Marketing Data: Using predictive analytics to make sense of big data
One buzz word/phrase that became very popular in business circles this year was “big data.” And, even though the term is trendy and probably overused, the overall concept has major implications for marketers.
Marketers are awash in campaign data, more so now than ever before. Email marketing campaigns produce data about open rates, clickthroughs, unsubcribes, and more. Visitor activity on company websites can be tracked, and in the case of registered users or leads flagged for scoring, that activity is not only tracked but also attributed to a particular individual.
Elements tracked can include the website visit itself and activities such as downloading Web content or watching embedded video. That tracking can get pretty granular, such as combining a series of website activities, or exactly where in an embedded video the viewer stopped the playback.
Taken as discrete pieces, all these data points are essentially meaningless. Taken together, they can provide insight into the tracked individual. Furthermore, subjected to deeper analysis, they can provide insight into what the most promising prospect or customer with the most long-term value looks like for the company.
This is where predictive analytics come into play. To provide more insight into predictive analytics and big data, I interviewed Omer Artun, CEO and founder of AgilOne, a cloud-based predictive marketing intelligence company. Omer also has an academic background in pattern recognition, data mining and complex systems.
