I already talked about the GMail aliases feature in my previous blog. It’s something I use EVERY DAY! I used to share this trick as is, but this time I want to what are the consequences regarding Big Data and marketing.
What are GMail aliases?
The Google Mail service has a really interesting “hidden” feature: with one address, you actually have an infinite amount of aliases.
There are actually two kind of aliases:
- Adding dots, because GMail is “dot blind”. That means it does not take account on dots in an email address. So, if your address is firstname.lastname@example.org you can use both email@example.com or firstname.lastname@example.org (source).
- Adding “+alias” between your username and the “@”. So, you can use email@example.com, or firstname.lastname@example.org, etc. You don’t have to configure anything, just use it and emails will reach your inbox (source).
The first interesting use of this feature is that you can easily filter your emails using specific rules. So you should consider using a different alias for each website you’re using, it greatly helps to sort your emails!
For example, let say you’ve created a new Amazon account using email@example.com to sign up. One day, your inbox is full of spams from websites you don’t even know. If all these spams are initially sent to firstname.lastname@example.org that means Amazon has “sold” your email address (or it has been hacked). The good news is you can easily filter all the emails sent to john.doe+amazon and automatically delete them if they’re not from Amazon.
How it can “disrupt” Big Data?
The biggest challenge in digital marketing is to collect clients and prospects data. When brands don’t have enough information, they can buy or rent other databases and try to match them with their own data.
One of the best criteria to match a person one the web is to use his email address. It’s not necessarily the only one but a lot of matching methods are based on email addresses. As an example, here is how Facebook matches data between their users and a brand database.
Once the matching is done, you can decide to push precise ads to your customers specifically (loyalty) or match look-a-like prospects you don’t know yet (conquest).
So, now imagine Amazon wants to match his clients on Facebook to push them ads for articles related to their last purchases.
If you’ve registered on Facebook and Amazon using the following adresses:
- Facebook: email@example.com
- Amazon: firstname.lastname@example.org,
That will make you be really hard to match.
GMail aliases are always a good thing to use as they let you filter easily your emails (especially in case of spams). Moreover it can muddy the waters when it deals with matching your “Big Data” information across databases.
Big Data is not Smart Data yet!