What does Big Data mean for marketing?


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Yes, Big Data is currently at the peak of a Gartner hype cycle. Like many new trends in information technology with ambiguous names and all-encompassing abstractions, it became a focus for cult-like adoration and a salvation for data oppression. Is every data-intensive challenge a Big Data problem? May be not. Let’s move beyond the hype to understand what this means for marketing.

Generating useful information from raw data has been a challenge from the first days of business computing. The evolution of more and better sources of data over the decades has been a two-edged sword: As the diversity and quantity of data increased, the challenge of linking and correlating raw facts to distill new learnings grew exponentially. Marketing often had a voracious appetite for anything these initiatives could yield.

Relatively new technologies (e.g., network ubiquity, digitalization, sensing mechanisms) generate transactional data about human activity that are orders of magnitude larger than anything seen in the past. At the same time, some of that new data lacks traditional structures that our spreadsheet-oriented culture is familiar with (words, fields, discrete values, precise mathematical representations). We now have mountains of visual and audio data (pictures, videos, recordings) to contend with as well.

Coping with and making sense of the onslaught requires new tools and ways of thinking. This is what Big Data is about. The old challenges, tools and processes are still there, but we shouldn’t think of them as Big Data. The scale of Big Data makes it a different animal.

So what does this mean for marketing?

Marketing has always been, to a large extent, data-driven. Big Data simply offers unprecedented opportunities to understand prospects and customers in ways that were not possible until recently. Consider these data sources, some of which did not exist a decade ago:

  • log data from websites
  • feeds from a support administration system
  • social media (e.g., Facebook, Twitter, LinkedIn, blogs)
  • external suppliers (e.g., Acxiom, Census, Alexa)
  • paid sources (e.g., Google AdWords)

And now consider these older and well-established data sources:

  • feeds from a CRM (sales) system
  • feeds from a support administration system
  • marketing campaign responses
  • offline activities (e.g., tradeshows)

By themselves each data source provides some information, but not enough to produce any significant insights or breakthroughs. Imagine the power of linking and correlating these sources; and maintaining them with real-time updates. That changes the rules of the game. Two very popular uses of Big Data are customer insights and predictive analytics. They help to answer these kinds of questions:

  • What real-time offers do prospects prefer?
  • What are the best web pages to serve based on an identified prospect’s interests?
  • What is the probability of closing a sale with a potential customer who just filled in a web form?
  • What promotions work best at particular times of the day?
  • What is the probability that a prospect targeted by a nurturing campaign will make a purchase in the next six months?

Marketers understand that their biggest challenge is communicating the right offer at the right time in the right channel to prospects or existing customers. To that end, digital interactions (email, SEO, paid search, display advertising, social media) with a target audience are far more valuable than offline equivalents. Those transactions, if corralled and used intelligently, enable businesses to build sustained relationships with while supporting behavioral modeling and probabilistic forecasting.

Platforms and tools for managing Big Data enable companies manipulate large multi-channel datasets for such insights. Examples of such vendors are AsterData, Netezza and Greenplum.

If you’re just starting out as a data-driven marketer, try mastering “small data” before tackling Big Data. You’ll learn, through experiments and practice, more about your precise needs. This clarity will serve you well when the time comes to move to the next level and commit a larger amount of effort and money.

Republished with author's permission from original post.

Shreesha Ramdas
Shreesha Ramdas is SVP and GM at Medallia. Previously he was CEO and Co-founder of Strikedeck. Prior to Strikedeck, Shreesha was GM of the Marketing Cloud at CallidusCloud, Co-founder at LeadFormix (acquired by CallidusCloud) & OuterJoin, and GM at Yodlee. Shreesha has led teams in sales and marketing at Catalytic Software, MW2 Consulting, and Tata. Shreesha also advises startups on marketing and growth hacking.


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