Month: May 2022

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Confronting myth and marginalization

Confronting myth and marginalization

Today, cultural dichotomy means more than just differences in traditions or recipes. For the Asian American community who has experienced so much trauma in the past few years, being seen when tuning into media helps create a sense of connection and empowerment. The media industry has also pledged to do its part to invest in content that paints more accurate representations of diverse communities. This year’s Asian American Native Hawaiian and Pacific Islander (AANHPI) Diverse Intelligence Series report explores if we have made any progress when it comes to Asian representation.

The good news is yes—there has been a significant increase in Asian representation on screen in 2021. Across the top 1,500 shows in broadcast, cable and streaming video on demand (SVOD) in the U.S., SVOD led the way with 11% Asian share of screen compared to broadcast (3.2%) and cable (2.7%). The presence of Asian talent in top-rated shows like FBI, Equalizer and Chicago Med, and the debut of Asian-led programs like FOX’s The Cleaning Lady indicate the industry is responding to growing calls for more Asian-inclusive content.

Overall, Asian representation across broadcast, cable and SVOD increased to 4.6% in 2021 (up from 3.5% in 2020). The report notes a significant improvement in representation in the top 10 most-watched shows on broadcast and cable. In 2021, half of the top 10 programs had some Asian talent representation, compared to 2020 when none of the top 10 most-watched shows did. Asian women were present in three of those shows (NCIS, Equalizer and Yellowstone) and Asian men were present in two (Chicago Med and FBI).

But just being present on screen isn’t enough. The stories that are told and the roles played by Asians are also critical to shaping people’s perceptions about the Asian American community. In 2020, the dominant themes in the stories when Asians were present were cerebral, thoughtful and good, which reinforced the model minority myth. In 2021, there was a greater diversity of thematic attributes such as friends, teamwork and creativity.

Despite this progress, media content still falls short in meeting the demands of Asian American audiences who want more accurate portrayals. The results of a recent study found that 2/3 of Asian Americans feel there is not enough representation of their identity group on TV, and when they are seen on screen, more than half of Asian American respondents feel the portrayal is inaccurate.

The media industry has tremendous influence on people’s beliefs and bias. Now is an important time for the industry to highlight Asian characters, stories and experiences on screen through culturally inclusive programming. Accurate representation on screen can lead to greater understanding, inclusion, engagement and peace off screen.

Download the report
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Unless the numbers reflect people, they’re just numbers

Unless the numbers reflect people, they’re just numbers

For the media industry, the period between March and May is go time. Across the many upfront events that span the media landscape, which are no longer bound to individual platforms and technologies, the expanding content marketplace presents both a wealth of opportunity and an expanse of information for ad buyers and sellers to navigate, especially amid the increasing conversations about big data for measurement. 

For advertisers, numbers are critical this time of year. And as TV consumption fragments amid rising digital engagement, they take on even greater importance. How important? An Ampere Analysis study found that total spend on content in 2021 totaled about $220 billion, led by streaming powerhouse Netflix. And advertisers, knowing that Americans streamed almost 15 million years’ worth of video last year, are rallying, as worldwide digital ad spend surged more than 29% in 2021 to eclipse $491 billion. 

What’s more, consumers have no plans of changing the trajectory of the streaming industry, as 93% of streaming subscribers say they plan to increase their usage over the next year. That doesn’t mean, however, that traditional TV content is out of the picture. Quite the opposite, as the average adult spends more than twice as much time per day with live TV than they do with connected TV (CTV) content.

The increasing abundance of content presents a growing wealth of choice for consumers, but the myriad platforms, devices and services can present measurement challenges for advertisers. Additionally, the explosion of choice has not created more time to engage with content, nor did it create more people. But big data, including that which comes from smart TVs (ACR) and cable boxes (RPD), has a way of suggesting otherwise. The data from cable boxes and smart TVs also provides little insight into streaming activity: Cable boxes, by definition, provide traditional TV data, and ACR often shuts off when audiences use native apps, including Netflix.  

In addition to never being intended to be used for measurement, big data isn’t reflective of actual people. There is no mistaking the value of RPD and ACR, as they provide scale to measurement, but big data is reflective of devices, not of actual people. The data by itself can’t tell you who’s watching and who’s not—which is a fundamental need for advertisers. And when people are removed from the equation, the numbers just won’t add up.

Take ACR data, for example, which identifies images on the screens of smart TVs. This data can be very useful in audience measurement, but by itself, it does nothing more than identify what’s on a screen. RPD data is similar, yet it lacks the ability to even verify that a TV set is on. That’s why one-fourth of all set-top-box impressions come from TVs that aren’t even on.

In addition to not knowing who’s using a device or a screen, big data is inherently biased, and the bias depends on the data type. In order for big data to truly represent the U.S. population, every TV household would need to have the exact same TV set and access programming through the exact same data stream. That’s why all big data sets need to be level set—calibrated—with people-based panels that reflect the diversity of the U.S. population.

Importantly, the World Federation of Advertisers, the Association of National Advertisers and the comparable organizations in over 30 other nations have unanimously stated that the future audience measurement system for screen media must be a combination of quality panel and big data.

Without panel data, measurement doesn’t capture diversity. Not only do we know that all TV households will never access the same content on the same devices, we also know that household makeup is as varied as the fabric of the country that contains the TV households. That’s where big data-based measurement misses the mark—significantly.

For example, Hispanics represent just under 20% of the U.S. population, but big data significantly undercounts this audience, along with many others. But when measurement is based on RPD alone, Nielsen analyses have found that it underrepresents Hispanic homes by 30%. To put that into perspective, consider this: The 2020 U.S. Census determined that the Hispanic population was just over 62 million. If half of that population is watching TV at a given time and advertisers leverage RPD data for measurement, advertisers could be reaching 9 million more people than they’d be aware of.

Importantly, the 30% underrepresentation is an average. At a program level, big data can under- and over-represent by much bigger margins—for both the general population and diverse audiences. For example, a Nielsen study into the variances between big data measurement and its gold standard panel-based measurement found that RPD measurement overstated the total U.S. impressions for a primetime program by 69%. Comparatively, ACR measurement under-stated the total by 12%. For a sporting event, RPD measurement under-stated the Hispanic audience by 47%, while ACR data over-stated the same audience by 12%.

For advertisers, these measurement variances can be costly. The increasing supply of new data sources, however, does add complexity to measurement, especially when it may not be connected to real people. Publishers and advertisers will always want the biggest reach possible, but certainly not without the analytical rigor needed to validate it.

As linear and digital converge, big data sources are critical inputs for measurement. But they’re not trustworthy as measurement sources by themselves. As consumers engage with more devices and more channels, it will be easy to point to data that claims potentially over-inflated engagement. Advertisers would certainly welcome the audience sizes that many alternative audiences suggest, but if they place their ad buys against those numbers, they’ll ultimately be paying for numbers that aren’t reflective of real people.

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