Big data might be a flashy industry watchword, but marketers can make use of it in practical ways – that is, if they pay attention to the “signals of demand.”
At least, that’s David Jonker’s take on everyday uses of big data. Jonker, the senior director of product marketing for SAP AG, was speaking from a session at Digital Day 3D, a Toronto-based conference for digital marketers, on Oct. 22.
“People talking about big data will say crazy things, like zettabytes, and how many mobile phones everyone will have by 2015. But those are crazy numbers that don’t mean much to me,” Jonker said. “So the question is, how do I take information that’s available to me and apply that? It’s very fuzzy for big data.”
The best way to do that, Jonker said, is to figure out the signal of demand. What do customers want, based on the data in front of you? And then, how do you find a way to meet that business need?
For example, he said, there’s a company that fits the very niche position of supplying gamers playing rounds of Battlestar Galactica, a massive online multiplayer game where players line up fleets of ships and use real-time strategy to duke it out.
Here’s where the company has gotten inventive with its data – it recognizes when players keep failing at certain points, like if they repeatedly lose to a particular player or during a certain round. Then it provides weapons or ammo or anything else that player can use, at just the right moment, to beat that other player.
Of course, that’s a very particular use for big data, but there are other places where it’s used for less niche purposes, Jonker said.
A good example is Société de transport de Montréal (STM), which runs the major metro and transit systems in Montreal, Que. Earlier this year, the public transport system has set up a special way of boosting brand loyalty through gamification.
Basically, transit riders can register their transit pass, as well as download an app called “Merci,” which rewards them for riding different routes by providing them with special offers from merchants based on their location. For example, a small coffee shop that wants to compete with the corner Starbucks can offer a free donut to a transit rider passing through the area, encouraging the individual to stop by again instead of only visiting the neighbourhood franchised Starbucks or Tim Hortons.
“You might think of a transport agency, and think ‘innovation in digital? Really?’” Jonker said. “But they’ve handed out more than 7.2 million different offers.”
And of course, beyond upping the STO’s marketing offers, it has also been able to gather data on where the most transit cards are being used, giving it data on where to set up more bus lines.
Still, while it’s great there’s a lot of hype surrounding all of the things big data can do, businesses need to have a strategy in mind if they’re trying to leverage big data, Jonker said. That’s a much better alternative to asking the IT department to collect large amounts of data, and then try to figure out business uses for it afterward, he added.
“It’s not a volume question. But having big data makes [business operations] go faster,” he said.
There are four steps to this: first, figuring out an offline strategy. What is your business looking to do with big data? Does it want to boost key performance indicators, like sales, or the volume of sales? Secondly, companies need to align business imperatives with their data. They need to ask a specific question, and then find the data that will answer it.
Third, businesses need to figure out how to apply their data to their operations, or “where the rubber hits the road,” as Jonker put it. Finally, the fourth step is when the technology finally comes in, he said. This is where you finally get use all of the analytical tools that come with collecting big data.
And using big data isn’t limited to enterprise, Jonker said. Small businesses can also take advantage of it, but they just need to be more specific about the business question they want to answer.
That means making sure that within an organization, both the leader and the IT department both agree on the question they want to answer – and how they will collect the data that will answer it.
