How DAM Systems Can Make People Feel Seen
- Critical importance. Inclusivity and diversity in marketing are not just ethical imperatives but also crucial for business, particularly in the beauty industry.
- Metadata matters. The quality and depth of metadata in digital asset management systems play a significant role in personalizing and making marketing feel inclusive.
- ROI analysis. Advanced analytics can provide insights into the effectiveness of inclusive marketing strategies, helping brands align better with diverse customer needs.
In commerce worldwide, there’s a complicated and evolving debate about diversity, equity and inclusion (DE&I). Brands are criticized regularly for doing too little, doing too much, or doing DE&I the wrong way or for the wrong reasons. DE&I wins and blunders have moved stock prices, sales and cultural norms.
Let’s take a look at inclusivity and diversity in marketing.
We believe that inclusivity is both a matter of ethics and smart business — and that martech systems like digital asset management (DAM) determine how inclusivity and diversity in marketing is experienced. In few industries is this issue so important and so visible than in beauty.
We’ll start with an exploration of why inclusivity became critical but difficult in beauty. From there, we’ll explore how DAM systems can connect shoppers to content and products in which they see themselves, and how you might measure the ROI of that.
Diversity in Marketing: 40 Shades of Fenty
Beauty is a $430 billion industry, according to consulting firm McKinsey & Co., and is tracking toward $580 billion by 2027. The industry encompasses makeup, skincare, haircare, fragrances and more. The challenge for beauty inclusivity is not just the product or its audience but how people shop.
Growing up, Gen Y and Gen X people of color in the US rarely saw themselves in beauty marketing. Cosmetics were designed mostly for light skin tones, and hair products were designed around longer, straighter hair rather than curlier and kinkier styles. This forever changed when Fenty Beauty, founded by pop star Rihanna in 2017, launched with 40 foundation shades — which sold like crazy through Sephora and later, Ulta Beauty. Fenty is now worth at least $2.8 billion.
Arguably, physical stores cater to inclusivity and diversity in marketing. Just by seeing customers, sales associates can infer what products they may need. It’s also easier to test and compare 40 shades of foundation in person.
Between 2019 and 2022, though, ecommerce beauty sales grew 21% thanks to COVID-19 and remain the fastest-growing channel. Unlike human salespeople, ecommerce sites can’t see users. Rather, users must identify themselves to the brand. What search terms, browsing categories and filters would shoppers use to do that? What are people comfortable telling a brand, or being told by a brand, about their appearance?
Related Article: 5 Ways Diversity and Inclusion Impact the Customer Experience
Metadata’s Role in Inclusivity
Research makes clear that inclusivity and diversity in marketing matters to buyers — especially younger ones. In one survey, 40% of Gen Z and 31% of Gen Y respondents ranked diversity and inclusion as the top brand value they consider when shopping for beauty products. How they perceive inclusivity comes down to the content brands create and the technology they use to distribute it.
The images we discover by searching, browsing and filtering online are surfaced based on metadata tagged to them. Search “black hair” and Google image results will be varied. Google doesn’t know whether you’re searching for images of any hair that is black in color, or hair representing black women and the culture of black hair. The aim is to define metadata such that a user from anywhere finds their hairstyle, skin tone and cultural heritage in the results.
Let’s take Shiseido, a 150-year-old beauty brand from Japan (disclosure: they are a customer of Acquia, the author’s company). Its homepage has a section called “Find Your Formula” where users answer questions about skin tone, concerns (e.g., puffiness), goal, age, gender, lifestyle and more. The product metadata and content metadata are synced. Shiseido sells 30 foundation tones and has at least 30 different models representing them. If you buy products for, say, the Cedar tone, Shiseido can serve up product recommendations, advertisements and email offers for you.
Notice that Shiseido uses racially neutral words, like Cedar. The brand can add metadata on where models are from to serve up localized content (e.g., Swedish models for Swedish shoppers). But there’s not one tone for people who might identify as white or black, Han Chinese or Korean, Egyptian or Moroccan. There is immense diversity within these social constructs, and inclusive metadata reflects that.
While Shiseido’s product line makes skin tone important, Dermologica (another Acquia customer) is more focused on expanding skincare beyond a traditionally female audience. On the company’s website, you’ll see models across the gender spectrum. And its Face Mapping analysis doesn’t care how or if you identify yourself. It’ll serve up product recommendations based on a machine learning algorithm. That, too, is a good approach to inclusivity.
Bottom line: the quality of that metadata ultimately determines how well you can personalize marketing offers, content recommendations, and more — and how inclusive that personalization feels.
Related Article: How Can Generative AI Improve DEI in the Customer Experience?
Will You Know if Inclusivity and Diversity in Marketing Works?
Building out metadata taxonomies in DAM (and product information management, aka PIM) enables some analysis of their impact. Among any top DAM vendor, analytics should show which assets are downloaded, by which users, where, and for what purpose. If an online retailer in Texas uses a lot of images tagged with five distinct skin tones, and sales of those tones are rising at that retailer, you have a case that inclusive content is driving sales (granted, correlation isn’t causation).
Similarly, analytics on share links — which enable non-DAM users to download assets — can illustrate what DAM users prefer to share with partners in a given region. They may pass along highly diverse images with partners in the US but not in more ethnically homogenous countries, like Japan or Iceland — because shoppers wouldn’t see themselves in those images. Migration (and colonization) throughout history means that inclusivity looks different depending on where you are. Indeed, people may see themselves most deeply in models who look nothing like them but represent their ideals of beauty.
A warning, though: When brands have tried to do inclusivity using AI-generated models, it has backfired. Shoppers feel that inclusivity isn’t just about the appearance of a model. It’s about a dignifying transaction in which that model is paid for representing people who often feel underrepresented.
Virtuous Reality of Inclusivity and Diversity in Marketing
The beauty industry thrives because people desire to be seen as they see themselves. Our beauty choices are personal and often grounded in cultural heritage. That is why inclusivity in this space is so meaningful — and sensitive.
Inclusivity done well is a personalized experience for a shopper. Whether that personalization is based on online quizzes (like Shiseido’s), browsing filters or purchase histories, brands ultimately need a metadata taxonomy to determine who will see which products and content, and why. AI may play a bigger role in this process by, for instance, matching skin tones and hair qualities based on user-submitted photos. If AI is trained on diverse data (a common oversight), that could play out well.
Most importantly, we need less fraught and awkward conversations about diversity in marketing. Inclusivity is not a box to check. It’s good business and the right thing to do.
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