An in-depth look at how Facebook correlates with TV-viewing habits (April 2013)
An in-depth look at how
with TV-viewing Habits
Dan Benyamin, CEO CitizenNet
Dr. Arshavir Blackwell, Chief Scientist CitizenNet
We live in a world awash in data. From our digital pedometers to
the words we write, the amount of data generated in two days is
greater than all data collected from the dawn of time to 2003. Of
course, in business, there has always been a desire to use data to
inform decision-making. Few business sectors, however, have
seen the same level of growth and adoption of data – especially
social data – as publishing businesses.
Content, and content delivery, are increasingly becoming a Web
business. Netflix, some have argued, is now on pace to stream
more than any cable TV network. Netflix has shown, even from its
inception, that algorithms can choose content quite well for
audiences, and has even applied that thinking to first-run projects
such as House of Cards.
Netflix of course has a myriad of signals to measure and assess the
likelihood of a show being a hit; the ratings users have provided
have famously spawned Netflix-sponsored competitions to improve
on their own in-house algorithms.
While these content predictions are fine for Netflix, is it possible to
make content predictions more broadly?
Social data is a new proxy for
consumer behavior – even
offline behavior such as TV
Facebook behavior is indeed
correlated with TV viewing
The most correlated metrics
are the number of people
who have liked a show’s
Page and click-through rate
For each 3% increase in
Page likes, there tends to be
a 1% increase in TV
What drives TV viewership is
the right message to the right
audience, at scale
Understanding audiences is
paramount to driving quality
2 CitizenNet, April 2013
“Word at mouth
can scale brand
Today more than half a billion Tweets and 2.7 billion Facebook®
likes are generated everyday. At CitizenNet, we are big believers in
the power of social. Not only is the volume of social data huge, but
in a world of device fragmentation, social identity will inevitably be
more robust than cookies and other browser-based behavior.
So what are some proof-points around the marriage of Social and
There certainly has been a lot of activity. Just this past February,
Twitter acquired Bluefin Labs, who built a product that “marries the
data from TV shows and commercials to audience reactions in social
media. This means real-time feedback for marketers about the
performance of media plans and creatives, something that hasn’t
been available until now.”
Facebook has also been active in the social TV landscape, with Kay
Madati, the company’s head of entertainment strategy stating, “If the
90s were about browsing, if the new millennium was about search,
today and into the future, we believe it’s about discovery. It’s
structured around helping content producers surface their excellent
content and leverage the idea that word-of-mouth at scale can raise
While interest in social platforms is certainly understandable, is this
enough to justify a budget for a social media campaign?
A recent study by Nielsen indeed confirms a correlation between
Twitter volume and TV ratings, with an 8.5% increase in Twitter
volume corresponding to approximately 1% increase in ratings for
So while there clearly is a relationship, this raised a lot of questions
for our team. Specifically, what does the Twitter volume look like?
Is it lots of conversation by few people? Is it related to content
produced by the show? We set out to answer some of these
questions, but this time, on Facebook.
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While there are many different datasets available to a Facebook
advertiser or Page administrator, we wanted to find something as
broadly applicable as possible. Shows are marketed in a variety
of ways, with differing budgets, so we stayed away from pure
advertising metrics, and instead focused on Facebook Page
Insights. These Insights account not only for ad-boosted
impressions, but also such rich information as click traffic,
“People Talking About This” (PTAT), and viral impressions.
Working with our broadcast partners, we gathered insights for 77
different TV shows, across both broadcast and cable, from 2011
and 2012. Since season premieres are such highly scrutinized
events, we decided to look at two weeks of data for each show,
culminating on the premiere date. For each day, we utilized over
twenty different metrics provided by Facebook’s Insights tool. We
then performed a linear regression analysis to see if these
metrics would correlate with Nielsen viewership for that episode.
The result? The actions that people take on Facebook preceding
a premiere are indicative of how many people tune-in to that
show, as shown in Figure 1.
Figure 1: A significant relationship exists
between Facebook Page Insights and TV
4 CitizenNet, April 2013
Of the dozens of metrics used to make the linear
regression, which ones are the most significant?
What are the important things marketers should
watch out for? Our team performed Principal
Component Analysis to find out how all these metrics
fit together. The findings are illustrated in Figure 2.
