AEJMC 2022: Thursday Highlights
Content Ratings, Influencer Marketing & Advertising Effects, and Audience Choice
Iwas pleased to be flown out to participate in the 2022 conference hosted in Detroit by the Association for Education in Journalism and Mass Communication (AEJMC) and there were more sessions than any one person could attend. That being said, I had the good fortune — and relentless determination — to attend as many sessions related to media and entertainment as I was able.
What follows is a summary of my favorite things that I learned on the Thursday, the second day of the conference. Thursday was actually the day that I participated in a panel and, as I mention below, I will publish a separate post dedicated to our panel discussion of XR.
Additional posts—for each of the subsequent day s— will follow this one. The conference ran Wednesday, Aug. 3 through Saturday, Aug. 6.
Quantifying Success: Innovations in Measuring Box Office, News, and Beyond
TV Ratings
Todd Holmes, Ph. D., associate professor at Cal State Northridge, kicked off the day with a fascinating presentation about Nielsen ONE. Basically, the long and short of it is that, Nielsen, the firm that has long-provided the standard measure for TV audiences, has realized that with audiences consuming content across platforms at a variety of times—and in a variety of places—their measurement tactics need to change. To better reflect a world in which audiences are watching TV both live and on-demand, across a variety of different streaming services and devices, they are launching a new platform, Nielsen ONE, which will update the company’s core metrics.
This means that Nielsen will now capture reach and frequency across platforms, including: linear TV, streaming, connected TV (CTV), computer, and mobile. And their measure will offer greater consistency, coverage and comparability. In particular, with regard to comparability, this means more granular measurements.
“It’s really three different things they use: unifying watermarks, enhanced signatures and a partnership with Extreme Reach,” said Holmes, associate professor at Cal State Northridge. “The company really made an effort early this year, updating and enhancing their watermarking capability. And the idea here is that each individual ad will be watermarked by the company it represents; this enhancement has really allowed for the 15 second sub under one minute measurement to be possible.”
Here are some of my favorite things I learned from Holmes:
- deduplication: This means that Nielsen is only counting a household or audience member one time. This is done so that they can determine “reach,” which is a key metric used by advertisers. Reach is the total number of unduplicated persons or households included in the audience of a station or a commercial campaign over some specified period of time. It is usually expressed as a percentage of the total market population.
- C3 and C7 ratings: They’ve always looked at the average rating over the entire program. This means that all commercials aired during a program have the same rating attached to them. (Nielsen One will now allow for the measurement of individual ads. Therefore, a 30-second commercial for Geico may end up generating a higher rating than an ad for Red Bull that is airing in the same 30-minute or one-hour program.)
- content/commercials for credit: A program generates one rating that is “live” and the other that is based on DVR viewing. Since these numbers are essentially added together, it is important that the exact same program and commercials are displayed to the audience both live and via DVR viewing for the summation of the numbers to be accurate. This is what is meant by “C3 and C7 ratings have been dependent on the same content/commercials being viewed for credit.”
The new platform formally launches in Q4 2022. And Holmes has some thoughts about what this means for the present and the future:
“For the present, it means that Nielsen will be able to match up data points with actual people. Their current National TV measurement uses TV panels that are statistically representative of the country’s population by such demographic measures as age, ethnicity, and income levels. The combined insights of these panels and big data received from cable boxes (and other devices that allow for return path data) will be able to inform Nielsen about who specifically is watching at any given point in time. To be truly holistic and representative, measurement needs to leverage big data in conjunction with panel data.”
“For the future, this will mean upholding the rigorous and scientifically-sound audience measurement that Nielsen has been known for over the firm’s existence. The principles of consistency, coverage, and comparability will maintain their relevancy moving forward and Nielsen will certainly aim to improve upon the precision and ubiquity of each. It is critically important for Nielsen to be as precise as possible moving forward as AI plays a larger role in audience measurement. Data needs to be clean and representative to ensure that machine learning models are producing good data. If data is not clean and representative from the start, AI will only multiply this bad data.”
“The future business landscape will be different and better because now traditional linear forms of media, like broadcast and cable television, will be measured in a similar way as digital platforms. Having comparable metrics for each will make it easier for advertisers to see the true impact of their ad messages, especially for linear platforms as individual commercials will be measured. Being able to measure the impact of individual commercial spots provides them with a capability they have long awaited and it brings linear measurement to level that is closer to that of digital. While Nielsen One brings a unique set of challenges to the forefront, the benefits to advertisers will greatly outshine these difficulties and will, ultimately, result in a brighter and more data-driven future for content creators, advertisers, and audiences alike!”
