A Funny Thing About Cord Cutters

Much has been written about the Nielsen position on cord cutting…that it is, in fact, happening, but among segments that are not of core importance to the TV industry.  A key paragraph from the June 2010 Nielsen press release below:

“….shifting to online video mainly appears to be happening in small pockets of the population, including young, emerging households.  Households with no cable subscriptions at all, but who subscribe to a broadband service, also reflect a younger population of college graduates and lower to middle income consumers who may not be fully convinced of the need to pay for digital cable.  However, Nielsen data shows that these individuals are typically light TV viewers who watch 40% less TV per day than the national average.   And while they stream about twice the average amount of video, they still only stream about 10 minutes per day, hardly an indication of a monumental shift to online-only viewing.”

The delicious irony of this position:  according the the theory of disruptive innovation (Clayton Christensen, The Innovator’s Dilemma, 2003) it is precisely among non-core audiences that disruptive innovations gain their initial foothold.   Check out this quote from The Innovator’s Dilemma:

“First, disruptive products are simpler and cheaper; they generally promise lower margins, not greater profits.  Second, disruptive technologies are typically first commercialized in emerging or non-significant markets.   And third, leading firms’ most profitable customers don’t want, and indeed initially can’t use, products based on disruptive technologies.   By and large, a disruptive technology is initially embraced by the least profitable customers in a market…”

So…according to the Nielsen analysis, disruption of the TV business is proceeding more-or-less in textbook fashion.  Not what they intended I’m sure.

One niggle to applying the concept of disruptive innovation to cord cutting: disruptive innovations generally deliver a lower quality product (offset by other benefits).   At least at first.   While most cord cutters would likely argue that the quality of their viewing experience through their over-the-top devices is quite as good as they would get from multi-channel providers.

I’d argue, though, that cord cutters are sacrificing ease-of-use.  Not every mainstream consumer would know what over-the-top device to get or how to hook it up.  Lack of knowledge and a hassle factor keeps the disruption isolated among demographic pockets with greater economic need and some technological savvy…who are willing to go through a little trouble.

We’ll have to see, as going over-the-top gets easier for consumers, whether cord cutting expands beyond (and how far beyond) these initial demographic pockets:

  • Will exclusive content, only available through multi-channel subscriptions, become a critical barrier against the increased penetration of cord cutting?   Live sports, in particular, may be a key factor that keeps people from cutting the cord so long as there is no easy/legal way to get major sports events over-the-top.   If this is true we may see female-headed households and those who care less about sports become the next demographic frontier for cord cutting.
  • Is there a psychological barrier for a significant portion of TV viewers (particularly heavy viewers) against the on-demand only type of experience that cord cutting entails?   To the degree that there is an inherent need to surf channels…there may be an anti-cord cutting barrier for heavy viewers.
  • Will the TV Everywhere initiatives of the major TV players help forestall disruption of their business?

It will be fascinating how it all plays out…

But one thing we must not do is dismiss the phenonomenon based on its initial audience characteristics.   Quite the contrary.


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Is Increasing Visit Frequency A Realistic Goal?

For the content sites I’ve worked on there’s often been the following mantra on the business and editorial teams:

We want to our site to be part of our users’ lives.   We may be a quick, entertaining mid-day mental snack, but one our users can’t live without.   We want them to come back (practically) every day.

But there have been a number of times that I’ve run into the following pattern…a pattern that makes me wonder about the soundness of this goal.   I’ve reproduced it here using Quantcast data that’s in the public domain, starting with site ranked number 22 (Huffington Post) so as to exclude the very top-ranked utilities and running down to number 89, excluding sites that are not quantified or block access to their frequency data.   There are 32 sites on the chart – the point is that I wasn’t cherrypicking.

Quantcast Loyalty Data For 32 Highly-Ranked Sites - 6/28/2011

The chart breaks the number of pages per monthly visitor into two components, page views per visit across the x axis and visits per visitor across the y.   These are the two components of loyalty, how deep do people go into the site when they visit and how often they come to the site.

The striking thing is that all the sites fall into a narrow range on the visits/visitor axis.   Almost all fall between an average of 1 and 4 visits per month.   Of the 32 sites there are a couple of outliers..to all of about 4.5 and 5.5 visits per visitor per month (those would be Tumblr and Drudge Report).   The pages per visit axis has the wider range, with the majority of sites in the 1-10 range and outliers into the low teens, one into the 20’s (Tumblr, 4share and Drudge Report).

The admitted flaw here is that I’m working with averages.   If you break down the average of 1-4 visits a month and look at the distribution of visits across visitors there are sure to be segments that visit the site every day, several times a day.

