AI and Personalisation

By Covatic

The rise of Artificial intelligence (AI) has well and truly captured the public’s imagination - and media news headlines - in recent years. However, the way in which AI will impact the world around us is still being debated by those companies developing AI applications; especially within the media and broadcast industry. 

Surveying the landscape of machine learning within the industry, it’s easy to point to the trailblazing brands that are using it to connect with their customers. The implementation of the technology to deliver personalised content experiences for consumers is a key strategic tool for companies like Netflix, Hulu, Spotify and Amazon.

Clearly, by implementing algorithm-driven viewing recommendations and engaging machine learning to optimise content systems, these digital-first brands have disrupted what it means to be a media company in today’s competitive business landscape. 

The next challenge we all face taps into a topical societal issue; how to balance the need for accurate content delivery against the right to privacy for individuals. The recent high-profile cases of data being misused have left people hyper-aware of the risks to privacy when it comes to companies gathering and storing data; especially in the face of new legislation – the European General Data Protection Regulation – which places a large onus on companies to keep citizens’ personal information private and secure.

Clearly, with accuracy and privacy at odds, media businesses must find a way to use machine learning so that it benefits both themselves and their customers. The most effective strategy to achieve this is to keep the data ownership within the realm of the individual. Analysing the data on a users’ own device (whether that’s a phone, tablet or laptop) means that businesses can keep private information just that: private. Machine learning can be applied to work within a person’s device, and provide recommendations and additional details to inform companies about what content should be delivered, when, and which format will suit best. 

This ‘machine learning at the edge’ means you can use multiple data points to understand the context of your consumers; their location, their activity and what content they enjoy engaging with. Essentially, it’s your customers’ own personal content assistant. 

At the end of the day, you don’t need to know somebody’s private details to use machine learning to deliver a truly personalised content experience. To be a leader in machine learning, you must invest in technology that can protect the privacy of your customers, as well as deliver the content experience of the future.