CTV (connected TV)

Driving Greater Campaign Reach and Relevancy Across Formats

Driving Greater Campaign Reach and Relevancy Across Formats

Sharmilan Rayer – GM, Amazon Publisher Cloud


Sharmilan Rayer of Amazon Publisher Cloud discussed an approach to empowering addressability as legacy identifiers (cookies and mobile IDs) fade. This approach, called durable addressability, includes the sharing of first-party signals across publishers, advertisers and third parties. Its three pillars are first-party signal investment, secure signal collaboration and machine learning (ML) powered modeling. The Amazon Marketing Cloud is their new advertiser clean room which takes this approach. It allows advertisers to combine their first-party signals with Amazon’s publisher ones and any third-party’s in a privacy compliant way. Key takeaways:
  • Durable addressability starts with each member investing in first-party data from a resource, funding and technology perspective.
  • Sixty percent of advertisers report planning to leverage first-party data for ad placements, and 47% of publishers say their first-party data is the answer to cookie deprecation.
  • The first-party data advertisers would bring to this strategy includes customer engagement, conversions and proprietary audiences.
  • Amazon has access to publisher first-party data across CTV, web, mobile and audio. Having access to this first-party data allows for determining which ad opportunities are best for a particular campaign.
  • As cookies deprecate, clean rooms will begin playing a more important role, according to Amazon.
  • Modeling by machine learning has increased reach 20-30% on unaddressable supply, Amazon claims.
  • A new product called Performance Plus combines Amazon Ads signals, advertiser conversion signals and machine learning to generate predictive segments. It has been observed boosting conversions 30-80%.

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Rough Waters? Downstream Effects from the Transition to Streaming Via Smart TVs

Rough Waters? Downstream Effects from the Transition to Streaming Via Smart TVs

David Tice – Consultant, HUB Entertainment Research

Justin Fromm – Head of Insights & Thought Leadership, Samsung Ads

Justin Fromm from Samsung Ads and David Tice of HUB Entertainment Research discussed how consumer behavior is changing due to greater Smart TV penetration and usage. Streaming has become the default method of watching TV for a large swath of viewers. Streaming audiences have also increasingly become more receptive to advertising. Another important trend, Smart TV operating systems (OEMs) are constantly upgraded and made easier to use. As a result, home screen interactions continue to grow. Home screens have played a significant role in content discovery, although TV brand is a moderating factor. Home screens have even helped accelerate the rise of FAST services. In an era of constant churn, coming up in a home screen search and having an advertising model or tier have become critical to retention. Key takeaways:
  • Two-thirds of people in all TV households use a CTV to stream content.
  • Home screen usage is up 140% due to manufacturers consistently improving the user experience and helping viewers find content.
  • Nearly two-thirds of Smart TV users spend most of their time with streaming. In 2022, 70% of all viewing minutes were streamed, and 62% of viewers spent more than half of their time with streaming content.
  • In Q4 of 2023, 64% of respondents said they would rather watch ads and save money on a subscription, up 7% from Q4 of 2022. Ad tolerance has been stable over the last three years.
  • All streaming was up 22% in 2023. AVOD was up 50%, while SVOD use was down 7%. Home screen use was up 117%, and deep link use was up 59% from 2020.
  • Content discovery occurs about 50% of the time from the TV’s home screen, and 50% from an app’s home screen. This varied substantially by brand. For instance, only 38% of TCL owners (bottom of the batch) found content from the home screen.
  • While most found shows from TV promos (61%) in the last year, 17% of respondents found their favorite new show through their TVs’ home screen.
  • Advertising to at-risk audiences on the TVs’ home screen increased retention by a factor of eight.
  • In the last two years, FAST services increased 16%, which was 6% faster than the two years prior. The number one reason people used a FAST app was, they found it on their TV’s home screen (36%).

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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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CTV Ads: Viewer Attention & Brand Metrics

CTV Ads: Viewer Attention & Brand Metrics

Rohan Castelino – CMO, IRIS.TV

Mike Treon – Programmatic Lead, PMG

Representing the Alliance for Video Level Contextual Advertising (AVCA), Rohan Castelino (IRIS.TV) and Mike Treon (PMG) examined research conducted with eye tracking and attention computing company, Tobii. The research endeavor focused on the impact of AI-enabled contextual targeting on viewer attention and brand perception in CTV. Beginning the discussion, Rohan examined challenges with CTV advertising. He noted that advances in machine learning (ML) have empowered advertisers to explore AI enabled contextual targeting, which analyzes video frame by frame, uses computer vision, natural language, understanding, sentiment analysis, etc., to create standardized contextual and brand suitability segments. Highlighting a study of participants in U.S. households, the research specifically aimed to understand if AI-enabled contextual targeting outperformed standard demo and pub-declared metadata in CTV. Additionally, they wanted to understand if brand suitability had an impact on CTV viewers’ attention and brand perception. Results from the research found that AI-enabled contextual targeting outperformed standard demo and pub-declared metadata in CTV and increased viewer engagement. In closing, Mike provided the marketers’ perspective on the use of AI-enabled contextual targeted ads and its practical applications. Key takeaways:
  • Challenges with CTV advertising: Ads can be repetitive, offensive and sometimes irrelevant, in addition to ads being placed in problematic context.
  • In addition, buyers are unsure who saw the ad or what type of content the ad appeared within. A recent study by GumGum showed that 20% of CTV ad breaks in children’s content were illegal (e.g., ads shown for alcohol and casino gambling).
  • Advertisers have begun experimentation with contextual targeting in CTV, as a path to relevance.
  • A study conducted with U.S. participants that examined the effects of watching 90 minutes of control and test advertisements, using a combination of eye tracking, microphones, interviews and surveys to gather data found that:
    • AI-enabled contextual targeting attracts and holds attention (e.g., 4x fewer ads missed, 22% more ads seen from the beginning and 15% more total ad attention).
    • AI-enabled contextual targeting drives brand metrics (e.g., 2x higher unaided recall and 4x higher aided recall).
    • AI-enabled contextual targeting increases brand interest (e.g., 42% more interested in the product, 38% gained a deeper understanding).
  • Research to understand if brand suitability had an impact on CTV viewers’ attention and brand perception found that:
    • Poor brand suitability makes CTV viewers tune out ads and reduces brand favorability (e.g., 54% were less interested in the product, 31% liked the brand less).
    • AI-enabled contextual targeted ads are as engaging as the show.

