Which Music Do We Prefer? Interesting New Research

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We know that Spotify, Apple, and others have developed sophisticated algorithms to recommend music to listeners. While many customers know precisely what they would like to listen to, others are open to discovery. Spotify distinguishes between those listeners with a "closed" vs. "open" mindset. The algorithms are especially important for those with an open mindset. 

Now we have a new academic study that suggests an interesting way to predict the music we will enjoy. David Greenberg, Sandra Matz, Andrew Schwartz, and Kai Fricke have published a paper titled, "The Self-Congruity Effect of Music" in the Journal of Personality and Social Psychology.  They write: 

Across three studies we show that people prefer the music of artists who have publicly observable personalities (“personas”) similar to their own personality traits (the “self-congruity effect of music”)... Our findings are largely consistent across two methodological approaches to operationalizing an artist’s public personality: (a) the public personality as reported by the artist’s fans, and (b) the public personality as predicted by machine learning on the basis of the artist’s lyrics.

The scholars also show powerful evidence of a gender effect in music preferences.  They write:

The present article also provides the first evidence that listeners tend to prefer music from artists of the same sex, which had been previously theorized but not studied empirically (Hagen & Bryant, 2003). That is, the gender-fit between the listener and artist was found to be a significant predictor of musical preferences.

So, the next time you say yourself, "I love that artist's music."  Ask yourself, what do you know about that performer?  Why do you like that artist?   For companies, of course, the implications are profound.  Can they use this finding to refine their algorithms, without intruding on our personal privacy?  What if listeners were willing to disclose more about their personality in order to discover more music they would enjoy?   

Big Data and The Analytics Paradox

Kellogg School Professor Eric Anderson describes an interesting phenomenon that he calls the “Analytics Paradox.”  Anyone grappling with how their firm employs data science as part of their business strategy should become familiar with this concept.  Anderson explains:  

A young firm starts out making many mistakes. Eager to improve, they collect lots of data and build cool new models,” he says. “Over time, these models allow the young firm to find the best answers and implement these with great precision. The young firm becomes a mature firm that is great at analytics. Then one day the models stop working. Mistakes that fueled the models are now gone and the analytic models are starved."

Anderson offers an example of the analytics paradox.  Imagine that a firm provides two-day delivery services.    They consider using data analytics to help make an important decision: whether or not to offer one-day delivery services to customers.  You might be hard-pressed to answer that question using analytics.  Why? An effective organization becomes proficient at executing two-day delivery.  If the firm can't meet two-day delivery deadlines on a regular basis, processes and systems are changed.  Employees who can't meet the two-day delivery schedule get admonished or even dismissed.  In short, a firm that is very effective at execution will drive all variability out of its "production" system.  Yet, without variability, you will find it very difficult to use analytics to drive improved decision making. 

What can you do to conquer the analytics paradox?    Put simply, you need to inject variability into your system by promoting thoughtful and systematic experimentation.   The use of experiments enables you to test different models and systems, and in so doing, generate the type of data that can be analyzed effectively to enhance decision making.