Characterized by its volume, velocity, value, and variety; big data is being produced at a rate of over 2.8 zettabytes (ZB), or 2.8 trillion gigabytes, each year. Every day, 2 million blogs are posted, 172 million users visit Facebook (spending a combined 4.7 billion minutes on a single social networking site), 51 million minutes of video are uploaded, and 250 million digital photos are shared. We continue to generate 294 billion emails each day, even though many consider email an outdated form of communication. It is expected to explode to over 40 ZB per year by 2020; and to stay ahead of the pack, businesses need to start tackling big data today. Investments are being made faster than ever before to improve productivity, create value, stay competitive, spot new business trends, and to generate exciting analytic solutions. Big data is becoming a hallmark of the start of the 21st century where it is being consumed and utilized by more and more businesses.
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You can generally split big data into two different types, structured and unstructured. The 294 billion emails being sent per day can be considered structured text and one of the simplest forms of big data. Financial transactions including movie ticket sales, gasoline sales, restaurant sales, etc., are generally structured and make up a small fraction of the data running around the global networks today. Other forms of structured data include click stream activity, log data, and network security alerts. Unstructured data is a primary source of growth in big data as well. Music is an ever increasing variety of data and we are streaming nearly 19 million hours of music each day over the free music service, Pandora. Old television shows and movies are another source of variety in the non-structured realm. There are over 864,000 hours of video uploaded to YouTube each day. MBAOnline.com even found that we could pump 98 years of non-stop cat videos into everyone’s home for endless hours of boredom, fun, or insanity!
Businesses have been segmenting customer markets for decades, but the era of big data is making segmentation more essential and even more sophisticated. The challenge is not just to gather the information; rather it is a race to understand customers more intimately. Segmentation is a foundational element of understanding customers. In its simplest form, customers are grouped based on similar characteristics. As the data improves (demographic, attitudinal, and behavioral), the approaches to segmentation become more sophisticated. Right now, enterprises are practically drowning in all the data being collected and if they are not careful, they can spend all their time staring at it and not putting it to good use to make better business decisions. The dissection time can be limitless without yielding actual results, so having a proven and scalable analytics system in place can drastically cut down this segmentation time.
Businesses from all sectors recognize that knowing your customer well leads to improved and personalized service for the buyer and this results in a more loyal customer. In the effort to know their customers better, businesses have traditionally employed advanced analytics systems such as Google Analytics to segment their customers into groups based on demographics, geography, and more. Although this type of segmentation helps, it often fails to not only define important differences between customers, but lacks in offering consistent innovative features. For example, a basic traveler segmentation from an airline might define a customer as a male, 37 years old, lives and works in Raleigh, and makes frequent Business trips to London.
A better approach is to classify by the customer’s choices, preferences and tastes based on all his interactions with the business. But to accurately micro-segment their customers, businesses need to recognize a broader range of customer characteristics many of which are found beyond the structured information in Reservation, Departure Control and Loyalty systems of an airline. A rich set of additional information about customers can be found in customer interaction like emails, call transcripts, chat, SMS, social media and more. Businesses should have the ability to understand the meaning in customer dialog, and can do so automatically through newer types of analytics systems. Link here: https:/