TV Targeting: Case Study

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TV Targeting
A data-driven approach

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Executive Summary
• The Nielsen rating system has never been ideal for targeting
political/issue advocacy TV buys
o Nielsen is good at helping you target consumer groups, for example
‘Adults 18-49’.
o However, campaigns and issue advocacy groups tend to have
much more specific targeting, reliant on variables such as party
affinity, issue preference, turnout likelihood, and basic demographic
variables (e.g., age, gender, ethnicity).
• Utilizing set-top-box data, we can derive custom ratings for
target groups that campaigns or issue advocacy orgs actually
want to target
o For example, a campaign might want to contact unmarried, high
turnout, 18-28 year old women; with set-top box data, we can very
easily establish a custom rating for that group across all channels
and timeslots.
• Set-top-box targeting confers several benefits to campaigns
and issue advocacy organizations:
o This data allows you to have a much more detailed understanding
of where your political target universes watch their TV.
o This data allows you to apply niche messaging to niche groups,
likely increasing the efficacy of your ad campaigns, when compared
to messaging targeted to broad Nielsen ratings groups.
o In our experience deploying this targeting approach across multiple
statewide campaigns, we have achieved 20-50% efficiency gains
(in terms of average cost/delivery to target), helping our campaigns
get a lot more “bang for their buck”.

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Part I:
Understanding the “points” status-quo

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The Nielsen “points” status-quo
Even if you are not involved in buying television ads, you have undoubtedly
heard of Nielsen ratings and their ubiquitous “points.” The day after the Super
Bowl, you might wake up hearing reports of how many points the Super Bowl
attracted and how that compares to previous years. Interestingly, most people
are not aware exactly of what a “point” is, even though they may have been
willing to purchase ads and think in terms of points for decades.
In the Nielsen rating system, a point is set to be
equal to 1% of the viewers in any given Nielsen
category (see the table to the right for a list of
such categories). The two most commonly
used categories are “households” (everyone
with a TV in their home) and “Adults 18-49.”
This latter category is typically considered
more valuable (i.e., more expensive) because
people age 18 to 49 tend to buy more
consumer goods than those outside of this
demographic category.
Anyone involved in electoral or public opinion contests might immediately spot a
problem in relying on a category as broad as “Adults 18-49.” Influencing potential
voters is very different from selling consumer goods. How often does your
pollster tell you that you need to make inroads with “Adults 18-49?”
In the world of political and advocacy campaigns, target populations tend to be
much more specific and stratified by factors such as party identification and
likelihood of turning out for elections. For example, a campaign might be
interested in targeting high turnout Republican primary voters not identified as
strongly supporting the opposition candidate, and who are highly concerned
about their tax burden. Alternatively, an advocacy group might want to target
non-married women, age 18-35 who are likely to vote in the next general
election, with a message about “the war on women.” The Nielsen rating system
and its broad consumer categories is fundamentally ill equipped in providing the
kinds of specific information most important to campaigns and advocacy groups.
Nielsen Estimates 2012-2013
Households
People 2+
Adults 18-49
Women 18-49
Men 18-49
Adults 18-34
Women 18-34
Men 18-34
Adults 25-54
Teens 12-17
Kids 2-11

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The 1000-point rule
Unfortunately, the dominant Nielsen paradigm has instilled a sort of carpet-
bombing mentality in many campaign and advocacy operatives, in which the goal
is to “saturate” a market by buying 1000 points. Remarkably, there is no empirical
evidence to suggest that this is the most effective approach to getting one’s
message across. We have interviewed dozens of industry experts and have yet
to hear a single piece of empirical, quantitative evidence supporting the 1000-
point rule of thumb. When we challenge “experts” on the value of the 1000-point
rule, they often return with another rule of thumb, that buying 1000 points will
results in the average target seeing the ad 10 times. If you have the nerve to
push for empirical support on this second claim (we have a lot of nerve), the
answer is usually unsatisfactory. Organizations end up having to take it on faith
that 1000 points, not 900, not 1300, not 725, is the magical round number
needed to do a good job on TV.
The carpet-bombing approach is ineffective for two main reasons. First, it does
not actually do a good job of directing which shows to buy. You can buy 1000
points of Kim Kardashian, and 1000 points of This Old House. Both would get
you to the magical goal of 1000 points, neither probably does a good job of
hitting your political/issue advocacy target. If we continue with the bombing
metaphor, it is generally good to know whose side is getting bombed (the 1000
point rule is curiously apathetic about this little detail). Second, unlike carpet
bombing in real life, which has an economic justification, paying for message
space using the 1000-point approach does not save anyone money (in fact, it
tends to do quite the opposite). The only useful function of the 1000-point rule we
have discovered thus far is helping so-called “experts” avoid tough conversations
regarding their reasoning behind a buy. We like to think that a better approach to
carpet-bombing is using precision strike weapons, which is what we discuss next.

