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Posts Tagged ‘internet traffic’

Failure to understand how users and money flow through the Internet costs media and etailers a lot of money every day.  There are huge misconceptions about where the “value” actually lives for user data, advertising performance and profit margins on all this high tech.

The following figures attempt to disambiguate some of the confusion.  The summarized conclusions come from a variety of data sources and real life experiences analyzing financial statements, traffic reports, advertiser analysis and experimentation.  Specifically one could get someone exact figures by combining comScore, Quantcast, Compete, Google Analytics, TNS, @Plan, SEC Filings, internal reports, revenue statements and DART forecasting as I have done several times.

This post is meant to be a demonstration of the core concepts, not a statistical treatise on the topic.

If you hate reading too much, skip to the end for a somewhat realistic example of how traffic flows.

Traffic on the Internet roughly splits 7 segments.  (as shown in the figures below).  These segments are defined by where the sit in the user experience by amount of consumptive behavior (clicks, reading, sharing, watching). How the user gets from segment to segment is not completely linear in actuality, but when you coagulate a users behavior you’ll roughly see a funnel in terms of time spent, pageviews and ad impressions.

Traffic Funnel

Traffic Funnel

The segments can be characterized also by their ad performance, ad targeting (how specific is the user in their activity), and their audience coverage (how much of the particular audience segment does a type of site/service reach)

Funnel Traffic Segments

Funnel Traffic Segments

Each segment has a different cost profile.  Here I look at labor costs to maintain and capital expenses to build and power.

Where's the Cost?

Where's the Cost?

As you can guess, each traffic segment has a different profit profile too.  This is largely the result of combining the advertising/revenue performance with the cost profile.  Certain Internet services simply do not have a strong profit opportunity because they borrow old models and/or cost more than the market is willing to pay. (Perhaps that will stabilize one day, but I think software tools and low cost hardware disrupt the demand curve A LOT because users can often supply their own demands once the cost gets too high, hence why TOOLS are the most profitable segment.)

Profit Margins by Segment

Profit Margins by Segment

Make no mistake about what I’m presenting here.  The profit online is all in retailing, portals/search and tools/utilities.  The stuff in the middle of the funnel is highly susceptible to competitive displacement and has very little intellectual property protection.  You can verify this conclusion by reviewing revenue statements and SEC filings for the big tech and internet companies.

The advent of citizen journalism and self publishing flattened the media market.  Owning a printing press was once “high tech” and a capital investment barrier.  Owning the right location on the main street was once a logistical barrier.  High speed computers and difficult programming languages was once a technical barrier.  Those 3 feature are gone.  Media is now, well, almost purely a creative barrier.  There’s a huge pool of creative talent constantly struggling against each other.  Creativity is worth a lot once it rises above everything else.  That happens so rarely to make it a bad investment.  Every minute more and more people enter the creative market (how many blog posts per hour? how many videos go up each day?… a lot.)

organizing, sifting, filtering, distributing, aggregating… that’s the sweet spot.  There is a technical hurdle, but the investment is worth it as there will never be less of a need to filter, sift, find, distribute.

This week we had a beautiful illustration of these concepts with the Presdential Inauguration.

Most of the US users watched the Inauguration, most on TV, a lot with online video streams and 2 million in person.  During and Immediately following the inauguration the Internet lit up with content creation and massive usage.  The portals and search engines featured as many new links and breaking stories to the news coverage.

The social networks shot pictures, tweets and status updates around, occassionally referencing links to the confirmation gaff, benediction speech text, and satelite pictures from DC.

Micro bloggers summarized everything as fast as they could, while the search engines and utilities sucked in that content.  The original content creators probably released a previously composed story and put that live.

Mainstream users shut down their video streams and took to the portals and search engine, seeking more info on what just happened or insight into a specific moment.  Most times they ended up at CNN or NYTimes.  Many times, but less frequently, they hit a blog that had some recent content.  Most users probably ran into a wikipedia reference link or youtube video.

Some users ended up on amazon to buy Obama’s books or some inauguration swag.  Finally as the day concluded and original content creators finally had enough time to craft something, users might find themselves falling asleep to a good OpEd on the history of the day or an interview with the Michelle Obama dress designers.

By 3 days later the amount of content available on the inauguration is 1000x greater than within the first 10 minutes.  Original content creators are hopelessly buried amongst the blog posts, tweets, continuosly AP feed CNN articles and YouTube embeds.  The bloggers are buried by other bloggers.  The news stories give way to other news stories.

The utilities that sort, sift, filter and monetize on it all just got a 1000x better experience and continue to catch the huge volume of user investigation and digging.  The own the head, the trunk and that dreaded long tail and collect user targeting data all along the way.

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Check out this bit of info from the Official Google Blog!

Based on their graph of searches per hour and assuming at $5-7 eCPM on searches  and an estimate of 500,000,000-1,000,000,000 searches per day (or 21,000,000 to 42,000,000 searches per hour)* ….

Google lost over 20,000,000 queries during the inauguration or $100,000-140,000 on search.

Assuming a similar loss of general traffic across the web it cost Google an additional $40,000-60,000 in AdWords revenue.

Wow.  that’s a lot of ad revenue to lose for about a 1 hour interuption.  I suppose CNN, Facebook, and other news outlets picked up that extra ad traffic.

*Based on 2008 comscore reports and UU estimates from quantcast.

* My estimates for revenue per hour roughly equate to the quarterly earnings after you do all the math to roll it up.  So the eCPM and searchs per hour seem to be solid assumptions.

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Wanna get a good idea about why asking “who would do such a thing” is not an uplifting question to ask about events like Spitzer?

Check out these sites:

http://www.google.com/search?q=emperors+club

Think they benefited at all by those curious enough to go find out about Emperors Club’s services?

http://www.alexa.com/data/details/traffic_details/emperorsclubvip.com?site0=emperorsclubvip.com&site1=theemperorsclub.com&y=t&z=3&h=400&w=700&range=1m&size=Large

Spitzer and Internet Traffic Graph A

Two domain name variations on Emperors Club put those sites in the top 15,000 for a couple of days.  That usual equates to 50-90K unique users per day.

Wanna still get at the content like many others did that day? Use the wayback machine.

Think I’m making this up? Strange correlation here.   See it over a longer period to see archive.org traffic…

Archive.org and Emporers Club

So, methinks Spitzer isn’t the only guy looking for an escort.  If I dug down far enough I’m sure I could find out who actually went to these sites during this time.

Also interesting note is that EmperorsClubVip.com was hosted by Homestead.com, an Intuit company.  So… are they involved in the investigation….

Enjoy

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Consider the Democratic Primaries. Do we see any predictive power in internet traffic?

Quantcast Demographic Info:
Hillary
Barack

Compete.com: Hillary vs. Barack

Alexa:

Check here. 

Quantcast:

Conclusion:

It’s tricky! however, I think we need to normalize the traffic by demographic as raw volume is not a good predictor at all (very low correlation between results+exit polls and internet traffic). See here for detailed information on results and polls.

No conclusion yet…

Next Steps:

I will be mashing all this data together to show trends overtime. AND, i will be overlaying it on tools like PolicyMap to show how Internet (general and social networks) + real world policies + polling locations + business all works together.

Amazing that we have all these tools and an incredibly small set of people uses them. Oh, that’s not amazing nor surprising – perhaps frustrating.

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