Pandora, the Internet radio service, is plying a new tune.

After years of customizing playlists to individual listeners by analyzing components of the songs they like, then playing them tracks with similar traits, the company has started data-mining users’ musical tastes for clues about the kinds of ads most likely to engage them.

“It’s becoming quite apparent to us that the world of playing the perfect music to people and the world of playing perfect advertising to them are strikingly similar,” says Eric Bieschke, Pandora’s chief scientist.

Consider someone who’s in an adventurous musical mood on a weekend afternoon, he says. One hypothesis is that this listener may be more likely to click on an ad for, say, adventure travel in Costa Rica than a person in an office on a Monday morning listening to familiar tunes. And that person at the office, Mr. Bieschke says, may be more inclined to respond to a more conservative travel ad for a restaurant-and-museum tour of Paris. Pandora is now testing hypotheses like these by, among other methods, measuring the frequency of ad clicks. “There are a lot of interesting things we can do on the music side that bridge the way to advertising,” says Mr. Bieschke, who led the development of Pandora’s music recommendation engine.

A few services, like Pandora, Amazon and Netflix, were early in developing algorithms to recommend products based on an individual customer’s preferences or those of people with similar profiles. Now, some companies are trying to differentiate themselves by using their proprietary data sets to make deeper inferences about individuals and try to influence their behavior.