In today’s digital landscape, data-driven choices are essential for staying competitive, with experimentation as a critical driver of innovation. To support this, we recently hosted a webinar with experts from Google Cloud and AdSwerve, focusing on how Google Analytics 4 (GA4) and BigQuery can enhance experimentation strategies. GA4 is essential for all marketing teams, providing advanced analytics that, when combined with BigQuery’s data consolidation capabilities, enables more effective testing, personalization, and digital optimization.
Meet the panel
Taige Eoff, Cloud Data AI Lead at Google, has been at Google for twelve years, leading data and AI initiatives for cloud marketing. Taige focuses on developing scalable solutions that support partners like AB Tasty and AdSwerve in optimizing digital experiences.
Alex Smolin, Senior Optimization Manager at AdSwerve, brings extensive experience in media, data, and technology. As a certified Google Premium Partner, AdSwerve provides data-driven brands with solutions ranging from A/B testing to advanced analytics.
Mary Kate, our roundtable host and Head of Growth Marketing for North America at AB Tasty, leads efforts to help companies create impactful digital experiences through AB Tasty’s suite of experimentation, personalization, product recommendations, and site search tools.
AB Tasty’s integration with GA4 & BigQuery
Connecting AB Tasty with GA4 gives marketing teams insights into visitor behavior through advanced analytics on CPA, conversion rate, bounce rate, SEO, and traffic. This integration allows teams to use data from either tool to measure the impact of experiments pre- and post-rollout, generating data-backed hypotheses and fostering innovation.
Google BigQuery, a fully managed cloud data warehouse solution, offers rapid data storage and analysis at scale. With its serverless, cost-effective structure, BigQuery allows businesses to analyze large datasets efficiently, making it easier to make well-informed decisions.
With Google BigQuery, users can effortlessly execute complex analytical SQL queries and leverage built-in machine-learning capabilities.
Why is data from GA4 foundational to any CRO program?
In experimentation, data is the catalyst that drives actionable insights. Data flows in from multiple sources, and businesses generate detailed reports by working with partners to integrate tracking and tagging. But the question then becomes: what comes next? That’s where experimentation enters. Using data from tools like GA4, teams can transform hypotheses into tests, uncovering which changes impact user engagement or conversions most effectively.
GA4’s role extends further by providing a consistent framework for testing across platforms. When integrated with BigQuery, GA4 allows teams to cross-reference test outcomes with other data points, revealing not just what worked but why it worked. As Alex noted, “We gather good data, run good tests, and then verify results across disparate sources like BigQuery to see if what we tested had the expected downstream impact.”
Data accessibility and agility are also important. Trends evolve quickly, with viral content or market shifts requiring rapid adaptability. “Having partners like Google, with all data in one place, and a platform like AB Tasty, where experiments can be quickly set up, is essential for staying competitive” Alex emphasized.
“Having partners like Google, with all data in one place, and a platform like AB Tasty, where experiments can be quickly set up, is essential for staying competitive.”
Alex Smolin, Senior Manager Optimization at Adswerve
How BigQuery powers scalable experimentation
With the growing volume of data, businesses need a way to consolidate and interpret it to drive impactful decisions. BigQuery, as Taige explained, is a robust cloud warehouse that streamlines data for meaningful insights, making it a key player in the experimentation ecosystem.
“Think of BigQuery as a filing cabinet for your organized data,” Taige noted. By consolidating disparate data sources, teams can create a unified view that informs testing and optimization efforts. Through this approach, tools like GA4 and BigQuery enable accurate decision-making that scales with the business. With BigQuery as the backbone, AB Tasty and AdSwerve can build on this structure to optimize user experiences through precise experimentation.
Beyond just data storage, BigQuery integrates with various Google Cloud tools and supports a wide range of use cases—from standard reporting to advanced machine learning. For marketers, this means fewer technical bottlenecks and quicker access to the data needed to stay agile. As Taige explained, “You may not need deep technical skills to access BigQuery’s benefits; the right partnerships and data structure can give you a powerful, accessible foundation.”
Leveraging BigQuery’s built-in AI and machine learning models
BigQuery offers an array of AI models for specific use cases—from translation and personalization to customer segmentation. These models add value by automating processes, such as localization or customer behavior prediction, allowing for smoother, more targeted marketing.
BigQuery’s flexibility means that companies can incorporate custom or third-party models, ensuring compatibility with a variety of AI solutions. This adaptability helps organizations innovate and iterate on experimentation programs, expanding what they can achieve with data.
Simplifying data access for marketing efficiency
For marketing teams, BigQuery’s role as a centralized data hub allows seamless data consolidation from platforms like Google Ads, Salesforce, and GA4. This integration ensures that marketers aren’t slowed down by fragmented data sources, freeing them to focus on insights and execution. As Taige highlighted, “The peace of mind that BigQuery provides comes from knowing that all data is consolidated and accessible, allowing teams to be nimble and creative.”
With BigQuery, marketers can view performance metrics, analyze customer journeys, and refine strategies—all within a unified environment. This lets teams optimize campaigns in real time as new data insights emerge.
Next-Generation capabilities enabled by Google Cloud
Looking ahead, digital is paving the way for more advanced experimentation capabilities. The conversation shifts to AI and machine learning, bringing new opportunities for personalization and optimization. As Mary Kate pointed out, while AI-driven insights can revolutionize customer experiences, many brands are still years away from realizing the full potential of these tools.
True value will come not from adopting every new tool but from understanding the foundational data supporting AI and asking the right questions about how these technologies can serve customer needs. Taige added, “If you don’t have a data strategy, you won’t have an AI strategy.” While AI amplifies data power, it requires organized, high-quality data to work effectively.
By consolidating and centralizing data through BigQuery, teams gain real-time insights and can make informed decisions. This data foundation enables the current wave of omnichannel strategies and sets the stage for future AI applications. Businesses that adopt this holistic approach—consolidating data, optimizing channels, and preparing teams for AI—will unlock new experimentation opportunities and drive impactful customer experiences.
With GA4 and BigQuery, businesses have the tools to streamline data consolidation and power next-generation experimentation. Ready to join your data and experimentation? Discover how AB Tasty can help bring data-driven optimization to life.
In the vast and competitive world of e-commerce, simply having great products isn’t enough. Your online store is like a stage, and how you present your products can make or break the show. Enter e-merchandising—the art and science of guiding your customers through a shopping journey that’s as smooth as silk and as engaging as a blockbuster movie.
Whether you’re looking to captivate first-time visitors or inspire returning customers, the right merchandising strategies can transform your site from a digital storefront into an experience that keeps customers coming back for more.
Ready to dive in? Here are five e-commerce merchandising strategies, with real-world examples, to help you create a shopping experience that truly shines.
1. Branding and Homepage Messaging: Your Digital First Impression
“Don’t judge a book by its cover” is a great mantra to apply to our personal lives, however, this proverb doesn’t apply to the e-commerce world.
Your homepage is more than just a landing page—it’s the welcome mat to your online store, and it needs to speak volumes. From the moment someone lands on your site, they should know who you are, what you stand for, and how you can make their life better.
Why it matters:
First impressions count. A compelling homepage can turn curious browsers into engaged shoppers.
Returning visitors want to see something fresh and relevant, not the same old same old.
Pro tips:
Tell your story boldly: Your brand story should be front and center. Use a powerful tagline or headline that captures your essence.
Test, test, test: Use A/B testing to find out what messaging resonates most with your audience.
Show, don’t tell: Include social proof like testimonials and customer reviews to build instant credibility.
Real-world example: Homepage
JOTT, a French clothing retailer, noticed that their homepage was experiencing a higher bounce rate than expected. Realizing that first impressions were crucial, they ran a no-code A/B test using AB Tasty’s experience platform to see if rearranging the homepage layout would improve engagement.
By moving product categories to the above-the-fold section and pushing individual product displays lower down, they achieved a 17.5% increase in clicks on the product category blocks. This optimization reduced bounce rates and guided more users deeper into their shopping journey, enhancing overall engagement.
2. Group Merchandise into Collections: Curate Like a Pro
Ever walked into a store and felt overwhelmed by choice? The same thing can happen online. Grouping your products into well-thought-out collections can turn chaos into clarity, making it easier for customers to find what they’re looking for—and maybe even discover something they didn’t know they needed.
Why it matters:
Curated collections simplify the shopping experience, helping customers quickly find what they’re after.
They also encourage customers to explore more, potentially increasing their basket size.
Pro tips:
Get creative with collections: Don’t just stick to the basics. Think outside the box—consider seasonal themes, trending items, or even influencer picks.
Use data wisely: Analyze purchase patterns to create collections that reflect what customers are actually buying.
Spotlight special collections: Use banners or pop-ups to draw attention to limited-time offers or new arrivals.
Real-world example: Collections
Balibaris, a leading French men’s fashion brand, revamped its e-commerce strategy by intelligently reorganizing its product displays to better match customer preferences and behavior. By dynamically sorting products and emphasizing best-sellers and seasonal items, Balibaris saw a significant increase in conversion rates compared to the previous year, even without special promotions. This strategic move not only enhanced the online shopping experience but also boosted overall sales while freeing up the digital team to focus on more impactful projects.
3. Showcase Products with Visual Merchandising: Let Your Images Do the Talking
In the world of e-commerce, a picture is worth a thousand clicks. Visual merchandising isn’t just about slapping up a few product photos; it’s about creating an emotional connection that makes customers want to reach through the screen and grab that item. High-quality images, videos, and even virtual try-ons can bring your products to life and help customers see how they’ll fit into their lives.
