Article

7min read

From Search to Checkout: 10 Data-Driven E-commerce Trends for 2025 

E-commerce has completely changed the way shoppers interact with their favorite brands.

From the continued rise of mobile commerce to virtual-reality try-on tools and AI customer service, some consumer trends have proven to be evergreen while others fall out of fashion in a season. As e-commerce marketers, it can be hard to know when to chase a trend or stick to being consistent. 

To help you better understand the mind of today’s consumers, we’ve broken down 10 key insights for e-commerce from our 2025 global report. Based on feedback from 4,000 consumers across the U.S., U.K., France, Italy, and Australia, this snapshot reveals how people discover new products, engage with AI, make purchase decisions, and much more.

1. Google Search is the first place for discovery

When it comes to starting an online shopping journey, Google Search is still king. Nearly two-thirds (63%) of global shoppers begin their hunt for a new product or service with a Google search. 

This underscores the ongoing importance of SEO for e-commerce brands. If your product pages aren’t optimized, you risk missing out on a massive audience at the very first step of their journey.

2. Mobile takes over, but desktop still matters

By the end of 2024, smartphones accounted for nearly 80% of global retail site traffic and over two-thirds of online orders. Mobile is now the primary device for browsing and purchasing in categories like clothing, cosmetics, and entertainment. 

However, desktop still plays a significant role in sectors such as travel and utilities, especially among older generations. Brands should continue to prioritize mobile-first design, but not neglect the desktop experience—especially for high-consideration purchases.

3. Millennials vs. Gen Z: Mobile app habits

Generational differences are shaping the future of e-commerce. For Gen Z, mobile apps are the second most popular starting point for shopping (48%), just behind Google. Millennials, on the other hand, split their preference between apps and brand websites (both at 35%). This means younger shoppers are more likely to use apps for discovery, while Millennials are equally comfortable with apps and direct website visits. 

Brands need more than just a mobile presence to capture Gen Z’s attention. They need apps built for exploration, speed, and flexibility. With Feature Experimentation and Rollouts from AB Tasty, teams can continuously test and optimize in-app experiences without a full redeploy, ensuring their app evolves alongside user expectations.

4. Comparison shoppers lead the pack

Not all online shoppers are the same. Our research found that the most common shopper persona is “comparison-oriented”—30% of respondents compare multiple products before making a purchase. Only 11% identify as “speedy” shoppers who want to check out as quickly as possible. The rest fall somewhere in between, with 21% being “review-oriented,” 20% “confident,” and 18% “detail-oriented.” This diversity highlights the need for flexible site experiences that cater to different decision-making styles.

If one size doesn’t fit all, then understanding your audience is the first step to building experiences that truly convert.

5. Reviews are more influential than discounts or brand names

When it comes to influencing purchase decisions, high-quality reviews top the list globally. Shoppers trust peer validation more than discounts, convenience, or even brand names. Written testimonials and customer photos are especially valued, providing the authenticity and detail shoppers crave. 

Make sure your reviews are visible, filterable, and packed with real customer insights to boost trust and conversions.

E-commerce moves fast. Get the insights that help you move faster. Download the 2025 report now.

6. The pop-up problem hurting conversions

Think you’re converting more by hitting new visitors with an email sign-up pop-up right away? Think again.

Too many pop-ups are the number one frustration for online shoppers worldwide, followed closely by slow-loading websites and difficulty finding products. While pop-ups can be effective for capturing leads or promoting offers, overuse can drive customers away. Use them strategically and ensure your site is fast and easy to navigate to keep shoppers engaged.

7. Loyalty is the key to better personalization

Personalization is more than just a buzzword—it’s a key driver of customer satisfaction and loyalty. The top way to make online shopping feel more personal, according to 35% of respondents, is by rewarding brand loyalty. Remembering preferences and suggesting relevant products also rank highly. 

Brands that recognize and reward repeat customers with exclusive perks or early access to new products can turn shoppers into advocates.

8. AI adoption is growing, especially among younger shoppers

AI-powered tools like chatbots and virtual assistants are gaining traction, but there’s still room for improvement. Just under a quarter (23%) of shoppers have used AI tools and found them helpful, while 32% haven’t tried them but are open to it. Younger generations are more receptive: 32% of Gen Z and 30% of Millennials found AI tools helpful, compared to just 13% of Baby Boomers. 

To win over skeptics, brands need to ensure AI support is fast, relevant, and seamlessly integrated with human assistance.

9. Shoppers just want frictionless experiences

When asked what would most improve their online shopping experience, the top answer was simple: removing frustrations like pop-ups, bugs, and broken pages. Tracking shipping, improving product search, and speeding up the shopping process were also highly valued. 

Before investing in flashy features, brands should focus on getting the basics right—smooth, intuitive journeys are what keep customers coming back.

10. The gap between personalization and perception

Personalization is supposed to make shoppers feel seen—but only 1 in 10 consumers say their favorite brands truly “get” them. In fact, the most common answer was “somewhat,” as 39% of respondents said the messages and offers they receive are hit or miss. Another 34% said brands mostly deliver relevant content, but not always. For the majority, the digital experience feels inconsistent. 

When personalization doesn’t land, it can come off as surface-level or even off-putting. The takeaway? Personalization isn’t just about using data—it’s about using it meaningfully, so relevance feels intentional, not accidental.

Conclusion

The bar for digital shopping experiences keeps rising, and today’s consumers are quicker than ever to click away when expectations aren’t met.

From discovery to checkout, each step in the customer journey has the potential to shape customer loyalty and long-term value. Our 2025 E-commerce Consumer report dives even deeper into generational trends, regional differences, and actionable strategies for optimizing your digital experience.

