It’s a simple concept really – people want to buy things that other people have already bought and have given their approval. Good recommendations for a product mean more people will buy, and bad reviews mean the product might flop. But here’s the thing: As an ecommerce business, you almost certainly need to have product recommendations available because of these stats:
54% of retailers reported product recommendations as the key driver of the average order value in the customer purchase.
75% of customers are more likely to buy based on personalized recommendations.
37% of shoppers who clicked a recommendation during their first visit returned, compared to just 19% of shoppers who didn’t click a recommendation
So, as an ecommerce company, how do we leverage product recommendations to maximize conversions and order size?
Today, we welcome Michael Prichard, CEO of Skafos.ai, to tell us about a new way to do product recommendations.
Deal Closers is hosted by Izach Porter and is produced by Earfluence.
Michael Prichard, an expert on product recommendation ecommerce, joins us to talk about an excellent tool that brings more sales to your ecommerce website. What he offers has a simple concept. With a mission to help shoppers find the products they want, Prichard created Skafos.ai.
The “Tinder of Online Shopping” concept takes a fresh approach to making online shopping more engaging and personalized. Think of it like browsing products just like you would on a social media app—swiping right if something catches your eye or swiping left if it’s not quite what you’re looking for. When ecommerce businesses implement this kind of system, shopping becomes more fun for the customers. It also learns preferences in real time, enabling it to create a recommendation system that goes beyond typical suggestions. Instead of relying on data from past purchases or basic demographic info, its job is to personalize recommendations based on the user’s actual choices. In other words, the system serves what the shopper wants right at that very moment.
Traditional recommendation systems often struggle to get the full picture of what shoppers want, especially as people’s expectations around privacy grow. By letting users actively participate in shaping their shopping experience, this “swipe to shop” concept offers more control and agency. It’s a modern response to the demand for personalized, privacy-friendly shopping, offering a solution where customers can help curate their own recommendations.
This approach does more than just suggest similar items—it learns from each swipe to show products that match the individual’s tastes, not just a general pattern. If boosting customer satisfaction is what you’re aiming for, this system is the way to go. Rather than relying on their previous interactions with your shop, the ecommerce store will deliver a responsive and personalized shopping experience. It will feel like an exploration rather than endless scrolling.
Before we explore Prichard’s tool, it’s worth understanding what makes a product recommendations engine so valuable for e-commerce. Think of it as a virtual shopping assistant, suggesting items that shoppers are likely to enjoy based on the data collected from browsing habits, past purchases, and even what other customers are buying. This feature adds a personal touch to the shopping experience, transforming it from a simple list of items into a journey tailored to each customer.
A big part of what makes recommendations effective is social proof. Just like a friend’s endorsement or seeing an item’s popularity in a physical store, these cues help customers feel more confident about their choices. Social proof in recommendations can highlight bestsellers or trending items, giving customers that reassuring nudge toward products that others have enjoyed.
By incorporating this feature across your e-commerce channels—whether on your website, via email, or on social media—you’re enhancing the shopping experience and increasing the chances of converting visitors into loyal buyers.
When running an online store, one powerful way to increase sales is through thoughtful product recommendations. By suggesting similar products on every product page, you can help guide shoppers toward items they might love, even if they weren’t specifically looking for them. For example, when a customer views or adds a particular item to their cart, recommending “frequently bought” items or products with similar features can encourage them to add a little extra to their order.
Additionally, consider adding sections like “recently viewed items” or “customer favorites” on your product pages. These sections serve as friendly reminders, letting customers quickly find items they liked during a previous visit. This is especially effective for people who might browse multiple times before committing to a purchase.
Another effective approach is to feature your store’s bestsellers or “you might also like” items on key pages. Highlighting these popular products can boost confidence in a purchase, especially for customers who value seeing what others enjoy. With these recommendations, you not only make the shopping experience more engaging but also create a subtle way to encourage larger purchases—benefiting both your customers and your business.
Using product recommendations on your online store offers several valuable benefits that can transform how customers shop—and increase sales in the process. For instance, recommending items based on a customer’s purchase history can create a more personalized experience, showing them options they’re more likely to appreciate. This makes customers feel understood, which often increases customer satisfaction and encourages them to return.
One big product recommendation impact is how they can help boost conversion rates. When you suggest “frequently bought together” items or related products, you’re giving customers the chance to find what they need all in one place. It’s a simple way to show customers other items they might be interested in, which can lead to larger orders and higher sales.
Additionally, recommendations make the shopping experience smoother. By presenting options that align with their browsing or past purchases, customers can discover new products without having to search extensively. This approach not only helps customers make quicker, more confident decisions, but it also builds trust in your brand, as shoppers know they can rely on your store to provide relevant, helpful suggestions.
Skafos.ai stands apart from traditional ecommerce recommendation tools by offering a more dynamic, adaptive approach that doesn’t rely heavily on historical customer data or generalized assumptions. While many systems use past browsing history to suggest products, they often fail to capture a shopper’s real-time needs or preferences, which can make recommendations feel static or outdated. Skafos.ai, however, analyzes product categories, descriptions, and images in real time, creating a more fluid experience that adapts as customers interact with it.
Using advanced AI models, Skafos.ai doesn’t just make typical recommendations; it effectively “searches” the catalog with each user interaction, instantly refining suggestions to provide the most relevant recommendations. By applying object detection, similarity, and metadata-based models, Skafos.ai can suggest items without needing a complete customer history. This means that even new customers can see personalized, relevant products from their first visit.
The tool feels intuitive and immediate. Customers are introduced to items that align with their current shopping journey, making them more likely to find products that truly resonate—boosting satisfaction and sales for ecommerce businesses.
The beauty of Skafos.ai is that, unlike traditional product recommendation tools, they don’t take the invasive route. While Prichard mentions that ecommerce store owners may feed the customer data that they have already collected, all Skafos.ai needs is the catalog.
Prichard also points out that there’s no need to rely on fake reviews to get your products recommended.
Paid product reviews can be costly, especially for small to mid-sized ecommerce stores trying to compete in a crowded market. Skafos.ai eliminates this expense by using its advanced machine learning models to make product recommendations that feel as tailored and trustworthy as reviews—without the need to pay for them.
Instead of relying on external factors like paid endorsements, Skafos.ai uses object detection and similarity analysis on product images, descriptions, and metadata to understand the appeal of each item in your catalog. This approach enables the platform to generate authentic, data-driven recommendations based on real user interactions rather than artificial signals.
For ecommerce owners, this means that Skafos.ai can suggest relevant products, increasing the likelihood of organic conversions. By eliminating the need for pricey reviews and invasive customer tracking, Skafos.ai offers a smarter, more ethical, and budget-friendly solution for boosting product visibility and sales.
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