Converged Analytics for Media Effectiveness

  |  May 5, 2014   |  Comments

Converged media (using one type of media to prop up another) is more effective in addressing the changing media consumption habits of online audiences than paid, earned, or owned media are when used in isolation.

Lately, I have been hearing the term "Converged Media" in connection with "media strategy."

In preparation for an American Marketing Association Virtual Conference that I am speaking at on the afternoon of May 8, I focused on the act of becoming a "Data Connoisseur" through measuring digital media effectiveness. My presentation demonstrates how analytics can help us to be more effective in the use of paid, owned/earned, and converged media, together with programmatic media as part of an omni-channel media strategy program.

Figure 1: Digital Marketing Trifecta -

Many marketers tend to focus on one type of media (paid, earned, or owned); it makes sense that "converged media" (using one type of media to prop up another) should be more effective in addressing the changing media consumption habits of online audiences than paid, earned, or owned media are when used in isolation.

For example, Ellen's selfie (a group shot of actors Bradley Cooper, Brad Pitt, Jennifer Lawrence, and Meryl Streep, among others at the 2014 Academy Awards) was said to generate up to $1 billion in earned media for the Academy Awards sponsor due to the massive press and social media coverage.

ellen-selfieFigure 2:

Turns out Ellen's selfie was a clever PR stunt planned in advance for Samsung by Publicis Groupe (Samsung is a Publicis client) and it rode to instant popularity because her selfie was timed and seeded on top of broadcast/cable media and distributed by social media, which was then quickly amplified by online press.

I suppose you can say that Ellen's selfie was a sophisticated form of "paid media" that generated drastically more "earned media" shares and retweets, etc. than it would have had it been released at a different time.

Figure 3:

Paid media timing is a vital part of a converged strategy and it clearly takes some sophisticated smarts to both ideate and deploy that strategy in a successful way.

Figure 4: Source: LinkedIn query formulated and run by Marshall Sponder

Using LinkedIn I created a query to look at individuals in the United States who work with various forms of media as well as those who work exclusively with one type of media (figure 4); I found most marketers tended to be focused on one form of media with some spill-over to another form (i.e.: roughly 75 percent of marketers who worked with paid media were exclusively focused on it, while the rest might work with owned or earned as well).

However, converged media appears to require an even more specialized focus or skill and there are far fewer marketers (probably in the 1:20 or even 1:30 range compared to the other types) who are dedicated to it.

Figure 5: Source: comScore Device Essentials, Monday 21 January 2013

Given the complexity of the Web and the multiple devices we use to read, interact, and process information, handling media attribution requires some specialized skills and many marketers simply do not have enough of them or the right set of platform tools and frameworks to assemble the right data in the right format.


Figure 6: Source:

Once the right cross-media attribution analytics model is selected and set up, you might end up with a dynamic display such as the one below (figure 7) along with programmatic buying and creative strategies that I will cover in a future column.

cross-media-attributionFigure 7 - Source:


Marshall Sponder

For over a decade Marshall Sponder has influenced the development of the digital analytics industry with his WebMetricsGuru writings that focus on social media metrics, analytics and media convergence. He also possesses considerable in-house corporate experience with roles at IBM,, Porter Novelli, and WCG while continuing to work with start-ups. Marshall is a Board Member Emeritus at the Web Analytics Association (DAA) and teaches Web Intelligence at Rutgers University and Baruch Business College. Marshall is the author of "Social Media Analytics: Effective Tools for Building, Interpreting, and Using Metrics," published by McGraw-Hill in 2011.

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