Adobe thinks it has the answer to Netflix’s “password sharing” problem that involves up to 46 million people, according to a 2020 study. TorrentFreak reports: Adobe believes that since every user is different, any actions taken against an account should form part of a data-driven strategy designed to “measure, manage and monetize” password sharing. The company’s vision is for platforms like Netflix to deploy machine learning models to extract behavioral patterns associated with an account, to determine how the account is being used. These insights can determine which measures should be taken against an account, and how success or otherwise can be determined by monitoring an account in the following weeks or months. Ignoring the obviously creepy factors for a moment, Adobe’s approach does seem more sophisticated, even if the accompanying slide gives off a file-sharing-style “graduated response” vibe. That leads to the question of how much customer information Adobe would need to ensure that the right accounts are targeted, with the right actions, at the right time.
Adobe’s Account IQ is powered by Adobe Sensei, which in turn acts as the intelligence layer for Adobe Experience Platform. In theory, Adobe will know more about a streaming account than those using it, so the company should be able to predict the most effective course of action to reduce password sharing and/or monetize it, without annoying the account holder. But of course, if you’re monitoring customer accounts in such close detail, grabbing all available information is the obvious next step. Adobe envisions collecting data on how many devices are in use, how many individuals are active, and geographical locations — including distinct locations and span. This will then lead to a “sharing probability” conclusion, along with a usage pattern classification that should identify travelers, commuters, close family and friends, even the existence of a second home.
Given that excessive sharing is likely to concern platforms like Netflix, Adobe’s plan envisions a period of mass account monitoring followed by an on-screen “Excessive Sharing” warning in its dashboard. From there, legal streaming services can identify the accounts most responsible and begin preparing their “graduated response” towards changing behaviors. After monetizing those who can be monetized, those who refuse to pay can be identified and dumped. Or as Adobe puts it: “Return free-loaders to available market.” Finally, Adobe also suggests that its system can be used to identify customers who display good behavior. These users can be rewarded by eliminating authentication requirements, concurrent stream limits, and device registrations.
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