Article

6min read

Test, Dress, Impress:  Top Fashion Consumer Trends 2025

Forget traditional shopping journeys, today’s fashion consumers are rewriting the rules! Our 2025 Fashion Consumer Trends report reveals the shifts in how consumers discover, decide, and commit to fashion brands today.

Introduction

In a recent webinar, 3 experimentation leaders came together to unpack the latest consumer trends shaping the fashion industry. The conversation brought together Ben Labay, CEO of Speero, Jonny Longden, Speero’s Chief Growth Officer, and Mary Kate Cash, Head of Growth Marketing for North America at AB Tasty. They shared valuable insights from AB Tasty’s recent global fashion consumer survey, highlighting what drives inspiration, conversion, and retention in today’s fast-evolving fashion landscape.

Social Media is Changing the Game 

Traditional search engines remain the top channel for fashion discovery, followed by direct website visits, Google Shopping, and Social Media ads. However, the differences between these top four channels are shrinking year over year, with social media rapidly gaining ground, especially among Gen Z consumers, where 60% of survey respondents highlighted Social Media ads as their preferred avenue to finding new products. Jonny predicts this trend will expand across all age groups. 

“Social and fashion just go so hand in hand. The big change that’s happened with social is that fashion itself has become more rapid in the way it changes, and so it’s really driving different consumer behaviour.”

Jonny Longden, Chief Growth Officer at Speero

Different Channels, Different Mindsets

People use search when they know what they want. Social media, on the other hand, encourages experimentation. As Ben pointed out, shoppers arriving from social media are often inspired to try new styles or connect with communities, engaging in “social shopping” and not just focusing on finding a specific product. This opens the door for more tailored experiences based on where customers are coming from and what type of inspiration they’re seeking.

Reward Loyalty in Meaningful Ways – When asked how brands could make customers’ experiences more personal, the top answer was clear: rewarding brand loyalty. Discounts, early access, or perks for repeat buyers make shoppers feel seen and increase the chances of account creation and repeat visits. 

Jonny pointed out that “the really interesting tension in this whole industry at the moment is the difference between what is the right thing to do and what is the profitable thing to do. about finding that balance is experimentation in the broadest sense of the word.”

Make Recommendations That Actually Fit – Consumers want relevant suggestions that go beyond basic personalization. Jonny compared it to having a personal stylist: a brand should know both the customer and the market, understanding trends and styles while matching these to individual preferences.

Personalization - fashion trends

What Actually Drives Conversions

When it comes to converting browsers into buyers, shoppers across generations are surprisingly aligned. 

Product quality leads the way across all age groups and regions. Shoppers are still willing to pay for craftsmanship, comfort, and durability, even in a price-sensitive market.

Discounts come next, but the strategy matters. Overuse can cheapen brand perception. As Jonny put it: “Fashion, especially the lower price point fashion has ended up in a kind of race to the bottom where discounting is the way to compete. […] and a lot of consumers wouldn’t consider paying full price. The challenge is how to be careful with the commerciality of discounting.”

Discounts - fashion trends

Sizing and fit clarity also ranks high, especially in fashion, where hesitation often comes from uncertainty about how something will feel or look. Ben noted that some major retailers are tackling this head-on, investing heavily in tools to improve sizing and try-on experiences.

For Gen Z, high-quality reviews and transparency around production methods, sustainability, and pricing are big drivers. Ben shared tactical approaches to transparency on product detail pages, like using engaging CTAs such as “Do you want to know a secret?” to reveal value props related to sustainability and ethical production.

Why Shoppers Abandon Carts

Cart abandonment remains a major friction point, and two reasons dominate globally:

  1. Not ready to buy – Many shoppers use the cart to explore shipping, delivery timeframes, or total cost before making a decision. Jonny explained it simply: “People use the checkout of an ecommerce website just to see what’s gonna happen. […] When’s it gonna be delivered? What are the delivery options? How much is delivery gonna cost? 
  2. Payment Methods not being accepted – This came in a close second, showing how overlooked payment flexibility still is. Buy-now-pay-later options like Klarna may move the needle, especially in fashion, where customers often purchase multiple sizes with the intention of returning some items. Jonny emphasized that payment method testing is one of the best arguments for AB testing and experimentation, as the “best practice” of offering many payment options doesn’t always lead to better conversion.

Retention: Loyalty Built on Familiarity

Finally, we explored what drives customers to create accounts with fashion brands, buy products from them, and what motivates them to stick around.

Loyalty Rewards Drive EngagementGlobally, the top reason for account creation is earning loyalty points, especially among Gen Z and Millennials. Discounts and sale updates follow closely behind.

Balancing Novelty and Trust – Shoppers crave both newness and familiarity: new products ranked highest in driving retention, but previously purchased items and trusted brands followed close behind. This balance is key to keeping customers engaged long-term.

Jonny raised an interesting point: a lot of loyalty programs end up rewarding people who would have come back anyway. Mary Kate added that tools like segmentation can help brands tell the difference between genuinely loyal customers and those just passing through, making it easier to design rewards that actually make an impact.

While conventional wisdom discourages forced account creation, Ben challenged this assumption, arguing it can work when paired with compelling promotions or rewards, especially in social ads. “Social ads that inspire and combine short-term promotions, rewards, and discounts are increasingly leading into forced account creation sequences.”