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Part 2:
Next-Generation TV Targeting

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There is a better way
The good news is that we do not have to resign ourselves to blindly following the
Nielsen rating system with its focus on excessively broad consumer categories
that are of little use in political and advocacy contexts. We can now make our
own custom ratings for the political target audiences critical to winning
campaigns and issue advocacy efforts.
The key to doing this is the little glowing box sitting beneath millions of
Americans’ televisions. It turns out it knows a lot about what people are watching
and shares its secrets (with 100% accuracy) to the cable or satellite company
that charges for its use. This information allows television companies to
understand what people are watching:
Before you get too worried about your privacy, no, cable/satellite companies will
not share what you, or John Smith for that matter, is watching. The moment they
do, they know privacy hawks will notice, and Congress will come down on them
with crushing regulation.
The key to harnessing this data is something that, to be frank, the Democrats
have already figured out. Dan Wagner and Carol Davidsen, working for the
Obama campaign, realized that they did not really care about what any individual
voter was watching, but rather, about what groups of voters were watching. By
requesting the viewership profile of whole groups of potential voters, the
cable/satellite companies are able to provide this information in a de-identified
format, which still protects the individual privacy of viewers, but allows for a much
more specific profile of what key target groups are watching.

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Our approach
In this new approach to targeting, we begin by selecting a group of voters whose
characteristics make them of interest to us. For example, if a campaign was
interested in pushing back on war-on-women messaging, we might go to the
voter file and take all of the registered, unmarried women, age 18 to 28, who
have moderate-to-high election turnout probabilities, and whose affinity scores do
not put them in the strong Democrat category. This segment of the voter file now
becomes a target demographic file. In the diagram below, you can see three
such target demographic files for three different groups (the above-mentioned
women, social conservatives, and undecided voters).
The next step is to take these demographic files and match them through a third
party vendor to the set-top-box data that the cable companies have, as shown on
the top of the following page:

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What results from this process is large sets of output data for each submitted
demographic file. These output files can be very large (e.g., 30 GBs for a single
day of data), and very unwieldy for typical processing due to their massive size,
temporal granularity, and extraneous meta-data. The important thing though, is
that inside these output files is the de-identified, second-by-second viewing
history for individuals who matched your target demographic.
As an example, if we pass in a group of 100,000 targets, and obtain 10,000
matches, we are left with what is equivalent to a very high sample poll of this
group’s viewing habits across the channel lineup. The best part of this approach
is that it does not rely on self-report data, which makes it superior to Nielsen
diary ratings. Self-report data is notorious for bias (e.g., social desirability) and
inconsistency (recall error). A male, blue-collar, social conservative may be
hesitant to admit that he is a huge fan of the America’s Next Top Model, but set-
top-box data does not lie.

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Group-specific viewership profiles
After reducing and performing several manipulations on the raw data, the result
is a custom rating for each of the audiences your campaign actually cares about,
for every TV program across the channel lineup. Nielsen points have now
officially become irrelevant. Who needs to know how well a program does on
“Adults 18-49,” when you can know how many of your high turnout war-on-
women audience will be watching? This approach leads to actionable insights
that directly inform when and on what channels to place TV ads in such a way as
to maximize the ad’s exposure to its intended targets. Now you can figure out
exactly how much of your target audience will be watching a given channel at
every hour of the day for every day of the week.

