The marketing catchphrase of the past 12 months has been ‘big data’. Two simple words that are both daunting and exciting. Daunting in that there are 2.5 quintillion bytes of data created globally every day. Exciting in that it can be a game-changer for businesses, with productivity and profit gains of between five per cent and six per cent better than competitors, according to recent research.
The term ‘big data’ was originally coined in Silicon Valley nearly 20 years ago as film houses raced into the world of computer- generated imagery and special effects, and needed major leaps in data storage to do so.
At the same time, direct marketers became more sophisticated, and embraced business intelligence and analytics, albeit in a very manual and time-consuming way. In more recent times, the rise of all things digital has driven exponential growth in available data, both within companies and externally.
And when you add today’s ability to automate predictive modelling, it is no wonder business intelligence and analytics folk have adopted Silicon Valley’s far sexier term.
Still, the fundamentals of data analytics have not changed. Collect data on your customers, pay attention to what that data is saying, and with that knowledge or insight, deliver a relevant solution. But marketers face an increasingly complex data environment in the quest to drive business growth. And consumers have a far higher expectation of companies getting those solutions right, and of receiving them immediately. But is it as daunting as businesses are making out?
While there has been incredible growth in available data, there have been even bigger leaps in computing storage and power. Data analytics programs that took hours to run five years ago take minutes today. And although there are many more sources of information beyond a company’s own databases, such as social media, smartphones and the web, most have been created in the digital age where there is a far more standardised data structure. So bringing that data together to assemble a single view of the customer, the holy grail for marketers, is much easier than a few years ago.
The wealth of external data we are gathering about ourselves is a major factor in the big data equation.
This individualised data is generated and captured by a range of connected devices. For example, using GPS tracking data, my phone told me that I’d walked 29 kilometres last month and, at a more granular level, it also captured that I had been to four different shopping centres.
As marketers start exploring how this location and behavioural data can be put to use in highly relevant and personalised communications, the opportunities rack up quickly.
Walmart and Target are two huge US retailers that have embraced big data. They both have a strong vision for using that data. They have built strong databases that provide a solid stream of consumer behaviour insights, enabling them to tailor offers. They’ve more recently added the ability to overlay external data, such as customers’ mobile phone locations and the weather conditions. The result is even more relevant and timely communications. The end game will be relevant offers delivered to customers as they shop in those stores, based on real-time data and insights.
Business such as these, as well as Amazon and T-Mobile, are leading the charge in the US, part of the 68 per cent of US businesses that undertook big-data initiatives in 2012. Yet only 32 per cent of Australian business leaders said they had done so last year.
Australian brands such as Qantas, Virgin, Myer, Coles and Woolworths have the advantage of large internal databases built off loyalty programs. Those programs provide terrific opportunities for developing deep purchase-behaviour knowledge and, in turn, personalised and relevant offers and service. So they are well advanced in embracing big data, and will be even further down that path when they overlay external data.
For businesspeople who feel they’re still at the starting point, it’s important to overcome the daunting confusion about where to start. Take simple first steps. Develop a great plan for data based on how it will help deliver the goals of the business. Or begin collecting data and storing it in a way that can be easily accessed by the right talent down the track.
Or use small pockets of that data to test and learn. Then it’s one step at a time, rather than trying to figure out how to leap straight to big data nirvana. Amazon’s big data play didn’t happen overnight. But by tackling it one chunk at a time, the possibilities are now extremely exciting.