To Boost Viewership, TV Networks Use Big Data To Think Like Performance Marketers

andy-fisher"Data-Driven Thinking" is written by members of the media community and contains fresh ideas on the digital revolution in media.

Today’s column is written by Andy Fisher, chief analytics officer at Merkle.

The TV network industry has seen extraordinary change over the past several years. Consumer viewing behaviors are in constant flux as they consume content on multiple devices and platforms, ranging from network programming on cable TV to on-demand streaming on a tablet.

Technology now allows the collection and analysis of dizzying amounts of data on viewer preferences and behaviors. Yet, even with all of the fragmentation, cross-platform developments and changing business models, one thing has not changed: the fundamental need for networks to use marketing tactics to drive viewership. Networks sell audiences, and more viewers equal more revenue.

This need may not have changed, but in this era of big data and technology, the ability to measure, optimize and improve tune-in rates is experiencing a rapid transformation. TV networks now have the ability to use performance marketing to boost tune-in rates.

Lose The ‘Spray And Pray’ Tactic

TV networks have never had a direct relationship with their viewers in the same way that banks have with their customers. Panels traditionally measured viewership, which was reported by third-party media researchers, such as Nielsen. TV tune-in marketing has typically not been scientifically measured in the same way as an ecommerce campaign. With today’s laser-like focus on margins, these “spray and pray” approaches to driving tune-in rates are no longer acceptable.

To address this, TV networks are starting to develop closer relationships with individual consumers by building infrastructures to support performance marketing initiatives. This direct connection serves two purposes.

First, it acts as a tool to optimize tune-in campaigns. By connecting marketing tactics to an individual household using set-top box data, TV networks can observe how those activities impact tune-in behavior. In turn, they can now begin to plan and optimize media as efficiently as ecommerce companies have been doing for years.

Secondly, this connection at the household level allows advertisers to bring their own data or third-party data into the network TV planning and buying processes, in order to reach and scale their audiences more precisely and efficiently.

The Role Of Big Data

The goal here is to leverage the same science and optimization capabilities for tune-in marketing that have been successful in multichannel marketing for ecommerce. To do this, common tune-in tactics, such as email, display, search and house TV ads, can be combined at the household level.

In this case, conversion is defined as watching the marketed show, or the tune-in rate. The same attribution approaches that are used in digital marketing can then be applied to tune-in campaigns. This technique allows for the ability to do detailed optimization, A/B and multivariate testing, personalization and other techniques that are so common to other industries’ marketing campaigns.

In addition, this allows networks to answer fundamental questions, such as how much house inventory should be used to drive tune-in vs. selling it or, if there is 5% opt-out rate per email drop, how many drops should be done to optimize tune-in while maintaining the integrity of the email list?

In other words, networks can now think like performance marketers.

In theory, it’s easy to see why these analyses make perfect sense, but in practice, they are very difficult. They are, however, now possible.

No matter how precise our targeted marketing tactics have become over the years, TV tune-in marketing has always continued to follow more of a “plan, buy and guess the results” kind of methodology. Now, with access to this new layer of data, analytics and technology, the very nature of TV tune-in marketing is moving into the world of direct, performance-based marketing.

Follow Merkle (@merkleCRM) and AdExchanger (@adexchanger) on Twitter.

 

Add a comment

XHTML: You can use these tags: <a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>