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Gender Analytics: How Gender-Based Insights Create Value

By Sarah Kaplan

Gender-based insights can inform innovative new ways of working, doing business and designing policy.

What do snow plowing, car safety,  investment management and face-recognition technologies have in common? While it isn’t obvious to most, they all have impacts that are gendered.

Take  snow  plowing:  Most  municipalities focus  on  getting the roads  clear after  a major  snowfall. But when  you clear snow from  roads  before you clear  the sidewalks, it turns  out that  you get many  more  slip-and-fall accidents — and  most  of these are women, because they  are more  likely to be out walking  kids to school in the mornings.

What  about car safety? Women are 47 per cent  more  likely to be injured and 17 per cent more likely to die when they get in a car accident because most vehicle crash tests are done with crash test dummies that are male-sized and have male features.

In the  realm of investment management, research shows that  women are highly likely to leave  their investment advisors when their  spouses pass away because those  advisors had never worked with them effectively. And facial recognition technology is coming under fire for many reasons these days, but one important one is that these tests are much  less accurate in recognizing women’s faces, especially women of colour.

The fact is, many  policies,  products, services and processes are gendered, and those  impacts are often  intensified when  you also consider other intersecting identities such  as race,  ability, Indigeneity, sexual  orientation or socio-economic class. And these dynamics shape  the  risks,  opportunities and  impacts of many  of your  organization’s activities and  outcomes. As I will show in this article, insights around gender can uncover hidden possibilities for innovation and value creation.

Putting Gender Insights to Work

Through my research and  teaching, I have  come  to understand that  one  of the  biggest  barriers to progress is that  people  don’t know  how to think  and  analyze with  gender. My hypothesis is that  if we all learn  how to generate intersectional gender-based insights, we  will be  able  to  create more  inclusive innovation. These innovations will be good for equity in our society and they will be good for policy impact and business performance.

 These dynamics shape the risks, opportunities and impacts 
of many of your organization’s activities.

Take the example of Ellevest, an app and  investment platform designed to support women in building their wealth. It was founded by Sallie Krawcheck, a former senior executive at several  Wall Street  firms.  Her  experience in this  male-dominated industry led her  to understand that  investment advice  had  not been  designed with women’s unique lived experiences in mind. The app’s tag line is: “Ellevest was built by women, for women. The financial industry wasn’t.” In fact, recent surveys show that 85 per cent of investment advisors are men.

Most investment platforms are designed with the  assumption  of a linear  earnings trajectory — meaning wages  generally grow  year  after  year  with  experience and  seniority — and  fail to factor  in the different life events that  motivate and  constrain women’s investment decisions. But when  it comes  to investing, men  and  women do have  different considerations. Because of the  gender pay  gap  and  career breaks to accommodate childcare, women earn less than men. And they also live longer, which means that  women retire with less money and  live six to eight years longer.

Keep in mind  that  the gender pay gap and career breaks to accommodate childcare are not caused by biological differences between men  and  women; they  are caused by long-standing ideas about what is appropriate or inappropriate for someone on the basis of their gender. By failing to consider how gender differently impacts women’s career choices, investment platforms do a disservice to female customers, because their product offerings do not factor in issues specific to women’s career trajectories.

Why is analytics important here? Ellevest’s product uses an algorithm that  incorporates factors that  are specific to women’s lives, such as the gender wage gap, earnings over the entire career,  career breaks, and  longer  lifespans. On the  app interface, women (and people  of all genders) are invited to input  information  about their  investment goals  and  financial status, and  the algorithm designs a portfolio and  investment strategy tailored to their goals. It celebrates successes and helps with adjustments if savings  goals aren’t  being  met.  This is a prime  case of an entrepreneur taking advantage of the fact that traditional products do not serve women well — and using analytics to innovate.