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Taking the next step: 0ptimizing
If your organization has decided to jettison the traditional rating scheme (Nielsen
points), you have done something quasi-revolutionary. The next step, however, is
all about adding value to this new approach. While the set-top-box data lets us
know which shows to target in order to reach our intended target audience, it is
most effective when coupled with ad pricing data.
Returning to our example of targeting the “war-on-women” segment of the
electorate, let’s say you collected pricing information from all the channels in a
market where your campaign is interested in placing ads. The campaign is all set
to communicate with the target audience to tell them just how pro-women the
candidate is. By combining target audience viewership with pricing data, we can
now look at views like this:
With this view comes the realization that some shows are much more efficient at
delivering your message than others. For example, you realize that trying to
deliver through Channel 8’s News at 6AM is going to cost you 7.74 cents per
delivery whereas buying in the next time block will mean you only pay 3.72 cents
per delivery (a 51% efficiency gain). You begin to realize that with this new
targeting information, you can beat the market because while the world is still
thinking in Nielsen terms, you can think in terms of the audience that matters to
you, and game the market to your advantage.
What if you could implement this approach across all of your TV buys? What if
you could buy your audience when and where it was cheapest to get to them? In
other words, not only could you target better, but also you could save money
doing it, and re-invest that money into more TV impressions, or whatever else
your campaign needed to do? You can.

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Part 3:
Example: Houston Buy

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A competition of sorts
About a year ago, a major TV buyer sat through the live presentation version of
this paper, and patiently waited until the end, taking it all in. At the end of the
presentation, he said, in a grandfatherly voice, “Guys, I get it, cool fancy
spreadsheets and doodads that let you do some pretty nerdy stuff, but I have
been buying TV longer than you guys have been alive, you don’t really think you
guys can beat me do you?” Our answer to his challenge was brutally honest –
“Yes… and we can prove it.” His grandfatherly demeanor became slightly less
grandfatherly with his follow-up question—“How the heck do you plan to do that?”
We were talking with this particular TV buyer about an upcoming primary race in
Texas, so we asked him to provide us with pricing for the channels and shows in
Houston and his most recent primary election buy pattern. He agreed to go head-
to-head with us, but only under certain conditions. He asked that we only
consider the four major broadcast channels and Fox News, since that was “all
[he] could ever see buying for the primary electorate.” Further, he asked us to
plan for a week’s budget of $225,000, since that is what his books would show
he spent for his last Houston primary buy. We agreed and the results of our little
contest are what follows.
Upon examining his books, we quickly discovered the buy pattern: local news
and the prime-time block for Fox News. No knock on him—this is a conventional
Republican primary buying rule of thumb. We decided to target differently. We
decided to target based on our set-top-box data in combination with pricing data
the buyer had given us. When we compared the traditional buy pattern to our
own buy pattern, we quickly realized that we called for many more spots given a
similar overall budget.

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Not even close
However, when it came down to comparing the overall effectiveness of our buy
compared to the traditional approach, the results were unequivocal. We almost
doubled the number of impressions achieved against the target audience (in this
case, proven primary voters), which had the effect of nearly halving the cost-per-
impression.
When we showed the buyer these results, he asked how this could this be
possible. He simply did not believe it. We showed him these three graphs:

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These three graphs synchronize three pieces of data by half hour time block on
the channel KHOU in Houston. The first piece of data (top graph) is simply the
pricing information the buyer gave us. The second piece of data (middle graph)
was the number of targets (primary voter households) projected to be watching
that channel at any time during the day. Lastly, we derived the third chart from
the first two by taking the cost for a time slot and dividing the cost by the number
of targets to yield our cost per impression line. Using this approach, we
effectively built “cheat” codes for beating the market.
We then overlaid the traditional buyer’s buy pattern, and our buy pattern, the
buyer looked on, frankly mystified. The campaign manager sitting beside him got
it. We knew what the buy efficiency was at every channel at every time of day for
our target audience, and could therefore beat anyone relying on the Nielsen
ratings (or similar non-voter file matched) buy scheme. Our approach literally
highlights when and where it is most cost-effective to reach the intended target
audience.

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Takeaways
• The age of Nielsen rating supremacy is over. We can now establish
custom ratings for campaigns and issue advocacy efforts based on
segments of the electorate you deem most important.
• Building custom ratings allows precision targeting of messages to the
intended audiences and can minimize exposure to non-intended
audiences.
• Using these custom ratings, you can now think in terms of contacts on
target (instead of ambiguous points), just like you do for internet, mail,
phones, and door knocks.
• Using custom ratings, you can beat the TV market, in our experience over
multiple statewide campaigns, by anywhere from 20-50%. For example,
on a $1M TV budget, you can expect to be able to buy 20-50% more
impressions with the same dollar if you target this way.
• Campaigns can design niche ads and deliver them to highly specific
audiences. This means young women can get your war on women ad, low
turnout republicans can get your charismatic turnout ad, and old Regan
Democrats can get your plans to keep the military strong and restore
integrity at the VA.