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    What You Can Learn from a Few Data Monetization Examples

    Most of us are familiar with software as a service (SaaS), the business model where one subscribes to a piece of software and pays a monthly usage fee. Data monetization can take a similar approach, where a company sells subscriptions to unique data it has created.

    The most widely discussed application of this model is when a company develops a way to derive marketable insights, based on aggregate data about its customers’ behavior or internal operations — and offers that data as a feed to paying customers. For instance, an insurance company might develop insights into homeowner behavior that it can sell to large remodelers, or a major retailer could develop insights into durable goods supply and demand that it could sell to hedge fund managers — the list could go on and on.

    Strategies and tactics

    There are many data monetization examples to consider. For instance, you could begin by developing an algorithm that creates insights and inferences on top of your organization’s data assets. First, you will need to scrub your data to improve quality and to ensure that you are not releasing Personally Identifiable Information about particular customers or partners.

    You could potentially package that data algorithm as a Python app, with a data repository sitting underneath it, such as Mongo DB. Then you could create a layer around that app — an application program interface (API) that allows users to access the data.

    Music to a data miner’s ears

    But there are other data monetization examples that are workable as well. In fact, two cases that I think are quite interesting and instructive are in an entirely different field: the consumer market for digital music. The music apps Pandora and Spotify have both found profitable ways to mine available sources of data regarding music and musical tastes. They’ve also created layers on top of their data that allow users to:

    • search for the data (that is, the music) they want
    • receive suggestions about similar data that may interest them
    • and use social media tools to share their data with friends

    The story of Pandora is especially illuminating. Its founder, Tim Westergren, started as a struggling composer for the indie movie market. To get a sense of what directors were looking for in the way of music for their films, Westergren would bring along a stack of CDs and play tracks to gauge the types of music the directors were seeking. As he said in a presentation at Chicago Ideas Week, “What I was doing at that time was an informal genome in my head of musical taste,” he told the audience. “I was running these people through the equivalent of a musical Myers-Briggs test.”

    Westergren had an inspiration, and began working with a group of musicians to build a database that codified songs according to roughly 400 possible descriptors or variables — that is, metadata. His initial company was called the Music Genome Project, but it wasn’t until his 348th pitch that he succeeded in getting the venture capital he needed to move forward and eventually found Pandora. “Most [new] ideas are by definition crazy,” Westergren notes, “because they’re not part of the existing intellectual structure.”

    At the heart of Pandora’s success is a machine algorithm that gets “smarter” over time. Every time a user clicks on a song and says they like it, the algorithm adds points to the metadata associated with that song — its artist, genre, beats per minute, etc. The app then uses that metadata to search through larger repositories of music to find every song or artist that matches, or at least comes close to, the metadata the customer already likes.

    Spotify, meanwhile, has a different twist on music as a data pile to be mined. Like Pandora, it offers a vast collection that tries to offer something for everyone. In addition, its social media feed allows users to easily share with friends whatever music tracks they’re currently listening to, favorite playlists and more.

    Both apps tap more traditional revenue streams as well, including selling advertisements that are seen by those consumers who subscribe for free. Another way Spotify is monetizing its data is to allow third-party developers to create apps that can be hosted within the Spotify desktop player — providing such capabilities as synchronized lyrics, music reviews and others. In essence, they’re creating an environment around their data that functions as a marketing platform on which other companies will pay to appear.

    The gold rush is on

    These data monetization examples point to the tremendous potential for development in this field. Even though we’re still in the very earliest stages of data monetization, there are already several blueprints for ways to turn data into revenue.

    The key is to first ensure that the data you’re selling presents risks that you can tolerate. Then you must identify needs in a marketplace, whether it’s B2B, B2C or something else, for the kind of data that only you can produce and deliver. If you can figure out these fundamentals, your organization could be the the way to creating a new revenue stream — one that could flow far into the future.

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