consumer behavior

Tune-In to Discover What is Making Audiences Tune-Out

Tune-In to Discover What is Making Audiences Tune-Out

Travis Flood – Executive Director of Insights, Comcast Advertising

Duane Varan, Ph.D. – CEO, MediaScience

Travis Flood (Comcast Advertising) and Duane Varan (MediaScience) presented research, which explored improving ad pod architecture, aimed at better engaging audiences by understanding what makes them tune-out. To provide framework to their research process, Travis indicated they started with a literature review, to understand the existing viewer experience. Focus was placed on the quantity, quality and relevance of the ads, in addition to media effectiveness studies (e.g., pod architecture, ad creative, getting the right viewers, etc.). Duane indicated that the literature review unveiled gaps, particularly in the examination of the content within the middle section of an ad pod. Based on this, the goal of the subsequent research was to understand the optimal duration of ad pods to optimize both the viewer experience and brand impact, difference in impact (e.g., more ads vs. fewer ads in the same break duration) and the impact of frequency on viewers and brands. The research included 840 participants who watched a 30-minute program with structured ad breaks. Feedback was measured using a post-exposure survey, neurometrics and facial coding. Results revealed that shorter pod length, grouping consistency in ad length and capping frequency at two to three ads per program as most effective. Key takeaways:
  • Optimal pod length: Two minutes or less leads to better results. After viewing 2 minutes of ads, recall begins to decrease. Recall is 2x higher at 2 minutes vs. 3 minutes, and after 3 minutes, recall is at its lowest point.
  • Viewers are more engaged as ads begin. Using facial coding data showed that for a heavy clutter cell, there was marginally less joy in the first 5 seconds of the ad, indicating that ad load impacts how viewers experience ads.
  • Facial coding data revealed that ad clutter can diminish how funny scenes are for viewers.
  • Consistency is key in ad lengths within a pod. Viewer testing showed that when ads had different lengths in a pod, it made the ad break feel longer compared to pods with ads of the same length.
  • Ad frequency was optimized at two per program. There was significant boost in ad recognition and purchase intent going from 1 to 2 exposures in a program. Capping frequency at 2-3 per program can positively impact recognition and purchase intent.

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Big Data helps solve for Fragmented TV Viewing

Big Data helps solve for Fragmented TV Viewing

James Alexander – Product Director – Measurement, Inscape

Rich Guinness – Associate Director, Data Licensing, Inscape

James Alexander and Rich Guinness at Inscape utilized big data to uncover current trends in TV viewing. Streaming continues to cut into linear. Ad supported platforms are growing more popular. People’s thirst for new subscriptions has plateaued. Streaming viewers churn constantly, following the content they want to watch from one platform to another. Bingeing occurs on both linear and streaming, but those who use both binge the most. In today’s environment, new streaming apps grow quickly in both viewership and minutes viewed. Even though they are still a small slice of the pie, FAST apps continue to grow at a rapid pace. Key takeaways:
  • Over the last three years, there’s been a 10% increase in those whose only TV experience is streaming.
  • In Q4 2023, 6.5% of Vizio monitored TV viewers no longer watched cable or satellite, up from 4% in Q4 2022. That number is still growing.
  • The average number of native apps or all apps viewed on CTV (including YouTube) has plateaued at 5.5. This number is not increasing, which is due to churn.
  • Bingeing occurs evenly between linear and streaming, but those who have both are doing it the most. This is likely due to when people miss a few episodes of a favorite show on linear, then hop onto the streaming app to catch up.
  • FAST apps, the “new cable,” are growing rapidly. Fifteen percent of first app opens are a FAST app, and 72% of these viewers are not watching on an MVPD.
  • In their case study, 87% of SVOD users also watch a FAST app, which suggests that viewers are willing to pay top dollar for the right content—a finding that bucks current thinking.

