QuadPlay and Beyond

Since Google changed the face of Internet advertising, many companies have adopted solutions based on Contextual Advertising, where ads are selected based on the contents of the Web page/Email/IM. In some cases, the user’s profile, if known, is also used to better match ads.
Mobile Advertising is also based on the context but usually relies heavily on the user profile as each handset is directly associated with a specific user. Since mobile operators can collects a lot of information about their users, the generated profile greatly assists in the matching process.
Unlike Internet and Mobile domains, it’s not trivial to understand the context of a video stream and to identify the person watching it. First of all, how can we know whether the TV is turned on when even the Set-Top-Box (STB) is unaware of it? and even if we knew that the TV is on, how can we identify who is watching the program?

In the early days of Interactive TV (iTV), applications used to require users to ‘login’ to the service however this approach was not accepted by users and was neglected along the way. Solutions that attempt to build user profiles for TV watching habits must find a way to bypass these constraints, either from the STB side or with assistance from a server-side application.

But the questions still remains – is it really worthwhile to target ads on the TV domain? In their article “Using Data Mining to Profile TV Viewers“, Spangler, Gal-Or and May show that targeting TV viewers creates a lifting effect that increases the changes of having a household person that matches the ads’ intended segment.

“The table here outlines the gains from viewer profiling for five target gender/age segments defined by Nielsen Media Services, Inc.; for a discussion of segmentation strategies. If an ad is sent to everyone, 25.18% of the recipient households will include a female aged 18 to 34. If an ad is sent only to households selected by the profiling system, 58.06% will include such a person. A household selected by the
profiling system group is therefore 58.06%/25.18%, or 2.3 times more likely, to be of the desired type than one selected at random. This ratio is the model’s “lift”; the lifts in the table are different for different demographic groups because some groups’
characteristics are easier to predict based on their viewing patterns”.

Several companies have already started to work on such solutions, although most of them are in stealth mode. An example of such company is Invidi, which already provides tools for various sorts of targeted advertising.

TV viewing has undergone profound changes in the past few years with the emergence of Digital Video Recorders (DVR), and especially those with time-shifting capabilities, that turned TV watching into a non-linear experience. This change in watching behavior jeopardizes traditional advertising models and are likely to raise the need for alternative advertising solutions and models. Forrester Research has predicted the viewing of television commercials could decrease by 20% in just a few years. Forrester analyst Joshua Bernoff suggests this is because the DVR “degrades the value of advertising,” predicting the eventual disappearance of an important social element of the television viewing model, that is, “television in which you sit through the commercials is about to be replaced.

But time-shifting is not the only threat facing TV advertising. It is already apparent that TV advertising revenues are generally in decline due to several reasons:

1. Decline in total audience of “traditional” channels
2. Fall in the attractiveness of TV advertising compared with alternative media such as the Internet where ads are adjusted to specific users, selected according to the context and can be effectively measured (CPM, CPA).
3. Reduction in the effectiveness of TV advertising due to time shifting, PVRs, VOD and poor measurement capabilities

Advertisers and services providers need to adapt to these changes and the opportunities they create for new solutions that will tackle these problems through new models and technologies. Such solutions will allow advertisers to target their preferred audience by means of segmentation (possibly using DVRs for generating watching patterns and profiles), and offer an advertising model that will be resilient to ad-skipping and time-shifting.

So, why don’t we see such solutions on the major cable networks? believe that the main reasons are the difficulty to penetrate STBs, the complex business model that requires cable/satellite operators and broadcasters to join forces (and share revenues) and the reluctance of broadcasters to produce accurate measurements of real ad exposures.

A mid-way solution that is already tested in several cable networks is based on geographic targeting where the ads are inserted (using splitters) at the Video Hub Office (VHO) nearest to the end-points (STBs). VisibleWorld is one of the leading companies offering such a solution. VisibleWorld has trials in Comcast, Cablevision and other cable operators. Another company playingin this domain is Navic.