What does feminism mean to U.S. women in 2025? The #MeToo movement is waning while toxic masculinity appears ascendant. Will the gains made by previous generations be lost? Our latest nationally representative survey of 739 women aged 18 and older uncovers a complex and often divided landscape. Views on feminism, gender equality, and social progress are shaped by significant generational, cultural, and racial differences. While the term “feminist” remains contentious, its ideals are supported by a vast majority of women.

Download the report here.

Feminism: A Brief History of a Divisive Identity

The first wave of feminism started in the mid-1800s with the suffrage movement. However, not all women supported it. Many believed that men and women had distinct but complementary roles: men in the public sphere (politics, business), women in the private sphere (home, family, church). The suffragists, however, were successful in gaining equal property rights, educational access, and most notably, the right to vote in 1920.

The term “feminist”, however, didn’t gain popularity until the second wave of feminism in the mid-60s when a new generation of women fought for reproductive freedom and workplace equality. Popular films and television shows of the era like Norma Rae and Mary Tyler-Moore celebrated strong, independent women, while songs like Aretha Franklin’s Respect and Helen Reddy’s I Am Woman became popular empowerment anthems. Around 30% of women identified as feminists at that time, but even then, the term was polarizing.

By the 1980s, during the Reagan era, feminism started facing a backlash. Movies like Fatal Attraction and sitcoms like Family Ties either demonized women or suggested that they return to more traditional roles. This trend continued into the 90s with conservative commentators like Rush Limbaugh coining the “feminazi” label, which conflated feminism with extremism. Powerful women at the time like Hillary Clinton felt pressure to conform to traditional roles and, in Clinton’s case, change her last name from Rodham to Rodham-Clinton to just her husband’s name, Clinton.

The early 2000s saw the rise of conservative media personalities like Sean Hannity, Bill O’Reilly, and Laura Ingram, who promoted traditional gender roles and mocked feminist ideals. That changed in 2017, when the #MeToo movement and the Women’s March (a reaction to Trump’s first term) reenergized the conversation around reproductive rights, workplace harassment, and gender-based violence.

A New Backlash

In 2025, under Trump’s second presidency, the pendulum appears to be swinging back toward cultural conservatism. Thus far, we have seen the following:

• Increased limits on access to reproductive care
• The dismantling of diversity, equity, and inclusion (DEI) programs
• The removal of gender-related material from federal agency websites
• The rescinding of gender identity protections under Title VII

Feminism Today

Today, American women are nearly evenly divided on the term “feminist”:

• 37% embrace it
• 31% reject it
• 32% are unsure or avoid labels altogether

This topline, however, masks deeper generational and racial divides. Our research found that Asian women lead in self-identifying as feminists, but they also express the most uncertainty. Gen Z women are the least likely to reject the label, whereas Millennials are the least likely to adopt it.

These trends suggest a growing discomfort with ideological labels, even as support for feminist principles remains high.

Low Awareness of International Women’s Day

Despite decades of activism, only 44% of women in the U.S. know that March 8th is International Women’s Day (IWD). This limited awareness may be tied to IWD’s roots in European socialist and labor movements, and unlike Mother’s Day or Valentine’s Day, IWD isn’t easily monetizable, so major U.S. retailers and media don’t generally promote it.

Some key facts from our study:

• Awareness is lowest among Boomers (28%) and Non-Hispanic Whites (36%)
• However, 70% of Hispanic and Gen Z women are aware of IWD
• Of those who are aware, only about half participate in IWD activities
• Overall, just one in five women report taking part

Feminism’s Core Values

Our research found that most women define feminism as promoting gender equity, eliminating discrimination, and advancing equality. Gen Z women are especially likely to view feminism as fairness across genders. Yet despite broad agreement on its goals, fewer than 1 in 5 women believe society views feminism positively. Nearly half say it’s perceived negatively.

Other findings include the following:

• Only 40% of women rate gender equality in their workplace or school as “high” or “very high”
• Roughly one in three women say they have faced gender-related challenges, with Gen Z reporting the highest rates
• About one in three have experienced gender-based discrimination, especially in public or professional settings
• Encouragingly, one in three women have noticed greater male involvement in gender equality discussions, although older women are less likely to perceive this shift

What’s Blocking Progress?

Women identify the biggest obstacles to gender equality as:

• Cultural and social resistance
• Lack of education and awareness
• Underrepresentation in leadership
• Economic inequality
• Toxic online content targeting young women, especially noted by Gen Z respondents

What Needs to Change?