New Models Are Emerging

While Ellevest  was among the first to embrace gender analytics, companies across industries are following suit. In order to distinguish  itself  in the  market, an online  grocery  delivery company in the U.S. called  Boxed took advantage of data  about the ‘pink tax’ — whereby women and girls are charged more  for products marketed to them, whether it be razors, shampoos, deodorant or shaving cream. In the U.S., many  states also charge sales tax for feminine hygiene products, thus  increasing their  cost  even though they are a necessity, not a luxury. So, Boxed developed its Rethink Pink strategy, charging equal  prices  for equal  products no matter whether they were being marketed to men or women. They also reduced the list price of feminine hygiene products in states that taxed them. On top of this product strategy, they have also been  testifying in front  of legislatures to get ‘tampon taxes’ repealed.

This  wasn’t  just a strategy to do good.  According to Nitasha Mehta, director of vendor marketing, “We’ve  had  tens  of thousands of customers sign up for Boxed in response to our Rethink  Pink initiative, and  we’ve seen  greater loyalty  from  these customers as a result. After launch, our customer service  team received hundreds of positive emails from current as well as new customers applauding our initiative.” This is important because in online  businesses, customer retention is one of the most  important metrics associated with long-term profitability.

Real estate development is another male-dominated field: Only about one-quarter of architects in Canada are women, and the  numbers are  even  lower  amongst developers, city planners and construction firms. In Toronto, an all-women team of developers has redesigned what housing should look like. The condominium development team spearheaded by Taya Cook at Urban Capital came up with ideas based on intensive engagement with the  community, using  surveys  and  community meetings to understand people’s  needs. They focused particularly on women’s voices — which tend  not to be heard in traditional planning processes  — and ended up with a design that included stroller parking on each  floor, mini-marts on the  premises so people  could pick up staples such as diapers and milk, ample bicycle storage, and hallways that had no blind corners so women would feel safe. The condo also included units that could accommodate multiple generations of family  members. In short, it was an  innovative new approach to housing.

Gender analysis is not  just about women or family  needs. When  we say ‘gender’, we mean all genders. For example, there are  lots  of insights to be had  about men  and  masculinity that could benefit businesses. You might  remember the 2019 ad that Gillette launched as part of its The Best Men Can Be initiative. The  company’s tagline has for 30 years  been, ‘The best  a man can get’. But in response to the #MeToo movement and attention to ‘toxic masculinity’, they developed a new campaign that went along  with a social responsibility program to donate to community organizations working on helping men be their best selves.

The  ad was controversial. Just check  out Twitter or all of the media articles on it when it was launched. Some worried that it might  alienate Gillette’s core customers, but when  you think that  Millennials and  younger shoppers are  being  enticed away from  old razor  brands by Dollar Shave Club and  other online upstarts, a marketing strategy like this — which gets them back in the game with younger consumers who have different notions of gender roles — makes a lot of sense.

Avoiding Downside Risks

So far I’ve focused on the innovative upsides that can come from Gender Analytics, but I also want  to address how these kinds of insights can help avoid downside risks. Most of these risks come about because of what we call the ‘male default’. That is, we think somehow that a man is a stand in for all people.

Many  readers probably saw  the  story  from  2019 that  two women astronauts from  NASA  couldn’t go on a spacewalk together because there weren’t two appropriately sized space suits for them to wear. It would have been too dangerous for a woman astronaut to go out into space with equipment that didn’t fit her. But this type of danger occurs all the time: From settings such as biology  labs to construction sites  to bus driving,  uniforms and equipment are  often  designed for men’s  bodies, thus  creating real hazards for women.

I indicated earlier that  women are much  more  likely to be injured or die in a car accident because safety  tests  have  used male-sized crash  test  dummies. When  automotive companies finally realized this,  they  reduced the  size of the  dummies but didn’t  change the anatomy, so it didn’t  help the problem. Government regulators didn’t have policies in place to force companies to fix this. And, the same  is true for pharmaceuticals, many of which are not tested on women and the results of testing are not disaggregated by gender. So in many  cases,  women are taking drugs  that  won’t actually work for them or might  cause  different side effects.

