Schenectady, New York, is home to the most commonly used ZIP code in the United States.
However, it’s not why you’d think. Its population of just over 65,000 doesn’t account for the millions of people who list Schenectady’s ZIP code as their own.
What accounts for the discrepancy? Schenectady’s ZIP code is 1-2-3-4-5.
Children writing Santa or those looking to create a fake profile online revert to this series of numbers, which dramatically distorts the popularity of the city.
The messy world of data
This example illustrates the complex nature of data. It can be too big, unstructured or just plain messy.
And yet, unless you are aware of nuances like 1-2-3-4-5 and Schenectady, you could mistakenly focus your marketing or sales efforts on a town that isn’t as promising as your lead form submissions indicate.
Not to mention, with a quintillion bytes of data created every day, the problems of data are just compounding.
Data science to the rescue
Data science was introduced as a means of managing this flood of data.
In my previous article, I outlined how data scientists are using predictive analytics to transform raw and unstructured data into actionable insights.
This process is helping organizations use historical information to do things like predict customer behavior or determine which campaigns are most likely to have favorable outcomes.
However, in order to make this possible, data scientists must have an intimate understanding of their business domain.
Data doesn’t come with a user manual or easy-to-use search functions.
Before diving into the data, scientists must first understand which “levers” they need to manipulate in order to achieve specific results.
Only then will they know which kinds of data they need, where to look for it, and how to bend it according to their needs.
Unfortunately, this is a very laborious process. That’s why XANT is working to automate as much of it as possible.
XANT and the Predictive Cloud
For more than a decade, XANT has collected data on nearly 90 billion sales interactions.
On top of that, XANT has access to contextual data on things like stock prices, weather patterns and even social media discussions.
It’s access to that kind of information that XANT is offering to organizations of all kinds.
Back in September, XANT announced the Predictive Cloud™, a first-of-its-kind open platform that enables businesses to build rich, powerful and predictive apps that drive business results.
The Predictive Cloud platform offers API usage for a way to leverage all of XANT’s data to help data scientists combine it with their own.
Once they have that contextual data and the associated business data they bring with them, we help these organizations filter out messy and unstructured data so they end up with a cleaner data set.
The end result? Companies can realize the full potential of predictive technologies in their own applications and business processes, regardless of their function.
To learn how you can use data to become a predictive sales organization, download this free ebook.
Free eBook: The Science of Lead Scoring, Prioritization & Sales Success
79% of marketing leads never convert to sales. That means inbound reps waste a lot of time chasing the wrong leads.