It turns out that many of the Facebook metrics are
related to each other. For example, the number of
people who are friends of people who like a
Facebook Page is obviously related to the number of
people who like the page, which is related to the total
number of impressions of a page. So many metrics
are statistically correlated with each other, in fact, that
we can put all the Facebook Insights into just two
categories and still largely preserve all the original
In the figure, we see 20 different Facebook metrics,
each radiating from the center of the diagram. The
names of the metrics, such as “The number of people
sharing stories about your Page” and “People who
have seen any content associated with your Page”,
are taken directly from Facebook. The two categories
are represented as green and blue lines. The further
out from the center, the more ‘influence’ that metric
has on the category. So, for the green category the
“Number of people who have engaged with your
Page” is the greatest factor, whereas for the blue
category it is the “The number of people who are
friends with people who have liked your Page”.
An important property of PCA is that the two
categories themselves are uncorrelated; that is,
where green has a high value, blue has a low value,
and vice-versa. That means that what comprises the
green metric are similar to each other, and dissimilar
from the blue metrics. Looking closer at the green
metric, in fact, we see things that are largely related
to an action that someone takes: “clicked”, “liked”,
The blue metrics, however, are things largely
associated with the number of people seeing your
content: “impressions”, “seen”, “saw”.
Figure 2: A plot of each much each metric contributes to an overall engagement measure (green), and an overall awareness measure
5 CitizenNet, April 2013
Figure 3: Even using just two metrics shows
Armed with this analysis, we see that there are really only two
metrics publishers need to spend most of their time on, and it’s
the ones that are the most basic: awareness and intent. You
can’t expect to have large numbers of people tuning into a show
without lots of people being aware of it – this is the blue metric.
At the same time, lots of people who are aware but completely
disinterested in what you have to offer does no good either –
there has to be some level of engagement, and this is
represented by the green line.
To further show this relationship, we can use this as a guide to
simplify your own analysis. Let’s use only a combination of:
• The total number of people who have liked your Page
• The total CTR of your content
With these two values alone, we still see a correlation with TV
viewership, as shown Figure 3.
From this relationship, if we hold the total CTR at the average of
2%, we can estimate that for every 3% growth in Page likes, there
tends to be a 1% increase in viewership.
The Two Metrics
that Matter the
6 CitizenNet, April 2013
It’s unfortunate that so many marketers are caught up in the myriad
of measures and scores that come out of online advertising. As we
have shown, many online metrics are correlated with each other, so
not having a useful dependent variable makes some people come to
the conclusion that social media metrics are a waste of time. By
looking to see how online influences offline, we can see that
successful marketers are just trying to appeal to people’s nature –
regardless of the medium.
It’s also unfortunate that so many marketers have focused purely on
cost-efficiency and Facebook likes. As much as every industry
needs to stretch their ad dollars, there’s no use in advertising to the
wrong people. Our advice is to start building simple benchmarks of
past performance, with an eye to overall CTR (not just paid, but
organic as well), as well as cost efficiency.
How do you drive up CTR? It’s through smart segmentation of your
audience and experimentation with content. With CitizenNet’s
analysis and predictive targeting, on top of Facebook’s rich
channels, it’s not as hard as it seems.
Take the Figure 4 for example. This is a collection of over one
hundred different audience segments for a recent campaign done by
Figure 4: A CitizenNet Audience Map,
which diagrams how different audiences
have been segmented and respond to the ad
7 CitizenNet, April 2013
Figure 5: A close-up of a particular section
of the map.
Every circle is an audience predicted, with the advertiser’s
guidance, to engage with this content. Circles placed near each
other are similar to each other in some regard. Once these
audiences are targeted, the circles are colored to indicate their
level of intent – with red higher performing than yellow. Notice
how this map is ‘smooth’ – where you see red, the neighboring
circles are red. Where there is underperforming yellow, there
tends to be other yellow.
Let’s focus on just one area of this map, shown in Figure 5. This
‘branch’ is of audiences targeted by their retailer interests. While
most of these audiences actually showed tepid interest in the
show, there is one group (marked with the arrow) that showed the
most interest. It turns out that Urban Outfitters was the highest
performing group here. Now imagine how valuable it would be to
show to advertisers that, with positive proof, fans of Urban
Outfitters are highly engaged in your content, all before the show
This brings us full circle with the original objective of this study.
We have established that Facebook behavior is a great proxy for
TV tune-in, and that marketers should focus on finding highly
engaged audiences at scale. In addition, by segmenting those
audiences to find the high engaging pockets, marketers can
establish exactly which types of audiences are consuming what
type of material, and can present those case studies to
About CitizenNet Inc
CitizenNet, a Facebook®
Preferred Marketing Developer,
is ushering a new era of social
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advertising. Its predictive
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