Matching Mobile Music with Content and Consumer

Wall Street Journal - June 19, 2007

WITH THE avalanche of mobile music and entertainment Content slated to come online this year, service providers can no longer afford to bet their bottom line on the understanding that consumers know what they want — and are prepared to navigate confusing mobile portal menus to find it.
Indeed, that business model ignores the shortcomings common to mobile phones (notably small screens and tiny keypads) and puts far too much emphasis on user- pull (where a user requests specific content) which is quite an assumption when it comes to content such as music, which changes faster than fans can keep up.

Put simply, selling new mobile entertainment content is about content-push —where a service-provider offers content before a user asks for it, based on an understanding of the individual user’s purchases, passions and past click-behaviour. It’s even more compelling if the technology can learn users’ likes and dislikes to suggest the tight content to the right users at the right time.
Enter recommendation engines and a variety of bleeding-edge technologies designed to play matchmaker between mobile music and entertainment content and their fans. In its basic form, recommendation technology — modeled on the approach of online bookseller Amazon — suggests content on the basis of what like-minded customers consume, and so connects users with relevant content that their peers recommend.

Mobile music is already a huge market, with recent research from London-based Informa Telecoms & Media pegging global user spend on mobile music services at $13 billion by 2011. But this figure doesn’t factor in the windfall expected when operators, service providers and content companies harness recommendation technology to suggest similar content, and encourage the all-important impulse buy, says John Giamatteo, president, technology products and solutions and international operations, at RealNetworks Inc., a Seattle-based provider of digital media services and software.
“Recommendation is the new must-have ingredient in the mobile music services mix,” Mr. Giamatteo explains. “It’s all about making it easier for consumers to find and buy the music they love in a seamless way.”

To this end, RealNetworks recently acquired Sony NetServices, a Salzburg, Austria-based company best known for its heritage in music discovery and technology that “learn” subscribers’ music tastes over time and exposes them to music relevant to their tastes — even as they change. This is made possible via the platform’s personalization engine, which allows customers to “teach” the music service their personal likes and dislikes.

Xiam, an Irish provider of content discovery solutions, gleans user music preferences from data, including billing information, mobile browsing logs and purchase history, to deliver a variety of related content, including news, images, wallpapers, video clips, games — and whatever else the service provider might offer.

“Music is more than tracks; it’s an experience that spans across multiple categories and platforms. The challenge is to bring it all together for the customer,” says Kurt Lillywhite, vice president of marketing. To this end, Xiam has developed a “super” recommendation engine, which enables a holistic view of the subscriber and allows the operator to respond with a well-rounded offer of entertainment content.
Japanese mobile operator NTT DoCoMo has developed a service that combines relevancy and retail to deliver content around the songs users hear on the FM radio built into selected handsets. Using mobile search technology from Mobile Content Networks (MCN), a U.S. provider of real-time mobile search services, NTT DoCoMo can match merchandise and mobile advertising to the song users hear as they hear it.

When the song changes, so do the search results served up by MCN. The results can be further customized to fit the individual user’s profile, preferences and context.
But not all mobile music services revolve around connecting users to content some focus on enabling music fans to connect with each other. My Strands, a pioneer provider of content discovery and recommendation technology, has harnessed the dynamics of social networks to develop what it calls a “social recommendation engine.” In a nutshell, the company’s patented technology analyzes how people listen to and organize their music and learns from these patterns to suggest the right content to the right users in realtime. When enough users listen to song A followed by song B, the system concludes that the two songs are similar. Using this insight the system can suggest other similar tracks. The system also creates Community by allowing users to see the listening patterns of friends, as well as peers the recommender system has identified as a perfect match.

The text of this Special Advertising Section was written by Peggy Anne Salz, a freelance journalist.

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