Why it matters:
Stunning visuals can make or break a sale. They help customers imagine the product in their own lives.
Lifestyle images and videos build an emotional connection, making customers more likely to hit “Add to Cart.”
Pro tips:
Go high-def: Invest in top-notch photography that shows your products from every angle.
Tell a story: Use lifestyle images or videos to show how your products can be used in real life.
Mix it up: Consider adding videos or 360-degree views to give customers a more immersive experience.
Real-world example: Visual Merchandising
Galeries Lafayette, one of France’s most iconic department stores, sought to enhance the online shopping experience by testing the impact of different product image styles. They compared standard packshot images to premium images featuring models wearing the products.
The results were striking: the premium images not only increased clicks by 49% but also boosted the average order value (AOV) by €5.76, adding a potential €114,000 in profit. This shift towards higher-quality visuals resonated with customers, leading Galeries Lafayette to prioritize premium images across their site, significantly improving user engagement and sales.
4. Implement Effective Site Search: Help Shoppers Find Their Perfect Match
When a customer knows what they want, nothing should stand in their way—especially not a clunky search function. A well-oiled site search is like a personal shopper, helping customers find exactly what they’re looking for, faster.
Why it matters:
Customers who use search are often more ready to buy, so it’s crucial that they find what they’re looking for quickly and easily.
An effective search can turn casual browsers into buyers by surfacing relevant products.
Pro tips:
Optimize filters & facets: Let customers narrow down their search results with relevant filters like size, color, and price.
Smart error detection: Make sure your search can handle typos and synonyms—because we all make mistakes.
Autocomplete magic: Help customers out by suggesting popular search terms as they type.
Never show a dead end: Avoid zero-results pages by offering suggestions or related products instead.
Real-world example: Site Search
VAN GRAAF, an international fashion retailer, recognized the need to elevate their online search functionality to meet the high standards of their physical stores. By integrating AB Tasty, VAN GRAAF significantly improved the customer journey on their e-commerce site. The results were impressive: online orders from search increased by 30%, conversion rates rose by 16%, and the average order value (AOV) saw a 5% boost. Additionally, the share of sales from search grew by 4.3%. This transformation not only enhanced the shopping experience but also reduced the time the team spent managing search functionalities, allowing them to focus on other critical optimizations.
5. Cross-Sell and Up-Sell Products in Your Shopping Cart: The Power of Suggestion
You’ve done the hard work of getting a product into a customer’s cart—now’s your chance to suggest a few more. Cross-selling and up-selling are subtle yet powerful ways to increase the value of each sale by offering customers items that complement what they’ve already chosen.
Why it matters:
Personalized recommendations can boost your average order value and make customers feel like you really “get” them.
It’s a win-win—customers discover more great products, and you see a bump in sales.
Pro tips:
Personalize everything: Use AI to suggest products based on what’s already in the cart or what similar customers have bought.
Bundle it up: Show products that are frequently bought together as a bundle to encourage more sales.
Test placement: Experiment with where you place these suggestions—product pages, the shopping cart, or even during checkout.
Real-world example: Cross-sell and Up-sell
Figaret, a high-end French shirtmaker, significantly boosted its online sales by integrating personalized product recommendations. By strategically placing recommendation blocks on product pages and in the shopping cart, Figaret achieved remarkable results: 6% of visitors used these recommendations, contributing to 10% of the site’s total revenue. Additionally, these users spent on average 1.8 times more than those who didn’t engage with the recommendations. This approach not only enhanced customer engagement but also drove substantial revenue growth.
Measuring Success in E-merchandising: Are You Hitting the Mark?
You’ve put in the work, but how do you know if your e-merchandising strategies are actually working? Measuring success isn’t just about looking at sales numbers; it’s about understanding how each element of your strategy contributes to the bigger picture.
Key Metrics to Watch:
Website traffic: Keep an eye on where your visitors are coming from and what they’re doing on your site.
Conversion rate: This is the percentage of visitors who actually make a purchase—one of your most important metrics.
Sales data: Analyze overall sales, average order value, and revenue from specific merchandising strategies.
Average basket size: Track how many items customers are purchasing per transaction to gauge the effectiveness of your cross-selling and up-selling efforts.
Pro tips:
Set benchmarks: Compare your metrics against industry standards to see where you stand.
Use analytics tools: Platforms like Google Analytics or Matomo can give you insights into how visitors interact with your site.
Keep iterating: Don’t settle for good—strive for better. Regularly review your data and tweak your strategies to keep improving.
Conclusion: Trial and Better—The Heart of E-Merchandising Strategies
E-commerce merchandising isn’t a “set it and forget it” task—it’s a continuous journey of trial, error, and improvement. The best strategies evolve over time as you learn more about your customers and the market. So don’t be afraid to experiment, take risks, and, most importantly, keep pushing for better. Every tweak, test, and change you make is a step towards creating an online store that not only meets but exceeds customer expectations.
Ready to take your e-commerce merchandising to the next level? Download our comprehensive guide on e-merchandising best practices or schedule a free demo with AB Tasty today. Your journey to better starts now.
For e-commerce success, added revenue from existing customers can be more efficient than constantly pursuing new ones. Returning buyers are a vital piece of this strategy. We recently sat down with industry experts to discuss how optimizing customer experiences can drive upselling and cross-selling opportunities. They shared practical approaches for boosting average order value (AOV) while nurturing customer loyalty and retention.
Our speakers, each experts in testing, optimization, and conversion rate, provided insights into how brands can increase revenue through personalized, thoughtful customer engagement.
Meet the experts
Nicole Story: Co-Founder & Director at Hookflash Analytics, leading experimentation in testing, optimization, and personalization.
Gerred Blyth: Chief Product Officer at Giftory, with a background in interaction design and e-commerce.
In this article, we’ll explore actionable strategies from the webinar to help you personalize to existing customers and drive growth through upselling and cross-selling—not just new customer acquisition.
1. Optimizing the cart for upsells
Upselling at the cart and checkout stages can significantly increase AOV, but it requires a carefully planned approach. As Colette Carlson explains: “Before implementing anything, it’s crucial to understand how you’re going to measure success and ensure that your conversion rate is solid. When it comes to the cart and checkout process, if those aren’t optimized, adding upsell and cross-sell strategies will only introduce more noise.” Shoppers who have reached the cart are already primed to convert, so it’s important not to disrupt their momentum with irrelevant or poorly timed offers.
“Before implementing anything, it’s crucial to understand how you’re going to measure success and ensure that your conversion rate is solid. When it comes to the cart and checkout process, if those aren’t optimized, adding upsell and cross-sell strategies will only introduce more noise.”
Collette Carlson, Director of Optimization at Astound Digital
Coordination with internal teams is also important when designing upsell strategies. For instance, if an upsell is introduced at checkout, the process should be seamless – will the original product be automatically removed from the cart if the customer selects an upgrade, or will they need to make the changes manually? Likewise, if you’re offering a bundle or cross-sell, is your system prepared to handle it without disrupting the customer experience?
Effective upsell offers are relevant to the customer’s purchase. Suggesting complementary items or upgrades can boost AOV, as 80%of consumers are more likely to complete their purchase with brands offering personalized experiences. From upsell testing experience at Giftory, Jared advises against pushing unrelated or overly expensive items, which can confuse or deter customers altogether.
Using product recommendation algorithms can streamline upselling. Automating this process ensures that customers receive relevant suggestions without the need for manual curation, creating a smoother experience for your team and the customer. AB Tasty’s product recommendation engine allows upsells based on several criteria, including most recent products, associated products, similar or more expensive items, complementary items, and top promotions.
2. Strategic product recommendations for cross-selling
To effectively cross-sell, brands must identify the right moment in the customer journey. If you offer relevant products at key points without disrupting the experience, similar to upselling. But first, establishing cross-selling metrics can lead to stronger effectiveness.
The primary metrics will vary depending on what you’re testing—whether it’s an algorithm change, a new carousel design, or a different recommendation format. There are some essential KPIs to consider:
Engagement: Track how often customers interact with cross-sell offers, such as clicks or add-to-basket rates.
Conversion rate: Measure how many customers who engage with offers complete their purchases.
Average order value (AOV): Gauge how effectively cross-sell strategies are increasing the total order value.
Items per order: Monitor if cross-sell efforts lead to additional products being added to the cart.
Overall revenue: This ultimate metric reflects the total impact of your cross-sell strategy.
Once these metrics are in place, refine your strategy by determining where cross-sell offers should appear. For example, adding a cross-sell option in the mini cart or as a pop-up at checkout can add complexity, so testing can help avoid disrupting the customer experience.
Testing cross-sell algorithms in action
Nicole Story shared a valuable example of testing product recommendation carousels. Inspired by Amazon’s success, many brands rushed to implement carousels on their websites, but forgot the importance of context. Placing multiple carousels on the homepage often leads to irrelevant suggestions and a poor experience.
Nicole’s team tested various algorithms by tailoring product recommendations to the customer’s journey. On product detail pages (PDPs), carousels that showed “related product suggestions” outperformed those with generic recommendations. The tests revealed that adjusting algorithms based on context and customer behavior was far more effective than placing standard carousels throughout the site.
As Nicole explains: “Simply introducing product recommendations and checking that box off the roadmap isn’t going to deliver real value. The key is continuous optimization and discovering what works across the entire customer journey—that’s where the real value lies.”
