Download: Analyzing Customer Behavior posted on Thu, November 15 2012 in Analytics, Big Data


Consumers are no longer what they used to be. Before they buy, they look around. A lot. And when they buy, they talk about it. To everybody. They want to be treated as unique. They want you to listen to them. They want you to talk to them. They want you to thank them. In short, they are influencers and mini-tyrants who will leave you in the blink of an eye if you don’t do as they please. And that’s the easy part. The problem is that they change so fast that you actually need to know what they want, before they do. Your organization needs to outrun them. It needs to be agile and fast.

The good news is that you have all the information needed to understand, catch and nurture these empowered consumers. Because they literally leave traces everywhere. Not just inside your company, nicely stacked in shiny databases, but outside as well, in a colossal mass of unstructured external data. I’m talking about data from Twitter, Facebook, industrial sensors, geotracking data, voter registrations, sports statistics, website logs and much much more. They are there for the taking. You only need to link the two and unlock their value. And be able to store them, of course.
Not only does Big Data come with real predictive insights but with actions and results as well. Churn prediction, in-store behavior analysis and recommendation engines are just some of the cool possibilities that it fuels. It’s customer-centricity on speed.
‘Analyzing Customer Behavior’ is a new publication dealing with these themes. With this booklet, we want to offer you views on the way markets are changing and how you can make informed use of these trends to strengthen your company’s position.  ‘Analyzing Customer Behavior’ is the third in a series of booklets published by Across Technology on behalf of EMC Greenplum. You can download ‘Analyzing Customer Behavior’ using the link below.



by Amit wadhe on January 24, 2013

"I like to know more detail on this technology to give my efforts. How we can identify the behavior of customer from the post and comments on the social media. ??"

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