What about TV Viewability? An Interview with TVision’s Luke McGuinness

Viewability is a challenge for many forms of visual media. How do we really know if a piece of content is seen? “IPG recognized that the industry had never effectively measured TV viewability,” explained Luke McGuinness, President of TVision. “Ads are bought and sold on the assumption of 100% viewability - but that assumption is false.” He should know since his company tracks a range of television consumption behavior.

IPG MAGNA recently released a TV Viewability study using TVision data. The study noted that regardless of device, viewability indicates whether an ad has the opportunity to be seen but it does not guarantee that a viewer has actually seen the ad or whether that ad is effective. The difference between digital and TV viewability is that with digital, the consumer is present, but not all served ads appear on the screen. With TV all ads appear on the screen, but the consumer may or may not be present.

In this interview, McGuinness discusses the study, its genus and its future.

Charlene Weisler: What prompted the study?

Luke McGuinness: More than $59 billion will be spent on TV ads in 2019, without knowing if the ads are viewable. IPG sought to quantify TV viewability so that TV advertising could be measured in a manner similar to digital. 

Weisler: What are its implications?

McGuinness: The study from IPG Media Labs, using TVision data, found that 29% of all TV ads are not viewable. That means they air to an empty room. When we think about the $59 billion (or more depending on the source), ads that air to an empty room are costing advertisers quite a large sum. By evaluating TV for viewability, advertisers can determine what is working, what is not, and optimize for better performance, and therefore substantially reduce ad waste.

Weisler: Please give me an overview of the methodology.

McGuinness: IPG reviewed 6 months of TVision data, tracking 5,388 individuals in a nationally representative panel, tracking 39,464 hours of ads for households, 2,992,414 unique ads, 5,961,757 impressions, persons 2+ and C3. Programming and ads were captured via ACR (automatic content recognition). Participants opted to install TV viewability detection technology in their household. Viewability and attention were measured by using computer vision algorithms.

Weisler: Would you say that the results show that TV and digital are comparable in delivering ad messaging? What are the differences that you found?

McGuinness: The fact that the size of the Viewability issue for TV very closely mirrors digital video shows that the two face similar (29% for TV; 31% for digital video) challenges in delivering ad messaging, but there are some natural differences in viewability for TV and digital. And these differences are rooted in the fact that digital video on PC and mobile are inherently different experiences. With digital, it is presumed that the consumer is there because of the nature of the medium. Someone just clicked to watch a video. As we all know, TV is very different. People leave the room or even leave the home with the TV on. 

Advertisers measure digital viewability as they do not want to invest in ads that do not have the opportunity to be seen. The way we measure viewability for TV delivers the same value proposition - investing in an ad strategy that will deliver an opportunity for people to see the ads. The methodology is different, given that are different mediums, but the value proposition is consistent. 

Weisler: What is your recommended course of action to improve ad delivery and consumption?

McGuinness: TV’s viewability challenge lacks uniformity. It is not limited to specific networks, times of day, programs or content types. While viewability varies across industries, no advertiser is immune. Every brand can improve their return on ad spend with this data. The best step forward for brands is to measure what’s working or not for their historical TV advertising, benchmark versus competitors’ performance, plan a more effective strategy along with their existing planning tools, and measure and optimize on an ongoing basis. By using TVision viewability and attention data, combined with other data such as cost and ratings data, brands can identify higher performing opportunities. For example, the study suggests that brands can find value by buying ad spots outside of prime, and outside of the first spot in the pod.

Weisler: What about pod position?

McGuinness: In general, the first position in an ad pod may not be worth a premium. Ads appearing first in a pod had 72.2% viewability. Ads in the middle of the pod had 70.3% and those at the end of the pod had 69.9%. Longer ads have higher viewability but doubling the length of an ad does not double the viewability, so the cost of longer ads must be considered.

Weisler: What are next steps?

McGuinness: The immediate next steps are for advertisers and networks to incorporate viewability into their ad buying and selling - and many have already started to do so. They can do this by analyzing their historical performance for viewability, analyzing their competitors’ performance, and learning from that.  Additionally they’re leveraging broader planning data from TVision to best optimize their campaigns and they are now measuring their specific performance on an ongoing basis.

This article first appeared in www.Mediapost.com

Pod Behavior - And How to Modify It

This is an article I wrote for Mediapost in November 2007 which is still very timely today. The informationn contained in this article will form a good backdrop to a future post - an interview with Daniel Fischer. Stay tuned....

Pod Behavior - And How to Modify It
by Charlene Weisler


AS THE INDUSTRY FOCUSES ON commercial ratings, my company has embarked on a series of research initiatives that shed light on viewer behaviors in and out of pods. Some of our preliminary conclusions are eye-opening and challenge commonly held industry beliefs.

While "C3" " (average commercial with three days of playback) is on the top of everyone's mind, "C3" is fast becoming irrelevant. This is really just an interim step to exact minute. To that end, we have begun to explore behavior within the commercial pods on a minute-by-minute basis to see if there are any patterns that can be tracked and strategically exploited.

Questions asked: Is there a pattern to pod performance? And if so, is it standardized and therefore modifiable? What can be done to mitigate pod erosion and speed rating recovery through the pod?

Using Nielsen N-Power total day, minute by minute demographic ratings, we examined ten networks - five with a target demo of A25-54 and five targeting W25-54. The time frame was four months of the past year - one month of each quarter to take into account any seasonality. Each minute was tagged as majority program, promo or commercial. Pod placement was added. We then compared the last majority program minute to the individual commercial pod minutes through the pod until the first few back-to-program minutes.

Here is what we have found:
• All networks that we examined lost audience going into the pod -- some to a greater extent.
• Generally speaking, commercial pod performance across all these networks have a similar pattern -- an initial deep drop in the first minute (A), another drop in the second minute (B), then a leveling off in performance until a gradual recovery (often, but not always) back to pre-break levels.
• The leveling off in erosion from the "C" position to the end of the pod was notable and might indicate a level of commitment to the program tantamount to affection or engagement. It also says that if a network can hold a viewer in the first two minutes of the pod, it will substantially improve the rest of the pod performance.
• The frequency of the breaks in combination with the lengths of the breaks impacts the level and degree of ratings erosion. In the networks that we examined, we found that longer duration/fewer interruptions generally hold audiences better than shorter/more frequent breaks. Younger-skewing networks may have different patterns of viewing.
• There is no difference in the rating of a promo in the "A" position and an ad in the "A" position. But this does not mean that we advocate replacing promos in A with ads. We are currently experimenting with promo creative that can help improve "A" position performance and thus improve the performance of the entire pod.

Next steps: . Among the many ideas to deal with pod performance include focusing on hard starts and modifying our promo creative based on findings in brain behavior research. It is clear from this preliminary study that we need to concentrate on improving the performance of the "A" position as much as possible because the lower the loss into A, into B, the less the overall loss for the entire pod.
Stay tuned....