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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.