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Cross-Platform Measurement Options from an Agency Perspective

Cross-Platform Measurement Options from an Agency Perspective

Audience measurement is changing at an unprecedented rate. Concurrently, identifiers such as cookies are fading, and traditional models and incumbent suppliers are being questioned. In reaction to all these happenings, new measurement initiatives and a new Joint Industry Committee (JIC) have risen to establish a path toward a new video measurement framework. In 2023, the Online-Offline Metrics Working Group, within the ARF Cross-Platform Measurement Council, conducted anonymous, in-depth-interviews (IDIs) with eight key decision-makers from major agency holding companies. The IDIs focused on three major issues involving the metric situation confronting the advertising industry. This report summarizes the learnings from those interviews.

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Making the Right Impression

Making the Right Impression

  • INSIGHTS STUDIOS

Key Takeaways

Paul Donato – Chief Research Officer, ARF Pete Doe – Chief Research Officer, Nielsen Pete Doe brought some clarity to how Nielsen currently approaches linear TV measurement and how it will evolve throughout 2024 in this detailed presentation, describing Nielsen’s integration of big data with panel data in its national TV measurement, participation in auditing and accreditation, exploration in defining impressions and conversations with the industry about time requirements and duration weighting. Other topics discussed in the Q&A that followed covered definitions of calibration and campaign reach measurement, panel adjustments for STB/ACR data, personalization and the differentiation between 30-second ads, 15-second ads and 60-second ads in terms of equivalization and measured impressions. These are selected excerpts from the session’s presentation and Q&A:
  • While big datasets are necessary to capture the fragmentation in the market, panel measurement—with its details on the persons viewing and devices being used—is essential to create a holistic view of audiences. Nielsen’s philosophy does not prioritize one over the other; instead, each informs the other.
  • After listening to industry publishers, agencies and clients, Pete assured the audience that Nielsen will still be offering C3 and C7 metrics in addition to new offerings of individual commercial metrics as of September 24th, 2024. He outlined a three-step process in Nielsen’s overall approach to its big data solution, starting with providing one year of impact national STB (set-top box) data that will then be audited by the MRC and submitted for accreditation. Pete noted that some clients were open to using non-accredited data in the interim, with buyers and sellers agreeing to available data that enables transactions.
  • Nielsen’s currency roadmap for 2024 begins with the currently available data streams that include both panel-only C3 and big data. They are planning to extend their national big data to include Comcast’s STB data calculated from sub-minute crediting in January and fully release their new currency combination of panel and big data, produced to C3 and C7 standards, in September, subject to auditing and accreditation processes. Pete also provided details on Nielsen’s approach to local TV measurement by introducing a calibration methodology, along with top line national demo findings in age groups and increases in Hispanic and Black audiences from Q1 2023.
  • Pete addressed the importance of having a consistent definition of an impression and how Nielsen worked to achieve more granularity in measurement with the sub-minute level of data. Referencing the MRC’s cross-media measurement standard and the continued debate around time requirements (at least two consecutive seconds) and duration weighting, he said Nielsen found no complete consensus from different sides of the industry, although there seems to be more support for two continuous seconds without duration weighting. Nielsen’s exploration in defining impressions assumed that equivalization as a kind of duration weighting will be assessed as deals are made.
  • Nielsen compared the impact of 1s, 2s and 5s using their sub-minute panel plus big data measurement against panel data and the average commercial minute, and, when adding duration weighting, found significant differences in impressions across varying age groups, households and day parts.
  • In terms of deals, one of the benefits of moving from the average commercial minute in a program to individual commercial metrics is the ability to look at the position in the commercial pod. In an example from a daytime broadcast show, Pete illustrated how first-in-pod ads typically deliver a higher audience than the rest of the ads in the pod, finding 99 percent of ads in the first pod indexed higher with 18 percent higher impressions than the average across 160 placements.
  • Nielsen’s national measurement’s “big data” encompass 30-35 million homes including Comcast, DirecTV and DISH return-part-data (RPD) from STBs. Smart TV ACR data from Roku and Vizio adds to the 30-35 million total with some overlap. In local markets, Nielsen does not currently use smart TV data as local stations are not all measured or supplied in its numbers so they focus instead on RPD augmented with Charter data. Because of its deals with DirecTV and DISH, Nielsen has a presence in every market.
  • Nielsen has streaming meters in about 50% of the homes in its national panel currently and is focusing on building those numbers. It also has local CTV measurement capability.
  Nielsen’s key takeaways:
  • Panel+big data means higher audiences, better stability, fewer zero ratings.
  • Overall patterns of viewing are pretty consistent between panel and panel+big data.
  • Two-second qualifier increases available impressions, while duration weighting deflates them.
  • Individual commercial minute data enables pod position considerations in deals.
 

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