The most popular use cases that we have at Parseur are food delivery, extraction of leads for real estate or mortgage, and e-commerce as well. For those industries, our customers have to extract specific information from their emails such as their contact details including their addresses.
Why should you parse and normalize an address?
Let's take the case of the coffee shop owner who receives his orders online. From the orders received, he has to extract data manually and send in the delivery addresses to his drivers. For real estate agents, it's more or less the same process where they receive leads from real estate platforms such as Zillow, Realtor, and Trulia. They will not only need the contact details of the potential buyers but also the location of the interested property (street, zip code, country).
Unfortunately, addresses often come in all sorts of shapes and format, especially if they have been entered by a person without being verified.
When you receive hundreds of emails on a daily basis, it can become time-consuming and difficult to extract address data quickly, especially if you need to integrate those into a CRM such as Realvolve, Surefire CRM, or an ERP system.
How to automatically parse and normalize an address in a document
As a powerful email parsing engine, Parseur can automatically parse and normalize an address location. The street address, city, zip/country code, state, and coordinates can be easily extracted from emails and other email attachments such as PDF documents. With Parseur, the unstructured data can be split into individual address fields, that is, in normalized form.
A unique feature that Parseur provides and other email parsers do not have is the power to determine the geographical location from the address and provide a google map link.
Forward the lead or document to your Parseur mailbox
For this article, we will take an example of a lead email from Zillow.
The address automatically geolocated!
The address parser will normalize the above property location and provide you with the coordinates as well:
| Parsed data | |
|---|---|
| PropertyAddress.normalized | 12345 Random Rd, El Mirage, AZ 85335, USA |
| PropertyAddress.address1 | 12345 Random Rd |
| PropertyAddress.city | El Mirage |
| PropertyAddress.zip | 85335 |
| PropertyAddress.state_code | AZ |
| PropertyAddress.country_code | US |
| PropertyAddress.lat | 33.1234567 |
| PropertyAddress.lng | -112.1234567 |
| PropertyAddress.map | link to Google Maps |
As you can see, Parseur has provided you a link to the location as well. It has split up the text and standardized the field values.
If receive emails emails and want to normalize all addresses from them, you can easily do it with Parseur. Once your email arrives in your Parseur mailbox, you just have to highlight the data that you want to be extracted (in this case, the location) and create data fields for them. The field formats are used to normalize data, thus teaching the data parsing solution on how to normalize this particular field.
Capturing a parsed address will save you countless hours of manual work as the parsed email data can be easily exported as well in any CRM application of your choice. In this way, you have a complete automated workflow set up for you.
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