Google’s Gender Bias in the clothing industry

In the world of digital marketing, understanding how search engines influence consumer behaviour is crucial. At Space & Time, we’re committed to analysing trends in SEO that impact businesses across industries. One fascinating area of study is how gender plays a role in Google’s search results, particularly within the clothing industry. I recently conducted an in-depth review of how search terms related to clothing yield results that seem to prioritise one gender over another. This blog delves into those findings and offers insights into what brands need to know about potential gender bias in Google’s search algorithms. 

It is important to acknowledge that I recognise that ‘gender’, especially when it comes to clothing, is purely what society has deemed it to be. This piece is not looking to stereotype or promote any kind of gender-normalisation in clothing. It is merely an observation of how brands are using this language in their content to appear on the web.  

Is Google Steering Gender Bias in Fashion? 

Over years of working in SEO for eCommerce, a recurring observation has come up: search terms that should provide gender-neutral results often skew towards one gender, primarily women. For example, searching for something as basic as “coat” tends to return pages filled with women’s clothing, leaving those searching for men’s options to refine their search with a “men’s” modifier. 

Though this isn’t a huge inconvenience, it highlights potential biases in how Google presents fashion results. These nuances could affect user experience (UX) and the visibility of certain products. So, is this trend driven by Google’s algorithm or by consumer preferences? Let’s dive into the data. 

Breaking Down Google’s Gender Bias in Clothing Search Results 

For ease of viewing, the graphs have been broken down into sections where there are at least 5 of the top 10 results in each category. In total there are 85 bars because some terms there were an equal split of non-specified and gender (i.e. 5 of each). 

Here’s what the data revealed: 

• 34 results showed at least 50% non-specified gender. 
• 30 results were dominated by at least 50% women’s clothing.
• 26 results were dominated by at least 50% men’s clothing. 

In total, there were: 

• 350 non-specified results, 
• 253 women’s clothing results, 
• 243 men’s clothing results, 
• 2 results explicitly targeting both men and women. 

The most balanced result we found was for the search term “jacket,” which was evenly split between men and women. Interestingly, some categories leaned heavily towards one gender, while others remained more neutral. 

Key Factors Behind Gender-Specific Results

1. Products Don’t Always Specify Gender

In many cases, Google shows results for products that don’t specify gender at all. A prime example is the search term “gloves,” where work gloves (typically not gender-specific) are more common in the results than fashion-oriented mittens. 

2. Clothing Items Traditionally Associated with One Gender

Some clothing items are almost exclusively marketed to one gender. Take the term “ballgown,” for instance. While all the search results were for women, none explicitly stated “for women.” This suggests that brands assume consumers will know the intended audience, relying less on gender-specific keywords. 

3. Navboost: Consumer Behaviour Driving Search Results

There are many good definitions and details out there about how engagement on the search results affects what is shown.  But one of the best comes from a SparkToro article: 

“Google examines clicks and engagement on searches both during and after the main query (referred to as a “NavBoost query”). For instance, if many users search for “Rand Fishkin,” don’t find SparkToro, and immediately change their query to “SparkToro” and click SparkToro.com in the search result, SparkToro.com (and websites mentioning “SparkToro”) will receive a boost in the search results for the “Rand Fishkin” keyword.” 

In our example, users may be searching for ‘Jumper’ and originally the results may have been split between men and women (in this example the film Jumper (2008) also appears).  However, over time, more women have clicked on results that specify ‘women’ in the title, and consequently the results are now populated with predominantly women’s clothing.   

The same could be said of ‘Bomber jacket’.  The results show a gender mix, but predominantly men’s clothing, and the women’s clothing is now further down the page. Eventually women’s clothing might get pushed out completely as the data has led Google down this path.  

4. Search Volumes and Gender Preference

Search volumes also play a significant role. Data shows that search terms related to men’s clothing often have higher volumes than their women’s counterparts. For instance: 

• “Men’s joggers” sees 18,000 searches per month, while “women’s joggers” only gets 6,000. 

• “Men’s t-shirt” is searched 4,000 times, compared to just 900 searches for “women’s t-shirt.” 

Google’s algorithm is likely responding to these trends by prioritising men’s clothing when gender is unspecified in the search.  

There is also a lot more of what I would call Google’s classic algorithm criteria being fed in e.g. how many links go to the men’s pages vs the women’s ones: 

How Does This Impact Your SEO Strategy? 

Understanding Google’s potential gender bias is critical for brands in the fashion industry. If you sell specifically men’s clothing, it may be more effective to optimise for gender-specific keywords like “men’s blazers” rather than general terms like “blazers.” Additionally, ensuring your product data is correctly structured with the use of product schema and rich media can help secure better visibility on search results pages that include images and shopping features. 

One other thing to consider is the level of personalised search Google will add on top of this.  This element is not new to search or hidden by Google, as the goal of personalisation is ultimately to benefit the user.  Whether this personalisation knows users to a level of their gender specifically is harder to say.  Rather than a clear ‘person x is male’ the algorithm is just pulling a variety of signals to form an impression of a consumers likes and dislikes. 

Finally, analysing how consumers interact with your listings is key. If your high-ranking page has a low click-through rate (CTR), it might be time to revisit your keyword strategy and adjust your content to align with consumer expectations, whether gendered or not. 

Conclusion: Take Charge of Your SEO in a Gender-Biased Landscape 

Brands can’t afford to overlook how search engine algorithms and user behaviours shape their visibility. Whether you’re in fashion or any other industry, being aware of how Google might categorise and display your products is crucial for an effective SEO strategy. At Space & Time our SEO experts are here to help you navigate these nuances and ensure your products get the attention they deserve. 

Ready to optimise your search strategy? Get in touch with our SEO team today and let’s ensure your brand reaches the right audience—without bias. 

Note on Methodology: 

To do this experiment I took 80 terms, types of clothing without stating gender e.g. ‘coat’ and not ‘mens coat’ and looked at the top 10 results for each. 

I found that some terminology was not suitable, for example ‘braces’ was most commonly showing the things you put in your teeth rather than suspenders.  Therefore, these items were removed from the list.   

For the benefits of this study, I did not look at multiple variations on a term e.g. ‘coat’, ‘coats’, ‘warm coats’, ‘summer coats’ etc. There are a couple of exceptions to this for example ‘bomber jacket’ and ‘bow tie’ are both used, but these have been used as they will alter the clothing style enough.  

The method for establishing the gender was via using the content in the <title> tag of the page and whether ‘man’, ‘male’ or similar terms were used, and the same process for women.  Where neither gender terminology was used it was categorised as ‘non-specified’. Finally, there were some examples where both existed categorised as ‘both’.   

This data also only considers ‘standard’ organic results and not any of the featured results such as image blocks, product sections, videos etc.  This has been chosen to ensure that most of the terms tested come to approximately 10 results.