Gender Differences in Product Usage Analytics

Credit cards were built with men as the default user; women came in as add-ons Only 12% of India’s credit card holders are women, and just 1 in 4 digital payments users is female. By those odds alone, I was never meant to be CRED's "average" user. But I’ve become one of their most engaged. Not just paying bills and logging out, but staying to explore, shop, and discover. Somewhere along the way, I started buying shampoo, artisanal honey, and the kind of niche brands I’d never have otherwise stumbled across. (Credit card rewards introducing me to a new conditioner was not on my 2025 bingo card.) Here’s the thing: for decades, credit cards, and most financial products, have been designed and optimized for men. When women are factored into design, adoption looks different. The data supports it: female-led fintechs see around 58% women customers on average, compared to just 35% for others. That isn’t coincidence; it’s perspective at the design table translating into participation in the market. The assumption was that men were the primary earners, spenders, and decision-makers. Even today, much of the UX, product marketing, and rewards design is still anchored in that mindset. Which is why my experience feels interesting to me. CRED, for me, is less about cashback or coins and more about discovery and delight. And in the process, I’m pretty sure I’m doing my bit for their DAU and MAU charts. Kunal Shah has often pointed out how underrepresented women are in financial products, both as users and as product shapers. When women are factored into design, adoption looks different. Data shows that female-led fintechs serve almost twice as many women customers as others. That isn’t coincidence, it’s what happens when diverse perspectives shape products from the ground up. So while I may not be the “average” user, maybe that’s the point. Sometimes the non-average users, the ones buying shampoo and honey on what’s technically a bill payment app, end up reshaping how the product is experienced. And if you’re looking at your charts right now, CRED, you’re welcome. What about you? do you find fintechs are building for you, or are you just fitting into what was built for someone else?

❓ Do Men and Women Use ChatGPT Differently ❓ New data from OpenAI suggests the answer is YES. The conversation around Generative AI's impact often focuses on the technology, not who is using it. The latest research on ChatGPT usage by millions of people reveals two key shifts: a move toward gender parity and distinct differences in how men and women are leveraging the tool. Let's start with usage. The initial significant GenAI gender gap has nearly disappeared. In the first few months after ChatGPT's launch, around 80% of active users had typically masculine names. By June 2025, that number had declined to 48%, with active users slightly more likely to have typically feminine names. This signals that ChatGPT usage basically mirrors the general population. Now, let's look at usage behaviour. While adoption is reaching parity, men and women still favor different applications: ♀️ Users with typically feminine first names are relatively more likely to use ChatGPT for 'writing' and 'practical guidance'. ♂️ Users with typically masculine first names are more likely to use it for 'technical help', 'seeking information', and 'multimedia' (e.g., creating or modifying images). Beyond usage, users with typically masculine names are more likely to engage in "Asking" behavior ('seeking information' or 'advice for decision support'), while users with typically feminine names show a greater propensity for "Doing" messages (requesting the model to complete a task, such as drafting a document) within their work-related messages. This aligns with the topic differences, where "Doing" is heavily skewed toward 'writing', a category more frequently used by those with typically feminine names. Here's a 🔗 to the academic article containing the full results: https://lnkd.in/eFQPEgEc IMD #GenerativeAI #FutureofWork #ChatGPT #GenderParity #AILiteracy

Women are adopting AI tools at a 25 percent lower rate than men on average. This is according to research by Harvard Business School Associate Professor Rembrand Koning. With my product hat on, I’m curious about why this is. This is “despite the fact that it seems the benefits of AI would apply equally to men and women.” Ok. But is the build designed to address the needs for all… in order to achieve these benefits? Why the gap? The research suggests women are concerned about the ethics of using the tools and may fear they will be judged harshly in the workplace for relying on them. Are these real pain points of women being addressed? A good place to start is at the root, by listening, understanding, and addressing the needs of users. Koning noted: ➡️ Women appear to be worried about the potential costs of relying on computer-generated information, particularly if it’s perceived as unethical or “cheating.” ➡️ Women face greater penalties in being judged as not having expertise in different fields.  They might be worried that someone would think that even though they got the answer right, they ‘cheated’ by using ChatGPT. To design and build for all AND achieve intended outcomes: “It’s important to create an environment in which everybody feels they can participate and try these tools and won’t be judged for [using them],” Koning says. It really comes down to understanding and addressing the real pain points and concerns that women have. #AI #AILiteracy #ProductManagement

