Big Data, Big Value: Master Marketing Performance With Merged Datasets

By Ross ShankenMay 5, 2014

Consumer generated information is exploding.  Whether you are talking about stock trades, advertising or lead gen, the trend is toward merging datasets to drive predictive analytics, consumer intent and even personalization. Smart marketers gain profound performance marketing insights and actionability.

About 2.5 quintillion bytes of data are created every day, with 90 percent collected in the past two years alone, from websites, sensors, social media, images, transactions and mobile. And that’s only the beginning of the story. Blend that data together and gain surprising insights culled from disparate datasets.

Consider high frequency trading, one of the more controversial uses of big data and a hot topic these days. Michael Lewis, author of Flash Boys, was recently interviewed on NPR’s Fresh Air and 60 Minutes. Flash Boys is a hot title, currently #2 on the Amazon bestsellers list. Lewis explores the formerly secretive world of high frequency traders. The latest figures from the Securities and Exchange Commission show that more than 50% of U.S. equities trading volume is high frequency trades, according to a March 2014 report. Lewis explains how high frequency traders profit from capturing mispricings that last a matter of milliseconds.

There are big dollars in big data when you know how to mix your industry metrics. Performance marketers who think outside the static data box get the best and most actionable picture of the consumer and overall intent. That translates into more sales.

The lead generation industry is in a perfect position to profit from dynamic datasets. In the past, lead data was confined to somewhat static snapshots, with lead verification and lead scoring standing as the best tools available to aggregators and advertisers. With literally hundreds of new data points available to the marketer, it’s now possible to add in behavioral, engagement and intent metrics, raising the bar to get the precise value of every lead.

Real-time lead gen data mashup examples include:

  • Consumer Origin. Know the facts about the consumer’s path from inception to the present moment.
  • Lead History. Know the lead’s path, and get information on the exact time elapsed, as the lead passed through the supply chain to you.
  • Consumer Behavior. What is the consumer’s intent and engagement level?
  • Demographics. Match a consumer profile with age, gender, income level and other data points.
  • Fraud Risk. Gauge the likelihood that a lead is a real consumer.
  • Compliance. Ensure brand and federal compliance at a lead by lead level.

Now there is a geometrically more powerful dataset for lead buyers and sellers to harness big data and have the deepest insights yet. With data derived from previously unconnected events, you enable your business to make far more effective decisions about consumer intent. As a marketer, you can apply resources more effectively. You win and the consumer wins.

In the world of lead gen, value is everything. New real-time data combinations create a far more intelligent and actionable marketplace for all players, empowering the marketer to make better decisions and drive performance.

 

 

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