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September 24, 2015
The Evolution of Data Science in Marketing
Data science is all the rage. Agencies and advertisers are scrambling to find analysts and number crunchers to harness the torrent of data spilling out of the web, e-commerce, social, and mobile channels.

The promise of aggregating mountains of diverse data from many sources, dicing and slicing them to produce personalized, nuanced, relevant, and valuable messages or offers to hundreds of millions of individuals in real time is the customer intimacy fantasy driving the investment and the hype.

Integrating data from multiple channels is considered the only way to get a complete picture of customer interactions, interests, and intentions; the missing link for finding the most effective ways to communicate, motivate, and capture customer loyalty. The challenge is to make sense of data in ways that personalize the experience for consumers and sell more stuff.

The operative assumption is that the richer the data sets, the richer the reward. The underlying marketing conceit is that data mash-ups will yield more personal, relevant, and valuable ways to enrich our lives, expand our connections to each other, and make our money go further.

The commercial use of data is simultaneously creepy and useful. Studies have shown that the vast majority of Americans will trade off private data for utility or value. As data sets automatically talk to each other, this trade-off will be perceived as less scary and more useful. We are used to being pinged, cued, and prompted based on observed or volunteered data.

But the science of data isn’t quite that easy. Mobilizing data science in service to brand growth faces these five immediate challenges.

Data Torrent. Forty-five percent of all the data on the planet was generated in the last two years. We are generating data with every breath, click, or step. The number of data generation pathways is increasing daily. Collecting, comparing, and coordinating data feeds and deciding which data sources are useful and predictive is a big challenge. Marketers have to decide which data points drive business growth and customer satisfaction.

Missing Match Keys. There is no universal data scheme or format. Each data set is constructed on its own. AT&T mobile data is not necessarily connected to a Facebook page or a Gmail account. Individuals leave a trail that marketers can’t automatically follow. Even when we collect or aggregate the records, we don’t necessarily have a common variable to match up the data about an individual.

Multiple Personalities. Matching, profiling, and segmenting is further complicated by the fact that individuals have several email or social accounts, multiple mobile devices, and endless screen names. Finding any particular person and matching observed, volunteered, purchase history, or behavioral data is extremely difficult. Ironically, the old-fashioned analog postal address is often the most useful and efficient way to match records or append aggregated data.

Silos. Different organizations and different departments within organizations collect different bits of data, often in different formats for different purposes. They may or may not talk to each other. More often than not, the structures used to house and analyze data are not compatible. Getting a 360-degree portrait of an individual, even within a single company, is not a slam-dunk.

Privacy. A growing number of people don’t want to be profiled, tracked, messaged, or engaged. And if they do, they want to do it on their terms. Their data is none of your business. They are opting out, complaining to regulators, and advocating for stricter data protection policies and laws.

Data and data science are useful and here to stay. Our progress in applying these principles to marketing at scale is still evolving.

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Danny Flamberg, EVP Managing Director of Digital Strategy and CRM at Publicis based in New York, has been building brands and building businesses for more than 30 years.Prior to joining Publicis, he led a successful global consulting group called Booster Rocket, as Managing Partner. Before becoming a consultant, he was Vice President of Global Marketing at SAP, SVP and Managing Director at Digitas in New York and Europe and President of Relationship Marketing at Amiratti Puris Lintas and Lowe Worldwide.
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