Piano Announces the First Release of LT[X], a Machine Learning Algorithm That Predicts Users’ Likelihood to Subscribe

Piano, the leading subscription management and customer experience technology provider, today announced the launch of Piano LT[x], a machine learning framework that gives companies the predictive power they need to change how they interact with users — promising to alter how the entire media industry approaches its audience experience. LT[x] (Likelihood To [act]) predicts the likelihood of individual browsers to take a given action on a site. The first propensity model released under the LT[x] framework — LTs — allows for precision targeting to the browsers most likely to subscribe.

Media companies today are trying to determine the best way to maximize revenue by striking the right balance between advertising and subscriptions. The most common approaches to finding that balance are to use a pageview meter or lock a fixed set of premium articles or videos. While these approaches are effective, they treat every user the same — and that leaves opportunity on the table. Faced with this challenge, Piano has established its LT[x] model to understand and automatically determine the factors that correlate with users’ future actions, including their chances of subscribing.

“One-to-one business rules and customized commercial experiences are the media industry’s future. That’s the vision Piano is already working towards, and the unveiling of LT[x] is a major step forward,” said Trevor Kaufman, Piano CEO. “Artificial intelligence and machine learning have the ability to fundamentally shift how media companies react to evolving audience demands and expectations. Piano is fueling the solutions needed to do that.”

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In determining likelihood to subscribe, LTs considers a starting base of 76 unique metrics. Factors such as the time of day or week a user visits a site, or the device they visit from, together form predictable patterns that demonstrate how users will eventually act. The LTs algorithm automatically fine-tunes its predictions based on an individual site’s converters, adjusting for each audience accordingly. This is already generating more subscribers and more revenue for several of Piano’s media customers.

“By demystifying exactly which metrics are most important for driving conversion, LT[x] allows us to better understand where to invest our resources for maximum return,” says Claudius Senst, head of subscriptions for Piano client Business Insider. “And by predicting user actions, we’ve been able to substantially increase our subscription conversion rates, while also nurturing relationships with prospective subscribers.”

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Piano’s LT[x] model also promises to go beyond the walls of media. By building advanced capabilities to optimize for any user behavior, Piano’s foresight into the future of digital will arm a breadth of companies with the ability to turn data into action.

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