Apache Hadoop needs no introduction when it comes to the management of large sophisticated storage spaces, but you probably wouldn’t think of it as the first solution to turn to when you want to run an email marketing campaign. This collection of open-source utilities are primarily designed to help solve issues related to distributed storage, which is normally associated with crunching large numbers and tracking information that comes in from multiple sources. Ironically, these features make it ideal for those who want to run complicated marketing campaigns.
Since Hadoop is designed to work with large computer clusters made from inexpensive commodity-grade PC hardware, it’s uniquely attractive to smaller businesses that need the same kind of power found at larger organizations without the upfront infrastructure investment. Some groups are turning to Hadoop-based data mining gear as a result. Nevertheless, there’s no reason why people can’t also rely on it to manage their lists and sendmail daemons as well.
Managing Mail with a Distributed File Structure
By exposing underlying storage resources as part of the Hadoop Distributed File System, Apache’s platform has given technicians the power and freedom to work with their data the same way they would with any other discrete file resource. If they have a management utility that can mount HDFS volumes, then they can simply treat them as any other drive. That means they could manually update mailing lists that are stored this way as though they were any other database.
Creative users who have lower-end needs could even store their lists as a simple flat file, which could be manipulated with any modern text editor. Over time, these same users could write relatively small shell scripts that would perform most of these maintenance chores for them.
Each time an outside user signed up to receive their email blasts, the aforementioned shell script could update the list database and store everything inside of the very scalable HDFS structure. Unlike most other competing technologies, there’s no reason to believe that the limits of this kind of cluster could be reasonably met by most email marketing campaigns.
In fact, you could store all of your content in a single area using this kind of technology. Coupon experts have been using a somewhat similar technique to process large coupon databases, which would normally prove rather difficult to work with.
Creating One Centralized Storage Location
As the name might suggest, YARN technology splits up the resource manager and job scheduler into separate services that are connected by a piece of what amounts to digital string. Through a wise application of this system, a majority of the maintenance tasks that most server administrators and email marketing list magnates might have to go through could be largely avoided.
That makes creating a single storage repository relatively simple. Placing all emails in one place empowers marketers, since they then won’t have to go around looking for messages that are waiting to be sent. Each time they manually compose one, they’ll know where to store it.
Those who use extensive automation systems can point all of their mail merge apps at this repository and then let them run based solely on a schedule. As a result, they won’t have to worry about the possibility of some merges failing due to the inability to find certain messages.
Those who want to build the most effective system they possibly can might also want to investigate the possibility of letting Hadoop figure out when messages should be sent and to whom.
Leveraging Hadoop’s Predictive Analytic Potential
Predictive analytics based around Hadoop libraries are quickly becoming a mainstay of the meteorological community, which might seem to indicate that they’re not capable of managing more immediate problems. Nothing could be further from the truth, however. Assuming that you’ve migrated all of your email marketing data to a single HDFS location, there’s no reason that you can’t also rely on Hadoop’s prediction tools to figure out when the best time to send messages is.
Customer patterns can be somewhat difficult to track, which is why retail industry representatives originally started to deploy Hadoop-based solutions. These were designed to run through loads of existing data in order to find patterns they can act on.
Chances are that you have a fair amount of data about who responds to your messages and who throws them away. You might have even requested confirmation packets when someone reads an email. Others may include a single pixel’s worth of graphics data to track who opens emails and who doesn’t.
Try feeding all of this information into a Hadoop-based predictive analytics routine. While such an arrangement isn’t going to provide results overnight, it can certainly help you to further get a handle on the right way to frame your next major email marketing campaign.