Based on items you’ve viewed recently…
Items on your wishlist
Frequently bought together
Similar to your past purchases
Categories to browse
What do these ecommerce headings have in common?
Simple: they’re all AI-driven recommendations based on shoppers’ previous searches and purchases. And they’re something else as well: lucrative tools for online stores. Because when shoppers get curated suggestions from a product recommendation engine — ostensibly just for them — there’s a good chance they’ll find something, or something else in addition to what they’re about to buy, that they can’t wait to get their hands on.
By enabling once-unimaginable personalized shopping experiences, this technology has proven to be a win-win: it’s charmed the majority of shoppers by catering to their needs, and AI product recommendations have proven to handily boost conversion rates and customer loyalty.
What’s the secret to giving shoppers such highly relevant, intriguing recommendations?
When that’s set up, highly personalized suggestions can be easily compiled.
However, the process doesn’t end there. Machine-learning recommendation algorithms are applied to continually study the input, and produce suggestions that reflect shoppers’ evolving interests.
The information most helpful for recommending online content includes attributes such as:
These are just some of the attributes and events collected for online stores. The business data and events you choose will likely be even broader.
To ensure that item recommendations hit home, the AI can parse people’s viewed and preferred product data, including:
In addition, stock levels can be noted to ensure that any recommended products are currently available.
Contextual understanding allows AI systems to not just personalize based on static user preferences, as would have been the case years ago, but adapt dynamically to external factors, including:
AI-powered product recommendations provide:
Well-targeted recommendations are an effective form of personalization, making each shopper feel understood and valued. Recommendations enhance the shopping experience while potentially boosting conversion and AOV as products are promoted for individual customer preferences. They work so well that shoppers have come to expect their online experiences to be tailored to their preferences and needs; 76% get frustrated when this doesn’t occur.
For example, an online bookstore might use AI to recommend books they know shoppers will love. By analyzing people’s past purchases and browsing histories across thousands of sessions, a product recommendation system can suggest similar titles, such as novels in the same genre or by the same author, while also introducing them to new material. If a shopper loves Terry Pratchett’s “Discworld” novels, they could be pointed to work by Ben Aaronovitch and Eoin Colfer. This level of personalization optimization can’t help but make shoppers feel that their needs are understood, which, of course, can significantly boost the likelihood of a purchase.
Relevant product suggestions can keep shoppers engaged on a site or in an app for longer periods of time. When they land on your site and see that “Based on items you have viewed recently” is full of items they find attractive, this can lead to increased engagement that raises the likelihood of their ending up on product pages to check out details, followed by happily purchasing.
Let’s look at a use case from the world of fashion. AI-based recommendations can help a shopper pull together an outfit that expresses their unique taste by suggesting accessories that complement what they’re viewing. If they’re looking at a cocktail dress, for instance, the system might recommend style-appropriate sparkly heels and color-coordinated clutches. As shoppers find themselves being tempted by these kinds of outfit-completing items, their average order value can be easily inflated by this effective upselling, resulting in higher profits.
Satisfaction and loyalty are both key to retaining shoppers. Relevant recommendations can help a company create a personalized experience that shoppers love to the point of their becoming considerably more loyal. Statistics show that 56% of online shoppers are more likely to return to a site if it recommends products. And over time, this loyalty can translate into repeat purchases and a higher lifetime value.
A good example would be a beauty-product subscription service that utilizes AI to curate monthly goodie-box deliveries based on people’s skin types and product usage preferences. When shoppers feel that their skin care needs are being catered to, and that a company is on top of any changes, they’re likely going to want to continue being pampered with new item collections designed just for them.
AI-powered recommendation systems continually learn from customer interactions to provide current insights on shopper preferences and behavior. A retailer’s amassed treasure chest of first-party data can be used to refine product offerings, marketing strategies, and user experiences alike, helping ensure that a company remains competitive.
For instance, let’s say an electronics retailer determines that its shoppers often buy additional controllers and games along with gaming consoles. Using this insight, the company’s merchandising team can either curate bundled offers, which not only simplifies the shopping process but improves customer satisfaction, or let the AI automatically learn which items are often purchased together and display them. Regardless of whether the latest Super Mario or Legend of Zelda games are part of the bundles, AI-driven insights can ensure that the retailer stays ahead of consumer demand.
Ready to put smart personalized recommendations to work for your online business? As you can see, they’re an exceptionally powerful tool for improving the online customer experience and boosting ecommerce sales. And by leveraging Algolia’s search and discovery tools, you can make real strides toward securing a competitive edge in the crowded digital landscape. Get in touch with us to chat about the promising possibilities for your business.
John Stewart
VP, Corporate Communications and BrandPowered by Algolia AI Recommendations
Jon Silvers
Director, Digital MarketingCatherine Dee
Search and Discovery writerVincent Caruana
Senior Digital Marketing Manager, SEO