“Simply introducing product recommendations and checking that box off the roadmap isn’t going to deliver real value. The key is continuous optimization and discovering what works across the entire customer journey—that’s where the real value lies.”
Nicole Storey, Co-Founder & Director at Hookflash Analytics
Relevance is everything
Cross-sell strategies must be highly relevant to what the customer is already doing. As Gerred Blyth from Giftory mentioned, “We have high expectations as customers and irrelevant offers can break that trust.” Customers expect brands to know their preferences and behaviors, so it’s important that recommendations feel personalized and timely.
3. Experimentation and testing for long-term loyalty and CLV
Continuous experimentation is critical for building long-term customer loyalty and increasing customer lifetime value (CLV). Instead of relying on a single strategy, brands should constantly test and improve their approach. Colette points out that starting by analyzing existing order data can uncover natural cross-sell patterns. This provides valuable insights into which products are frequently purchased together.
For first-time visitors, bombarding them with upsell offers might backfire. Instead, let them become familiar with your brand and key products before introducing additional offers. In contrast, repeat customers may be more open to cross-sells that align with their previous purchases.
Upselling with product recommendations
According to our data a customized UX can boost revenue and increase basket size by up to 10%. Product recommendations can be seen as a form of personalization and, as our panel pointed out in the webinar, experimenting with different formats—such as carousels, quizzes, or other interactive tools—can help identify what resonates with your audience and drives engagement.
We use AI to analyze visitors’ site interactions and purchase behavior, delivering targeted recommendations, each with a specific goal. This means you can better understand which products to offer, to whom and when during the customer journey:
Product Page: Guide users to explore more products or categories.
Last Seen Products: Help users quickly resume their browsing.
Add to Cart: Encourage users to add complementary items to their basket.
Cart Page: Suggest additional items to increase order value.
Homepage: Showcase personalized content and help users navigate the site.
Our panel also discussed how different types of algorithms are necessary depending on your vertical. You can divide your algorithms into three distinct types and choose how you prioritize:
Convert: These recommendations would offer top sellers, top trending products, top converting products, top reviewed products etc.
Upsell: This could suggest most recent products viewed, associated products, similar products, compatible products etc.
Personalize: This could suggest last visited products, last bought products, user affinity or similar or associated to cart products
If you work for a beauty site, customers will replenish their favorite products, whereas home and decor might recommend accessories or similar products. While personalization drives relevance, maintaining control over the recommendation process means you can speak directly to your customer’s needs.
Giftory: fostering loyalty with timely engagement
Giftory is beginning to focus on lifetime customer value. Their approach involves using cross-sell and upsell strategies similar to a CRM initiative, introducing customers to a broader range of products both during and after their purchase. They gather data on why customers buy gifts, such as birthdays or anniversaries, and use that information to send timely product recommendations in the future.
By reaching out to customers at the right moment, such as 11 months after an anniversary purchase, Giftory can re-engage them with relevant offers without overwhelming them with constant promotions. This creates a personalized experience that encourages long-term loyalty and repeat business.
4. Subscription models for upsell and retention
Offering subscription products to upsell can improve both immediate revenue and CLV. The challenge is to find the right balance: How can you encourage customers to subscribe without overwhelming them, while also ensuring the offer feels relevant and valuable over time?
Before launching a subscription model, look at your data to understand customer behavior. Consider the difference between a one-time purchaser and a subscriber. While offering a small discount for subscribing may lower the initial AOV, the long-term benefits of recurring revenue from a loyal subscriber can make up for it.
Testing and data-driven strategy
Launching a subscription model requires more than just adding an upsell feature—it involves a data-informed approach. Starting small with a minimum viable product (MVP) allows you to test how customers respond and fine-tune the offering. Metrics like renewal rates, engagement, and overall CLV will help guide decisions about whether to scale the program.
As Gerred advises: “Walk before you run. Start with the first test—an MVP. It doesn’t have to be the final version you’ll roll out, but that initial test will help you understand the value and prove the benefits. From there, you can evolve and continuously improve. It’s easy to feel overwhelmed when you hear about advanced strategies and algorithms, but you don’t need to get there all at once.”
“Walk before you run. Start with the first test—an MVP. It doesn’t have to be the final version you’ll roll out, but that initial test will help you understand the value and prove the benefits. From there, you can evolve and continuously improve. It’s easy to feel overwhelmed when you hear about advanced strategies and algorithms, but you don’t need to get there all at once.”
Gerred Blyth, Chief Product Officer at Giftory
Offering personalized options, such as different subscription tiers or flexible renewal cadences (monthly, bi-monthly, quarterly), can make the experience more appealing to a wider range of customers. Testing, refining, and adapting based on customer feedback will ensure that the model evolves in a way that meets both business goals and customer expectations.
Wrapping up
Just as you approach CRO with care and precision, cross-selling and upselling require a high level of attention.
Upselling and cross-selling don’t have to be complex when you have the right tools and the right strategy. If you want the expert’s opinion – watch the webinar below:
In today’s mobile-first world, where smartphones dominate more than half of global web traffic, optimizing for mobile has never been more crucial. Mobile usage surpassed desktop in the US in 2022 and in the UK in 2023, signaling a clear shift in consumer behavior. Brands are now urged to design with mobile in mind first, adapting for desktop as needed, rather than the reverse. This shift may seem daunting for teams, but it’s a necessary evolution to meet the expectations of today’s users.
Whether your customers are researching products or making purchases, their mobile experience can make or break their journey with your brand. While it’s clear that more shopping is done on mobile devices than on desktop, the real question remains: how significant is mobile shopping overall? Today’s mobile-savvy consumer isn’t just using their device for convenience, but to blend their in-store and online shopping into one seamless experience. In fact, nearly 80% of shoppers globally use their smartphones to browse a retailer’s website while shopping in-store, and 74% use the store’s app. However, only 33% of consumers prefer making purchases on their phones, with 49% reporting a smoother experience on desktop or tablet. This highlights just how important it is for brands to enhance their mobile offerings for a seamless experience across all devices.
To delve into the complexities of mobile optimization Mary Kate, AB Tasty’s Head of Growth Marketing for North America, teamed up with Allie Tkachenko, a UI/UX Strategist at WPromote, for a webinar on mastering mobile. AB Tasty’s platform enables brands to deliver personalized customer experiences, while Wpromote helps design and optimize engaging web experiences that convert. They emphasize a key message: mobile optimization isn’t just about resizing for a smaller screen – it’s about creating an intuitive, seamless journey that aligns with today’s mobile-first consumer’s behaviors and expectations.
It’s critical that mobile websites excel in areas like speed, navigation, and user-friendliness. Let’s dig into three actionable strategies from the webinar to help your brand stay ahead and deliver an improved mobile experience for your customers.
1. Maximizing limited space
One of the biggest challenges in mobile design is maximizing limited screen space without overwhelming users. The key is to keep crucial content above the fold—on mobile, this means placing essential elements like navigation bars, CTAs, and product highlights in a prominent position, visible without scrolling. This is particularly important on search landing pages, the homepage, and other high-traffic areas. A well-organized and streamlined navigation system that helps users quickly find what they need can lead to higher engagement and reduced bounce rates.
While desktops offer ample space to break down navigation into detailed categories, mobile design requires a more simplified structure due to space constraints. Consider grouping categories under broader buckets like “Top Categories” or similar, allowing users to easily explore the site without feeling overwhelmed by too many options. Another key strategy is leveraging responsible design, such as implementing sticky navigation bars or menus that stay visible as users scroll. This approach, widely adopted across industries, ensures easy access to important links and minimizes the effort required to navigate the site.
AB Tasty in action
The UX team at Clarins wanted to make their product more visible on their category pages. In the original layout, filtering and sorting functions were stacked, removing space from the second row of products appearing. After testing a column layout for the filtering and sorting menus, the team saw a significant improvement—bounce rates decreased, and clicks to products increased by 34%.
The “Thumb Zone” refers to the area of the screen that is easiest for users to reach with their thumbs, typically the lower portion of the screen. Since most users interact with their phones one-handed, placing critical CTAs, buttons, and interactive elements within this zone is important for accessibility and ease of use.
Consider this: a navigation bar that starts at the top of the page but shifts responsively to the bottom as the user scrolls. This keeps it in an expected spot initially, avoiding any disruption to the user’s flow, and then moves it to a more reachable area as they continue browsing.
Another thing to keep in mind is sizing. Whether it’s buttons, images, form fields, or menu links, the size of these elements plays a huge role in usability. You can’t just shrink them to save space—you have to ensure they’re “tappable” so users can easily interact. While reachability is key, think about what doesn’t need to be within reach, like informational banners or logos. You can place those outside the thumb zone, saving prime space for interactive elements.
Brands that prioritize the thumb zone in their mobile designs see improved user engagement and lower frustration levels. This small shift can make a significant difference in usability and customer satisfaction.
AB Tasty in action
The team at Club Med, a leading travel and hospitality brand, observed that their original mobile site displayed a navigation bar at the top of the page, which would disappear as users scrolled down. To increase user engagement with different category offerings, they created a variation of the mobile homepage with a sticky navigation bar which remained at the bottom of the screen while scrolling.
The results of the A/B test revealed a 12% increase in click rates, a 12% increase in access to the transaction funnel, and a 2% decrease in the bounce rate for users showing the variation with the sticky navigation bar. This approach effectively makes information more physically accessible.
