While multicultural audiences are becoming key drivers of growth, many brands are still struggling with how to accurately predict what resonates and what doesn’t with people of color. Only 5% of media spends goes to multicultural audiences, and 64% of African American and Asian American and 52% of Hispanic audiences are not satisfied with how they are portrayed in the content. David Wellisch, co-founder and CEO of Collage Group, a consumer research company that works with brands like Twitter, Coca Cola, Nestle, and more and uses machine learning and facial tracking to reveal critical differences in emotional response to ads in different demographics, explains the importance of “cultural fluency.”
Why does only 5% of media spend go to multicultural audiences?
Consumer brands are aware of the demographic landscape shifts that this country is undergoing. They know that 100% of the population growth and 75% of the expenditure growth has come from multicultural consumers. However, they also have their proprietary segmentation models that are based on psychographics and brand usage models. At best, brands apply multicultural overlays on top of their segments. At worst, demographic overlays are not part of the equation, missing a significant opportunity to connect with their target markets’ identities, which should have an important impact on response rate.
Is it common for content that appeals to one group be a turn-off or even offensive to another?
We see this happening all the time in our AdRate analysis of video ads. It does not mean that companies are not able to create advertising that resonates with multiple segments at the same time. However, that can only take place if they understand the cultural traits of the different segments and their responses to different treatments. This is where machine learning can help. What drives ad effectiveness by segments – and why – is critical to know in order to avoid crucial mistakes that The New Wave of American consumers will not forgive.
Even seemingly innocuous content can have the opposite effect than intended. For example, facial tracking analysis of Bertolli’s 2016 “Dance” commercial, which featured a biracial couple, revealed a remarkable difference in emotional response between white and multicultural consumers. Other data shows that humor and inspiration are key to positive reaction from white consumers but are not as important for Asian, Hispanic and African American consumers. So yes, it is common.
How can advertisers navigate such a complex landscape? How can facial recognition tech help?
Most brands have swung between two extremes – from a dedicated ethnic lens, to a “Total Market” approach, which is intended to appeal to all consumers. The former is expensive and the latter has led to lackluster results of under penetration and market share loss.
Instead, brand marketers need to become Culturally Fluent in order to cost effectively win in today’s America. Understanding cultural traits and attributes of different segments is essential to success. When such understanding is combined with a data driven approach to ad resonance, brands can begin to decipher clusters that can react well to similar messages.
We are fortunate that we can now use machine learning and facial tracking to help reveal crucial differences in emotional response to ads and the drivers behind those responses. Through the utilization of these technological advances, and frameworks such as Cultural Fluency, brands are able to build a roadmap that helps effectively and authentically speak to these diverse segments. This ends up driving critical marketing outcomes, like purchase intent and brand favorability.
Can you offer an example of backlash a brand has gotten from a misfire?
Certainly the 2017 Dove ad showing a black woman taking off a brown t-shirt to reveal a white woman in a white t-shirt is the most jarring example of brand missteps – of seemingly innocuous content can have the complete opposite effect.
