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Quinnipiac Social Media Social Media Class

Quinnipiac Assignment 10 – ICM501 – Television and the Internet

Television and the Internet

Nielsen estimates that there are some 115.6 million television households in the United States. According to the US Census, over ¾ of all households have at least one computer, and about 95% of those households use it to connect to the Internet. That’s about 122 million households. It’s clear that many American households have both.

Ratings

Quinnipiac Assignment 10 – ICM501 – Television and the Internet
English: Primetime television ratings for 2008 in the Philippines: Blue = GMA Red = ABS-CBN The charted points represented the TV program that had the highest rating for that station for that day. (Photo credit: Wikipedia)

Nielsen has had some issues in counting Internet viewing in its ratings systems. A part of this is due to differences in the advertisements delivered (if any), and part is due to pirating. However, Nielsen appears to be, if not getting the details and minutiae quite right, is at least getting the essence correct. What it lists as popular really is popular (although the degree of popularity might not be perfectly represented). As Engler, C. (2011, January 20). The truth about TV ratings, online viewing and sci-fi showsBlastr. [Link] says, “…highly rated shows are streamed more frequently online, sell more DVDs, have higher sales on Amazon and, yes, are pirated more often. When you account for variables that impact all these metrics (e.g., some movies bomb in theaters but later sell well on DVD, younger viewers are more apt to watch things online than older viewers, etc.), we don’t see the crazy variances that you’d expect if ratings weren’t very accurate.”

Adding Online Streaming

As recently as 2013, Nielsen began making the effort to better include online streaming and other means of television content consumption. As Kelly, H. (2013, October 28). Nielsen adds web viewers to its TV ratings. CNN.com. [Link] says, “Shows that don’t include the same ads online as on TV will be counted as part of separate Nielsen Digital Ratings. Shows streamed directly by networks through their own sites and apps typically include the same set of ads, and those viewers are counted towards the traditional Nielsen totals.”

Nielsen’s main purpose is to count program viewership for the purposes of understanding advertising potential. E. g. the more viewers, and the more sustained viewers, the more a content provider can charge for commercials. Hence the need for a connection between online and offline delivery that includes the same advertisements. The networks and other content providers need to, as closely as possible, compare apples to apples. Comparing the delivery of different commercials makes it that much more difficult to achieve a reasonable comparison.

If Nielsen’s purpose were purely to measure viewership without wedding it to advertising, counting clicks and downloads would be easier, and it would not matter what was advertised during any delivery of the content.

Piracy also wouldn’t matter quite so much, at least not vis a vis counting an audience. But speaking of piracy …

Piracy

For networks and other content providers, piracy was and is a huge issue. When we talked about copyright and copyleft, the question arose about the grabbing of content. Who enforces copyright? Who pays when it’s violated? And are the punishments excessive at all?

The uneasy marriage of television and the Internet has spawned an interesting child – Hulu. After all, why were people pirating televised content in the first place? Perhaps, as DVRs made time shifting possible, it was just to allow for platform-shifting. Hulu arose as a response to that, and a way to monetize a far better version and offer it to a paying customer. The thinking was that quality would be its own advertisement, and that most people just wanted a platform shift. They didn’t want to steal from artists or cheat networks. As Braun, J.A. (2013).  Going over the top: Online television distribution as socio-technical system. Communication, Culture & Critique 6(3), 432–458. [Library Link] says, “With Hulu, the networks decided they would instead attempt to draw viewers away from pirated content by hosting higher quality versions of the same videos themselves, while selling advertising against them in an attempt to reclaim some of the revenue they believed they were losing to other sites.”

Most People Are Honest

Many users are more than happy to receive a better quality product and pay a commensurate fee for it. But there are still some who insist on pirating, practically seeing it as a right. As Newman, M. Z. (2012). Free TV: File-sharing and the value of televisionTelevision & New Media 13(6), 463–479. [Library Link] says, “Sharers [also] insist that DVR recordings and downloads are ethically equivalent. The difference between recording a show oneself using a VCR or DVR and skipping commercials and downloading a commercial-free file via BitTorrent is regarded as ethically insignificant.” (Page 469)

So, what is it? Theft? Sticking it to ‘the man’? Platform-shifting by (more or less) innocents? As the debate continues, it shifts away from ethics and morals and into monetization. If content providers can make a little cash even from peer to peer networks, and pay artists, can they continue to claim that they’re being harmed?

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Analytics Quinnipiac

Quinnipiac Assignment #04 – Media Convergence

Quinnipiac Assignment #04 – 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?


Media Convergence

Wishing and Hoping AKA The Past

Back even before television was three channels, data was gathered via Nielsen ratings. Nielsen started in the 1920s but didn’t really get into media analysis until the 1942 radio index. In 1950, it was followed by the television index. (Nielsen, 90 Years, http://sites.nielsen.com/90years/). By 2000, Nielsen had gotten into measuring Internet usage.

Throughout most of this nine-decade period, media was siloed. Radio was analyzed 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, http://www.nielsen.com/us/en/newswire/2012/celebrating-25-years-of-the-nielsen-people-meter.html), an effort was made 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, the People Meter was used 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.

Adventures in Career Changing | Janet Gershen-Siegel | Quinnipiac Assignment 04 – ICM 524 Media Convergence
Martha Stewart on Topsy, June 9, 2014

 

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.

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 become part of television advertising, and our Tumblr images are being slipped into online newspapers. All of this and more can be seen 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 +one anything anymore, when a lot of people seem to reflexively vote up their friends’ shared content?

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.

With media convergence, the number of touches in a campaign can begin to come together. Facebook likes can be measured for all channels. Tweets can be counted for whichever messages are being sent 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?