YouTube: An Analytical Approach (Part 1)
According to Alexa’s listing of most visited websites (sorted by country), YouTube is ranked as #4 in the United States, #6 in the United Kingdom, #4 in Japan, #5 in South Korea and #2 in Germany. It has the power to launch overnight public-relations Cinderella stories such as the Blendtec miracle (yes already, it will blend) as well as broadcast to millions a reputation-killing moment even more swiftly. These occurrences, once posted to YouTube, are available indefinitely.
How does one quantify the amount of successful or damaging exposure YouTube is causing them? Here are some measurements of a micro-case study crafted specifically for this blog post: an analysis of YouTube exposure of the television infomercial product, Kinoki™ Detox Foot Pads. Note: The Bivings Group and the ImpactWatch service are in no way affiliated with Kinoki™ Detox Food Pads.
I manually aggregated data by watching YouTube videos related to the “miraculous detox system”. You can see the raw data that I collected here in an excel spreadsheet. Using the resulting data, I created some visualizations within ImpactWatch:
The first graph shows a raw view of the types of videos people watch related to the Kinoki™ Detox Foot Pads. This is strictly measured in views, which is valid because YouTube only counts views once from each unique IP address, and only if an overwhelmingly large portion of the video was viewed. However, this unit of measurement is often misleading and does not give an accurate representation of the actual exposure Kinoki foot pads have received. That’s where a little spreadsheet manipulation and the second graph comes into play.
In the second graph, a new unit was analyzed. Taking the number of views for each video and multiplying them by their length in minutes, an adjusted unit that more accurately represents total “exposure” was created. This graph provides a much better metric for gauging the float-or-sink status of the product. Although in both graphs negative exposure was dominating (I wonder why?), the adjusted quantity of “YouTube minutes” shows that it wasn’t as bad as just a raw views count might have initially demonstrated.
In the next part of this two-part case study, comments for the videos and other forms of responses will be analyzed.
