What is data-driven marketing?

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Once considered a mysterious black art, marketing is now a quantifiable, data-based function. But how do businesses use data analytics to drive marketing decisions?

In words tinged with somber acceptance, today's digital marketers proclaim customer data as their new master. No marketing decision shall be made without closely consulting the data-analytics tea leaves. Marketing's black art has just become quantifiable, but what does data-driven marketing really mean?

"Arguably, the most important evolution in the history of marketing is the ability to understand what data you have, what data you can get, how to organize and, ultimately, how to activate the data," says Mark Flaharty, executive vice president of advertising at SundaySky, a tech vendor leveraging customer data to create and deliver one-to-one marketing videos.

Where does data come from?

Customer data can sprout from just about anywhere. Sales transactions lie buried in a company's CRM and ERP systems. Customer interactions in marketing and customer service steal away in silos. Out on the edge, social listening, online surveys, consumer feedback and Internet of Things produce boatloads of data every day.

[Related: CIOs Have to Learn the New Math of Analytics ]

Then there are external data providers such as Avention, formerly OneSource, which offers business-to-business data about customers and prospects, which a company blends with internal data and feeds into an analytics engine to spit out marketing insights. Avention data helps companies better target prospect and manage the customer purchasing lifecycle.

"We're throwing fuel on the marketing-analytics fire," says Avention CTO Hank Weghorst. "All of these groups are trying to gain competitive edges by using data." Marketers are by far the fastest growing segment of Avention's business.

Paralysis by data analysis

No wonder marketers often feel overwhelmed -- even paralyzed at times -- by the sheer amount of customer data suddenly available to them. They're under enormous pressure to make data pay off with real-time decision-making. They're expected to be data experts: Three out of four consumers want retailers to gather and use personal data to improve the shopping experience, according to Monetate.

Data comes from many sources but not all contribute equally. Marketers also have the unenviable task of separating the good data from the bad data. It's a work in progress, and CIOs can help CMOs learn about the many internal and external data sources and their value to marketers. Tech vendors can assist in this difficult process, too.

"We help [CMOs] understand the quantification of the measurement metrics of the use of that data," Weghorst says. "We find out which data is truly useful and predictive, and which data is just noise so that we can drop it and potentially add other sources."

With so much data and analytics and technology thrust in the face of marketers, it might be wise to start with the end goal: What exactly do you want to achieve with all this data?

Forrester says the right data can identify customer preferences and uncover unmet customer needs. A clothing retailer, Forrester says, smartly used behavioral and location data to learn that young women, age 13 to 24, window shopped at their stores only to purchase lower-cost alternatives elsewhere. This led the retailer to create a low-cost line of clothing specifically targeting these shoppers.

Building better relationships

Customer data isn't just about sales prospecting, either. Marketers can wield data to improve the customer relationship. Airports use real-time data and recognition technology to identify passengers, bags and staff, in order to free up bottlenecks and prevent delays, Forrester says. When a flight is canceled, one airline has an app that suggests rebooking options on the spot for affected travelers.

[Related: Getting a handle on marketing technology ]

Forrester also cites another example of creative customer service fueled by data: Mattersight draws on billions of interactions stored in a customer database, as well as predictive algorithms, to match a customer with a customer service representative who shares a similar communication style and behavioral characteristics.

Data is the starting point to make all of this happen.

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