When asked which areas need the most urgent attention, women pointed to:

1. Pay equity and workplace opportunities (63%)
2. Gender-based violence (53%)
3. Reproductive rights and healthcare access (50%)
4. Parental leave and childcare policies (46%)
5. Women’s political representation (43%)
6. Rights of marginalized racial and ethnic groups (39%)
7. Gender-inclusive education (31%)

The report breaks out those findings by ethnic and generational differences. With some issues like pay equity resonating most with Boomers at 78% vs. 49% of Gen Z, and others like stopping gender-based violence resonating with 61% of Latinas but only 39% of Black women.  Despite these priorities, optimism about the future of gender equality remains muted. Only 43% of women report feeling optimistic. Optimism is highest among Asian women and Boomers, while Gen Z and Hispanic women are notably more skeptical.

Conclusion

While much work still needs to be done to achieve true gender equality, 43% of women are optimistic about improvement, while only 19% express pessimism. Support for gender equity is strong, but the feminist label remains polarizing. Younger and diverse populations, however, are picking up the mantle and pushing the conversation forward.

At ThinkNow, we believe in amplifying diverse voices to inform brands, policymakers, and advocates on where the conversation on gender equality is headed. Whether or not women embrace the label “feminist,” the values behind it, such as equality, justice, and dignity, remain widely shared. Those ideals matter, regardless of what we choose to call them.

Download the report here.

Veterans are undoubtedly our nation’s heroes. They bring with them a set of skills honed through years of service, skills that, if clearly communicated, can achieve the same success in business that they achieved on missions. The key to transferring these skills to civilian roles is breaking down what was done in a military context into terms that show hiring managers how those capabilities can drive results for a company.

Yet too often, employers overlook or diminish this value. Without awareness, unconscious bias and outdated stereotypes can pigeonhole veterans into narrow roles. The reality is that the discipline, strategic execution, and situational awareness cultivated in service are exactly what organizations need to navigate the complexity of the marketplace and rally teams toward common goals. Employers who are intentional about being inclusive and who make the effort to understand these skills gain access to a high-performing, job ready talent pool.

Community-building within organizations amplifies that impact. Veterans’ networks, for example, offer mentorship and onboarding support from the start of the hiring process. Once hired, employee resource groups provide safe spaces that foster belonging, educate allies, and dismantle biases, ultimately creating an inclusive workplace culture. Even smaller companies can take meaningful steps by partnering with local veteran groups to source talent or provide job training.

In this episode of The New Mainstream podcast, Ari Friedman, Talent Development Manager, Global Early Careers at Microsoft, offers strategies for translating military skills into business impact and creating workplaces where veterans can thrive, benefiting both talent and employers alike.

Synthetic sample is changing how we think about data. Once static, data is now dynamic, opening up possibilities we’re only beginning to understand.

What is Synthetic Sample?

No, we’re not talking about bots or fabricated data. These are intelligent models generated from real data that allow us to simulate behaviors, attitudes, and responses of specific populations with a level of precision and control that traditional methods simply can’t deliver. It’s a way to fill the gaps where panels fall short, whether due to logistical limits, participation bias, or market fatigue.

Why Does It Matter Now?

It matters because the landscape has changed. It’s harder than ever to get people to participate in surveys, especially within diverse and underrepresented communities. There’s fatigue, there’s distrust, and there’s noise.

And while the industry continues chasing the “ideal respondent,” at ThinkNow, we’re building robust analytical models based on real data that allow us to generate insights with more agility, diversity, and depth.

The benefits of synthetic sample are clear:

• Speed: No need to wait days to collect responses, we model scenarios in real time.
• Smart Representativeness: We blend panel data, external sources, and demographic attributes to build models that reflect the complexity of the real world.
• Privacy & Ethics: By using anonymized data and advanced modeling techniques, we reduce exposure of sensitive information without compromising analytical value.
• Scenario Exploration: Want to know what happens if one variable is changed or how a group reacts to X or Y? With synthetic sample, you can explore possibilities without launching a new survey every time, saving time and costs.

It’s important to note that synthetic data is not a replacement for people. It’s an amplifier.

Synthetic doesn’t replace human voices, it only enhances them. It enables us to utilize our existing data in more strategic and responsible ways, such as helping to fill data gaps, anticipate trends, and design better questions.

And when we combine that with our real, culturally diverse communities – people who are genuinely motivated to share their opinions – the result is a robust, more agile, and far more representative insights ecosystem.

How we do it at ThinkNow:

Step 1: Integrate real data from our multicultural research.
Step 2: Apply AI and machine learning techniques to model specific audiences.
Step 3: Validate models through observable behavior and direct feedback.

We do all of this with a team that understands culture, context, and the responsibility of representing authentic voices within synthetic models.

The future isn’t just digital, it’s hybrid.

We’re moving past methods that only work “when everything goes right.” We’re investing in research that’s more resilient, more human, and yes, more intelligent. Because in the end, it’s not just about collecting responses. It’s about understanding people. With synthetic sample, we’re opening new ways to do exactly that.

