Video Analytics? Thoughts on Web Analytics for Internet Video

By | November 2, 2014

Measuring video content with web analytics isn’t super difficult, but it has its nuances and challenges. I’ve been thinking a bit about it lately, and have had some good conversations with a few people. Folks I know are playing around with the likes of Joost, Vuze, and Hulu, TVUNetworks, as well as using BrightCove and Videoegg. And, man, the popularity of BitTorrent and other swarm structure 4th gen P2P networks is larger than ever.

Simply speaking video measurement can be divided into the following types:

  • Instream measurement. Refers to measuring the video itself and the various abstract elements of the video experience, such as duration metrics (average viewing time) and interaction metrics (number of stops, plays, pauses, rewinds, fast forwards, and clicks on video content).
  • Outstream measurement. Refers to measuring the content environment and user experience surrounding the video, such as the conversion metrics (percentage of visits downloading or viewing a video), behavioral metrics (referrers to the video page, players used), and content metrics (percentage videos per channel, percentage videos viewed by topic, percent videos viewed by file type).

By categorizing the web video analytics into these two buckets, you are better able to answer meaningfully the following questions, which must be considered prior to any roll-out:

  1. What are the business objectives for rolling out video features on the site?
  2. What format are the videos in?
  3. Are the videos downloads or streams?
  4. Am I using a content distribution network or streaming video network?
  5. Does my web analytics tool have the features necessary for video measurement? Or should I look for a third party, niche vendor?
  6. What data collection method should I use?
  7. Do I understand event models?
  8. What KPI’s are relevant and important based on my business goals?

To help you formulate answers to those questions, here’s some thinking:

  • Business objectives. You, the analyst, must understand why your company is rolling out video. In other words, what’s the goal and what strategy underpins the goal? While video is “the rage” right now, simply rolling out video because “everyone is doing it” is no strategy (though doing so may yield a strategy ;). A goal for video deployment could be “to generate leads,” thus you measure the scenario conversion rate for the funnel resulting in the lead generation and video download (outstream video analysis). The objective might be “to keep visitors on the site longer,” then you would measure duration and interaction (instream video analysis). As you all know, I firmly believe that it the business goal that allows you to contextualize what you’re measuring so that you may build KPI’s.
  • Video format. Lots of different video file types exist: mpegs, qt, mov, swf, flv, avi, wma, ra, wmf, mp4 and more. You’ll need to identify the video types you want to track so you can configure your web analytics tool to measure them. Removing or adding filters or changing your tag’s javascript might be necessary.
  • Download or streams. Videos can be downloaded (by right clicking) or spawned in a media player. They can also exist embedded on the page or in another object for on-page streaming. Thus, the way you instrument your pages will differ based on the way you present the video content. For example, if you are streaming videos, you may want to use javascript (or a vendor provided scripting language) to instrument your pages to track the video. If you are just hosting downloads, you may simply want to run your logs to detect the number of times videos were downloaded.
  • Content distribution network or video network. If your video content is distributed by a CDN or a video network, you will have to apply page tags on all the pages rendered by combining your server’s content with the content served by the CDN. Some video networks provide basic reporting that you can extend with a client-side page tagging solution. Alternatively, you can process the logs provided by a CDN. The challenge with CDN log file processing is that you will most likely not be able to merge the data with your log files for the same site, resulting in two “profiles” of analytics data related to one site: one profile with the site analytics data and one with the CDN analytics data.
  • Data collection method. If you’ve read this far in my blogivation, you probably picked up that the data collection method you have at your disposal will constrain or enable the way you measure video. Page tags will enable you to instrument your pages with onclick functions that pass values to the javascript and in turn to the analytics server. Packet sniffers and log files enable you to measure downloads without modifying code. If you need modify your web analytics tool or tag configuration to track video filetypes, you can reprocess logs to access the data. With tags any data related to downloads or interactions with the video object prior to the config change will be lost.
  • Web analytics tool features. Many web analytics tools will allow you track a video play or download in your page view reports, but only two tools support true event models: Unica NetInsight and Google Analytics.
  • Third party tools. With the convergence of internet and television, we’re not many years away from having a single-screen for viewing the internet, tv, and movies. Many of us already connect our TV’s to our computers (Windows Media Server), use Slingbox, have had Tivo for years, use BitTorrent and perhaps even consume content from the sites I listed at the beginning of this post. Companies like Visible Measures, Zango, VidMetrix, and Maven Networks already provide some flavor of a video measurement solution too.
  • Event models provide the conceptual and logical framework for measuring interactions that are subordinate, equal, or a replacements for the page view. Without getting into much detail, “events” are interactions such as the play, stop, pause in a video stream, or the pan, zoom events in a online mapping experience. In order to articulate the instream video experience, you should understand what an event model is and how it applies in Web Analytics 2.0.
  • KPI’s.Based on business goals resulting from site strategy, you can build KPI’s related to instream and outstream video measurement. For example:

Instream:

  • Percentage high duration streams
  • Percentage medium duration streams
  • Percentage low duration streams
  • Average viewing time per stream/overall across all streams
  • Percentage visits who complete stream
  • Percentage visits that stop stream within 10 seconds
  • Percentage visits when this stream was the last video viewed
  • Percentage visits when this stream was the first video viewed

Outstream:

  • Conversion rates by video filetype, video topic, channel, taxonomy node, referrer, geography, keyword, and so on
  • Average streams per visit
  • Percent visits/views from different channels (such as email, organic search, paid search, direct, offline)
  • Average time since last stream/video downloads
  • Average time between stream/video downloads
  • Repeat visit rate for visits involving a stream/video download

The Internet has come a long way since I saw my first streaming video over 9 years ago (VIVO for those old timers out there). The options for consuming video content over the web are growing everyday (and not at all limited to YouTube, ay?). I firmly believe video on the Internet is still in its infancy, and video measurement technologies both inside and outside of “web analytics” are quite embryonic. What a huge space for growth!

As the internet-originated video becomes even more pervasive for home entertainment and for business communication, companies will need to employ analysts who know how to create frameworks measuring video content. Do you?

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