The "Communication Tools Survey 2025, conducted by the Tokyo University of Technology among 1,589 new students, reveals how digital habits are evolving—and how gender plays a key role. 📊🧑🤝🧑 📱 LINE remains the top communication tool (99.3% usage) 📸 #Instagram climbs to 2nd place, with rising male engagement narrowing the gender gap 👩🦰 #BeReal—an app encouraging spontaneous, unfiltered photos—sees high usage among female students (nearly 50%) 🎮 Male students heavily favor #Discord (57.1%) for both messaging and community interaction 🎥 #YouTube continues to dominate; #TVer, a Japanese online streaming service, steadily gains popularity 📲 #iPhone use remains high (78%) but is slightly declining in favor of other systems 💳 96.8% use digital payments; Japanese payment system #PayPay leads, growing 2.3× in three years 🛒 Online shopping is nearly universal, with ~90% of students participating These results show interesting gender patterns: female students are more active on visual and lifestyle platforms like BeReal, Pinterest, and TikTok, while male students favor interactive tools like Discord. Overall, the data points to a digitally fluent generation whose platform choices increasingly reflect their social identities, communication styles, and everyday routines. #SocialMediaTrends #YouthDigitalBehavior #BeReal #GenderDigitalDivide #CashlessSociety #ECommerce #JapaneseHigherEducation #InnovativeJapan https://lnkd.in/gzY7d9V9

As GenAI becomes more ubiquitous, research alarmingly shows that women are using these tools at lower rates than men across nearly all regions, sectors, and occupations.   A recent paper from researchers at Harvard Business School, Berkeley, and Stanford synthesizes data from 18 studies covering more than 140k individuals worldwide.   Their findings:   • Women are approximately 22% less likely than men to use GenAI tools • Even when controlling for occupation, age, field of study, and location, the gender gap remains • Web traffic analysis shows women represent only 42% of ChatGPT users and 31% of Claude users   Factors Contributing the to Gap:   - Lack of AI Literacy: Multiple studies showed women reporting significantly lower familiarity with and knowledge about generative AI tools as the largest gender gap driver. - Lack of Training & Confidence: Women have lower confidence in their ability to effectively use AI tools and more likely to report needing training before they can benefit from generative AI.   - Ethical Concerns & Fears of Judgement: Women are more likely to perceive AI usage as unethical or equivalent to cheating, particularly in educational or assignment contexts. They’re also more concerned about being judged unfairly for using these tools.   The Potential Impacts: - Widening Pay & Opportunity Gap: Considerably lower AI adoption by women creates further risk of them falling behind their male counterparts, ultimately widening the gender gap in pay and job opportunities. - Self-Reinforcing Bias: AI systems trained primarily on male-generated data may evolve to serve women's needs poorly, creating a feedback loop that widens existing gender disparities in technology development and adoption.   As educators and AI literacy advocates, we face an urgent responsibility to close this gap and simply improving access is not enough. We need targeted AI literacy training programs, organizations committed to developing more ethical GenAI, and safe and supportive communities like our Women in AI + Education to help bridge this expanding digital divide.   Link to the full study in the comments. And a link also to learn more or join our Women in AI + Education Community. AI for Education #Equity #GenAI #Ailiteracy #womeninAI

Recent studies show that women use generative AI technologies significantly less often than men. According to research of Bank for International Settlements, 50% of men in the United States have used artificial intelligence (AI) in the past year, compared to just 37% of women. Weekly usage rates show a similar disparity: 19% of men use AI tools regularly, while only 12% of women do so. World Economic Forum highlights that this gender gap exists across all age groups, but it is most pronounced among Gen Z. For example, 71% of men aged 18–24 use AI weekly, compared to only 59% of women in the same age group. Even when occupying similar roles, women are less likely than men to use AI at work, according to the study published by Anders Humlum of the University of Chicago and Emilie Vestergaard of the University of Copenhagen. For instance, only one-third of female teachers use tools like ChatGPT, compared to half of their male counterparts. In software development, nearly two-thirds of men use AI, while fewer than 50% of women do. This gap persists even within the same companies and roles, where women are 20 percentage points less likely to use AI. According to UN Women report, women have less access to technology and spend less time online compared to men. This digital gender gap worsens the lack of representation in AI training datasets, resulting in algorithms that reinforce both gender and racial stereotypes. A study of 133 AI systems revealed that 44% exhibited gender bias, while 25% displayed both gender and racial biases. For instance, when asked to create a story involving a doctor and a nurse, the AI typically portrays the doctor as male and the nurse as female, regardless of the prompt. These patterns persist because the AI's training data encode societal stereotypes, embedding associations between specific genders and roles or skills. Implications for society If this gap remains unaddressed, men, as the primary users of AI, will gain advantages in career progression and higher salaries, while women will face additional barriers in the workplace. AI technologies predominantly developed and used by men risk being tailored to male preferences while neglecting the needs of women. This could lead to algorithms that are less effective or even discriminatory against women. https://lnkd.in/dTJyV54r