We all kind of knew that Twitter’s path to making money was paved with data, and the announcement on Monday that it’s buying analytics startup Lucky Sort makes it official. Unless I’m totally misreading the writing on the wall, this move is all about giving advertisers — and anyone, in theory — the tools to learn about what people are talking about.
Word that Lucky Sort is shutting down and that several of its team are joining Twitter’s revenue engineering department suggests this is exactly what the acquisition aims to accomplish.
As it stands, companies use Twitter as a way to track how people are talking about them and maybe, if they’re really advanced, do some sentiment analysis. If they’re willing to pay a third party, Datasift and Gnip are more than happy to broaden marketers’ views to encompass the entirety of Twitter’s data, both real-time and historical. What companies really can’t do, though, is run their own advanced analytics about topics straight from the Twitter platform.
One view of the Lucky Sort dashboard
The value proposition from such a product should be obvious at this point. Facebook, Google and Yahoo all collect a lot of data about how people are using their platforms and what topics are trending, and they all offer it up via a variety of products targeting marketing types and the public at large. If Twitter wants to be taken seriously as a venue for advertising budgets and a platform for measuring the pulse of the nation, people need to be able to ask questions of its data without relying on an intermediary or the occasional Twitter blog post.
As a journalist, I’d love to have access to this type of tool to track trending topics in real time and spot possible stories as they’re happening. The appeal to marketers should be obvious. As IBM’s Erick Brethenoux told me recently, “[Marketers] talk a good game about social data. Very few actually leverage it effectively today.”
At Twitter, though, data is a slightly different beast than at other web companies. Twitter’s value lies largely in real-time data — topics can be peak, crest and all but vanish within a 48-hour window. This situation has hampered some of Twitter’s efforts to surface optimal search results, and it has spurred the decision to buy companies such as Backtype (for its streaming-processing Storm technology) and parallel-processing startup Ubalo.
The latter move, which happened last week, should help Twitter’s development team create new features without worrying about the intricacies of making them run — and run fast — across a cluster of machines. (You can learn a lot more about how companies such as Google, Facebook and Box are rethinking infrastructure to handle their unique data needs at our Structure conference next month in San Francisco.)
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