A New Currency To Measure Audience Quality: qaCPM

bryan-gernertData-Driven Thinking” is written by members of the media community and contains fresh ideas on the digital revolution in media. 

Today’s column is written by Bryan Gernert, CEO at Resonate.

Over the past decade, digital marketers have relied on quantity-based measurement as a proxy for campaign performance. The volume of impressions, clicks and likes signifies that people see ads and find them relevant and compelling enough to publicly share with friends and colleagues.

But increased transparency into viewability, bot fraud and ad blocking issues reveals striking evidence that these traditional metrics do a poor job measuring true engagement and sales. Just this week, a joint study released by the Interactive Advertising Bureau and Ernst & Young confirmed the $8.2 billion threat of the broken digital supply chain to the advertising industry.

The marketing industry needs to step up its measurement game. One way is to provide marketers with new metrics that provide a more accurate way to measure quality, in addition to quantity. Establishing and implementing objective, quality-based metrics into a brand’s dashboard would enable marketers to significantly amplify their understanding of whether they are reaching the right audiences with their digital campaigns.

A good place to start is to begin measuring the quality of audiences reached through a new currency I call qaCPM (quality cost/m). It calculates how much a marketer pays to reach a quality audience. This is a concept that has broad impact and shines a spotlight on an entirely new way of thinking around measurement.

The Expanding Definition Of Audience Quality

For marketers, delivering the right ad to the right person at the right time at the right place, or variations thereof, has become the holy grail. To date, the right person has been predominantly defined by many demographic and behavioral traits, including age, gender, household income, browsing history and historical purchases, among others.

But over the past couple of years, ad tech has matured and technological sophistication has increased, advancing marketers’ ability to build a more complete picture of consumers – and a better understanding of audience quality. For example, through eye tracking and facial coding, marketers can now see specific feelings, such as the sadness, joy and surprise that consumers may experience while viewing a video ad.

These emerging dimensions – emotional engagement and underlying motivations – will continue to expand marketers’ view of what constitutes a quality audience. And, over time, these new facets of consumers will surely become a standard part of brands’ strategic media plans. Imagine a world where marketers can target, for instance, high-earning men aged 24 to 35 years old who are motivated by a sense of adventure, which may inspire happiness viewing ads of Jeeps conquering rocky, back-country environments

Defining A New, Quality-Driven Measurement: qaCPM

The adage, “If you can’t measure it, you can’t manage it,” has become dogma in our data-driven industry. While audience quality remains paramount to brands, marketers aren’t armed with the proper metrics to discern this information. The qaCPM metric I propose takes a step toward providing marketers transparency into audience quality.

A simple formula to determine qaCPM is: (audience reach x target audience percentage) x audience score / impressions = qaCPM.

Breaking down the proposal above involves taking multiple components into consideration, defined as:

  • Audience reach: Total number of people reached
  • Target audience percentage: Percentage of people that fell within the brand’s desired segment
  • Audience score: A range of 1 to 10, representing the relevance of an audience segment to the brand. An audience score of 1 represents an audience with low fit to the brand; an audience score of 10 represents the brand’s most probable buyers
  • Impressions: Total campaign impressions

In qaCPM scenario, quality is captured through two variables. The first is target audience percentage, a measure of how many of the people exposed to a campaign were actually consumers a brand wanted to reach. The second variable is audience score, which reflects whether a campaign reached an audience segment that is important to the brand. By factoring in these two components, marketers will have better insight into audience quality.

A measurement like qaCPM is just one proposed solution, and there are certainly other valid ways to calculate audience quality. The point here is that advertisers, publishers and technology partners should begin to embrace the concept of measuring the quality of audiences, rather than the quantity of user activity, such as clicks or shares, as those in the ecosystem think about the next generation of relevant and valuable metrics.

In the back half of 2015, we’ve heard compelling new ideas and progress on industrywide currencies that provide advertisers with deeper, actionable insight. For example, one ad tech company recently proposed a cost-per-human metric to address nonhuman traffic. The Financial Times also made strides implementing a cost-per-hour metric to gain deeper insight into consumer engagement.

But missing from today’s measurement discourse is the relevance of the end consumer. In the above examples, even if we proved that a human viewed the ad or that a human viewed a branded content ad for more than five minutes, was it the right human?

By widening industry dialogue to new measures around audience quality, digital advertising will further prove its worth for a bigger share of marketers’ total ad spend budget.

Follow Resonate (@resonatetweets) and AdExchanger (@adexchanger) on Twitter.

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  1. Bryan, very insightful article – you are onto something big here. The piece of the equation regarding (TA%) – are you reaching the desired audience has long been understood in TV with Nielsen as the currency. With so much digital bought on behavioral or psychographic profiles there is no quantitative measurement, only qualitative through surveys. With big data, you would think someone would have enough good data to actually do this, but instead each element is assigned to a cookie and not an individual and the data remain fragmented and inferred. We did this to ourselves by not asking more about the data we are buying. “Target audience of sedan shoppers” was taken as exactly what we were told. So agencies rely on performance proxies based on digital KPI’s to provide “currency” to their clients. It’s sad. We did that back in the late 1990’s and we haven’t grown up. Your Good luck with your quest.

  2. This still isn’t very helpful for the following reasons:

    Target audience percentage – is this talking about demo data, or 3rd party behavioral data? Audience validation, if there is any, would only be applicable to contextual buys. Otherwise, the data segments used to target a programmatic buy should match up exactly with the validation paired with the buy. If that doesn’t happen, the audience validation on one end or the other is faulty.

    Audience score – is this just a conversion rate? How does context and correct messaging play in to this? Optimization analysis/machine learning would be more helpful that creating this value model.

    Also, if a eCPM = spend/impressions x 1000, why is qaCPM =(audience reach x target audience percentage) x audience score / impressions? It’s a bit contrived and makes no mention of spend, which is what we need to put efficiency into perspective.

    Any programmatic buyer worth their salt easily knows how to adjust their eCPMs for viewability and fraud wastage, and evaluate audience relevance by the eCPA per targeting segment.

  3. Guido Juliano

    I couldn’t agree more with your observations regarding the increasing difficulties in the Digital era (bots, fraud and ad blockers) – it’s a plague. I do want to comment that in my opinion, the equation you offered can be relevant to facebook and/or completely transparent sources. In my opinion, the main issue with moving towards a more quality oriented business model is the technology. I think that there are some steps the industry must go through in order to mature into you’re proposed model\paradigm of thinking.

  4. Could not agree more – 2016 must be the year of data transparency – where 3rd party suppliers must describe how they classify their data. Well done!

  5. Dorothy Higgins

    And exactly how do we ascribe this quality to 50% pixels for 2 seconds? The value of this approach is compromised by the viewability issues still rampant.