Is Online Video Growth Really Slowing Down?

The headline on data that Nielsen released last week: growth in online video time spent is outpacing the growth in the number of users.   Growth in total minutes for the most recent period, August 2010-August 2011 (9%) is more than double the growth for users (4%) and higher than the growth for streams (7%).  Thus the average stream is longer than it was a year ago.  The chart is shown below.

This trend in rising length of stream is apparent from data in Nielsen’s January press release…I pointed it out here and noted that the finding makes sense; the audience for online video is becoming saturated while the upside for time spent is tremendous.

According to Nielsen’s June data, time spent for the average online video viewer is 4 hours/30 minutes per month.   Compare that to average time spent per TV viewer at upwards of 30 hours per week and it’s clear that online video time spent has nowhere to go but up particularly as consumers increasingly watch long form content over-the-top on their TV screens.

That said, what seems odd about the chart, not explicitly called-out in the accompanying text, is that growth for all the online video metrics has slowed severely over the past 12 months.   I’d expect growth in users to plateau (as per the chart) and I’d expect growth in number of streams per user to slow as people watch longer streams.  But it’s surprising that total time spent with online video is growing at less than 10%, year over year.

It’s particularly surprising since the base for time spent per user is so low.   To get a sense of how low it is, take a look at Nielsen’s June press release and do a little back-of-the-envelope on time spent per stream.  It’s only 2 minutes/40 seconds, on an average of 101.5 streams per viewer per month.  So, according to this information, the average viewer of online video watches somewhere around 3½ streams per day at less than 3 minutes apiece.   You would think, given Netflix, Hulu, Amazon and other long-form streaming options, that total time spent would be growing at more than 10% off this base.

Another interesting back-of-the envelope:  calculate the average streams per user and time per stream for the three competitors for which the press release provides monthly user, streams and time spent data.    For YouTube (with 108M users): 81.7 streams at 1:55 (mins/secs) apiece.   For Hulu (13.5M users):  46.7 streams at 4:45 apiece.    And for Netflix (8.0M users) 24.1 streams at 21:18 apiece.    The total is tilted toward the YouTube number with its enormous user base, but the Hulu number is surprisingly short and even the Netflix number, at less than half an hour per stream, makes me wonder about what might not be included in the data.

The shape of the Nielsen growth curve is contrary to the one below, for example, from Cisco data cited in ReelSEO earlier this year.   This predicts that bandwidth demands for streaming video will not decelerate but in fact accelerate over the coming years.

I suspect the Nielsen time spent growth curve is affected by a particular aspect of their methodology; they monitor online video activity on their respondents’ computers.    The way I read it:

Online video that people access through games consoles, Internet-connected TV’s, Roku boxes, Apple TV and any other device that circumvents the computer  and ports online video directly to the TV screen are not included.   This is probably not a substantial piece of the puzzle on a user basis or on a streams basis.   But the absence of this piece may understate the growth of time spent per stream with the increasing consumption of longer streams concentrated in non-PC devices.

This isn’t a knock on Nielsen’s methodology – it measures no more and no less than it says it does.  But if we’re really going to understand the total picture of online video behavior we’ll need to get a handle on the whole ball of wax no matter what devices the video streams flow through.

TV Everywhere: How To Assess Success?

With widespread availability of TV Everywhere in North America as of this summer according to a Parks Associates study and with a consumer awareness campaign recently launched by Turner Broadcasting it is interesting to think about what success would look like…in terms of what distributors and their network partners are trying to achieve.

It seems to me there are three goals:  (1) stimulate cross-platform uptake and usage of programming and thereby extend the reach of the advertising, (2) reduce the likelihood of cord cutting behavior and (3) reduce cannibalization of cable or satellite time by on demand alternatives.  Though there’s some payout associated with the first goal the second two defensive goals are the key to why TV Everywhere platforms are being implemented…to subsume evolving consumer behavior into the existing business model and prevent the dynamics that disrupted the music business from affecting TV.

