Marketers’ Top Measurement Concerns
CIMM: the Coalition for Innovative Media Measurement, has identified critical issues that should be addressed this year. Read more »
CIMM: the Coalition for Innovative Media Measurement, has identified critical issues that should be addressed this year. Read more »
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:Rebecca Fine – Associate Director, Marketing Insights, Samba TV
Cole Strain – VP, Head of R&D, Samba TV
An examination of the top H1 2023 streaming shows for household viewership found that original streaming content captures audiences, according to Rebecca Fine and Cole Strain of Samba TV. Bingeing behavior is a significant household viewership change as viewers have shifted from linear to streaming TV consumption. When given the choice, viewers prefer to binge. Seventy-two percent of U.S. adults identify themselves as binge watchers. A consistent trend across viewership for streaming program premieres was that 44% of viewers watched the program in the first four days, and 78% watched the program by day 15. Bingeable shows are more likely to be completed and have higher average retention between season premiere and finale. Forty-five percent of households will finish the whole season when the season drops in bulk, but retention drops to 35% when the show is released weekly. Netflix’s propensity for the bulk release model means that more of its shows get fully consumed. Rebecca and Cole analyzed the top 50 streaming programs and found that most platforms have a high percentage of households who only watch only one show. However, 73% of viewers watch more than one top show on Netflix. Key takeaways:Nicole Lawless DesJardins, Ph.D. – Sr. Director Data Science, iSpot.tv
Leslie Wood, Ph.D. – CRO, iSpot.tv
Leslie Wood and Nicole Lawless DesJardins, both of iSpot.tv, provided an overview of advertising trends in linear and streaming over the last two years in terms of ad creative, campaign and measurement. They defined linear as any content purchased on a national schedule and noted that variations exist across industries. Among their findings: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:James Ambalathukal – Director, Strategy & Insights, Magid
Mike Bloxham – EVP, Global Media & Entertainment, Magid
Mike Bloxham and James Ambalathukal of Magid partnered with twelve networks and streaming services in a study to identify factors of cultural authenticity in drama, comedy and unscripted programs. With research into the creative elements that resonate with diverse populations from qualitative studies and online surveys, Mike and James described the importance of authenticity in how audiences relate to emotional content, how they see themselves in the content and ultimately, how they perceive the content itself. The various levels of signals that diverse audiences assess as good or bad representation include storytelling components and physical production elements, which help separate out what drives positive and negative perceptions of these shows for actionable results. Key takeaways:Spencer Lambert – VP, Product & Partnership Success, datafuelX
Matthew Weinman – Sr. Director, Advanced Advertising Product Management, TelevisaUnivision
Reach and frequency planning requires access to unique viewership data, which has become increasingly restricted due to identity restrictions. However, challenges exist with panel-only measurement, including the undercounting of Hispanic and Spanish language coverage, stated Matthew Weinman (TelevisaUnivision). Panel data undercounts Hispanics audiences by upwards of 20%, even for broad demographics. The benefits of big data exist across audience planning, viewership measurement and outcomes. Excessive frequency can be limited while maintaining or expanding reach, as well as improving ROAS. However, there are barriers to working with big data, including PII compliance. Additionally, the size and scale of big data leads to lengthy ID forecast times and computing costs. Spencer Lambert (datafuelX) presented details of their approach to ID-level forecasting which included their reach and frequency clustering methodology. Key takeaways:Jeff Bander – President, eye square
Sandra Schümann – Senior Advertising Researcher, RTL Data & Screenforce
Marvin Vogt – Senior Research Consultant, eye square
Screenforce conducted a series of studies beginning in 2020, examining reach, success, mapping moods and impact in relation to attention. They mapped the impact by investigating when does which type of communication work best and why? There were 8,304 ad contacts in-home, 285 participants in a natural way (living rooms). They also examined 64 brands in three countries. The largest media ethnographic study in Europe examined usage situations and scenarios. There were four different scenarios: 1) Busy Day scenario (2-6PM Mon-Fri, people are distracted and focused on other things), 2) Work is Done (after 6PM, first lower part of concentration, seeking for better mood), 3) Quality Time (8-10PM, prime time, high activation of quality time, “Super Bowl moment,” high focus on screen), 4) Dreaming Away (10PM-1AM, typically alone, before sleep, dreamlike situation). Each of the 64 ads was tested in all four scenarios. The study included a technical objective criteria, subjective feeling and creative approaches. Eye square found a way where no additional material is needed other than an instruction book, webcam and GSR. Key findings:David Kurzynski – SVP, Data Science, Nielsen
Kyle Poppie – VP, Data Science, Nielsen
It is challenging to measure the smaller audiences of local TV and measurement challenges include false zero audience metrics and instability. Kyle Poppie (Nielsen) reviewed the evolution of local TV measurement, and this presentation demonstrated how Nielsen’s approach enables accurate measurement. Calibrating big data to a probabilistic panel controls for biases in the big data population that cannot be accounted for by weighting alone. The panel provides accurate and unbiased measurement at aggregate levels while big data provides greater coverage of granular behavior. An example demonstrated how the calibration of panel data and big data resulted in a more accurate weighted audience size. David Kurzynski (Nielsen) presented a case study that applied calibration to live data from a secondary station in New York. The improved result included fewer zero ratings and smoother trends. Key takeaways:The use of specific sounds, audio cues and music for branding is nothing new — it has been employed since radio became a mass medium. But as many marketers are rediscovering, sonic branding researchers are exploring best practices for today’s media environment.