AI and Analytics for Business
Analytics: When Less Really Can Be More
Eric Bradlow, the K.P. Chao Professor, Professor of Marketing, Economics, Education, and Statistics, and Vice Dean, Analytics at Wharton, at the Wharton School of the University of Pennsylvania.
Raghu Iyengar, Miers-Busch W’1885 Professor of Marketing, and Faculty Director, AI and Analytics for Business
Analysing marketing data too finely may lead to costly errors; here’s a new way to do it
Consider England’s mysterious crop circles, or the ancient, gigantic animal-shaped mounds of the Americas. At ground level, they don’t look like much. It isn’t until you go further up—on a hillside or in a plane, perhaps—that you get to the “sweet spot” where the pattern sharply emerges. If you continued going up, the pattern would eventually blur and vanish.
Similarly, managers who rely on data analytics, tracking sales or usage over time, must decide on the level at which their teams should examine a given set of data. Should analysts be measuring daily, weekly, or monthly activity? Or if it’s location data, should they look at a census block? A postal code area?