Optimizing the thumb zone
Bottom Navigation
Sizing
Reachability
3. Improving processes
Lengthy forms and cumbersome checkout processes are major obstacles to conversion in mobile digital experiences. Mobile users expect a seamless, fast journey, and frustration with complex forms often leads to abandoned carts. Streamlining these processes—especially form fills and checkouts—can reduce friction and improve conversions. We’ve all experienced the annoyance of having to redo a form, fearing progress might be lost, which can lead to users abandoning the process entirely. Key areas for optimization include simplifying checkout by offering guest checkout options and exploring one-click payment methods.
Search and product discovery also present unique challenges on mobile devices due to limited screen space. With condensed menus and site navigation, users often rely heavily on the search function. Optimizing your search results pages to help users quickly find specific products can drastically improve the user experience. The space constraints of mobile mean that every element, including search results, should guide users efficiently to what they’re looking for.
Lastly, page load speed plays a vital role in retaining users. A slow-loading site can deter users, leading them to abandon your site altogether. Reducing load times is crucial for keeping users engaged. Understanding your audience and continuously optimizing these processes will help ensure your site meets their needs and encourages conversions.
AB Tasty in action
Travel insurance company, DirectAsia, needed users to fill out a form to generate an insurance quote. The team observed that customers were not completing the forms as smoothly as expected. To address this, they implemented a variation in the test where bolded check marks appeared to validate each completed field. This change created a sense of progress for users as they navigated the form and alleviated any uncertainty about needing to go back to correct errors.
As a result of this test, DirectAsia achieved a 1.97% increase in quote generations and a 7.78% increase in transaction rates. By reassuring users throughout the form-filling process, DirectAsia successfully guided more customers through their quote generation form.
Optimizing mobile processes
Checkout
Search and discovery
Speed & image loading
Wrapping up
Mobile optimization is about much more than making your website look good on a smaller screen; it’s about crafting a seamless, user-friendly experience that enhances the customer journey. Whether you’re focusing on improving site speed, optimizing design for better accessibility, or streamlining complex processes, the suggestions above provide a solid foundation for mastering mobile optimization. By understanding the nuances of mobile behavior and catering to the needs of your users, your brand can create a frictionless experience that drives conversions and fosters customer loyalty.
Stay ahead in the mobile-first era by ensuring your website design and processes align with the expectations of today’s consumers. AB Tasty can help achieve this goal by providing innovative tools and data-driven testing to enhance your mobile strategy. As mobile usage continues to grow, so does the importance of providing a smooth, engaging, and conversion-focused experience.
If you want to get all the details. – watch the webinar below.
The future of digital experience optimization has arrived and it’s driven by AI.
Are you ready for it?
AI can often be a sensitive subject, as loud voices in the room will boast how AI can replace people, careers, or even entire sectors of society. We’re scaling back the dystopian imagery and instead finding ways where AI can be your sidekick, not a supervillain.
There are two sides to the coin with AI: it can help optimize your time and boost conversions, but it can also be risky if not used properly. We’ll dig into the ways AI can be a helpful tool, as well as some considerations to take.
The positive impact of AI on your customer experience roadmap
In one of our last pieces about AI in the CRO world, we discussed 10 generative AI ideas for your experimentation roadmap. Since the publication of this article, we’re back with even more ideas and concrete examples of successful campaigns.
1. Display reassurance messages to visitors who value it
Some shoppers value their privacy and data safety above all else. How can you comfort these visitors while they’re shopping on your website without interfering with other visitors’ journeys? While salespeople can easily gauge these preferences in face-to-face interactions, online shoppers deserve the same personalized experience when they shop independently.
Let’s see an example below of how you can enhance the digital customer experience for different shoppers at the same time:
MAAF, a French insurance provider, knows just how complex buying auto insurance can be for visitors. Some shoppers prioritize safety and reassurance messages, while others don’t. With AI systems that segment visitors based on emotional buying preferences, you can detect and cater to this type of profile without deferring to other shoppers. “Intuitive” profiles are receptive to reassurance messages, while “rational” profiles tend to see these extra messages as a distraction.
The team at MAAF used advanced AI technology to overcome this exact challenge. Once the “intuitive profiles” were identified, they were able to implement personalized messages ensuring their commitment to their customers’ data protection. As a result, they saw an increase of 4% in quote rates for those directed to the intuitive segmentation, and other profiles continued on their journey without extra messaging.
2. Segment your audience based on their shopping behavior
With so many online shoppers, how can you possibly personalize your website to give each shopper the best user experience? With AI-powered personalization software.
Some online shoppers have a need for competition. Don’t we all know someone who loves to turn everything into a competition? These “competitive” shoppers are susceptible to social-proof messaging and are influenced by the opinions of other customers while searching for the best product. One of the best ways to personalize a listing page for competitive shoppers is to show ratings from their peers.
Meanwhile, what works for competitive shoppers, will not work effectively for speedy shoppers. Shoppers with a need for immediacy will appreciate a clear, no-frills browsing experience. In other words, they don’t want to get distracted. Let’s look at the example below.
This website implemented two different segments targeting online shoppers with a need for “competition” and “immediacy.” These two segments brought in a 9% increase in conversion rates and a 2% increase respectively. The campaign was a success, but how did it work?
Using AB Tasty’s AI personalization engine, EmotionsAI, this online shop identified its visitors’ main emotional needs and directed them toward a product listing page best suited for them. EmotionsAI turns buyer emotions into data-driven sales with actionable insights and targeted audiences.
Want to learn more about EmotionsAI? Get a demo to see how AI can impact your roadmap for the better!
3. Automate and personalize your product recommendations
European backpack designer, Cabaïa, used an AI-powered recommendation engine to generate personalized recommendations for their website visitors based on user data collected. The team at Cabaïa previously managed product recommendations manually but wanted to shift their focus to improving the digital customer experience.
AI recommendation tools put the right product in front of the right person, helping boost conversions with a more tailored experience. Since implementing this AI-powered recommendation engine, they’ve had +13% revenue per visitor, increased conversions by 15%, and raised their visitor’s average cart size by 2.4%.
4. Innovate your testing strategy with emotional targeting
According to an online shopper study (2024), traditional personalization is no longer enough. Personalizing based on age, location, and demographics just isn’t as precise anymore.
The team at Groupama, a multinational insurance group, wanted to take A/B testing a step further and better adapt their approach to fit their customers’ unique emotional needs. By using an AI-powered emotional personalization engine, they were able to identify two large groups of website visitors: emotionals and rationals.
They created an A/B test based on these customer profiles. One variation catered to the “emotional” buyers by showing reassuring messaging on the insurance quote to protect their data, and the other catered to “rationals” that displayed the insurance quote without any extra messaging that allowed them to have a distraction-free buyer journey. Within 2 weeks, Groupama saw an instant win with a 10% increase in quote submissions.
5. Simplify the customer journey and build buyer confidence
Like many financial services, purchasing insurance is inherently complex. Consumer behaviors and expectations in insurance are quickly changing.
As a leading insurer in Singapore, DirectAsia has embraced innovative technologies to better serve their customers. By pioneering new technology, Direct Asia was able to segment their visitors based on emotional needs.
The team at DirectAsia identified that the ‘safety’ segment (buyers needing reassurance) was the top unsatisfied emotional need for visitors on both desktop and mobile devices. With these insights, DirectAsia ran an experiment on ‘safety’ visitors, displaying two banners to reassure them and move them further down the form to the quote page.
The banners led to + 10.9% in access to the quote page for one, and +15% in access to the quote page for the other.
The potential risks of AI on your customer experience roadmap
Artificial intelligence has been evolving (very quickly!) over the past few years and it can be tempting to run full speed ahead. However, it’s important to find the right AI that works for you and helps you achieve your goals. Is AI powering something you need, impacting your business, or is it just there to impress?
With that in mind, let’s consider some precautions to take while using AI:
Unfactual or biased information on data reports, website copy, etc.
When researching or asking for data sources, it’s important to keep in mind that artificial intelligence can get it wrong. Just as humans can make mistakes and have biased opinions, AI can do the same. Since AI systems are trained to produce information following patterns, AI can unintentionally amplify bias or discrimination.
Lack of creativity, dependence, and over-reliance
Excessive reliance on AI can reduce decision-making skills, creativity, and proactive thinking. In competitive industries, you need creativity to stand out in the market to capture your audience’s attention. Your roadmap could suffer if you put too much faith in your tool. After all, you are the expert in your own field.
Data and privacy risks
Protecting your data should always be a top concern, especially in the digital experience world. You will want a trusted partner who uses AI with safeguards in place and a good history of data privacy. With the fast-developing capabilities of AI, handling your data correctly and safely becomes a hurdle. As a general best practice, it’s best not to upload any sensitive data into any AI system – even if it seems trustworthy. As these systems often require larger quantities of data to generate results, this can lead to privacy concerns if your data is misused or stored inappropriately.
Hallucinations
According to IBM, AI hallucinations happen when a large language model (LLM) thinks it recognizes patterns that aren’t really there, leading to random or inaccurate results. AI models are incapable of knowing that their response can be hallucinogenic since they lack understanding of the world around us. It’s important to be aware of this possibility because these systems are trained to present their conclusions as factual.
Conclusion: Using AI in the Digital Customer Experience
As with any tool or software, AI is a powerful tool that can enhance your team – not attempt to replace it. Embracing the use of AI in your digital customer experience can lead to incredible results. The key is to be aware of risks and limitations, and understand how to use it effectively to achieve your business goals.