At this point I haven’t seen uptake or usage numbers surface in the public domain.   The NYT piece about the Turner campaign launch cites nothing more definitive than “millions”of users of HBO GO…since its launch earlier in 2011.   I’ve seen no numbers for Fancast Xfinity TV though its been available to Comcast subscribers since December 2009.

But, per my argument above, if we want to asess the success of this strategy, uptake is only part of the story.   We’d want to show that availability and usage of TV Everywhere makes customers less likely to cut the cord…because they can get a wide variety of on demand TV shows and movies along with their cable subscription they are less likely to cancel that subscription in favor of on demand options.   And we’d want to show that those who use TV Everywhere are less likely to dip into Netflix streaming or other on demand options…that one behavior effectively supplants the other.

It is possible, for example, that people likely to adopt TV Everywhere are a different set of people than those likely to cut the cord.   The former may be happy to expand cross platform options from their cable or satellite provider for no extra cost while the latter, under stronger financial pressures, feel a need to reduce their spending.   One group coming in the top may not forestall others falling out the bottom.

Of course there is serious question about how many people really are falling out the bottom.   As cited in a previous post,  a J.D. Power and Associates study released in June showed just 3% of cable or satellite customers had cut the cord, 6% among Generation Y…

Still, if one of the goals of TV Everywhere is to keep this behavior from expanding to more customers and to the industry’s core demographics, we need to explicitly monitor if A really does prevent B.    And if it does not, if cord cutting continues despite operators providing and touting TV Everywhere, we have to consider (as per recent comments by Bernstein Research’s Craig Moffett) that perhaps prices are simply too high; offering cross platform access for the same price just can’t match the allure of a la carte on demand services replacing the cable bill:

“Perhaps the most consistent theme in our research over the past two years has been the widening disconnect between flat-to-declining consumer disposable income, particularly in the bottom two quintiles of household income, and the rising price of media and telecommunications services”

The other defensive role that should be monitored is whether TV Everywhere customers are actually less likely to tap into on demand video services like Netflix or Hulu.   This is another case of whether A really does prevent B.   One question to keep an eye on is whether consumers tend to use TV Everywhere for a limited, specific purpose…catching show episodes that they’ve missed… while continuing to use competing on demand services for another function, mining a broad movie database for something, old or new, that fits their mood and preferences.   Just as Netflix, entering the streaming world, is struggling with their lack of new and original program content, cable and satellite operators expanding their presence in the on demand world may need to tweak the depth, organization and positioning of their offerings to keep on demand competitors at bay…for all the needs that people want from these services.

Implementing a defensive plan is an important first step for the industry.   The next step is making sure that it is, in fact, serving all the intended defensive functions.

 

Are Heavy Online Streamers Watching Less TV?

Nielsen’s first quarter 2011 Cross-Platform Report shows that the top quintile of online video streamers, in terms of time spent, consume somewhat less TV than other groups.    We can’t assume causality from the findings; heavy online streaming may reduce TV viewing or heavy online streamers may be lighter TV viewers to begin with.    But it’s an interesting finding either way, as shown by the squiggly line charts in the Nielsen report that I’ve lifted below:

There are a couple of different threads that can be pulled out of this information; an alternate visual may help to tease them out.   What I’ve done below is plot the online streaming quintiles, ranging from lightest (5) to heaviest (1), on a scatter plot with average daily minutes of streaming and average daily minutes of TV as x and y axes.   I’ve put 4Q 2010 and 1Q 2011 on the same scatter plot and connected the dots for each time period.

The first thing to note from this reconfigured chart:  TV viewing for the first quarter is on a much higher plane than the fourth quarter.   The two periods seem so different, as far as media behavior is concerned, that we can’t really trend between the two; all we can do it see if there’s consistency to the story.

