Dion Heal is about to graduate from the Advertising Diploma here at Macleay College and he explores the real results and new role of Big Data.
There is an almost infinite amount of data collected about us every second. Right now, multiple companies know how long you’re spending on this page and what the content is. They know where you mouse is hovering, where you physically are, when your last jog was and even how you slept. Creepy? Probably. But if we break it down this ‘big data’ is nothing but a bunch of numbers. Who cares that you spent 33.2 seconds on the BuzzFeed homepage this morning after your 8h 22m sleep if we don’t know how to read it and generate BIG and RELEVANT insights from it.
Big businesses (evil or not) get so excited about the abundance of data. Like a kid in a candy store. But like the kid in the candy store they have two choices: They can go crazy, buy everything, get a massive sugar high and pass out an hour later. Or they can carefully select the best candies, take them home and get the best out of them.
But selecting the best candy is often where businesses fall over. To do this, you need to know how your consumers ‘tick’. You need to know the basics of what motivates them and how they live their lives, so you can select the data relevant to them, analyse the data and come to an insight that is far better than if you were to use every single scrap of number and percentage at your fingertips, purely because you can.
‘Big Data’ alone is also noisy, messy, constantly-changing, in hundreds of formats and virtually worthless without analysis and visualisation. Companies must learn to combine business, technology, and data expertise to get intelligent customer engagement. (If they want to save money that is. And who doesn’t.)
An example of looking through the crap and applying the useful stuff through BIG insights is the prevalence of ‘recommendation engines’ like those used by Netflix or Amazon. They gather relevant data about the consumer and prior interests (which the consumer entered) and previous purchases to suggest things the consumer may be interested in. Some credit-card companies are even making unusual associations while mining data to evaluate the risk of default: people who buy anti-scuff pads for their furniture, for example, are highly likely to make their payments.
In these situations, companies are looking specifically at one set of data to arrive at the BIG INSIGHT, instead of getting all excited about the ‘Big Data’ they have, trying to use every bit of it and eventually failing.
Especially with the rise of Gen Z/Millennial consumers who are very tuned in to knowing when they’re being watched too much, marketers need to learn how to use a small amount of the big data they gather to target consumers in a way that is highly effective, but also not alienating by appearing ‘creepy’.
Keep on stalkin’ us. We’ll love it; if you get big enough insights 😉