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TV Audience Targeting Must Reflect the Dynamics and Speed of Consumer Behavior

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With Disney and Netflix becoming the latest providers to succumb to the inevitable commercial realities of the subscription-only TV business model, it makes you think about the amount of advertising consumers now have to endure and what this means for both customer experience and advertising effectiveness. 

With the ad-supported tier, Disney+ and Netflix are planning about four minutes of advertising per hour, roughly in line with competitor AVOD services such as, Discovery, Peacock, and HBO Max. Many other providers (even Disney-owned Hulu) and commercial broadcasters already have ad-loads significantly in excess of this.

Empirical research suggests that most TV viewers would ideally prefer not to have any advertising, although will trade off the access to free, or low cost, content in return for consuming ads, so long as they have some relevancy. However, we are in a media age where consumers are exposed to up to 1,000 ads per day at multiple touchpoints: home, work, transport, and many more. Most of these ads are untargeted or poorly targeted. At what point does this just make consumers tune-out or, worse, drop-out of subscriptions altogether? 

A recent report from the Internet Advertising Bureau (IAB) spelled out bluntly the changes in consumer expectations of advertising. It found that consumers are tired of traditional advertising, especially when it manifests as interruption to digital video consumption. Lower consumer tolerance and higher expectations of brands are impacting the composition and size of audiences for ad-supported media and entertainment brands.

To achieve a clearer understanding of consumer affinities, you must acquire more data. The challenge is the complexity and cost of turning viewing data into audience attributes that advertisers want. Consequently, the demands of targeting on addressable TV advertising require high levels of automated AI, both in development and maintenance. That means reducing the manual aspects of the analytical process to a minimum. As well as ensuring high velocity of development and adjustment, it also dramatically reduces the cost of offering TV behavioral targeting to advertisers.

Nonetheless, consumer data can be divided into two categories; behavioral and stated.

  • Behavioral 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 sums up the issues with relying just on data sources that are based on what consumers say they do, as opposed to their actual behavior.

Another factor in data quality not to be overlooked is recency.  In the case of third-party data, some of it might be more than 12 months old. This has profound implications for the accuracy of targeting.

Even those TV operators who have built behavioral-based targeting from their own viewing data need to be mindful of two big problems that may not be immediately obvious in the excitement to deploy analytical resources to explore such a rich data set. 

  • Viewing behavioral attributes are expensive and slow to develop using data scientists or modelers.

It can take some weeks to properly develop reliable and accurate attributes that work. The more attributes you need, the more time and costs are incurred. When we launched AdSmart at Sky, my data science team successfully developed many useful behavioral attributes, but it took a few weeks to create and operationalize each one. When I calculated the headcount cost incurred, it came to tens of thousands of pounds for every attribute. 

  • An attribute set needs to be constantly monitored and maintained in order that attributes reflect the changing dynamics of the viewing customer base.

If this is not done, the attribute set will ossify and produce declining uplifts. Attribute maintenance also takes time and costs money.

It is true that transforming viewing data into accurate affinity targeting attributes is challenging, but the opportunity to offer advertisers targeting based the actual behavior of an operator’s viewers (rather than externally matched data) enables a TV operator to offer the kind of powerful first-party data-based targeting that advertisers value in digital channels.

Most TV operators are now sitting on a huge and powerful behavioral 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.

The targeting variables deployed must match the dynamics of the addressable TV ecosystem. In effect, attributes must change and update as viewer behavior changes. While some behavioral patterns may be slow to shift, and therefore demonstrate established viewer habits, we have also seen very sudden, yet persistent, transformations in viewing behavior that reveal potential lifestyle changes or intent to purchase. A relatively static set of TV targeting attributes will miss these valuable triggers.

Fortunately, there are solutions that can facilitate rapid implementation of an automated advanced analytics engine that creates and updates TV targeting attributes in tune with the dynamics of consumer behavior, like the ThinkAdvertising solution. For a TV operator or broadcaster, it offers the ability to sell advertising inventory using 160+ affinity attributes derived from its actual viewer behavior that align with international digital audience taxonomies. Crucially, the solution can be deployed in 2-3 weeks (no need to wait) and then run as an automated, dynamic and cost-effective process.

[Editor's note: This is a contributed article from ThinkAnalytics. Streaming Media accepts vendor bylines based solely on their value to our readers.]

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