Gender Analytics is a process to embed insights about gender and its intersections with race, ethnicity, disability, sexual orientation, Indigeneity and other factors to create inclusive product, service and policy design. Despite having a potentially significant impact on society, the idea of inclusive analytics is often misunderstood, though it’s recently risen to prominence as companies prioritize equity and diversity not just in their talent management but also in how they go to market. And there’s a significant upside to the shift: Applying a gender and inclusion lens to product and service development could result in new opportunities for businesses.

“Companies across industries are embracing inclusive approaches to analytics to distinguish themselves from competitors, reach underserved customers and markets, and catch up with shifting cultural and social norms ⁠— especially among younger consumers who have different notions of gender roles,” says Lechin Lu, associate director of the Institute for Gender and the Economy (GATE) at the Rotman School of Management. We spoke to Lu and Professor Sarah Kaplan, director of GATE, who busted five myths about Gender Analytics.

Myth #1: Gender equality is just an HR issue

“When we say Gender Analytics, everyone thinks that it’s a talent management issue,” Kaplan says. “So the conversation doesn’t move beyond HR team to the people who are involved in product and service design.”

But while talent management and HR teams are integral to increasing the diversity of an organization, the impacts of gender and other intersecting identities go well beyond these departments. Insights from an intersectional gender-based analysis need to be considered at all stages of the development and launch of a product or service, Kaplan says. What’s more, this approach can have a positive spill-over back into HR. “By making inclusion central to product market strategies, it will drive demand for more diverse talent.”

Myth #2: Inclusivity isn't profitable

“Women represent half of the population. There are some missed opportunities in the market and a missed segment of customers that companies don’t understand how to serve,” Lu says. “This is opening opportunities for more innovation.”

Kaplan points to uniform provider McCarthy Uniforms as a case study. Needing a turnaround strategy after near bankruptcy, it reevaluated its products and services using a gender lens to discover unmet market needs. For example, bus driver uniforms were only made in male sizes and were designed for male body types. “When McCarthy offered a wide range of female-sized and fitted products, they were able to win more contracts,” Kaplan says.

Or take the world of personal finance, where 85 per cent of investment advisors are men. It’s not uncommon to hear that, when a male spouse passes away, the female spouse will often switch investment advisors. “That’s because the [original] advisor never respected her, understood her issues, or figured out how to work with her,” Kaplan says. “Designing a new way to work with your clients that is more gender inclusive would lead to huge bottom-line gains for investment advisors.”

Myth #3: Products like cars and credit cards don’t have gender

There are plenty of products we consider “genderless” – take credit cards and cars for example. But Kaplan says even these items often have gender embedded into them.

For example, car safety tests were originally designed with male-sized crash-test dummies. “When they finally figured out that they needed a female-sized crash test dummy, they took the male one and made it smaller, as opposed to adapting it for women’s real physical differences,” says Kaplan. As a result, women are 73 per cent more likely to be hurt in car accidents.

Or take credit cards and credit scores. Kaplan points to a couple who applied for the new Apple Credit Card when it was launched in 2019. Despite sharing finances, they were given different credit limits. “The bank that backed the credit card said, ‘We don’t factor gender into our credit scoring.’ But of course, there are other things, such as spending patterns, that are gendered. You can’t eliminate gender bias simply by not considering it,” she says.

“Gender Analytics insights can help avoid downside risks caused by the ‘male default,’” says Lu. “That is, we think somehow that a man is a stand-in for all people.”

Myth #4: Machine learning and AI will eliminate bias

It’s tempting to view technologies such as AI and machine learning as the “magic wand” that can remove bias from analysis and evaluation. “But machine learning is learning from people in the end,” Lu explains. And people have biases.

If anything, machine learning and AI could be making the problem worse, amplifying these biases, Lu adds. Kaplan points to facial recognition technologies, which were designed predominantly by white men. “Those technologies have been shown to do a decent job of recognizing white men’s faces, but a terrible job in recognizing, for example, black women’s faces.” Ultimately, seemingly objective or neutral items are still designed by people, which means they can be embedded with biases.

To combat the inequalities in machine learning and AI, Lu says that developers first need to examine the data that’s used, how it’s collected, why certain pieces of information are included or omitted, and its potential impact on people. “If we don’t question how data is collected and handled, and the power imbalances between the people who are collecting data and the people who are having data collected about them, machine learning and AI risk embedding biases into algorithms.”

Myth #5: Inclusive practices are an add-on to your day job

“Considerations of gender and its intersections with other identities are often seen as ‘nice to haves’ or as an add-on to how people think about designing their products and services,” Kaplan says. But she believes that gender and equity considerations should be an essential part of any company’s go-to-market strategies.

Kaplan explains that many organizations and business leaders view inclusivity as something they consider after their core business objectives or needs are met. But Gender Analytics can be baked in from the ground up. “The idea is not to do what you regularly do, and then add on this layer where we say, ‘Is there any gender or racial bias in this product or program?’” she says. “Instead, you should use inclusive analytical approaches throughout the process of figuring out what products or services or programs you’ll design, and how you’ll design them.”

Want to learn more about Gender Analytics? Sign up now for the Gender Analytics: Possibilities conference, running April 27 to 28, 2023, for engaging panel discussion, workshops and online exhibitions to see how inclusive analytical approaches can generate new products, services and policies, and make real progress on equality.