Quinnipiac Assignment #04 – Media Convergence
A look at media convergence.
Once again, we did not have to prepare a YouTube video. Therefore, instead, I am going to reprint one of my essays, in its entirety. This one is about media convergence.
As media (print, television, Internet, etc.) all becomes deliverable on one piece of hardware (generally a smartphone or an iPad), and one is advertised or copied or shared on another, should our metrics and means of measuring reach, etc. on these platforms also diverge? And what does that mean for the future of measurement?
Wishing and Hoping AKA The Past
Back even before television was three channels, Nielsen ratings gathered data. Nielsen started in the 1920s but didn’t really get into media analysis until the 1942 radio index. In 1950, they added the television index. (Nielsen, 90 Years). By 2000, Nielsen was measuring Internet usage.
Throughout most of this nine-decade period, there was a siloing of media. They analyzed radio one way, television another, etc. But it was mainly counting. How many people watched a show? How many listened to a particular radio station?
With 1987’s People Meter (Nielsen, 25 Years of the People Meter), Nielsen made an effort to gather more granular data, and to gather it more rapidly. However, Nielsen’s efforts were still confined to extrapolated samples. Was their sampling correct?
In 1992, they used the People Meter for the first time in an attempt to measure Hispanic viewing habits. But even in 2012, the total number of people meters in use was in a mere 20,000 households. Were the samples representative? It’s hard to say.
Here and Now AKA It’s Better, But …
Social media qualitative measurements, including sentiment analysis, are an effort to understand viewer, user, and listener behaviors. Nielsen and the like measure quantifiable information such as time on a channel (or page). But qualitative measurement goes beyond that, in an effort to understand why people visit a website.
Topsy, for example, measures the number of positive and negative mentions of a site, product, service, celebrity, etc. Yet a lot of this is still quantitative data. Consider Martha Stewart as a topic of online conversation.
All we can see are numbers, really (the spike was on the day that a tweet emerged claiming that Martha Stewart had a drone). This is still counting. There are no insights into why that tweet resonated more than others.
What is Media Convergence?
Media convergence is mashing everything together in ways that audiences probably didn’t think were possible even a scant thirty years ago. But now, we watch our television shows online, we are encouraged to tweet to our favorite radio stations. Our YouTube videos are becoming part of television advertising. And our Tumblr images are slipping into online newspapers.
We can see all of this and more on our iPads. Add a phone to this (or just use an iPhone or an Android phone instead of an iPad), and you’ve got nearly everything bundled together. How is this changing analytics?
For one thing, what is it that we are measuring? When we see a music video on YouTube, are we measuring viewer sentiments about the sounds or the images? When we measure a television program’s Facebook engagement, is it directly related to the programming, to the channel, to viewer sentiment about the actors or the writers, or something else?
What does it mean to like or +1 anything anymore, when a lot of people seem to reflexively vote up their friends’ shared content?
Where Should Our Analysis Be Going?
I believe that our analysis has got to converge as our media and our devices converge. After all, what is the online experience these days?
On any given day, a person might use their iPad to look up a restaurant on Yelp, get directions on Google Maps, view the menu on the restaurant’s own website, check in via FourSquare, take a picture of their plate and upload it to Instagram, and even share their dining experience via a Facebook photo album, a short Vine video or a few quick tweets.
If the restaurant gets some of that person’s friends as new customers, where did they come from? The review on Yelp? The check in via FourSquare? The Vine video? The Facebook album? The tweets? The Instagram image? Or was it some combination thereof?
Avinash Kaushik talks about multitouch campaign attribution analysis (Avinash Kaushik, Web Analytics 2.0, Pages 358 – 368), whereby customers might receive messages about a site, product, service, etc. from any number of different sources.
On Page 358, he writes, “During the visits leading up to the conversion, the customer was likely exposed to many advertisements from your company, such as a banner ad or Affiliate promotion. Or the customer may have been contacted via marketing promotions, such as an email campaign. In the industry, each exposure is considered a touch by the company. If a customer is touched multiple times before converting, you get a multitouch conversion.”
Kaushik reveals that measuring which message caused a conversion is an extraordinarily difficult thing to do.
Media Convergence: Takeaways
With media convergence, the number of touches in a campaign can begin to come together. You can measure Facebook likes for all channels. And you can count tweets for whichever messages go out on Twitter, whatever they are about. Will attribution be any easier? Hard to say, but if the number of channels continues to collapse into one, will it matter quite so much in the future?