“Big Data” is often hailed as the new competitive advantage in marketing, but what happens when marketing data simply becomes too big to analyze in any meaningful manner? Professor Patrick Wolfe, executive director of the University College of London’s Big Data Institute, claims that the rate at which data is being accumulated “is rapidly outpacing our ability to analyze it,” and that transforming these massive data streams from a liability into a strength is key to harnessing the power of data.
In this post, marketers will learn our three tips to glean the most valuable insights from large, overcrowded data sets.
Segmentation involves categorizing data together by similar traits (such as age, income, or email behavior). By segmenting data into “clusters”, important attributes may reveal themselves even if they initially escaped marketers’ attention.
When it comes to segmenting large amounts of data, we recommend carefully planning your short and long term strategies to analyze this information. Instead of trying to sort through all the information at once, begin with the segments you’re already aware of—or those with immediate, short-term importance—and commit to carefully exploring the less obvious groups later on. Segmenting data can reveal powerful insights, but be careful with how you use these insights. Remember when Target grouped customers by purchase history in order to recommend new products? They famously predicted one teen girl’s pregnancy before her own family.
In 1973, statistician Francis Anscombe demonstrated the vital importance of visualizing data for understanding patterns when conducting analysis. He claimed that discerning trends from a crowded datasheet is nearly impossible, not to mention time-consuming. The same data that might seem useless in numerical format, however, can provide valuable insights when transformed into visual form. For example, although you may feel that you have a surfeit of information about this month’s email metrics, differences across the individual campaigns could be invisible until they are visually graphed together.
Not only is visualization necessary when determining which data is most important, it is also essential when searching for patterns and insights. Interactive data visualization tools allow users to interact visually with their data, instead of simply looking at static graphic representations of the numbers. Interactive tools like Tableau Software enable researchers to transcend common two dimensional tools like pivot tables, and instead utilize interactive and imaginative methods to visually represent their information.
Do you find yourself being weighed down by massive amounts of data? Find out how we helped Molex, a $3.5-billion world leader in electrical interconnect and electronics solutions, access data from multiple sources and visually analyze their data.
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