Spend any time in IT and you’re bound to hear the expression, “You can’t manage what you can’t measure.” Metrics is the mantra of many data center managers or network architects. How else do you know who consumes what resource how often and how that affects things such as WAN performance?
For chief marketing officers (CMO), however, useful metrics are hard to come by. Yes, like most executives, the CMO is buried in statistics: Same-store sales, inventory data, production data, CRM data, transaction data, sales and promotions data and so on. There’s lots of data, but little of it will tell you what your customer will do next or why they did what they did, like abandon an online shopping cart.
Transaction data doesn’t improve the customer experience unless you combine it with some other data to learn something new and useful. The same is true if customer data is collected at multiple touch points—Web, brick and mortar, mobile—but isn’t tied together on the back end or accessible to call center reps in real time.
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Take everything you know about your customers and prospects, though, and combine that with new data sources ranging from social media to location and weather data, and you enter the world of big data. What matters in this world isn’t so much the individual data points, but where they intersect. It’s the mashup that will give you the insights you need to improve your next-best offer or understand why customers do what they do.
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“The ability to collect vast amounts of data on individual consumers—their consumption habits, their preferences, their interactions with the company—and then analyze those data sets for predictive behavior and proactively apply those insights both to your existing customers and to customers coming into your call center or your website or your agents office, [that’s] the basis of competitive advantage in the future for the CMO because you can provide a better experience,” says Matt Jauchius, CMO of Nationwide Insurance.
While this is happening at a certain percentage of companies, most aren’t embracing big data or spending the dollars required to achieve the state of the art.
A recent, frustrating encounter with a new cell phone provider proves this to be true, even for multibillion dollar players in tech-centric industries rife with data that should make the customer experience memorable for all the right reasons.
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After ordering a smartphone online, only to have that order lost—which I learned only when the “overnight” shipment never arrived—I called customer service. That didn’t go well, either, so I went to a retail store. I spent 45 minutes giving the same information over and over to a very capable sales rep, who in turn had to deal with a rather inept back office, all to get one working smartphone when I had originally planned to get two.
In short, I had to work way too hard to give this carrier my money. Siloed product channels and data sets stifled cross-sell and upsell opportunities and left customer service reps unable to resolve problems or provide next-best offers or actions.
The moral of my story: Multichannel marketing is great only if you don’t experience a problem with one channel and try to resolve it via another.
Big data technologies such as Hadoop can flatten the data silos that support each of these channels and then feed the results to hyper-fast in-memory analytics platforms. These systems could inform call center reps in real-time that I, for example, placed an order online, called three times and had an outstanding unresolved order, a trouble ticket and a separate account for my office phone all causing customer service headaches at the same time.
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This is precisely why Nationwide, a 90-year-old company with many databases and myriad compliance obligations, is spending many millions on big data initiatives, Jauchius says.
But this is just one side of big data that CMOs need to consider. Among the advantage that big data bring to marketers, perhaps the biggest are the ability get out in front of customers and prospects and to conduct more effective predictive and prescriptive marketing, says Elana Anderson, vice president of IBM Enterprise Marketing Management.
“Marketing has long been on a quest to get to the individual,” Anderson says. “Smart marketers…have been trying to get beyond the demographic for a long long time. If you’re able to address the individual at an individual level, if you’re able to sense needs or meet needs before the customer is explicitly saying, ‘I have a need’, that requires Big Data and analytics in order to get to that point. We’re seeing tremendous value with uses cases around that.”
How an individual company will get started with big data depends on its use case, industry, available data and factors that depend on the outcomes it’s trying to achieve. In general, though, prescriptive marketing combines longitudinal knowledge of your customer with their larger patterns of activity. You then combine that knowledge with the broader patterns that affect your business—geography, demography, weather, social media activity or anything else you need—to get a more complete picture of your target audience.
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These patterns will provide the insights you need to reach customers in novel ways, says Olly Downs senior vice president of Data Sciences for Globys, a big data analytics provider to the telecommunications industry.
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Globys has been doing big data for more than 15 years, Downs says. The difference between today and years past, he says, is the volume of data and number of data sources. That said, the capability to capture, store and analyze that data has reached a price point that makes big data ROI achievable for marketing.
“By applying machine learning to big data, it’s doing the discovery for you,” says Downs. This means you can uncover 50 marketing scenarios you would never cook up on your own. “That’s the power. It’s not about any individual scenario that’s discovered, it’s about being able to surface many of these scenarios and act on all of them in a way that’s dynamic and meaningful.”
For example, Globys does a lot of work in the developing world, where the average prepaid mobile customer generates 29 pieces of transaction data per day, in the form of SMS messages, phone calls, top-up requests and so on. (A Facebook user averages just three, in comparison, and a Twitter user less than one.) All that daily data equates to a lot of upsell and cross-sell opportunities.
With its recommendation engine, Amazon is the poster child for this type of big data marketing. But Amazon isn’t doing anything you can’t, says Gartner Research Director Bill Gassman.
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Most marketing departments already use big data. It’s just buried in their analytics engines or customer experience management systems, Gassman says.
“There’s so much more data to play with, and it’s just so much easier to play with,” he says, noting that the ad-hoc query have advanced significantly since the term was first coined. In fact, queries can be done so quickly, and so inexpensively, that Gassman tells CMOs, “Don’t worry about how it’s done; worry about what you’re going to do with it.”
Allen Bernard is a Columbus, Ohio-based writer who covers IT management and the integration of technology into the enterprise. You can reach him via email or follow him on Twitter @allen_bernard1. Follow everything from CIO.com on Twitter @CIOonline, Facebook, Google + and LinkedIn.