Welcome to a world where chocolate isn’t just a treat but an experience—a world crafted by Hotel Chocolat, a British group with nearly 31 years of rich history. At the heart of their journey lies a realization: loyalty isn’t bought with discounts—it’s earned through authentic connections and shared values.
Recently, they shared this ethos at the CX Circle event by Contentsquare featuring Mel Parekh, Head of E-commerce at Hotel Chocolat. Mel took the stage to unravel the complexities of customer loyalty—a subject that has never been more critical in the fast-evolving world of eCommerce. The discussion centered around how Hotel Chocolat has navigated the challenges of a changing world while staying true to its brand values using the power of experimentation.
The Secret Ingredient: Authenticity and Quality
Hotel Chocolat stands out in the chocolate industry for its commitment to authenticity and quality. While most chocolate brands are content to source their cocoa, Hotel Chocolat went all-in, growing their own on the lush Rabot Estate in Saint Lucia. This direct control over their supply chain ensures that they use only the highest quality ingredients while helping craft a brand that’s as genuine as the cocoa it cultivates.
Hotel Chocolat has witnessed a constant change in the e-commerce landscape. They’ve learned to adapt to these changes while staying true to their brand identity. One of their key initiatives has been to clearly define who they are as a brand and to create compelling reasons for customers to return to their site time and time again.
A Changing Landscape
It’s no secret that the world of eCommerce is in constant flux. Prices are rising across the board—from raw materials to operating costs—and the competition for customers is fiercer than ever. In this environment, retailers must do more with less, finding innovative ways to stand out.
As customers increasingly engage with various digital platforms and experiences, the range of choices available to them has become almost overwhelming. In this crowded marketplace, standing out from the competition requires more than just eye-catching design elements.
Moreover, the explosion of data in recent years has made it possible for even smaller companies to leverage insights that were once only accessible to larger players. However, the real challenge lies in capturing this data, interpreting it effectively, and, most importantly, implementing it in ways that drive meaningful results. Hotel Chocolat has embraced this data-driven approach, using insights to refine their strategies and create a more personalized experience for their customers with both Contentsquare and AB Tasty.
Building Lasting Relationships with Customers in a Phygital World
Loyalty is the cornerstone of Hotel Chocolat’s strategy in this new era. As a premium brand, they understand that their customers aren’t just looking for a product; they’re looking for an experience that resonates with their values and desires. This understanding has led Hotel Chocolat to focus on building a brand that not only meets customer expectations but exceeds them by offering a unique, personalized experience.
One of the key strategies they’ve implemented is their “phygital” approach, which blends the digital and physical worlds to create a more personalized, engaging shopping experience. This approach is centered on three key principles:
Instant: Reducing delay or lag to ensure a smooth customer experience.
Connected: Creating a more personal connection with each customer.
Engaging: Giving customers a sense of control over their shopping journey.
Make the Experience Personal
With over 120 different chocolate recipes, Hotel Chocolat faced this challenge: how do you help customers find the perfect product without overwhelming them? Their solution was gamification—a method that makes the shopping experience more fun and interactive. In Spring 2023, they launched the “Chocolate Love Match,” a quiz that matches customers to one of six flavor profiles. This not only narrows down the selection from 120 options to 20 or 30, making it easier to shop but also helps customers find the perfect gift for friends and family based on their flavor preferences.
The personalization doesn’t stop there.
Hotel Chocolat also leverages machine learning and tools like AB Tasty to improve their customer experience further. For instance, they’ve been experimenting with “Add to Bag” personalized recommendations. This initiative is crucial, especially as acquisition costs rise, making it more important than ever to maximize the value of each customer interaction.
Using AB Tasty, they tested two variations: one that showed products frequently bought together and another that displayed recently viewed items for easy access. Both approaches tested positively, resulting in a 5.31% increase in average order value and a 2.87% boost in revenue.
Embracing Data for Optimization
Hotel Chocolat has also focused on optimizing its digital presence, particularly their website. Working with AB Tasty, they undertook a redesign of their homepage, recognizing that the layout and user experience across devices play a critical role in customer engagement. The goal was to create a more visually appealing and intuitive experience that could better connect with customers online—especially when you can’t taste or smell the products.
The results speak volumes. By optimizing the homepage, they saw a 10% reduction in bounce rate, a 1.67% increase in visiting time, and significant improvements in conversion rates—up 0.54% overall and a substantial 7.24% on desktop. This uplift was largely due to better highlighting the most attractive elements on the homepage, such as category tiles that drive higher conversion and revenue.
Loyalty from a Brand Perspective
Mel Parekh left us with three takeaways for building a brand that stands the test of time:
Embracing Change: It shows that your brand is up-to-date and ready to adapt. Staying agile ensures that your brand remains relevant and continues to serve your customers, no matter the circumstances.
Listening and Understanding Customers: If loyal customers aren’t heard and understood, they’ll lose their preference for your brand and start considering others.
Sticking to Your Values: Clearly reward loyal customers for their loyalty, and make sure to differentiate between who is loyal and who isn’t.
Conclusion
Loyalty isn’t just about offering a great product; it’s about creating connections that resonate. Hotel Chocolat has perfected this recipe by blending their commitment to quality with a data-centric culture. Experimentation and data from AB Tasty have allowed them to be able to improve in all areas – whether that is personalization, gamification of their loyalty scheme, or the link between their online and physical shops. Experimentation has improved more than just their CRO but has helped define who they are and what they stand for.
Most online shops implement e-commerce product recommendation systems. But if you take a look at how these recommendations are displayed, you’ll start to notice some big differences. Depending on how the recommendation system works, different variables impact which products are prioritized for personalization.
Since recommendations depend on personalization and user data, it’s possible to choose how to suggest items. In the following article, you’ll learn about various recommendation systems and how to choose the right one for your shop.
Relevant recommendations through personalization
Product recommendations are most impactful when they are relevant to the customer. It is possible to achieve maximum results by ensuring that the recommendations are personalized and account for the individual preferences of customers.
With this, it should be noted that the most effective type of personalization depends on the e-commerce product recommendation system and strategy used.
Personalization requires dialog
Personalization is very complex. It can be used at various touchpoints and implemented using different systems. It means presenting online visitors with product recommendations that are as close as possible to their preferences.
To do this, you first need data on the user’s click and purchase behavior. This can be collected through a dialog with the customer, which is established via their engagement with the online shop. Once data is collected, it can be combined with product data and expert knowledge in a knowledge base.
In this knowledge base, all data is processed and evaluated. One approach is to use artificial intelligence (AI). This allows data (click and purchase behavior) to be processed into information by experts via data mining. The information is then turned into knowledge using algorithms (reinforcement learning).
This knowledge is ultimately used to provide customers with relevant recommendations. Product data, click and purchase behavior, and expert knowledge all converge in the knowledge base.
Dialog-based AI
To provide customers with the best results, an algorithm must first determine what the customer needs. The term dialog-based AI is used for such processes.
A so-called response engine, which uses sensors to record and analyze click and purchase behavior, plays a crucial role here. This takes on the task of identifying a customer’s goals and interests from their engagement with the online store.
Various possibilities for dialog
There are many ways to track customer engagement with an online shop. These are distinguished in the form of:
Reactions
When a customer browses an online store, numerous reactions can occur. Based on user engagement with the content (clicks, product selection, purchases, etc.), you can find out what they want. Once you have this information, you can present customized content to customers.
Language
We naturally think of language when we hear the word “dialog.” That said, a dialog between humans and machines based on language has pitfalls. Many people who use voice-activated assistants understand this.
When discussing online stores, human-to-machine dialog occurs through search terms. This allows language to be used for a personalized search engine. Therefore, a product search can be understood as a personalized search system triggered by linguistic input.
With this, the following question arises. How can e-commerce operators use customer-driven engagement and dialog to provide suitable recommendations? There are various systems for achieving this, which are discussed in more detail in the following sections.
Classic recommendation systems (static)
There is a clear distinction between different e-commerce recommendation systems. They are primarily distinguished by the data and methods used to determine relevant suggestions to customers.
With this, there are two classic variants: collaborative and content-based systems. In addition, recommendation systems can incorporate other elements, including demographic data and time spent shopping. We’ll look into two “classic” recommendation systems below.
Collaborative recommendation systems
When online shop customers share click and purchase behavior, a collaborative recommendation system can be used. This analyzes data from different customers and finds similarities to suggest relevant products for a consumer group.
Recommendations generated by this system can follow a headline like “customers who were interested in this product found these relevant.” This is because the system will recommend items to multiple customers with similar patterns. On a related note, algorithms that deliver product lists with this approach are known as collaborative filtering systems.
Collaborative recommendation systems are used by major retailers like Amazon, among others. It is the method of choice when little or no personalization information is available for a customer. It’s also good when the product catalog contains minimal characteristics.
Advantages of collaborative systems
In e-commerce, the advantage of collaborative product recommendation systems is they can reveal relationships between users and items that aren’t explicitly apparent. Additionally. collaborative filtering can show customers products that differ from previous preferences, but may still be of interest. This means you can surprise your customers.
Disadvantage of collaborative systems
However, there is a disadvantage. It is referred to as the “cold start problem,” and occurs primarily with new users and products. With this type of e-commerce product recommendation system, it is necessary that there is a large number of customers with similar behaviors. If there is minimal customer engagement, it can be difficult to generate recommendations.
