Karalee Evans works in digital but still has her soul as well as a passion for writing, snowboarding and politics. Working in communications, digital and strategy for the past decade (there is no way to write that without sounding old), Karalee still isn’t an expert. But she can pour a pretty mean pint. This is her fourth column for MediaRound.
Data intelligence is by no means unique to the online era or indeed the year of Big Data; historically we’ve used everything from phone surveys, door-to-door polls and even mail-in coupons to identify our audiences’ needs and address them. Of course, digital data has given us access to far more insights than ever before – but more important than the source of data is how we manage it for the best results.
It’s an understatement to say that marketers and strategists have access to more data than ever before. Not only do we have a wealth of information coming in from online analytics and behavioural tracking, we’re also privy to offline consumer data from a whole range of sources. When you add online and offline data, you have a staggering volume of information at your disposal. If not carefully managed, that volume can end up as white noise and overwhelm your strategy and tactics instead of strengthening them.
The key to successful targeting is not necessarily the volume of data being used. What usually matters most is the accuracy with which the strategist can use the available data to understand the consumers it wishes to engage with, and calibrate its tactics to do so most effectively.
The potential for targeting grows even further when you combine both online and offline data. Until recently, most strategists have struggled with incorporating offline insights into their online strategy, and vice versa. However, continuous improvements in analytics and database technologies mean that we’re increasingly able to link online data to its offline equivalents, generating even more comprehensive profiles of consumer behaviours and values in the process.
Every aspect of the intersection between humans and technology is fed by, and feeds, the collation of data. Every time you check-in on Foursquare, Tweet, ‘like’ something on Facebook, send an email, make or receive a phone call, transfer money, purchase an eBook, search for a hotel in Darwin, download an episode of GoT, purchase the latest Gaga song, read about Clams licking salt on News.com.au, watch a cat swim in a bath on YouTube…. transactional and behavioural data is the result.
For example researchers have found a spike in Google search requests for terms like “flu symptoms” and “flu treatments” a couple of weeks before there is an increase in flu patients coming to hospital emergency rooms in a region (and emergency room reports usually lag behind visits by two weeks or so). So, if you’re a strategist for a Big Pharma, you should be using this intelligence to predict supply and demand for your cold remedies and conversely if you’re a Health Prevention Director, you can predict demand for health services before the epidemic.
Data alone is not the silver bullet. It is what this data is feeding that in my opinion, is the next disruptive innovation.
Data-driven strategy is now pretty mainstream. Everyone is talking about it, and trying to do it. Big Data is one of the most widely used, and misunderstood, topics of the modern-day tech bubble. But behind the hyperbole, there are real examples of innovation that will create new markets and change the course of how industries and specialties will deliver their value proposition. And it’s driving a new value network of roles and jobs. A report last year by the McKinsey Global Institute, projected that the United States alone needs 140,000 to 190,000 more workers with “deep analytical” expertise and 1.5 million more data-literate managers, whether retrained or hired just to sustain data-driven processes.
And when you consider this predictive power of data in fields like public health, economic development and economic forecasting, data-driven strategy is already becoming the next big disruption.
Connecting insights into both online and offline worlds lets marketers not only better target their actions, but also evaluate their results in a more comprehensive fashion. However, the sheer volume of data involved in these processes further highlights the need for a structured approach to targeting.
For their part, marketers can aim to structure available data according to business goals (like raising sales amongst a certain age group) rather than by traditional demographics. They can eliminate data not relevant to the scope of their campaign, or isolate the points which are most obviously actionable. But they need to remember that the full picture of data sometimes reveals far more than its parts. It’s a tricky balancing act between “too much” and “too little” data, especially as online and offline become increasingly interlinked. Marketers need to focus on the objectives of their campaigns to avoid being overloaded.
By bridging the gap between online and offline worlds, strategists can reach their audiences based targeted insights and not just demographic profiling. But the challenge to adopting data-driven strategy within your business is not the lack of available data; it’s whether you have invested in the skills and talent to really understand what’s meaningful and what’s just dirty data and hyperbole.