Ready to future-proof your e-commerce strategy? Download our report “Decoding Online Shopping: Consumer Trends for E-commerce in 2025” now.

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Article

7min read

Is Your Average Order Value (AOV) Misleading You?

Average Order Value (AOV) is a widely used metric in Conversion Rate Optimization (CRO), but it can be surprisingly deceptive. While the formula itself is simple—summing all order values and dividing by the number of orders—the real challenge lies within the data itself.

The problem with averaging

AOV is not a “democratic” measure. A single high-spending customer can easily spend 10 or even 100 times more than your average customer. These few extreme buyers can heavily skew the average, giving a limited number of visitors disproportionate impact compared to hundreds or thousands of others. This is problematic because you can’t truly trust the significance of an observed AOV effect if it’s tied to just a tiny fraction of your audience.

Let’s look at a real dataset to see just how strong this effect can be. Consider the order value distribution:

  • The horizontal axis represents the order value.
  • The vertical axis represents the frequency of that order value.
  • The blue surface is a histogram, while the orange outline is a log-normal distribution approximation.

This graph shows that the most frequent order values are small, around €20. As the order value increases, the frequency of such orders decreases. This is a “long/heavy tail distribution,” meaning very large values can occur, albeit rarely.

A single strong buyer with an €800 order value is worth 40 times more than a frequent buyer when looking at AOV. This is an issue because a slight change in the behavior of 40 visitors is a stronger indicator than a large change from one unique visitor. While not fully visible on this scale, even more extreme buyers exist. 

The next graph, using the same dataset, illustrates this better:

  • The horizontal axis represents the size of the growing dataset of order values (roughly indicating time).
  • The vertical axis represents the maximum order value in the growing dataset in €

At the beginning of data collection, the maximum order value is quite small (close to the most frequent value of ~€20). However, we see that it grows larger as time passes and the dataset expands. With a dataset of 10,000 orders, the maximum order value can exceed €5,000. This means any buyer with an order above €5,000 (they might have multiple) holds 250 times the power of a frequent buyer at €20. At the maximum dataset size, a single customer with an order over €20,000 can influence the AOV more than 2,000 other customers combined.

When looking at your e-commerce metrics, AOV should not be used as a standalone decision-making data.

E-commerce moves fast. Get the insights that help you move faster. Download the 2025 report now.

The challenge of AB Test splitting

The problem intensifies when considering the random splits used in A/B tests.

Imagine you have only 10 very large spenders whose collective impact equals that of 10,000 medium buyers. There’s a high probability that the random split for such a small group of users will be uneven. While the overall dataset split is statistically even, the disproportionate impact of these high spenders on AOV requires specific consideration for this small segment. Since you can’t predict which visitor will become a customer or how much they will spend, you cannot guarantee an even split of these high-value users.

This phenomenon can artificially inflate or deflate AOV in either direction, even without a true underlying effect, simply depending on which variation these few high spenders land on.

What’s the solution?

AOV is an unreliable metric, how can we effectively work with it? The answer is similar to how you approach conversion rates and experimentation.

You don’t trust raw conversion data—one more conversion on variation B doesn’t automatically make it a winner, nor do 10 or 100. Instead, you rely on a statistical test to determine when a difference is significant. The same principle applies to AOV. Tools like AB Tasty offer the Mann-Whitney test, a statistical method robust against extreme values and well-suited for long-tail distributions.

AOV behavior can be confusing because you’re likely accustomed to the more intuitive statistics of conversion rates. Conversion data and their corresponding statistics usually align; a statistically significant increase in conversion rate typically means a visibly large difference in the number of conversions, consistent with the statistical test. However, this isn’t always the case with AOV. It’s not uncommon to see the AOV trend and the statistical results pointing in different directions. Your trust should always be placed in the statistical test.

The root cause: Heavy tail distributions

You now understand that the core issue stems from the unique shape of order value distributions: long-tail distributions that produce rare, extreme values.

It’s important to note that the problem isn’t just the existence of extreme values. If these extreme values were frequent, the AOV would naturally be higher, and their impact would be less dramatic because the difference between the AOV and these values would be smaller. Similarly, for the splitting problem, a larger number of extreme values would ensure a more even split.

At this point, you might think your business has a different order distribution shape and isn’t affected. However, this shape emerges whenever these two conditions are met:

  • You have a price list with more than several dozen different values.
  • Visitors can purchase multiple products at once.

Needless to say, these conditions are ubiquitous and apply to nearly every e-commerce business. The e-commerce revolution itself was fueled by the ability to offer vast catalogues.

Furthermore, the presence of shipping costs naturally encourages users to group their purchases to minimize those costs. It means that nearly all e-commerce businesses are affected. The only exceptions are subscription-based businesses with limited pricing options, where most purchases are for a single service.

Here’s a glimpse into the order value distribution across various industries, demonstrating the pervasive nature of the “long tail distribution”:

Cosmetic
Transportation
B2B packaging (selling packaging for e-commerce)
Fashion
online flash sales

AOV, despite its simple definition and apparent ease of understanding, is a misleading metric. Its magnitude is easy to grasp, leading people to confidently make intuitive decisions based on its fluctuations. However, the reality is far more complex; AOV can show dramatic changes even when there’s no real underlying effect.

Conversely, significant changes can go unnoticed. A strong negative effect could be masked by just a few high-spending customers landing in a poorly performing variation. So, now you know: just as you do for conversion rates, rely on statistical tests for your AOV decisions.