Some  might  hope  that  machine learning and  artificial  intelligence will save us from  these kinds  of errors. But, take  the example of Amazon, which created a bot that  was meant to do a better job of hiring new recruits than  human recruiters would do. The  theory was that  it would  take  away human bias. However, they had to shut this down shortly after launch because the bot taught itself to discriminate against women. The data  used to train the bot was based on historical hiring, so it learned to filter out résumés that included the word ‘women’, as in ‘women’s chess club captain’ and graduates of some all-women’s colleges.

In the  medical field, one  major  healthcare system used  an algorithm called  Impact Pro  to  help  determine which  patients should receive what  level of treatment. A study  of its impact showed that  it favoured more  complex treatments for white  patients than  sicker Black patients. The problem was that  the algorithm predicted Black patients would  cost  less,  which  signaled to medical providers that their illnesses must not be that bad. But in fact, Black patients cost less because they don’t use healthcare services as much as white people on average due to lack of access and  a general mistrust in a system which  has not served them well in the past. The research showed that if this bias were eliminated, it would triple the number of Black patients receiving the additional help of more complex treatments.

 When we say ‘gender’, we mean all genders.

Similarly, when Apple launched its new credit card, it turns out that  it was giving women lower credit limits than  men.  This was true even for couples that shared finances. The defense that was offered by Apple and Goldman Sachs, the issuing bank, was that  the algorithm didn’t  even use gender as a factor, so it must be gender blind! But in fact, that  was just the problem: Because so many other factors used in credit calculations — such as prior credit history, where  you shop,  and  were  you live — have  been shaped by biased processes in our society, if you don’t  account for gender or race or other intersecting identities, you might just be reinforcing and  replicating that  bias. Being ‘gender blind’ is not enough. You need to do analysis to be gender aware  in order to counteract historical inequities.

These kinds  of biases  can  also  be perpetuated in internal organizational practices. Research on downsizing — something that  a lot of companies are doing  these days — shows  that  supposedly gender or race-neutral decision criteria can have  very gendered and racially-biased outcomes.

For example, in a study reported in Harvard Business Review, researchers found  that  if you do layoffs based on cutting positions rather than evaluating individuals, there was an immediate decrease in representation in organizations for white  women, Black men,  Hispanic men  and  women, and  Asian men.  When layoffs  were  based solely  on  tenure, white  women and  Asian men  were  disadvantaged. When  layoffs were  based on performance reviews, white  men  were the only ones to benefit — and the researchers explained that this is because performance evaluations tend  to be over-inflated for white  men  relative to other people  in organizations. So, supposedly gender-neutral criteria can actually undermine any work towards greater diversity that organizations have done. An intersectional gender analysis done in advance of deciding on furloughs would help avoid these negative consequences.

The Concept of Gender Identity

Up until  now, we have  been  talking  about gender as if it is a binary — either/or — system. But this is just a product of our culture. In  Western societies, we  are  finally  learning something long known  in other societies: that  a lot of people  don’t  neatly  into that  binary  or identify with  the  gender they  were  assigned at birth.  Some  readers will be quite  comfortable with terms such as gender identity, transgender and cisgender, for example. But some  might  not  be,  so it’s worth  taking  a moment to discuss some definitions and bust some myths.

First,  ‘gender  binary’  describes a social  system in which there are only two genders, a woman or a man.  These genders are expected to correspond to birth sex: female or male.  But we are learning that the gender binary is not representative of many people’s lived experiences.

The  term  ‘gender identity’ refers instead to each  person’s internal and  individual experience of their  gender. That  is, it is a person’s sense  of being a woman, a man,  both,  neither or anywhere  along the spectrum. It is important to note that a person’s gender identity may be the same as the sex they were assigned at birth,  or it may be different. ‘Gender expression’ is how people outwardly and publicly express their  gender. This could include clothing, hair, makeup, body language and voice. A person’s chosen  name and  pronoun are  also  common ways people  express their  gender. In English, my pronouns are she, her and hers. Someone who identifies as a man  might  use he, him and  his as pronouns. Other people might use they, them and theirs.

