The narrative that TV is dead couldn't be further from the truth. TV still reaches more than 95 percent of the U.S. population, Nielsen reports. In fact, the U.S. is experiencing a new golden age of television, with more money being spent on programming, more content providers distributing content, and consumers having more ways to watch than ever before.
What's more, according to Pew Research, 76 percent of suburbanites, 73 percent of urban‑dwellers, and 63 percent of those in rural areas are home broadband users — and smartphone penetration isn't much different. This means a significant portion of the population can't access digital content, including digital advertising. TV is still a dominant medium in the U.S.
However, what's good for reaching consumers can seem awfully intimidating for advertisers. With the explosion of consumer choices across all viewing devices, marketers face more complexity in TV advertising than ever before. A wait-and-see approach can be tempting, but smart marketers are resisting that notion by navigating the massive changes currently hitting the TV ecosystem. The masters among them are using audience data — not just context or indices — to improve everything from targeting to campaign measurement.
Here's how those new masters of TV advertising continue to hit their objectives while developing their skills to meet the needs of an evolving market and grow their careers.
Adopting a Test-and-Learn Approach
Changes in viewing habits and TV audience composition are driving much of the TV industry's development. Viewing of traditional, linear TV has been declining by small percentages off of a large base. Similarly, viewing of over-the-top (OTT) and streaming services has been increasing by large percentages off of a small base. At what level they stabilize and when that occurs is subject to variables and considerations beyond the scope of this article.
What's certain is the value of audience data to target these evolving TV viewing habits. Brands that leverage first-party or third-party data to target and measure TV advertising tend to have a significant advantage over those that don't, as measured by increased reach and improved business outcomes.
So what should marketers do about this? One of the best ways to navigate this change is to do what savvy digital marketers have done for years: set aside modest test budgets, use them to try new ideas and approaches, and learn from those tests quickly, all without putting the brands' or businesses' profit margins at risk. This approach also enables those who don't yet use data to optimize their TV strategies to start the learning process. Even through a relatively small campaign, advertisers can figure out strategies that will improve their advertising for years to come.
Why Data Matters
According to a recent IAB report, brands expect to increase the amount spent on data and data-related services in 2019. In the report, 69 percent of survey respondents said they had increased the amount spent on audience data and related solutions in 2018, compared with 2017. Some 78 percent expect to invest more in 2019.
Cutting edge practitioners of TV advertising are using data in ways that were hard to imagine 10 years ago. It's giving them an edge over those who continue to operate in the absence of data. Data allows TV advertisers to reach a higher percentage of their target audience or prospects and reduce customer acquisition costs; forecast days, weeks, and even months in advance which programs, networks, and dayparts will reach their audiences best; and reduce the waste that goes into reaching viewers who don't fit the target audience.
Data also gives TV advertisers an advantage by allowing them to deploy their first-party data or use it in combination with attributes from third-party providers to build a custom target audience of likely consumers, and to show which of their spots produce the most sales, website visits, and app downloads, among other insights. Combined, those advantages help advertisers optimize their audience segmentation and media delivery for future campaigns on both premium OTT and national linear TV inventory.
Take, as an example of a test-and-learn approach and the benefits of using data in a fast-changing market, the case of a home décor brand that wanted to drive downloads of its app, which it uses to power its e‑commerce business. Having not used TV much before, the brand devised a test campaign.
Based on its existing customers, the brand targeted women ages 25 to 54. Using advanced TV software, the brand was able to forecast which days, dayparts, networks, and programs were most likely to produce the most app downloads. Notably, with just $50,000 in media spend, the brand secured 112 spots spread over a one-week period on 12 national networks. The brand used just one 30-second creative piece. The campaign delivered 16.5 million impressions and reached 10.6 million people in the target audience, for an average frequency of 1.55. The brand used a pixel to track key conversion events and link them to ad viewing.
The insights the brand got for this investment were extraordinary. Using the pixel, post-logs, and some data science, the brand's marketing team developed a point of view on both short- and long-term attribution. Specifically, they arrived at a rule of thumb to estimate the total sales lift associated with their TV advertising: they could take the total amount of sales attributable to the first five minutes after an ad ran and multiply it by 2.5. (This factor is unique to every advertiser and depends on a variety of inputs, so it's critical that every brand determine it for itself.)
Armed with this information, the brand decided to optimize in a second campaign. This time around, the brand team created a plan that cost $80,000 and, learning from the first campaign, they shifted more budget to high-performing spots. The second campaign included 102 spots and 18 networks. This time, the campaign went with all 15-second spots. The media ran over one weekend, delivering 47.9 million impressions, reaching 23.1 million of the target audience, and realizing an average frequency of 2.07.
As a result of these tests, the brand improved its efficiency and lowered its cost of sales, making it much easier to run bigger, high-yielding TV advertising in the future. Using advanced TV data about the programming its audience will watch in the future, and linking ad viewing to download and purchase, the brand team was also able to make its second campaign revenue positive overall.
Where to Begin
Fortunately, nearly every brand can learn from the example noted above. Here are the elements TV advertisers should include in their planning when they test data-targeted TV:
- Define the conversion events. For example, a campaign can have a goal of increasing website visits, phone inquiries, purchases, and more. Defining this upfront can make campaign optimization easier to do.
- Pixel accordingly. Some first-time TV advertisers don't place a pixel in all the places customers may go after seeing their ads, resulting in an incomplete view of performance.
- Measure TV's short- and long-term effects. Develop a perspective and a plan, backed by observable data, to capture both the short- and long-term effects attributable to TV. TV is known for long-term brand building, but it can also drive more immediate results.
- Choose a "Goldilocks" approach to targeting. Target an audience specifically enough to be able to value some programs over others, but not so narrowly that the CPMs get too high.
- Get access to the best TV audience forecasting data possible. TV inventory always sells out in days, if not weeks or even months, before shows and ads air. In order to maximize exposure with any given audience, advertisers need to be able to reliably forecast what their audiences will watch in the future.
- Develop the right buying strategy. Is it dispersed, a pattern buy, or a more concentrated burst?
- Ensure the ability to buy broadly. Unlike search and social, a TV campaign will buy from several networks. Advertisers must ensure that they have access to as many of those networks as possible. This makes it much easier to activate based on the forecasting they do.
- Don't just buy cheaper spots, because they don't necessarily result in better KPIs. Sometimes context, including time of day and the nature of the show itself, may affect results.
- Measure results selectively. Measure those who are unexposed to any other marketing to isolate TV's unique impact, as well as those who are exposed to multiple channels, to get a read on the effects TV and other channels have on one another. In this way, marketers can determine their optimal media mix.
- Monitor and adjust the spend in other channels during a TV test, especially branded search. This will improve how advertisers measure their TV test and help them avoid spending more than is needed.
- Run a mix of 30- and 15-second spots. Thirty-second ads may result in a superior conversion rate, but 15-second ads are so much cheaper — as much as half the cost — that it can more than compensate for declines in the conversion rate.
The proof is in the pudding: there's no better time than the present to prepare for the future. By launching an experiment with an explicit purpose, brand advertisers can test on TV at low budgets and still produce meaningful results, giving them a solid base on which to build their TV strategies, and the experience to navigate in an uncertain and rapidly evolving ecosystem.
Matt Collins is the SVP of marketing at Simulmedia, a partner in the ANA Thought Leadership Program.