Big data has been the tech industrys favorite buzzword for the past few years and for good reason. Every website you visit, social page you like or follow, and wearable tech that you update is collecting data about your behaviors. For some, your car is building a database for your insurance company about when, where and even why you speed. And that implanted medical device that is saving your life? Yep, its also helping researchers better understand human habits when it comes to following the advice of doctors (surprise: we arent very good at it).
Every day, we collectively create 2.5 quintillion bytes of data. Over the last two years, technology has enabled us to create 90% of the worlds data. Those two facts alone mean one thing: the amount of data we currently have is really, really big and its growing really, really quickly.
So, when marketers started to use the term big data back in 2012, it was only because big was the only honest way to describe it. The data collected about users on Facebook or Twitter or Google was overwhelming but every CMO wanted to get to the bottom of it, figure out a way to make it affect the bottom line. The answer was in there, amongst all that data, a lot of it seemingly junk. The problem was how to get it out.
Thats when data scientists emerged, and everyone from Apple to The New York Times was hiring one (or a whole team) to dig through the junk, sort it and make profitable predictions based on tangible datasets that would alert executives to emails that wouldnt get click-thrus or iPhone color combinations people just wouldnt buy (looking at you iPhone 5c).
And that was always the problem with big data it was never very smart.
To utilize the massive amounts of data you collected, you needed to sort, to filter, to silo the information not just in one way, but in a multitude of different ways that would ultimately shine light on who your core audience or customer really was and then you could target those people, in ways that would engage them, and ultimately build your word of mouth proposition.
Break all of that down and heres what you get: big data is worthless unless it is actionable. Worse yet, even if it is actionable, it is still worthless if it isnt timely.
So, what does big data that is actionable and timely actually look like?
Lets take New York Citys big summer music festival, Governors Ball, as an example. In order to get the best headliners, you need to prove that you can pull in a crowd even if the multi-day, multi-concert location is a mud pit (like it was in 2013).
You use big data youve already collected to advertise to audiences who you know like the big headliners: Kanye, Kendrick Lamar, Kings of Leon. Cool you get the same people who came last year to come out this year, the ones who werent deterred by mounds of mud, even those found around the port-a-potties.
But how do you pull in a new attendees, the ones who last year decided to ditch the weekend concert series altogether for less muddy brunches in Manhattans West Village? You know, the people who dont really think they missed out on much by choosing food and drink over Kanyes performance?
What you need to do is segment your big data to let you see additional, less obvious interests not just which performers your RSVPers like. You need to know other ways to reach them, engage them, get them to spread your word of mouth worth so that when you announce this years headliners, the word mud isnt mentioned once.
Smart data does that segmenting. It buckets brand affinities and geo-locations, plus tons of other data points, so you can discover that 70% of people in the tri-state area who RSVPed last year are fans of Comedy Central shows. Better yet, 50% of them are fans of specific comedian-based shows on that media outlet.
Now, you can confidently target on social media outlets to those who like those shows, reach out to Comedy Central itself as a potential partner (with real world numbers backing why this is a great partnership for them), put some ads up on their streaming shows, and even pull in some of their talent to the concert-series itself.
Turns out, maybe your ideal audience didnt ditch the concert series for a West Village brunch. More likely, they were probably binge watching Comedy Central that weekend instead. Now, youre pulling them and their friends out to what will hopefully be a better-weathered event this time with a more diverse audience, better advertisers and a good joke sketch by Katt Williams who will most definitely mention all the mud from last year.
And youll laugh, because that mud no longer affects the bottom line.