Content-based recommendation systems
Unlike the above type, content-based recommendation systems do not work on the basis of users with similar engagement patterns. Instead, product attribute commonalities are used as a basis. In addition, individual customer engagement plays a role.
Content-based recommendation systems suggest items that are relevant to products a customer expressed interest in purchasing. To calculate recommendations of this kind, content analysis is required to determine product similarities.
When providing such recommendations, you can preface them with something like “similar products from your favorite brand.”
Advantages of content-based systems
Content-based recommendation systems have both advantages and disadvantages. One major advantage is that content-based recommendation systems can suggest items even if there are no clicks or purchases on the site. This counteracts the “cold start problem” of collaborative recommendation systems.
Disadvantages of content-based systems
A disadvantage of a content-based e-commerce product recommendation system is that it can be overly specialized. There’s no element of surprise regarding product recommendations. They are only based on the preferences of the individual customer.
If we look back at the example of “similar products from your favorite brand,” we can see another problem with content-based recommendation systems. The customer may wish to see products that have their favorite color, for example.
This makes it clear how important it is to understand preferences. In other words, a customer should be presented with a wide range of recommendation factors. It’s the best way to determine which, of the similar products, the customer actually wants.
Context-aware recommendation systems (dynamic)
Personalization goes beyond the matter of providing customers with desired content. Users increasingly expect content to be presented in the “right” context, one that’s familiar. This presents a challenge for personalization services.
To present customers with relevant recommendations in a context that’s suitable for them, dynamic information is required. This is in addition to static information (such as product similarities). Context-aware recommendation systems process this information.
Within this system, the context is another input for the recommendation system. It conveys what the customer is doing and where recommendations are displayed. The dynamic context-aware information and its interrelationships significantly improve recommendation quality.
Multiple recommendation contexts
When discussing e-commerce, multiple recommendation contexts refer to suggestions beyond products. This typically occurs in a personalized section of the website dedicated to the customer. It’s useful for keeping consumers engaged beyond their purchase(s).
It is possible to show a variety of recommendations in multiple contexts that are tailored to the customer’s preferences. This includes interactive elements and offers a mix of inspiration from similar products to content and entertainment.
If successful, this encourages customers to return to the online shop on their own, increasing consumer loyalty and website engagement. By discovering related content to their favorite brands, styles, etc, users may purchase more items.
This entertainment-driven approach is based on data already collected from the customer’s previous interactions with the website. The overall experience matches what consumers already know, creating a familiar environment for discovery.
Individual recommendation contexts
If your shop can’t host the content necessary for a multiple recommendation environment, you should ask what your customers specifically need. This will help you deliver the best individualized recommendations possible.
To illustrate how crucial it is to develop a suitable recommendation environment, let’s look at the following scenarios.
Product detail page
An online shop visitor is looking at a product (for example, a pot) on a product detail page. The customer is currently researching information and wants to buy a pot. To best lead the customer in the right direction, you can display similar products or products customers have also purchased. This can be shown below the product information in a recommendation widget.
Shopping cart layer
In this situation, a shop customer puts a product (for example, a bicycle) in the shopping cart and a pop-up appears with similar products. Here, the customer is one step away from completing the purchase.
They are about to buy a bicycle and have already added it to their shopping cart. When this happens, you should not display similar products under any circumstances. This will confuse the customer and delay the purchasing process.
To avoid them changing their mind at this stage, you should instead present complementary items like a helmet or bicycle lock. This is known as cross-selling and inspires customers to increase the value of their cart.
Compromises for individual recommendations
If only one or two recommendation methods can be presented on a product detail page, you have to select the best one. An example would be “similar products that you may also like.” It is important to take the term “similar” literally.
“Similar” here means products with the same characteristics as the product viewed are understood. This relates to a customer’s personal preferences. If done correctly, it will increase the quality of recommendations and drive sales.
For example: if a customer shows a particular interest in black items, other black items are considered to be very similar. Without this information about customer behavior, the product color would not help inform recommendations.
Hybrid recommendation systems
It may be necessary to mix or modulate recommendation systems. By combining content-based and collaborative recommendation systems, disadvantages can be minimized. This means that high-quality and relevant recommendations can be generated more quickly for online shop customers. If this occurs, this is called a hybrid recommendation system and ensures better results.
That said, truly relevant recommendations cannot be generated with a universal algorithm. They require the dynamic interconnection of a series of intelligent basic algorithms. The prerequisite for this is a modular software system that supports these basic algorithms in a compatible manner. It also requires experts to be able to configure such dynamic architectures with the right parameters.
Selecting the right recommendation system
We’ve covered a wide range of e-commerce product recommendation systems, alongside various methods and data uses. The final question remains. Which one is “right” for generating suitable recommendations?
Understanding your customer’s needs
The above question cannot be answered easily in general terms. The right recommendation system for your e-commerce platform depends on various factors. Recent developments in e-commerce reveal that previously static structures are becoming more dynamic.
In addition, the shopping environment is becoming increasingly important. With this, it goes without saying that it’s necessary to have a wide product and content selection available. The right recommendation strategy depends on the phase of the customer journey and product context. It’s best to explore a mixture of different systems for an optimal experience.
Expert knowledge as a prerequisite
To dynamically provide personalized recommendations in the right context, your website needs the right software architecture. It needs to dynamically combine a wide range of algorithms. With this, an understanding of the shopping environment context is necessary.
Configuring these architectures requires expert knowledge. This is because only trained individuals can identify the requirements of individual touchpoints for selecting the right recommendation system.
They will best know how to choose the best personalization type for the context. By using an expert, you’ll ensure individual recommendations are generated properly for customers.
Targeted combination of different recommendation systems
As you can see in this article, there are various recommendation systems. They each have their own advantages and disadvantages. The development of e-commerce shows that dynamic structures are becoming more important. Shop customers expect product recommendations in a familiar environment.
To meet such demands, different recommendation system processes can be combined with each other. It’s possible to facilitate this in a targeted manner, based on contextual information.
Since these hybrid systems are very complex, expert knowledge is crucial for success. This is because dynamic architectures need to be designed and personalized to generate relevant product recommendations.
With this, it’s important to understand customer preferences and ensure recommendations are appropriate for various stages of the journey. Following these recommendations can make a big difference in presenting optimal recommendations. All of this means more revenue from increased sales.
Any marketer will tell you that Digital Optimization is crucial to ensure successful e-commerce operations and yield the best possible return on investment (ROI). This practice includes both A/B testing and website personalization: every website presents a unique set of features and designs, which must, in turn, be optimized through A/B testing.
Building a great website is, unfortunately, not simply a matter of following best practices. Even within a single industry, users will hold varied expectations based on your brand, communication style, target audience, funnel, etc. And while browsing the same website, users’ expectations can vary, with some knowing exactly what they want and others needing to explore, check your returns policy, learn about your sustainability initiatives, and so on.
We have all heard the hype about how AI has been revolutionizing how marketers approach experimentation. Generative AI offers new opportunities for optimizing every aspect of the user journey, allowing marketers to:
streamline testing,
create new online experiences,
and create new types of user segments for more precise personalized experiences that drive conversions.
This guest blog post was written by Rodolphe Dougoud, Project Lead at fifty-five—a leading data company that helps brands harness the potential of Generative AI and mitigate associated risks effectively with a comprehensive and pragmatic AI strategy, among other services.
Below, we’ll explore these three perspectives in depth, with real-life examples gleaned from AB Tasty’s new algorithm, Emotions AI, and fifty-five’s work with its clients around GenAI.
AI in Action for Experiences that Matter
Streamline testing
When thinking about A/B testing, you might immediately picture creating an experiment and launching it live on a website. However, the most time-consuming phases of the A/B testing process generally come before and after that: finding new features to try out in order to create a testing roadmap and analyzing the results of these tests. Here, AI can increase test velocity by helping to reduce bottlenecks hindering both of the aforementioned stages.
Test ideation
Your roadmap must not only be top-down but also bottom-up: pay close attention to insights from your UX designers, based on benchmarks from your competitors and industry trends, and data-driven insights based on your own analytics data. Here, AI can facilitate the process by analyzing large datasets (e.g., on-site Analytics data) to find insights humans might have missed.
Result analysis
Similarly, it’s essential to analyze the results of your tests thoroughly. Looking at one KPI can sometimes be enough, but it often represents only one part of a bigger story. An aptly-calibrated AI model can find hidden insights within your testing results.
While we generally know what data we want to access, the actual querying of that data can be time-consuming. Applying a GenAI model to your dataset can also allow you to query your data in natural language, letting the model pull the data for you, run the query, and create instant visualizations for major time gains.
Content creation
While not necessary for most tests, creating new content to be included in the testing phase can take a long time and impact your roadmap. While GenAI cannot produce the same quality of content as your UX team, a UX designer equipped with a GenAI tool can create more content faster. The model used can be trained with your design chart to ensure it integrates with the rest of your content. Overall, adding a GenAI tool as a complement to your design arsenal can yield substantial gains in productivity and, therefore, reinforce your testing roadmap timeline.
Create new online experiences
Marketers should not hesitate to experiment with AI to create unique and interactive experiences. Generative AI can create personalized content and recommendations that can engage users more effectively.
Consider, for instance, fifty-five’s recent work with Chronodrive, a grocery shopping and delivery app. We used AI to address a common user challenge (and, frankly, near-universal issue): deciding what to make for dinner.