But my bet, looking at the averages, is that those highly loyal groups, in terms of visit frequency, will tend to be small, perhaps infinitesmal.  For the most part the online world is so noisy, there are so many options at people’s disposal, that it is difficult to generate visit frequencies to content sites of more than once a week or so.   The bigger driver of loyalty, the measure that creates more of a difference between the more sticky and more bouncy content sites, is how deep visitors go on each visit.   I believe the pattern implies…at least for content sites…that it is the pages/visit aspect of loyalty, the within session aspect, that deserves relatively more analytical and editorial focus.

What’s Driving Online Video Growth?

Nielsen and Comscore online video data look like they come from different planets, as ReelSEO points out.    The most dramatic difference is in hours per viewer per month; 4.7 hours for Nielsen as of January 2011 versus 14.2 for Comscore, December 2010.    Yes, both are for U.S.

But there’s another headscratch, aside from how different the numbers are.   Each source tells a totally different story about where the growth in online video is coming from.   Completely different answers to the question:  more users or more usage?

Take these Jan 2011 Nielsen numbers for example.  They seem to say that the audience for online video is saturated and growth comes almost entirely from dramatically more usage from the same people.  I calculated a number from those provided that suggests: growth in time spent comes from more videos per person; not so much from increased time per stream:

Nielsen Online Video Data: January 2011

Now Comscore, for Dec 2010, from their 2010 Digital Year In Review: 

Comscore Online Video Data: December 2010

Granted the measures are apples and oranges.   Still, the Comscore data seems to say that online video users are increasingly dramatically while usage growth is more modest.   A completely different story.

With no bias toward sources or methodologies, the Nielsen growth story is the one I tend to believe.   I would think, at this point in its evolution, most everyone who’s going to watch online videos is already doing so (not counting new users coming in as kids mature).    I would guess that growth is coming from changing behavior among those who’ve already caught the habit.

Clearly there’s tremendous usage upside for online video.   If you think about the higher (Comscore) number for online video consumption, 14.2 hours a month, and compare it to consumption of TV, at some 35 hours a week.   Given that comparison you’d sort of expect usage to be the dynamic driving online video growth.

The question for either story is the detail behind it:  

  • If new users are pouring into online video, as per Comscore, who are those users; what are their demographics or other characteristics?  
  • If the same users are consuming dramatically more streams as per Nielsen, how are there habits changing?   What types of video content are making up the difference?   

And why do both sources show the average length of stream to be so short (both under 5 minutes) and growing relatively modestly compared to the other metrics?  Wouldn’t we expect, given the growth of Netflix, for this measure to be growing dramatically?   

I would love to break this information down into user segments.   Because I would bet there are segments of consumers for whom average length of stream is much longer than the average and segments for whom this metric is growing more sharply than the others.   When those segments start to drive the total sample average, when average length of stream really starts to grow, that’s when disruption of the TV business will be under way.

Is Your Video Traffic Upside Down?

For content sites with a mix of text and video, are there times when it makes sense to shift the balance towards video?

I would argue, for these types of sites, video traffic tends to be upside down.   Video consumption will be relatively lower during the site’s peak traffic hours, toward the middle of the day on weekdays.  Video consumption will be relatively higher on weekends and in the evenings when total site traffic is at low ebb.

The way to see the pattern is with a calculated metric, video starts/page views.   Call it the video content ratio.   There are issues with this metric, as I’ll note in a couple of paragraphs.  But it indicates a rough percentage of the content consumption on your site accounted for by video, a useful thing to know.   And you can track it across time to see  interesting patterns in video consumption, as related to total site traffic.

What you will likely see, if you go through this exercise, is that page views and video starts both tend to peak during the week versus the weekend and they both tend to peak during the mid-day hours versus the evening.    But video will peak less sharply and trough less, so that the video content ratio will actually peak on the off days and off hours.

The implied consumer behavior behind the pattern is the most interesting thing.    During peak traffic times when people are at work they’re looking for information in quicker, tighter hits.   They’re consuming everything in high quantities, text and video, but there there are inherent efficiencies for text.   During soft traffic times when people are more likely at home they’re less likely to be online and cruising content sites.  But when they are using content sites they are more in lean-back mode… and in a more conducive mood for watching videos.   Perhaps somewhat longer videos as well.

There may be a number of ways for site programming to take advantage of this picture…

Before leaving the topic, a few words on the video content ratio.   What’s wrong with this, aside from being yet one more calculated metric, are the various fudge factors.   Are there auto starts on various pages?    That will distort the ratio.   Does the implementation count some video starts as page views; are these completely exclusive definitions or is there some fuzzy overlap?   So there are certainly issues…let’s call it crude.

And yet there are various uses, as you can see.   It’s helpful, when looking at video trends for a site to benchmark them against total traffic trends, to see if video is merely trending with the site or on a distinct trajectory.   It’s useful, when there are a number of sites in a portfolio, to see which are doing relatively better in delivering video content relative to each traffic base.   Accepting its warts, the video content ratio is a way to break down data silos and look at the big picture.   And that’s always a good thing.

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