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How Co-viewing and Other Factors Impact Viewer Attention to CTV

How Co-viewing and Other Factors Impact Viewer Attention to CTV

Monica Longoria – Head of Marketing Insights, LG Ad Solutions

Tristan Webster – Chief Product Officer, TVision

The research presented included an online survey of over 1,000 respondents incorporated with TVision’s 5,000+ U.S. home panel data. Questions asked: 1. Does CTV garner more attention? 2. Are consumers more likely to co-view CTV? 3. Does co-viewing negatively affect attention? TVision’s equipment includes their always-on panel, a webcam that can capture how many people are in the room and eyes on screen at a second by second, a router meter to understand which CTV device is on and detects apps. TVision measurement engine includes remote device management and ACR engine. Findings:
  1. CTV in general has 13% higher attention index. Attention increases due to purposeful watching. Co-viewing CTV has stronger impact in comparison to linear (75% higher).
  2. Streaming is a popular co-viewing experience with mostly a non-negative impact to attention. Households with kids are more likely to pay attention to streaming content and ads with 36% more likely to discuss what is seen on TV. There are three different types of co-viewing: family setup with different age group (increased attention depends on genre), adults only setup with similar gender and age (biggest impact on attention), mixed adults only setup.
  3. Streaming is gaining ground as a co-viewing method for watching sports. Watching sports is typically with other people.
Implications for brands and marketers:
  1. CTV offers opportunity to create more engaging ads with higher levels of attention. CTV has digital capabilities that garner more attention. There is a need to create ads that are specific for CTV (in contrast to linear).
  2. Co-viewing can be an opportunity to turn your brand into a discussion.
  3. Measurement providers give us new insights into viewer behavior.
Key takeaways:
  • There is a higher attention with CTV in comparison to linear.
  • Positive impact of co-viewing: Co-viewing on streaming platforms is popular and generally maintains or increases attention.
  • Streaming is increasingly preferred for watching sports in a co-viewing context, offering new opportunities for targeted advertising and engagement in sports content.
  • Implications for brands and advertisers: The engaging nature of CTV offers ample opportunities for more impactful ads. Co-viewing experiences can transform ads into discussion points among viewers, enhancing brand engagement.

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The Impact of Co-Viewing on Attention to Video Advertising

The Impact of Co-Viewing on Attention to Video Advertising

Duane Varan, Ph.D. – CEO, MediaScience

Impressions are measured everywhere, however, not all impressions are equal, and as such, we need to think about how to appropriately weigh them. The problem with CTV is that there is more than one viewer, and the device itself doesn’t tell you this. The question, then, is how do we account for these added impressions. From a value point of view, we need to understand what is the value of these additional viewers. There was a meta-analysis of MediaScience studies (n=11) on co-viewing. This is not conclusive but rather exploratory because these studies were commissioned by clients. These are premium publishers and not all TV is at that level of quality. The conceptual model of co-viewing: device level exposure data à add additional co-viewers à estimated additional co-viewers. How do we know that these additional co-viewers have the same values? We need to factor for what could be a diminished add impact. To do this: we need to adjust audience (factoring for diminished ad impact) à adjusted additional co-viewers (by impact). Results:
  1. Attention and memory effects are the two areas that matter the most when addressing co-viewing. The attention sphere is a small effect, and there is not a lot of variability with that effect. The real story is in memory—if you’re talking to someone it is difficult to process the ad. Memory retrieval when co-viewing decreases by 15-52% depending on the content.
  2. Co-viewing composition effect: Mixed gender viewing has a more detrimental effect than same sex viewing (decrease by 27%).
  3. Age effects: There are big differences by age but not a lot of difference in terms of the decline that is associated with co-viewing by age.
  4. Program effects: Majority of variability is in the program effects—between 22% and 58%. The co-viewing problem cannot be solved by industry averaging, but we would need program-level measurement. For instance, effect is worse with sitcoms than it is with sports. One of the theories is that in sports, a lot of human interaction happens at the moment, whereas in comedy this is saved for the ad break.
  5. Number of co-viewers effects: What happens when you increase the number of people in the room? In the studies, the maximum co-viewing is two. Looking at TVision data, they saw that for three or more viewers and above that impacts level of visual attention—from 3% drop with two viewers, to 18% drop with three viewers and 23% drop with four viewers or more. However, this is not significant because 97% of TV viewing occurs with one or two viewers, and only 3% of TV viewing is with three or more viewers (TVision data).
  6. Implications in terms of value proposition—the worst-case scenario is a detrimental effect of 58%. The net effect of co-viewers is negative 40. Average scenario— detrimental effect of 15%; net of 140 viewers in value.
Future research will focus on second screen device usage. Hypothesis is that the scale of this problem is bigger than the scale of co-viewing. Key takeaways:
  • Focus on co-viewing to understand the value of additional viewers.
  • Effect is seen in memory domain rather than attention domain.
  • Issue of variability by program means that the equation will differ between programs.