With our innovative solution, taking a picture of the inside of your fridge will allow the app to create a recipe based on the ingredients it identifies, while a photo of a dish – taken at a restaurant or even downloaded from social media – will generate a recipe for said dish and its associated shopping list.
Artificial Intelligence opens new creative options that weren’t available with previous LLM models. Chronodrive’s solution may not be applicable to most companies, but every business can think back on their typical user’s pain points and conceptualize how GenAI could help ease them.
Create new types of user segments for more precise personalized experiences
When a customer enters a store, a salesperson can instantly personalize their experience by checking if they want to be helped or just want to browse, if they are discovering the brand or are already sold on it, if they require guidance or know precisely what they want… A website, on the other hand, necessitates extra effort to present the user with a similarly personalized experience.
Online, segmentation thus becomes indispensable to deliver the most satisfying user experience possible. Even during testing phases, deploying A/B tests on user segments makes achieving significant results more likely, as increased precision helps mitigate the risk of obtaining neutral results.
AI can analyze a wide array of user interactions on a given website to determine which elements drive the most conversions, or how different users respond to specific stimuli. This analysis can allow brands to classify users into new segments that could not have been available otherwise. For instance, fifty-five applied AI to split Shiseido’s website users between low and high-lifetime value segments. This allowed Shiseido to run differentiated A/B tests and personalize their website depending on the expected lifetime value of the user, resulting in a 12.6% increase in conversions.
Going even further, what if AI could read your emotions? AB Tasty’s new AI algorithm, Emotions AI, can automatically segment your audience into 10 categories based on emotional needs.
If a user needs to be reassured, the website can emphasize its free return policy
If they need clarity, the website can highlight factual information about your product
And if they need immediacy, the website can hide any unnecessary information to instead focus on its main CTAs
The model estimates the needs of the user by taking into consideration all of their interactions with the website: how long they wait before clicking, whether they scroll through an entire page, where their mouse hovers, how many times they click, etc. This enables stronger personalization, both during testing phases and when deploying online features, by letting you know exactly what your users need.
Want to Learn More?
If you would like to dive deeper into current experimentation trends, watch our webinar replay here, where fifty-five and AB Tasty explored key CRO case studies and more. And if you have any questions or insights you’d like to share, please leave a comment – we would love to hear from you!
When shopping online, you’ll likely encounter suggestions for alternative or complementary items. This is meant to inspire customers to continue shopping and is part of an e-commerce product recommendation strategy. With this, shop owners can potentially increase revenue through bigger shopping carts.
Product recommendations can follow customers throughout their journey in an online shop. This usually includes the homepage, category and product detail pages, and shopping cart. It also extends to e-mail marketing.
In this article, we will show you where product recommendations make sense for e-commerce along with special features to consider. This is relevant for displaying recommendations at the right moments in an online shopping environment.
Ensuring relevant product recommendations for your e-commerce
In e-commerce, product recommendations are displayed through recommendation “reco” engines. As you might imagine, these deliver relevant recommendations tailored to the visitor. While customers are typically suggested products, recommendation engines can also be used for related content.
Recommendations are presented using widgets which provide the framework for relevant product or content suggestions. These can be customized to match the design and branding of your e-commerce platform.
Differentiate between new and existing customers
With the above information, you might be wondering how to effectively use an e-commerce product recommendation widget. You’ll first need to make a distinction between new and existing customers. This is important because you’ll need to tailor your recommendation engine to different audiences, based on shopping behavior.
In this context, it’s useful to know whether a customer has already visited your online shop, clicked through various categories, and completed a purchase. All of this is considered an online shopping history and only applies to existing customers.
Existing customers: online shoppers with history
If your customers have a history with your store, you can use data based on their previous habits or purchases to display suitable recommendations. You’ll want to analyze click and purchase behavior to personalize relevant product suggestions. In doing so, you’ll likely inspire more sales.
With existing customers, you can also suggest products or content based on their previous purchases. For example, if your customer recently purchased a skincare item that is usually purchased in a series, you can suggest other products to help them complete their skincare routine.
New customers: online shoppers without purchase history
While you can’t offer recommendations based on the personal interests of customers new to your shop, you still can make suggestions to inspire their purchase journey. These include using recommendation algorithms like top sellers, sale items, or new products.
Where to position product recommendations on your website?
There are many ways to position product recommendations in an online shop. You can add them to the homepage, product detail page(s), and directly in the shopping cart. It’s important to strategically place these at every step throughout a customer’s journey.
To help you keep track of the various options, we have provided an overview of suitable positions for online shop recommendations:
It should be noted that recommendations should be displayed in accordance with the best user experience for each page. To illustrate this better, we will discuss a few pages in detail below. Below we’ll show you what to look for on each page regarding optimal recommendation placement.
1. A dynamic homepage
As mentioned above, you’ll need to build different shopping environments for existing and new customers. If executed properly, this will change the look and feel of your homepage. Just like in a brick-and-mortar store where a salesperson knows the behaviors of regular customers, you can show users their preferences are understood.
For example, an online shop for pet products should know who is purchasing for cats vs dogs. If a cat owner is recommended dog food, they will find the shopping experience counterintuitive. The right products should be presented the moment a customer lands on your homepage, and can be as detailed or broad as needed.
However, as previously explained, if a new customer lands on your homepage, past purchasing data won’t be available. In this scenario, it’s advised to showcase top sellers, sale items, or new products.
2. Category pages
On product category pages, you can use personalized recommendations by analyzing online shoppers’ browsing and purchasing behavior. This approach will allow you to highlight products that are relevant to their habits and align with their interests.
The product detail page contains more specific information than a homepage. Since a customer is looking at one item, this allows for highly relevant product suggestions. With a product detail page, two types of recommendations make the most sense. These are similar and complementary items.
Create complete looks with product sets
On the product detail page, showcasing a complete product set is popular. These are recommendations that present customers with an entire look based on one product. The idea is that they can easily add complementary items with minimal effort.
For example, customers can create a stylish outfit, or purchase necessary camping gear in an instant. If you’re selling cameras and photographic equipment, you can offer a set with matching lenses, memory cards, batteries, and a carrying bag. With this, you can allow customers to save or purchase these items at once.
Similar products
An online shopper navigates to the product detail page because they find a particular item interesting. If they like the selected item but are still unsure, similar products can help them make a decision. This is where the customer can become aware of products that may better meet expectations. It helps them feel informed about their purchase, leading to a sale.
Cross-selling complimentary items
Complementary items are relevant for the product detail page. If the customer is convinced they need the selected product, it’s worth showing them matching items. With this, you can draw their attention to other products that may interest the customer. It might inspire them to continue shopping.
When doing this, you’ll want to take the customer’s preferences into account. These include sizing, brands, preferred materials, and dietary needs. The latter is particularly valuable if a customer doesn’t purchase items containing allergens or is vegetarian/vegan. Of course, with new customers, this isn’t possible.
It is also important to account for inventory. You don’t want to recommend products that aren’t in stock, as that creates a frustrating experience. The goal is to enable your customers to make additional purchases.
4. Showing alternatives on a zero results page
Sometimes users will search for items that you don’t offer in your shop. This will usually display a page showing “zero results.” To prevent customers from clicking off your site, you have the option of using a product recommendation widget on your page. You don’t want visitors to feel discouraged.
Recommendations on the zero results page can be alternatives from your product range. For example, if a user is searching for a particular brand that you don’t offer, you can recommend similar brands or styles. This will inspire your customers to explore these items and potentially discover new products.
5. Recommendations on the shopping cart page
Even though a user is confirming their shopping cart, it’s not too late to recommend other items. You can still increase sales at this stage of the process.
Add to cart recommendations
As customers click “add to cart,” you can provide quick personalized recommendations with a cart layer widget. This will pop up and show other products of interest.
Recommendations on the shopping cart page
The shopping cart page itself can display product recommendations. With this, it’s recommended that you don’t show similar products here as that can confuse the customer. Personalized complementary items make the most sense at this stage.
Additional products that complete the original product or relevant low-cost items are suggested for maximum sales potential. You can set up a checkout zone in your shopping cart that encourages customers to purchase small, cheap items. Think of how a supermarket places snacks near a checkout line.
When recommending items, you have different options for what information to collect from the shopping cart. You can either take the entire order into consideration or you can suggest items based on the last product added. The right approach will depend on your particular shop and strategy.
6. Wishlist
What better place to recommend more products than in your shoppers’ wishlist? This page is already a place where buyers keep up with their future purchasing desires. Product recommendations on this page can encourage a higher order value in the future.
7. Thank you page: more than just an order confirmation
A thank you page is a great place for product recommendations. It doesn’t have to just contain information about the order, it can also encourage future purchases. With relevant recommendations, you can lead customers back to a product detail page. This keeps them engaged in your shop for a longer time.
8. Content page: Recommendations based on topics
By now, it’s clear to see how relevance is key when discussing product recommendations for your e-commerce. With certain campaigns, you may want to execute this manually with marketing and thematic landing pages. Combining content and topic-related recommendations, you can use digital storytelling to emotionally engage online shoppers.
The goal is to create a digital story on a topic and tie it to relevant products. For example, you can do this with inspiration for skiing or surfing vacations. The combination of content and products is a popular approach to e-commerce product recommendation strategy. It moves away from the purely functional aspect of sales, creating a much more personalized experience.
Below you can see what a content page in an online shop looks like. In this example, the topic is a surf trip. In addition to information and a story about the perfect vacation, the page offers thematically appropriate product recommendations for inspiration.