Want to learn more about how ThinkNow is using synthetic sample to improve the accuracy and diversity of research? Reach out. We’re building the future of insights, and you can be part of it.

Are AI tools inclusive? 

We're halfway through 2025 and one thing is undeniable: AI is no longer on the horizon, it is in the room. For the market research industry, this has come faster than most expected. What felt like an existential threat just a year ago is now transforming how researchers approach everything from segmentation to recruitment to data analysis.

But as AI becomes embedded in our workflows, a critical question arises. Are the datasets powering these models truly inclusive? Do they reflect the diverse populations researchers aim to understand, or are they building the next generation of tools on top of the same old blind spots?

Why traditional datasets pose risks 

Market research has long struggled with inclusivity. Reaching Spanish-dominant Latinos, Gen Z respondents and even male participants has always been difficult. Despite decades of effort, many of these groups continue to be underrepresented in online panels and large-scale studies.

Now, imagine deploying AI on top of these incomplete datasets. Instead of closing representation gaps, AI trained on biased data risks amplifying them at scale. Biases that were once isolated can now be baked into algorithms and amplified across the entire research ecosystem, undermining the potential of AI to drive more inclusive insights. 

AI’s pivot from threat to tool

When AI began gaining traction in the industry, initial skepticism emerged among some researchers, particularly regarding the use of synthetic data and AI-powered moderators. These tools seemed impersonal, disconnected from the human insights that drive understanding and trust among respondents.

Yet, over time, AI has proven itself capable of complementing, rather than replacing, researchers’ work. Instead of diluting what makes insights meaningful, AI can expand them by enabling researchers to finally address representation issues that more conventional methods have never been able to. This shift has prompted a more intentional approach to innovation. If synthetic data is going to shape the future of insights, it must be inclusive by design, representing the full diversity of the populations it aims to model.

How market research drives ethical AI

The market research industry is uniquely positioned to lead in this space. While many tech companies face lawsuits for training AI on copyrighted or illegally scraped data, researchers have operated under strict privacy laws like GDPR and CCPA for decades. Upholding consent, data stewardship and adherence to ethical standards has been the norm.

Our datasets are not only large, but they are also permission-based and carefully vetted. This makes them ideal for training AI models that need to mirror real-world diversity.

But it is not enough to have access to data. The same rigor applied when building representative samples must be applied to training AI models. This means proactively identifying gaps, asking who is missing from the data and taking measurable steps to responsibly include them.

Rethinking multicultural market segmentation

This brings us to the future of multicultural segmentation. Relying solely on broad demographic categories or historical internal datasets is no longer sufficient. Today’s consumers are multidimensional, and AI gives us the tools to see them more clearly. 

To generate synthetic data that accurately reflects multicultural audiences, it is essential to incorporate information from historically underrepresented communities. This requires collaboration between technologists and cultural experts, as well as a commitment to designing systems that accurately reflect the reality of diverse identities.

For researchers generating synthetic datasets, combining privacy-compliant methods with culturally rich data points, powered by AI, helps ensure that communities often left out of the conversation are fully represented moving forward. 

The road ahead

AI is not a passing trend. It is here to stay, and it is reshaping how we segment audiences, recruit respondents and activate insights. However, AI’s success depends on the quality and inclusiveness of the data behind it, and the researchers guiding its application.

For market research professionals, this is a challenge worth embracing. With deep expertise, ethical frameworks and a foundation in representative sampling, the industry is uniquely positioned to ensure that AI serves all communities, not just the most accessible ones.

The future of multicultural segmentation will belong to those who successfully integrate innovation and intention because the question is no longer whether to adopt AI, but how to use it in a way that advances representation. 

Those investing in synthetic data and inclusive segmentation strategies play a crucial role in achieving this, and those seeking better representation in data must continue to demand it.

This blog post was originally published on Quirk's Media.

As the workforce becomes increasingly diverse—not just in terms of race and ethnicity, but also in education, language, and lived experiences—brands must rethink how they communicate internally. It's not enough to craft a compelling message if it isn’t understood, or worse, if no one cares.

For communication to be effective, it must be accessible, relevant, and actionable. Employees need to clearly grasp what the message means for them and what, if anything, they’re expected to do next. Without that clarity and connection, even the most well-crafted message falls flat.

Effective internal communication begins with empathy, which starts with recognizing the diversity of today’s workforce. Across a single organization, employees may span multiple job shifts, job functions, languages, education levels, and cultural backgrounds. Inclusive communication must be multilingual, multi-channel, and well-timed to meet people where they are, both physically and cognitively.

Traditional top-down communications often fall short because they’re designed for a single type of audience. However, when messages are designed with a broader range of identities in mind, and supported by data, feedback, and direct human connection, they drive authentic engagement and build trust. Employees feel seen, heard, and valued, and they recognize the company’s effort to include them.

