Yuval Fisher , CTO MVPD , Imagine Communications,

After acquiring knowledge, service providers can target OTT viewers with relevant ads that are personalized to the individual, adapted for the time of day, the location, and the device they are watching. Imagine Communications

India has more than 300 million smartphone users. It is no surprise that over-the-top (OTT) viewership is on the rise, and that broadcasters and content distributors would be wise to respond quickly to capture what should be long-term and potentially lucrative commercial opportunities.

The monetizing of OTT-delivered video falls into three primary buckets. The first is transactional: viewers pay a fee for each piece of content they view. The second is subscription-based: viewers pay a fee to access a library of on-demand (and sometimes linear) content. This all you can eat subscription model is best represented by Netflix and by Amazon, now making a major push in the market. The third route, and the one which appeals most to traditional players in the media industry, is advertising. Advertising has been extremely successful in funding broadcast, and it is easy to transfer those principles to new delivery methods.

OTT is inherently a personal experience. Consumers choose what they want to watch, when they want to watch it – increasingly on a personal device. The logical extension of that is to learn about the individual and provide targeted advertising. For those used to broadcasting, this means a move from a ratings-based approach to selling ads based on impressions.

OTT service providers should know something – and quite possibly a lot – about each subscriber. Using big data analytics, media companies can now build up a detailed profile of each individual. After acquiring that knowledge, service providers can target OTT viewers with relevant ads that are personalized to the individual, adapted for the time of day, the location, and the device they are watching.

This level of precision in targeting depends upon dynamic advertising insertion (DAI). Because the commercials are inserted at the point of delivery, every single stream can be tailored to the tastes and interests of the individual. Compare this to traditional, linear television advertising where commercials are targeted at a broad spectrum of interests and demographics. The same commercials are seen whether the programme is viewed live or via home recording, so they may not be relevant when the consumer gets around to catching up with the program..

In the traditional model, advertisers pay on the basis of the number of broadly relevant viewers. DAI turns the model on its head. The goal is not always to offer large numbers of viewers but rather high-value viewers who closely match the target demographic of the advertiser. Even more important, studies show that consumers who see only commercials that are relevant to them pay a lot more attention to those commercials. Messages are much more clearly retained, making it more valuable to the advertiser. A key technological requirement for DAI is that viewers should not be aware that it is happening. Early models for DAI were based on downloading and storing replacement ads in advance on the target device, then switching from the stream to the commercial as required.

This results in freezes and buffering while the display switches from one source to the other, before the commercials and when returning to the program. There may be a shift in perceived quality if the program and the commercials use different codecs and bitrates, resulting in resistance from consumers. Software ad blockers will also see the switch and could simply eliminate the advertising altogether. DAI inserts the right commercials into each individual stream at the point of delivery. By linking it to campaign planning, every commercial will hit its targets. For broadcasters providing both conventional channels and OTT, the advertising sales and delivery process is part of a unified advertising management programme, allowing campaigns to be sold over linear and nonlinear outputs and across delivery channels, targeted by demographics and by individual data analytics.

The technologies of DAI are proven, and will lead to the unification of advertising management around multiple services and applications, including linear, on-demand, and cloud DVRs. The result will be coherent campaign planning and maximized revenues, however the content is delivered.