Viewer behavioural targeting on TV is superior to both digital behaviour attribution and 3rd–party data

Whilst there is a lot of talk about better audience targeting on TV, there’s very little examination of the quality of data on which targeting is based.

Most of the targeting on addressable TV so far hasn’t really moved beyond basic geographic and census-based household data. It has its uses, but it is not especially helpful in identifying consumer likes and propensities.

To achieve a clearer understanding of consumer affinities, you must acquire more data. At a high level, consumer data can be divided into two categories; behavioural and stated. Behavioural data is collected as the result of people doing things: transactions, interactions, browsing, and viewing. Stated data is gathered from processes – such as surveys – that collect or monitor humans expressing attitudes, views and opinions.

However, as David Ogilvy once said; “the problem is that people don’t think what they feel, say what they think or do what they say”. This, for me, sums up the issues with relying just on data sources that are based on what consumers say they do, as opposed to their actual behaviour.

As much of the data available to TV operators commercially (3rd-party data) has been derived from stated behaviour or intention, this raises big questions about recency, accuracy and quality when used for TV Ad targeting.

Of course, it is also possible to match data from online browsing or purchasing behaviour to TV subscribers in an attempt to identify affinity but, in my view, that process often results in incomplete or inaccurate attribution.

On the other hand, most TV operators are now sitting on a huge and powerful behavioural viewing data asset, that can tell them exactly what their viewers are interested in. Moreover, the viewing patterns themselves can help better determine the composition of the household viewing, including age, gender and life stage.

It is true that transforming viewing data into accurate affinity targeting attributes is challenging but the opportunity to offer advertisers targeting based the actual behaviour of an Operator’s viewers (rather than externally matched data) enables a TV operator to offer its Advertisers the kind of powerful 1st-party data-based targeting that they enjoy in digital channels.

The solution developed by ThinkAdvertising turns real-time viewing data into highly accurate audience affinities in an automated process that’s quick, low cost to and easy to maintain.