Metia Insights: Why Big Data is Yesterday's News

14 May 2014

We recently released the Metia Insights 2014 report, which has expert opinion on different areas of digital marketing, including social media, PR and design. Here is one of the essays from the document - you can download the whole report here.

Big Data, Yesterday's news in more ways than one

Matthew Fontaine Maury was a promising U.S. Navy Midshipman assigned to a desk job after a crippling injury. Far from wallowing in his misfortune, however, Maury took it upon himself to analyze all the available data regarding the passage of naval and commercial ships across the world’s oceans. After plotting and analyzing more than 1.2 million data points, he published the Wind and Current Chart of the North Atlantic, which resulted in huge efficiencies in journey times and navigation. Maury didn’t publish it as an e-book, because his book was released in 1855.

So anyone who thinks that big data is new is sorely mistaken

The foundation of real-time marketing using big data is yesterday’s news. It’s hundreds of years old. What’s new are the tools and techniques to sort, manage, and use the data.

Further, big data is all about yesterday’s news. What happened yesterday that we can capture, store, analyze, and use to make our business and its products and services more compelling? What are our customers buying, at what time of day, in what weather, on what device, with what form of payment, and in response to what stimulus? What else are they discussing? And how can we use that to inform our proposition and marketing tomorrow?

How much data does it take to be considered “Big”?

It’s all relative. What represents a significant amount of data to a shopkeeper serving a few thousand customers in a single town would be the smallest drop in the ocean of Expedia’s data repository.

We should talk less about big data and more about “all the data” that any business can lay its hands on. And further, the data is simply a means to the insights it can deliver, which help businesses create and market products that better meet customer needs and desires. And that means that the visualization of data – enabling employees throughout the organization to make use of it – is going to be an imperative in 2014. Businesses must add qualitative insight from non-technical parts of the business to the quantitative value that data can bring if they are going to thrive.

Way beyond early adoption

Industries that employ dynamic pricing – airlines, for example – have been important leaders in big data for years. To the consumer, there may be no rhyme or reason to changing airline ticket prices from one hour to the next, when, in fact, the changes are the result of a complex analysis of real-time buying behavior allied to historical and environmental data.

Not only that, but exposing the results of data analysis to consumers during the purchasing process can move them further along the funnel. It was only yesterday that I was given the added sense of urgency over a ticket purchase by a message telling me that four other customers were currently looking at the same flight and there were only three tickets left at that price (who knows, there might have been 40 tickets left at tomorrow’s lower price!) Hoteliers are similarly enthusiastic users of data, and the more they can feed into the engine (preferably “all of it”), the more effective their marketing will be. More businesses operate in a sector where dynamic pricing applies than you might imagine. Data can empower businesses to propose spot offers and discounts and to give a salesperson the flexibility to strategically tweak pricing. The more data you collect on buyers’ sensitivity to pricing the better the results.

“Mash up” to get ahead

The true power of data comes through the ability to mash up and cross-analyze disparate sets of data. When I worked with a manufacturer of high-pressure washing equipment for domestic use, the company had sales data for the previous fifteen years, allowing us to plot the peaks and troughs of the annual sales cycle. Further, when that data was combined with historical weather data, we discovered that in those years that had an early spring, the company struggled to meet demand because consumers rushed to prepare their gardens for the good weather. Similarly, a late spring meant stock sat unsold on shelves for weeks, often ultimately discounted to sell.

Today, the company employs extremely accurate long-term weather forecasters as part of its business-planning process, and has seen results in efficiently managing its supply chain and meeting consumer demand. It’s likely that your business has historical sales data. But are you cross-analyzing that with external environmental data to deliver new insights? Even the smallest coffee shop might increase sales by promoting a festive drink a few weeks early if it knows there’s a cold snap coming. This is about using all the data you can lay your hands on, not just your own.