Consumers are connecting with entertainment in entirely new ways, and the days when one ratings company was able to tap into roughly twenty thousand homes and gain a comprehensive portrait of an audience’s viewing habits have disappeared. Audiences are fragmenting across time, across place and across platforms. This has been great for the individual consumer, but has been a challenge for those who measure television and for advertisers.
With viewers consuming countless movies, television shows and user-generated content on a variety of devices, a flood of raw data has been unleashed. How can advertisers and content providers navigate this increasingly complex ecosystem of platforms and content choices while filtering the information overflow? Today, companies are turning to predictive analytics for strategic intelligence about not just what people watch – but when and where they are viewing – in order to target specific audiences for advertisers, media buyers and content providers. Predictive analytics harnesses advanced data collection – from smartphone and tablet viewing data to market segmentation, socioeconomic statistics and online usage patterns – to illustrate a more comprehensive user profile. These enhanced viewer snapshots can help discern the likelihood of where a highly specialised demographic of viewers will be amidst the ever-expanding broadcast landscape.
Predictive analytics plays a key role in the future of ad targeting. The technology enables proactive, predictive viewing suggestions based on prior viewing habits and similar viewers’ choices, and even incorporates social network recommendations. This intelligence helps is more than about merely connecting with content; it’s about connecting with people and this connection will be incredibly valuable to advertisers.
However, predictive analytics only goes so far and new solutions are in development to “hyper-personalise” the entertainment experience for consumers. Hyper-personalisation refers to the next generation of personalised entertainment solutions, beyond bookmarks and simple recommendations. With hyper-personalisation, the TV and other devices are able to tap into viewing preferences, platform type, user behaviour, social networks and even context enabling the creation of highly relevant, engaging communication the consumer.
Hyper-personalisation contributes to a context-based entertainment experience that can deliver content (and potentially advertising) based on the user or group of users, and when and where they are consuming content. This enables decision making based on whether a person is alone, with friends or with the family. Depending whether users are on-the-go, alone, or at home together their viewing habits change, but with insight into exactly how, advertisers could deliver customised marketing to each individual consumer.
As testing continues and technologists continue to explore the possibilities offered by predictive analytics and sophisticated recommendations, it won’t be long before hyper-personalised experiences become more commonplace in households around the globe. With a more comprehensive understanding of the consumer, analytics benefits networks, providers and advertisers – but it’s the consumer, fuelling the predictive analytics engine, who will benefit most of all.
Charles Dawes is Global Strategic Account Director at Rovi.