When language barriers exist, translating core messages into employees’ native languages and using transcreation to adapt them for cultural context becomes essential. Communication plans must consider how different audiences will interpret a message, what cultural context might alter its meaning, and, most importantly, why they should care.

In this episode of The New Mainstream podcast, Jenna Marston, Communications Manager at BASF, shares how she uses inclusive, multilingual strategies to engage employees across geographies, leveraging an approach rooted in active listening, cultural awareness, and authentic connection.

Meet Jenna:

Jenna Marston is the BASF Communications Manager for Freeport, Texas. In this role she leads crisis management, government and community relations, and employee engagement. Prior to her joining the BASF team, Jenna was the Global Marketing and Communications Leader for the Corteva Agriscience Biologicals Business, collaborating across international sites to align corporate and product brand positioning while leading communications strategies to accelerate business results. 

With a belief that we can accomplish the greatest challenges of today together, Jenna’s career has focused on driving impact at corporations dedicated to supporting the harmonization of human health and productivity to solve some of the world’s greatest challenges.

Let’s face it! Traditional research panels aren’t cutting it anymore.

For years, market research has relied on large pools of pre-profiled individuals, often referred to as “panels,” to generate insights at scale. And while panels gave us reach and reliability, they also lulled the industry into a comfort zone, where respondents became data points, not people.

But the world has shifted. Audiences have evolved. Attention spans have shortened. Expectations have skyrocketed.

At ThinkNow, we believe it’s time to rethink how we engage respondents not as panelists, but as people.

The Problem with the “Professional Respondent”

You know the type, the person who’s in 15 panels, knows the right answers, and is simply rushing to the incentive. They’re the product of outdated engagement models where surveys are transactional, not relational.

This results in flat data, low authenticity, and insights that don’t reflect reality, especially when researching diverse, underrepresented communities where trust and context matter.

What Needs to Change?

The industry must move from mass reach to meaningful engagement. That means:

• Contextual Relevance: Surveys that reflect people’s lived experiences, languages, and cultural realities.
• Trust First: Especially with multicultural audiences, engagement starts with trust, not a survey link.
• Mobile-First, Human-Centric Design: It’s 2025 — stop designing surveys like it’s 2009. Respondents expect intuitive, mobile-first experiences.
• Rewarding Value, Not Just Time: Incentives should feel fair and respectful, not like a barter system for opinions.

Turning Panels into Communities

The online sample team at ThinkNow is building more than panels. We’re nurturing communities of people who want to be heard and willingly share their opinions, providing zero-party data brands can trust. Our approach combines cultural fluency, smart segmentation, and behavioural insights to go beyond checkbox answers.

We're also exploring new frontiers, including synthetic data modeling, AI-driven recontact strategies, and authentic content integration that makes surveys feel less like tests and more like conversations.

Because at the end of the day, insights don’t come from checkboxes. They come from connection.

The Way Forward

It’s time we ask ourselves: Are we collecting data, or are we listening? The future of market research lies in making every respondent feel like their voice matters, because it does. Let’s ditch the dusty “panelist” label and treat our respondents like what they truly are: individuals with stories, context, and value.

When we do that, the insights take care of themselves.

People make assumptions. While that may seem like a common character flaw, it can have serious implications on brand perception. When marketers rely on outdated stereotypes and beliefs about the American public, they are ignoring the complex reality in which consumers live. Today’s consumer is far more nuanced than the binary labels imposed upon them, e.g., Democrat vs. Republican. Clinging to binary frameworks in a rapidly shifting cultural and political landscape leaves brands vulnerable to costly missteps.

To avoid pitfalls, brands must do the work upfront. Trust in traditional institutions may be eroding, but people still want something to believe in. This creates opportunities for marketers to partner with market researchers to do a deep dive into the cultural drivers that activate and define the audiences being engaged.

But navigating today’s sensitivities requires more than curiosity. It demands intentionality. Brands must know who they are, know who they’re speaking to, and test their messaging, values, and assumptions across lines of identity. Many Americans share core values like freedom and fairness, but how those values are interpreted depends on who you ask. That’s why words matter.

There’s often a gap between what brands think their words mean, what they intend them to mean, and what people actually hear. Closing that gap is critical. But brands that attempt to please everyone risk saying nothing at all. Instead, marketers are encouraged to double down on their core identity and speak directly to their audience, even if it means not appealing to everyone.

In this episode of The New Mainstream podcast, Julia Glidden, Group President, U.S. Public Affairs and Ruth Moss, SVP, Senior Client Officer at Ipsos North America unpack the findings from the newly released “Know the New America” report that explores how political, cultural, and economic shifts are transforming the consumer and business landscape.