A transgender person is someone who does not follow gender stereotypes based on the sex they were assigned at birth.  A person whose  sex assigned at birth  is female and identifies as a man may also identify as a trans man. Similarly, a person whose sex assigned at birth is male and identifies as a woman may also identify as a trans woman. One  thing  that  is very important to know is that ‘trans’ and ‘transgender’ are adjectives, not nouns. So, we would  never  call someone ‘a trans or ‘a transgender’; it should always be trans man, trans woman or trans person.

People  might  also be ‘gender non-conforming’, or ‘non-binary’  or ‘gender queer’.  These are  terms that  are  becoming more  prevalent as we continue to recognize that  the gender binary doesn’t represent how many people  feel about themselves. Gender non-conforming people  might  identify and  express themselves as ‘feminine men’  or ‘masculine women’ or something  outside of the traditional categories of ‘boy and man’ and ‘girl and woman’. A person who is gender non-conforming may or may not identify as a trans person.

In many  cultures, gender has  historically been  viewed  as fluid.  Samoan culture includes the  category of ‘third  gender’. People  identifying as third  gender are  born  biologically male but embody both  masculine and  feminine traits. Third  gender individuals are considered an important part of Samoan culture. Among Indigenous communities in North America, ‘Two-spirit’ refers to a person who identifies as having both a masculine and a feminine spirit, and is used by some  Indigenous people  to describe their sexual, gender or spiritual identity.

Cisgender people, on the other hand, are those  whose  gender identity is in line with or ‘matches’ the sex they were assigned at birth. Some people say ‘cis’ for short. For example, when I was born,  they yelled,  ‘It’s a girl!’ and I still identify as a woman today. If we are going to label other gender identities, it is important  that  we also label the more  traditional one, and that  is why we need to use the term  ‘cisgender’ as well. The problem is, the Western context has been  built on assumptions that  people  are cisgender and that there is a gender binary. And as a result, when it comes to analytics, we often don’t have the data we need to understand the people we want to serve.

Think  about it. How  is data  collected? Usually  when  you apply for a driver’s  licence or a job, check in for a flight, or sign up for university classes, you are asked  for your gender, and the only options are male and female. So, people who don’t identify as either a man  or a woman or who change their  gender designation to align with their  identity will feel excluded and  will not be well-captured by the data. These kinds  of data  are used for all sorts  of analyses conducted by the  government and  researchers that inform policies and regulations, as well as corporate strategies.

In some places, governments and companies are coming up with better ways of capturing gender. For example, in the State of Washington and  several other U.S. states, as well as some provinces in Canada and jurisdictions around the world, people have  the  option  of marking ‘X’ or ‘other’  rather than  making a binary choice.

Organizations are  beginning to see ways that  they  can introduce new  products and  services to  include non-binary or trans people.  For example, Mastercard launched ‘True Name’, which  allows  people  to get a credit card  in their  chosen name that  matches their  gender identity even  if they  have  not  gone through the often  difficult and  costly processes to change their names officially.

Gender Analytics  as a methodology recognizes that  data collection and analysis on gender — if it only includes the binary definitions — is inadequate to capture the  realities of our societies,  markets and communities. While, for some,  the idea that there is a wide variety  of gender identities is new, surveys  show that  one-third to two-fifths of people  understand that  there is a spectrum of gender identities; and among young people, it is the majority. Policymakers and corporate leaders will be behind the times if they don’t embrace this reality.

In closing

Gender Analytics  can be useful  both  in creating innovation insights  that  improve impact and  in avoiding downside risks, and it should be embedded into everything an organization does. It’s not just an evaluation tool to analyze retrospectively what impact your  policy,  product, service  or process has  had.  It’s not  just a nice-to-have feature you might discuss from time to time. This is something that can shape how you strategize, plan, innovate and serve customers. 

Sarah Kaplan is Director of the Institute for Gender and the Economy (GATE); Distinguished Professor of Gender and the Economy; and Professor of Strategic Management at the Rotman School of Management. She is the author of The 360° Corporation: From Stakeholder Trade-Offs to Transformation (Stanford Business Books, 2019). In January, GATE launched a five-course Gender Analytics Specialization on Coursera. For details, visit

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