Conclusion

As shown in our 2025 Fashion Consumer Trends report, the e-commerce fashion industry is evolving, along with consumer expectations. To remain competitive, brands must go beyond simply selling products. They must deliver seamless, personalized shopping experiences that speak directly to the modern shopper’s needs.

This is where experimentation becomes a critical advantage. The most successful brands are those willing to test assumptions about everything from product discovery and presentation to payment options, loyalty strategies, and the evolving role of social commerce. Experience optimization is no longer a nice-to-have. It’s the foundation for building trust, loyalty, and long-term growth in the fast-moving world of online fashion.


Want a deeper dive? Watch the full webinar below to hear expert insights and practical strategies shaping the future of fashion commerce.

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Article

6min read

Minimal Detectable Effect: The Essential Ally for Your A/B Tests

In CRO (Conversion Rate Optimization), a common dilemma is not knowing what to do with a test that shows a small and non-significant gain. 

Should we declare it a “loser” and move on? Or should we collect more data in the hope that it will reach the set significance threshold? 

Unfortunately, we often make the wrong choice, influenced by what is called the “sunk cost fallacy.” We have already put so much energy into creating this test and waited so long for the results that we don’t want to stop without getting something out of this work. 

However, CRO’s very essence is experimentation, which means accepting that some experiments will yield nothing. Yet, some of these failures could be avoided before even starting, thanks to a statistical concept: the MDE (Minimal Detectable Effect), which we will explore together.

MDE: The Minimal Detectable Threshold

In statistical testing, samples have always been valuable, perhaps even more so in surveys than in CRO. Indeed, conducting interviews to survey people is much more complex and costly than setting up an A/B test on a website. Statisticians have therefore created formulas that link the main parameters of an experiment for planning purposes:

  • The number of samples (or visitors) per variation
  • The baseline conversion rate
  • The magnitude of the effect we hope to observe

This allows us to estimate the cost of collecting samples. The problem is that, among these three parameters, only one is known: the baseline conversion rate

We don’t really know the number of visitors we’ll send per variation. It depends on how much time we allocate to data collection for this test, and ideally, we want it to be as short as possible. 

Finally, the conversion gain we will observe at the end of the experiment is certainly the biggest unknown, since that’s precisely what we’re trying to determine.

So, how do we proceed with so many unknowns? The solution is to estimate what we can using historical data. For the others, we create several possible scenarios:

  • The number of visitors can be estimated from past traffic, and we can make projections in weekly blocks.
  • The conversion rate can also be estimated from past data.
  • For each scenario configuration from the previous parameters, we can calculate the minimal conversion gains (MDE) needed to reach the significance threshold.

For example, with traffic of 50,000 visitors and a conversion rate of 3% (measured over 14 days), here’s what we get:

MDE Uplift
  • The horizontal axis indicates the number of days.
  • The vertical axis indicates the MDE corresponding to the number of days.

The leftmost point of the curve tells us that if we achieve a 10% conversion gain after 14 days, then this test will be a winner, as this gain can be considered significant. Typically, it will have a 95% chance of being better than the original. If we think the change we made in the variation has a chance of improving conversion by ~10% (or more), then this test is worth running, and we can hope for a significant result in 14 days.

On the other hand, if the change is minor and the expected gain is less than 10%, then 14 days will not be enough. To find out more, we move the curve’s slider to the right. This corresponds to adding days to the experiment’s duration, and we then see how the MDE evolves. Naturally, the MDE curve decreases: the more data we collect, the more sensitive the test becomes to smaller effects.

For example, by adding another week, making it a 21-day experiment, we see that the MDE drops to 8.31%. Is that sufficient? If so, we can validate the decision to create this experiment.

MDE Graph

If not, we continue to explore the curve until we find a value that matches our objective. Continuing along the curve, we see that a gain of about 5.44% would require waiting 49 days.

Minimum Detectable Uplift Graph

That’s the time needed to collect enough data to declare this gain significant. If that’s too long for your planning, you’ll probably decide to run a more ambitious test to hope for a bigger gain, or simply not do this test and use the traffic for another experiment. This will prevent you from ending up in the situation described at the beginning of this article, where you waste time and energy on an experiment doomed to fail.

From MDE to MCE

Another approach to MDE is to see it as MCE: Minimum Caring Effect. 

This doesn’t change the methodology except for the meaning you give to the definition of your test’s minimal sensitivity threshold. So far, we’ve considered it as an estimate of the effect the variation could produce. But it can also be interesting to consider the minimal sensitivity based on its operational relevance: the MCE. 

For example, imagine you can quantify the development and deployment costs of the variation and compare it to the conversion gain over a year. You could then say that an increase in the conversion rate of less than 6% would take more than a year to cover the implementation costs. So, even if you have enough traffic for a 6% gain to be significant, it may not have operational value, in which case it’s pointless to run the experiment beyond the duration corresponding to that 6%.

MDE graph

In our case, we can therefore conclude that it’s pointless to go beyond 42 days of experimentation because beyond that duration, if the measured gain isn’t significant, it means the real gain is necessarily less than 6% and thus has no operational value for you.

Conclusion

AB Tasty’s MDE calculator feature will allow you to know the sensitivity of your experimental protocol based on its duration. It’s a valuable aid when planning your test roadmap. This will allow you to make the best use of your traffic and resources.

Looking for a free and minimalistic MDE calculator to try? Check out our free Minimal Detectable Effect calculator here.