There seems a great deal of consistency in the behavior patterns reflected by the streaming quintiles:

  • Quintile 5 in 1Q 2011: very heavy TV with virtually no online video.  I bet if we dug into this group we’d find an older demographic profile.
  • Quintile 5 in 4Q 2010 and quintile 4 across both time periods:  relatively light TV viewers with little online streaming.  This light/light pattern may reflect an upscale group that skews toward print rather than video and/or young males who skew low across all media.
  • Quintiles 2 and 3 across both time periods:  heavy TV viewers who are also moderate online streamers.   This may represent the mainstream condition.   At this point in the story, online streaming and TV viewing are rising in tandem…
  • And then there’s quintile 1, the heaviest online streamers who, as the Nielsen report points out, show somewhat lower TV consumption than the other groups.

A couple of points about the heavy online streamers:

  • Streaming behavior is highly concentrated into this top quintile.   The graphic shows how sharply the heaviest users pull away from the other four groups in terms of time spent with streamed content.  My back-of-the-envelope from these numbers shows that the top quintile accounts for 80% of the total time spent with streamed content; in contrast the top TV quintile accounts for just 45% of total TV time spent.
  • The data begs for deeper drill-down into this quintile; demographic and behavioral characteristics.  Why are they consuming so much streaming content?  Are they sending online video over-the-top to their TV’s?
  • Yes, their TV consumption is lower than other groups but not by a whole heck of a lot.   The graphic shows, as their online streaming behavior shoots away from the pack, their TV behavior declines a bit, but nowhere near in proportion to the way their streaming behavior accelerates.

Another way to look at this is to compare TV viewing for the top streaming quintile against average TV viewing across all the quintiles.   We can see the shortfall is slight; the under-consumption of TV is far smaller than the streamed content that these consumers have added to their lives:

  •  In 4Q 2010, among P2+, the heaviest streaming quintile consumed 14.5 minutes of online streaming a day but only 4.3 minutes less TV than average
  • In 1Q 2011, among P2+, the heaviest streaming quintile consumed 18.8 minutes of online streaming a day but only 8.0 minutes less TV than average
  • There’s a much sharper equivalence between minutes of streaming and dampened TV viewing among 18-34’s specifically.   Among this demo, for 1Q 2011, the heaviest streaming quintile consumed 27.0 minutes of online streaming a day and 21.5 minutes less TV than average.   It may be, among this demo, that the sharper trade-off reflects cord cutting; I cited a J.D. Powers and Associates study in previous post that suggests perhaps 6% of younger consumers have cut the cord.

I think this analysis shows a truism about people and media; the more options we throw at them the more media they consume.   Yes, the heaviest online streamers may consume a bit less TV than other groups but, as streaming becomes a bigger part of their lives, they consume more video overall.  They don’t trade-off one medium for another to the degree that they layer them all together.    The question then becomes:  how do they make these integrations and what role does each medium play?

 

Yahoo Data: “Online Primetime”

Yahoo/Interpret recently published results of an online video study updating a study conducted in 2009.   One of the findings is that online video, which previously peaked mid-day, now peaks in the evening hours; the Yahoo report refers to “online primetime”.   As per their key chart, cited below…

From "Phase 2 Of Video Evolution Revolution", Yahoo/Interpret, 6/2011

This startled me because of both the similarities and differences versus my post of a couple of days ago, Is Your Video Traffic Upside Down.   I argued, for major content sites with a mix of text and video, that people are relatively more prone to watching video on the weekends and in the evenings when total traffic for these sites is at low ebb.   The Yahoo data shows that people are consuming more video in the evenings on an absolute basis.

Of course I was talking about content sites with a mix of text and video while the trend cited above is driven by Hulu and Netflix, video-focused sites.  That may be the difference.   I also wonder, this being self-reported data, whether evening online viewing is more deeply engaged in and therefore better remembered, more likely to be reported in a survey.

But the key data point that that this chart seems to beg for:  how much of total “online” video content is being watched on a computer screen versus how much is being ported to a TV screen?  What’s the trend for this?

What’s of interest to me is how much online viewing is in typical TV lean-back manner, on a big screen 10-feet or so away…versus on a small screen 2-feet or so away.

Because I would think the “online primetime” trend and a “online 10-foot viewing trend” would be happening in tandem.

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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