AI For Personalized Recommendations: Optimizing Product Suggestions

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.

How personalized product recommendations work

What’s the secret to giving shoppers such highly relevant, intriguing recommendations?

  • First, collect data on shoppers’ actions at various touchpoints along the customer journey, make notations on how they browse and search or explore in an app.
  • Then, use artificial intelligence to aggregate the data and analyze users’ behavior and preferences. This facilitates the creation of a dynamic profile of each shopper.

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. Blog_ctabanner_Video_DDD

Data for determining accurate recommendations

The information most helpful for recommending online content includes attributes such as:

  • Clicks: Every click that a particular user makes can add concrete insight on and depth to their preferences.
  • Browsing history: The categories and products a shopper browses illuminate the general areas of their shopping intent.
  • Purchase history: Previous buys obviously constitute a gold mine of information, revealing not just types of preferred products but brand preferences, price sensitivity, and buying frequency.
  • Length of interaction: The amount of time a shopper spends looking at a product or category page supplies abundant clues on the intensity of their interest.

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.

Product information

To ensure that item recommendations hit home, the AI can parse people’s viewed and preferred product data, including:

  • Product descriptions: Natural language processing (NLP) techniques can let companies understand which product features, benefits, and attributes shoppers are interested in.
  • Categories: What a shopper generally finds enticing can help AI align their recommendations; for instance, by suggesting other items in a favored category.
  • Price: AI can deduce budget parameters from someone’s browsing and purchase history, then recommend products in their optimal range.

In addition, stock levels can be noted to ensure that any recommended products are currently available.

Contextual data

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:

  • Time of day: Taking note of when shoppers are typically online can facilitate a company’s timing of promotions, for instance.
  • Seasonality: To accurately target what people might want, product recommendations can be naturally cognizant of weather and seasons like winter and the holidays.
  • Device type: Whether a shopper is using an app on a mobile phone or looking at a website on a desktop PC can influence which recommendations should be made. For example, an online clothing retailer might observe that people shopping on mobile phones prefer impulse buys such as small accessories, while desktop users are spending more time doing product research and buying more-expensive items.

How AI recommendations impact the bottom line

AI-powered product recommendations provide:

Personalization at scale

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.

Better engagement

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.

Enhanced customer retention

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.

Continuous improvement

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.

Improve your conversion and loyalty

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.