Teaching The Next Media Currency

AU combines the probability of attention and the probability of outcomes into a single 0-100 placement quality score across 19 media channels. Media buyers and sellers increasingly use AU as a media quality metric, informing planning, valuation, buying, and campaign measurement.

When buyers and sellers in a market begin to rely on a new currency to value what they trade, everyone in the market, including those training to join it, must study and understand its dynamics.

Why education on a new metric is necessary

As media quality metrics grow in importance across the advertising industry, future media, marketing, and economics professionals should learn to understand, question, and apply them before starting their first jobs. Media industry practitioners often learn to use new data and metrics on the job, one client and one campaign at a time. An undergraduate- or graduate-level class setting introduces AU earlier, with the expectation that students will graduate into industry employment already versed in its fundamentals and able to apply what they have learned.

Academic engagement isn't only about educating future industry practitioners. Academic researchers are always looking for new ideas and topics to investigate, and their work moves industry forward. We are excited to be exploring the questions and interests of a wide range of academic professionals. We expect these collaborations to provide independent scrutiny, new applications, and opportunities to challenge our assumptions.

Adelaide treats academics as a new constituency with unique requirements, and our team is working to meet their needs by making our data and perspectives available for exploration.

What our own research taught us

In 2026, my colleagues and I published an article titled Commercializing New Predictive Metrics in B2B Markets: A Longitudinal Case in the International Journal of Market Research, which examined how AU was commercialized between 2019 and 2025. (doi.org/10.1177/14707853261493195)

The article explains that AU’s predictive accuracy was necessary but not sufficient for market adoption as a media quality metric. Much of its adoption came from educational work: explaining AU segment by segment to planners, buyers, and publishers who hadn’t been trained to evaluate media placement quality.

As Everett Rogers notes in Diffusion of Innovations (2003), innovations diffuse through a social system over time. For a new media metric, the undergraduate and graduate-level programs that train future planners and analysts are a key part of that system.

AU University curriculum structure

A Cornell University faculty member described curriculum development to us as follows: "A curriculum begins with the learning objectives. We first identify the main questions the course should address, organize them into a coherent sequence, and then build each section with the concepts, evidence, cases, and exercises, while continuing to incorporate new developments that keep the course timely and useful."

Adelaide’s AU University, our training curriculum for employees and the industry at large, follows the same framework. AU University describes four conditions we think a new metric must meet before it can function as a currency:

1. The metric must be validated by independent third parties.
2. It must correlate with outcomes.
3. It must not simply track price, since a metric that rises with cost adds no information.
4. It must be available wherever buyers and sellers transact.

These conditions apply to any metric that seeks to anchor a market, including AU, and they translate directly into learning objectives. A well-designed academic course should equip students to test AU against them and reach independent conclusions about the metric’s usefulness and applicability.

Our university class program

Our academic program has three components:

Curriculum support. We work with faculty to develop lessons on topics including how attention is measured and the limits of each method, why price and placement quality diverge, how DSPs and SSPs operate, why optimizing to a single KPI distorts campaigns, and how to incorporate placement quality into marketing mix models and other analytics frameworks.

Data. AU norms and an accompanying data dictionary are provided at no cost for teaching and non-commercial academic research. Norms are aggregated from placement-level measurement to a level agreed to with each instructor.

A semester-length class project. Supported by a written brief, a data file, an Adelaide coach and guest lecturer, a mid-term review, and feedback on final presentations.

Applications in marketing and economics

AU data can be examined in the classroom at several levels. Undergraduate media planning courses can examine how AU varies across channels and why, or how a plan changes when every placement must meet a minimum AU. Graduate-level marketing and business courses can test whether and how quality-adjusted impressions improve econometric models.

For economics, media quality is a standard information problem. Historically, media placement quality has been difficult to assess before purchase and sometimes afterward, making it what Darby and Karni termed a credence quality. Akerlof's 1970 analysis of the market for "lemons" describes the consequence: when buyers cannot distinguish quality, price ceases to signal it. Media markets show this pattern. In an analysis of more than 80,000 placements, we found no correlation between AU and CPM; overpriced low-quality placements exist alongside a larger number of underpriced high-quality ones. Students can study mispricing directly, along with the arbitrage that becomes possible once quality is observable, and extend the analysis to how quality thresholds affect price, the cost of a plan under a quality-adjusted CPM, or the relationship between ad load, quality, and pricing in ad-supported media.

The same Cornell faculty member I mentioned above raised a question that shows where this can lead: as advertisers have shifted video budgets toward digital platforms over time, which channels gave up that budget, and does the quality of the media bought explain the shift better than price alone? Budget share-shift is a classic subject in media economics, and placement-quality data adds a dimension earlier studies could not observe.

International markets offer further research opportunities. One project brief asks student teams to research and document media markets outside the US, including advertising regulation, ad load, permitted formats, and consumption patterns, so that we can study media quality contexts. Work like this gives students a substantive project to present to prospective employers, and the findings fill gaps in our own knowledge of those markets.

Preparing the next cohort

Media courses cover the basics of media planning: reach, frequency and cost. Media placement quality receives far less focus. Our objective is for graduates entering planning, buying and analytics roles to arrive able to understand an AU score, question it and apply it.

Academic work with AU data can contribute further scrutiny from other angles, including findings that challenge our own. Researchers with no stake in the outcome should always test a candidate currency.

We welcome faculty in media, marketing, analytics, and economics to explore how AU data can support their teaching and research. Educators interested in bringing AU into the classroom can contact Adelaide to discuss their academic needs. Students and faculty can also explore AU University, our free training and certification program, to start building a foundation in attention and media quality measurement and its applications.

Posted at MediaVillage through the Thought Leadership self-publishing platform.

Click the social buttons to share this story with colleagues and friends.
The opinions expressed here are the author's views and do not necessarily represent the views of MediaVillage.org/MyersBizNet.