanted characters, change the formats of dates and phone numbers, and more. This makes it easy to refine and organize your tables however you want.
Integrations with your cloud apps and APIs
With Mailparser, you can easily move your email data to Google Sheets, Excel Online, QuickBooks, Salesforce, and thousands of other cloud applications (thanks to our integration partners, like Zapier). Alternatively, you can also use a webhook to send data in real-time to an endpoint URL, or simply download parsed data as a file.
So whenever you receive new emails, you just forward them to your Mailparser inbox. After extracting data, Mailparser exports it to your app of choice directly or through a third-party integration. Each data point is sent where it’s supposed to be according to the pre-defined data mapping. The whole process is seamless and saves you a lot of time.
In Conclusion
Let’s recap what we learned in this 101 guide: email data extraction allows you to automate the process of identifying, extracting, and organizing key information from emails. Due to the unfathomable amount of information that moves around via email, extracting data is no longer a nice-to-have but a necessity. Common use cases include order processing, lead management, customer support, and a lot more.
As a matter of fact, companies in your industry are already using dedicated tools to extract data from emails. So relying on manual data entry puts you at a disadvantage. However, you need to choose the right solution to reap the expected benefits.
If you want to get started with email data extraction, sign up for a Mailparser trial account. Once you start extracting data from your inbox, you — as well as other people in your organization — will streamline workflows, improve data accuracy, and save valuable time for the work that truly grows your business.
Capture Data from Emails Effortlessly
Save countless hours of tedious data entry and streamline your workflows.
No credit card required.