9. Personalized shopping: relevant recommendations and more
If you want to offer users the best experience, you can create a personalized shopping area in your online shop. This is a section that is dynamically, intelligently, and fully customized for each customer. It offers a central location in the shop of the customer’s favorite brands, categories, and items.
This personalized area allows users to browse their own product and brand world. Online shoppers can also receive relevant content suggestions, such as blog articles, and receive shopping news in real-time.
Relevant product recommendations also play a role here. For example, recommendations can be integrated in the form of product sets. Additionally, users can easily access desired information via clickable, interactive elements.
With personalized shopping, the site learns sizing and preferences to display available and on sale items. There is also the potential for embedding interactive content for a unique e-commerce environment.
As you can see, a personalized shopping area offers a particularly high level of inspiration and goes beyond product recommendation widgets.
10. Newsletter: recommendations in real time
To keep your customers fully engaged, you’ll want to provide recommendations through email newsletters. Like everything else discussed, it’s important these are personalized to the customer. You’ll want to account for consumer preferences.
Since emails are read at various times, you want to make sure the recommendations are relevant to when the newsletter is opened. This ensures that all information (including inventory) is up to date. There are also various ways of showcasing recommendations.
Complementary items
With order confirmation emails you can suggest complementary items. This is a similar strategy to what was discussed on the thank you page.
Alternative products
If you want to remind inactive customers of products abandoned in their shopping cart, you can suggest both similar and alternative items.
Topic-related recommendations
Newsletters can focus on a specific topic and showcase thematically related items. With this, you can send topic-specific emails to ensure customers receive highly relevant recommendations.
Why recommendation visibility matters
Proper positioning is essential for product recommendations to perform optimally. As an example, it is important to not add suggestions “below the fold.” You don’t want customers to have to search for recommended items. There’s data to suggest that only 20% of content “below the fold” is seen by viewers.
This means that even if the right products are recommended, 80% of consumers would miss them if improperly placed. As you can see, this creates a drop-off in potential sales. It also means, since there was no engagement, less data is collected to inform better recommendations in the future. This insight is crucial for optimization.
If your recommendation engine is well set up, but recommendations aren’t seen then you’re missing valuable sales potential. Due to this, recommendation positioning on all pages of your website is important. This is particularly true for product descriptions where there’s a high chance of interaction.
Optimal e-commerce product recommendation placement for maximum potential
As you can see in this article, an effective e-commerce product recommendation strategy is a win-win. Your users become inspired while shopping and discover new products. As a shop owner, you can increase engagement and sales.
To fully benefit from the potential of a recommendation engine in your online shop, you should place product suggestions on the various stages mentioned. With the right data, you can anticipate the needs of your customers.
Also, make sure online shoppers actually see your recommendations and remain informed after they leave your site. It’s important to send personalized emails that contain real-time data.
This article shows the potential and power of useful product recommendations. Now it’s your turn to implement the best strategy on your site.
Optimally designed online shopping categories can increase sales. This long-term, strategic approach is often referred to as e-commerce category management.
In this article, you’ll learn more about what’s involved in achieving an effective approach. You’ll also discover how digital marketing plays a role.
We’ll go over how to implement an easy-to-follow 8-step strategy for maximizing engagement and sales. The key is to balance accessibility, unique merchandising, and customer behaviors. This can all be achieved through understanding and implementing data and analytics.
Read on to learn more, starting with the fundamentals of category management.
What is category management?
With category management, products are grouped together. The focus is placed on the needs of consumers. This structure allows for more efficient purchasing by anticipating customer demand.
The goal is to not cluster similar products together, but instead to group them by complementary items. That said, category management has been standard in physical retail for decades.
For example, you’ll find pasta alongside sauces in a supermarket. Similarly, hiking gear, clothes, shoes, and backpacks are usually in the same aisle of a sporting equipment store. This concept applies to e-commerce environments.
Cooperation between retailers and manufacturers
Optimal category management is based on functional cooperation between retailers and manufacturers. It works when both parties want to achieve a triple-win situation for satisfied customers, higher profits, and more sales. Within this context, product groups or service categories are viewed as strategic business units.
To be effective, a holistic approach that engages various departments in the company is needed. As mentioned above, this also involves input from manufacturers. Retailers control the product groups, while manufacturers provide detailed knowledge of categories. In the end, both sides are interested in finding the best possible solution for customers.
Tasks of a category manager
Typically, an effective strategy is managed by a dedicated person or team. They are usually responsible for the following tasks:
Analysis of shopping baskets
Definition, structure, and optimization of product groups (category management)
Strategic planning of featured products
Merchandising and purchasing for the respective product segment
Goals of category management
Category management is a strategic approach designed for long-term goals. The aim is to achieve higher customer satisfaction with an easy-to-navigate shopping experience. This generates more sales and improves the overall brand image.
Also, effective category management provides a competitive advantage. If done properly, consumers will come to trust a store for its expertise in certain product groups.
E-commerce category management
As you might imagine, category management not only applies to physical stores, but also to e-commerce. That said, it’s fairly new to the digital environment and there is room for development. Similar to brick-and-mortar retail, an online store can meet customer needs with structured product categories. This, along with an optimal user experience, can increase customer satisfaction and boost sales.
8 step strategy for E-commerce Category Management
Below, you’ll find an 8-step process that applies to both physical and online retail environments. This involves a systematic and structured approach. It’s encouraged to repeat the steps as much as necessary for implementing optimization and growth potential.
The basis for decision-making will come from data related to online marketing analytics. Therefore, close coordination with relevant teams and departments is essential.
Strategic coordination
Before the process begins, you should discuss strategies and goals with manufacturers for your future product groups. This will help avoid potential conflicts.
1. Understanding consumer behavior
This stage is about learning about your customers through purchasing patterns. You’ll want to pay attention to which products or services they choose. To do this, it’s helpful to analyze quantitative and qualitative data provided by market research.
2. Category importance
This step is where you’ll determine how important the selected product category is in your store’s overall portfolio.
3. Category evaluation
As soon as you have defined a product group, you’ll want to analyze its performance. Sales and various key figures on purchasing behavior will help assess where there is potential for development, particularly in comparison to competitors.
4. Category goals
To monitor the success of your product category, you’ll define specific and measurable targets. These relate to customer profiles, financial reports, market share, and performance. This is important for reaching specific audiences, achieving a certain turnover, and determining featured product categories.
5. Strategy development
At this stage, you’ll develop strategies to help achieve the previously defined goals. These are handled through marketing and procurement. Marketing involves conversion rate, average basket value, and profit. Procurement, on the other hand, focuses on more efficient processes.
6. Tactics for your category management
It is recommended that you and your team define a concrete action plan for the product range, placement, and visibility in your online store. With this, you’ll want to implement effective pricing, communication, and advertising strategies. If executed properly, these tactics will all help boost performance.
7. Implementation of your action plan
Now that you’ve defined goals, strategies, and measures, it’s time to implement your plan. For successful e-commerce category management, you’ll want to assign responsibilities and set deadlines.
8. Review
With your plan set, you’ll want to regularly review product group performance. This will help identify potential for growth and optimization. It’s important to implement improvements regularly. The aim is to design your product range as efficiently as possible.
E-commerce category management and digital marketing
There are many overlaps between e-commerce category management and digital marketing. You can use data provided by both areas to inform overall strategies. This will help determine the right campaigns to run to boost sales and performance.
Product allocation using keywords and clickstream data
As mentioned above, useful data is collected by digital marketing efforts. In addition, it’s important to pay attention to user behavior involving clickstream data and UX design.
This information helps determine which products belong to certain categories. It also is useful for identifying product range gaps so you can add relevant items to respective groups. Additionally, you’ll identify opportunities for cross-selling or upselling strategies.
Optimal website category structure
In addition to what’s already discussed, category managers can use data from digital marketing to identify optimal paths for product selection. This helps create an easy-to-navigate website for customers.
If your visitors are shopping through specific products or brands, you’ll want to make these entry points accessible. It should be simple to select paths through corresponding filter attributes in the search function of your website.
In some cases, themed landing pages, personalized product recommendations, or a brand landing page are worth pursuing. The aim is to offer visitors on your site an outstanding customer experience through an optimal website structure.
For example, category management can be used to structure “barbecue” products such as aprons, spices, charcoal, etc. This would be considered a product group. You may also want to create a themed landing page for barbecue season, which would not only feature products but also content for party inspiration.
Growth potential based on conversion rate and consumer behavior
With the help of digital marketing data, including website traffic, conversion rate, bounce rate, and average shopping basket value, there’s potential for growing sales.
For example, if you notice a category isn’t performing so well, you can improve discoverability in your e-commerce environment. With this, it’s possible to improve overall success.
As explained, you can increase customer loyalty and generate higher sales with strategically thought-out e-commerce category management. A long-term approach allows for enormous growth potential for your store. All these efforts can improve loyalty and customer engagement.
Frequently asked questions about e-commerce category management
What is e-commerce category management?
This is the management of the website layout and navigation, and SEO optimization of product listing. It also oversees product information accuracy, including images, descriptions, and pricing.
What are the goals of e-commerce category management?
The overall goal is to optimize the website for increased revenue. This includes conducting the right test for optimizing conversion rates.
What digital marketing data is useful for category management?
Category managers can analyze keywords and click performance to optimize category performance. It’s also worth looking at bounce rates.
What features help with online category management?
Features useful for category management include on-site search and product filters. The goal is to allow customers to easily find the product they want.
