It’s about a 5 min. read.
It’s an amazing time to be a music fan (especially if you have all those Ticketmaster vouchers and a love of ’90’s music). While music production and distribution was once controlled by record label and radio station conglomerates, technology has “freed” it in almost every way. It’s now easy to hear nearly any song ever recorded thanks to YouTube, iTunes, and a range of streaming sources. While these new options appear to be manna from heaven, for music lovers, they can actually create more problems than you’d expect. The never-ending flow of music options can make it harder to decide what might be good or what to play next. In the old days (way back in 2010 :)), your music choices were limited by record companies and by radio station programmers. While these “corporate suits” may have prevented you from hearing that great underground indie band, they also “saved” you from thousands of options that you would probably hate.
That same challenge is happening right now with marketers’ use of data. Back in the day (also around 2010), there was a limited number of data sets and sources to leverage in decisions relating to building/strengthening a brand. Now, that same marketer has access to a seemingly endless flow of data: from web analytics, third-party providers, primary research, and their own CRM systems. While most market information was previously collected and “curated” through the insights department, marketing managers are often now left to their own devices to sift through and determine how useful each set of data is to their business. And it’s not easy for a non-expert to do due diligence on each data source to establish its legitimacy and usefulness. As a result, many marketers are paralyzed by a firehose of data and/or end up trying to use lots of not-so-great data to make business decisions.
So, how do managers make use of all this data? It’s partly the same way streaming sources help music listeners decide what song to play next: predictive analytics. Predictive analytics is changing how companies use data to get, keep, and grow their most profitable customers. It helps managers “cut through the clutter” and analyze a wide range of data to make better decisions about the future of their business. It’s similarly being used in the music industry to help music lovers cut through the clutter of their myriad song choices to find their next favorite song. Pandora’s Musical Genome Project is doing just that by developing a recommendation algorithm that serves up choices based on the attributes of the music you have listened to in the past. Similarly, Spotify’s Discover Weekly playlist is a huge hit with music lovers, who appreciate Spotify’s assistance in identifying new songs they may love.
So, the next time you need to figure out how to best leverage the range of data you have—or find a new summer jam—consider predictive analytics.