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How We Watch: Examining the Shifting Trends in TV Habits

How We Watch: Examining the Shifting Trends in TV Habits

Mike Brooks – Global Head of Business Development and Partnerships, LG Ad Solutions

Mike Brooks of LG Ad Solutions described the current rebalancing among CTV users leaving subscription services to embrace ad supported streaming platforms. The trend continues at a brisk pace which spells good news for advertisers. CTV offers many opportunities and as ad supported grows, more viewers suddenly become reachable. People take a significant amount of time to select what they want to watch on CTV, LG’s survey found, and are equally driven to content from their TV’s home screen as from the home screen of their favorite streaming app. This creates an opportunity to help people find content. Most viewers are also doing something on their personal device while watching, which offers shoppable TV opportunities as well as the ability to connect one’s digital and TV brands in dynamic ways. Key takeaways:
  • LG found that 93% of respondents interact with a CTV, and 80% are using some form of ad supported TV. Of them, two-thirds (63%) prefer the ad supported to the subscription model.
  • Subscription cycling is the norm with 59% of respondents saying that they are willing to cancel a subscription-based platform after finishing the content that got them to sign up.
  • The shift from SVOD to AVOD is predicted to continue: 29% of respondents are expected to remove a subscription CTV service from their household within the next 12 months, while 29% will add a free, ad supported CTV service in that same timeframe.
  • A lot of time is being spent on selecting what to watch, five minutes 42 seconds on average, their survey found, between when the screen is turned on and when a piece of content is selected.
  • People discover content equally between the home screen (40%) and the homepage of a specific app (40%).
  • LG also found that 96% are media multi-tasking while they watch TV, usually with a mobile device or laptop. Of these, 48% are engaging with social media, 46% are gaming and 42% are shopping.
  • Shoppable TV is the future: 53% of respondents said they wished all TV ads had a quick option to buy the product, 51% said they wished they could shop using their CTV and 63% said they wished they could see their local store’s inventory on their TV. Twenty-nine percent had even purchased something through their TV before.
  • Of likely voters, 65% prefer streaming to linear TV, and 82% of those streaming with ads are open to political ads outside of political content.

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Having In-Depth Research on Consumers’ Values Yields Tremendous Benefits

Having In-Depth Research on Consumers’ Values Yields Tremendous Benefits

  • ARF Knowledge at Hand, CMO Brief

In decades past, demographic characteristics were considered the strongest predictors of consumers’ values, attitudes and purchasing behavior. Over time, however, they have grown to become weak predictors and correlates. In this Knowledge at Hand report, Dr. Horst Stipp, EVP at the ARF, summarizes the latest and most impactful research to date on consumer values and how researching them carefully can help shape effective business strategies and impactful ad messages.

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Top Topics at AxS 2024

  • Posted: March 15, 2024

Our analysis of the presentations during this year’s AUDIENCExSCIENCE conference shows which issues are marketers’ current priorities. 

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Making Prosocial/Cause-Related Ads More Effective

  • Posted: March 15, 2024

Last year, we reported on an analysis which found that many advertisements with prosocial and cause-related messages are not as effective as surveys on consumer attitudes suggest. This urged researchers to explore how to make such ad messages more effective. Several new studies provide insights on this issue.    Read more »

How to Use Machine Learning to Speed Up the Product Design Process

How to Use Machine Learning to Speed Up the Product Design Process

  • MSI

Aesthetic design significantly affects consumer evaluation of products. Nowhere perhaps is this truer than for the automotive category. However, in this industry, development cycles can be lengthy. As a result, mid-generation “facelifts” periodically occur to maintain appeal. However, this process can be expensive. Recent breakthroughs in machine learning may help speed up the process in an efficient and scalable manner. Not only is this option cost-effective, but it is customizable. For those who wish to infuse nature-inspired elements into an aesthetic design, deep machine learning offers many advantages.

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