New Route to New Revenue: Detect & Respond to Credentials Sharing

Rather than ignoring password sharing or solely seeking ways to prevent it, service providers are now in position to monetize it thanks to advances in behavioral analytics and machine learning.

Orly Amsalem, Product Manager Video Security, Synamedia

July 12, 2019

3 Min Read
New Route to New Revenue: Detect & Respond to Credentials Sharing

Credentials sharing is not a new problem for service providers. As the OTT and pay-TV landscapes continue to evolve to accommodate entertainment on multiple devices, credentials sharing has followed suit, increasing in both magnitude and in the diversity of motivations -- from casual password sharing among family and friends to large-scale, for-profit accounts run by fraudsters. The ability to watch from multiple screens has had the unintended consequence of contributing to illegal password sharing, which is expected to cost the US pay-TV industry nearly $10 billion by 2021, according to Parks Associates.

Truth be told, despite this growth in devices, casual password sharing has gone largely ignored by providers. Or if they aren't ignoring it, they're trying to find ways to prevent it. But why not see it differently and instead, find ways to monetize it?

Instead of wasting valuable resources to either prevent or shut down each password sharing account, what if there was a way to monetize the problem to turn it into a new revenue source? What if, instead of looking at credentials sharing as a war against non-paying customers, providers saw it as an opportunity -- an avenue to new revenue from viewers?

An evolutionary approach to addressing password sharing accounts would begin with the service providers, who can identify the different types of credentials sharing based on factors like usage patterns, locations and prevalence. Behavioral analytics and machine learning can be used to create predictive models to characterize the different types of sharing activities for a given population or user. Service providers then can categorize the different sharing types -- whether it is a casual sharing between family and friends, or a business sharing to make a profit. Armed with this data, service providers can make intelligent decisions to either upsell casual sharing accounts or shut down for-profit accounts run by fraudsters.

Having the power to make these decisions is an ideal outcome for OTT and pay TV providers, as it allows them to gain control of revenue loss/gain from credentials sharing -- in other words, providers can prevent revenue loss, and at the same time maximize revenue gain opportunity. Once the casual password sharers are identified, providers can offer an upgrade or a new premium package that allows for more active users per account. In this way, the sharing is controlled and beneficial for both the customer and the provider.

Many honest, casual users will be happy to pay a nominal, additional fee for a premium, shared service with a greater number of concurrent users. For example, a family member who is going away to college can opt-in to a premium service that will allow viewing content on the same account that the parents use back at home.

In the case of large-scale businesses by fraudsters who share passwords to make a profit, the data that can be obtained from machine learning models is key to shutting down these bad actors. In this scenario, password sharing is more than just a nuisance -- it is an industry threat that could lead to dangerous security breaches of customer data. Using behavioral analytics and insight teams to assign risk scores to suspicious accounts, OTT and pay TV providers can analyze the potential threat and take the necessary measures to prevent or shut down piracy.

As traditional monetization sources are drying up or stagnating, providers can take advantage of the changing landscape of entertainment to find new revenue streams. Addressing credentials sharing is a win/win situation for everyone. By identifying sharing accounts and taking informed, intelligent actions, pay TV and OTT providers can benefit from an incremental revenue stream, while keeping honest users honest.

— Orly Amsalem, Product Manager, Video Security, Synamedia

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About the Author(s)

Orly Amsalem

Product Manager Video Security, Synamedia

Orly Amsalem is Product Manager, Video Security at Synamedia. She brings 15 years' experience developing and analysing information systems, business intelligence, data architecture, machine learning and end-to-end solutions for enterprises and startups. Orly is responsible for Synamedia's anti-piracy and security portfolio. Previously, Orly was a data scientist at Cisco.

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