Audio Ratings
Bill Davie, stepping in as a pinch-hitter for the morning’s session, spoke about ratings for radio, audio and non-commercial content. He mentioned the Radio Research Consortium (RRC) and spoke about Triton, which belongs to I Heart Media.
He talked about the median age of NPR listeners and how the fact that people quit commuting during the pandemic really hurt radio, and especially NPR. His conclusion was that podcasts are the future. And, interestingly, he mentioned that for a radio station, launching an app really got its numbers to shoot up! This way local content can appeal to people farther afield. I thought that this idea of “reach” was an excellent point—having the app is not just about the technology itself, it’s about what it can do for getting the content in front of an audience.
Data Crunching
Anthony Palomba, Ph.D., MBA, of University of Virginia spoke about data collection and analysis. He encouraged everyone to get their Quantic digital MBA online for free. He raved about a Python bootcamp through Purdue. He talked about how APIs allow you to scrape data. And he also evangelized GitHub. All in all, a lot of tech focused leads to follow up on.
VR/AR/MR Research in Communication: Challenges and Opportunities
I had the pleasure of speaking on this panel and will be publishing a separate post to share all the great insights my fellow panelists shared. Keep your eye on this space for a link—or better yet, subscribe to my Medium feed—and you’ll get the piece as soon as it comes out.
Influencer Marketing and Advertising Effects
This was a poster session and I had the pleasure of speaking with and contemplating the work of several ambitious academics. I had a great chat with Abby Hendricks and Laura Bright (Texas at Austin) about the predictors of influencer engagement on Instagram. I met Harrison Gong (Texas Tech) and we talked about how parasocial attributes and sponsorship disclosure affect audience evaluations. Shuer Zhuo and I had a fascinating conversation about sensory VR, spurred on by his poster, jointly presented by him and Matthew Eastin (both Texas at Austin), titled “The Efficacy of Social Media Influencers in E-commerce in the Context of Sensory Richness. And finally, here’s a summary of a very cool presentation on Virtual Influencers:
Virtual Influencers in Advertising: The Role of Anthropomorphism-related and Technology-related Features in Influencer Attitude, Influencer Trust, and Influencer-Product fit
By Yan Feng (San Diego State), Huan Chen (Florida), and Quan Xie (Southern Methodist)
Introduction
“Given the differences between virtual influencers and human influencers, it would be important to identify the key features of virtual influencers that address the nature of virtual influencers and that affect consumers’ acceptance of them as brand endorsers.”
Process
They focused on three top US-based virtual influencers: Lil Miquela, Bermuda, and Blawko. For more about their process, I encourage you to reach out to them for access to their paper when published.
Key Takeaways:
- Anthropomorphism, attractiveness, and luminary play positive roles in consumers’ acceptance of Virtual Influencers
- Robophobia plays a negative role in consumers’ acceptance of Virtual Influencers
- Quality and trendiness play positive roles in consumers’ acceptance of Virtual Influencers
- Quality seems to be the most important Virtual Influencer-related feature; it significantly affected all three variables that represent consumers’ acceptance of VIs. (i.e., influencer attitude, influencer trust, and influencer product fit)
- Luminary seems to be the least important Virtual Influencer-related feature as it only significantly affected influencer trust
Challenges to Advertising Effectiveness
At this poster session, I spoke with Chen Ma (Communication University of China) and Kibum Youn (Tennessee, Knoxville), and I had a particularly memorable conversation with Xue Dou (Ritsumeikan University Japan) about the effects of skip-ad buttons, in light of the presented poster, “Enemy or Ally? Testing the Effect of Skip-Ad Buttons on Consumers’ Reactances and Brand Attitudes.”
Literature Review
Given my personal interests in choice and content, I made note of the following resources on these topics:
Familiarity and Choice
Exposure Effect (Zajonc, 1968)
Perceptual fluency theory (Jacoby & Dallas, 1981)
Choice Size Effects Are Somewhat Less Consistent, But Overall, “Choice Overload” Tends to Dominate
Movie options/video streaming sites (Bollen, et al, 2010)
Small, diverse options vs. traditional Top-N recommendations (Willmensen, et al, 2016)
Highlights from AEJMC 2022 continue…
with Friday highlights (to be linked once published).
