The complete guide to product recommendation on your e-commerce platform

E-commerce product recommendations are one of the most effective ways to improve your conversions and overall customer experience. This is why up to 71% of online retailers offer them to their users.  

AI-based recommendation systems are rapidly becoming the solution of choice for merchants looking to offer personalized product recommendations. With advances in AI and machine learning, product recommendation engines today have become incredibly sophisticated. The result is more accurate, targeted, and data-driven e-commerce product recommendations – a proven way to boost your business’ performance. with more accurate and relevant recommendations that help improve online conversions. 

This guide will offer a detailed look at how you can best use e-commerce product recommendations to grow your online business. 

What are product recommendation engines for e-commerce? 

Product recommendation engines are a key part of many e-commerce websites and apps. They use data about what customers have bought or viewed in the past to make recommendations for what they might want to buy or view in the future. 

They can be very simple, like Amazon's "Customers who viewed this item also viewed" feature. Or they can be much more complex, using machine learning algorithms to find the right products for each individual customer.

AI-powered product recommendation engines typically use large data sets to identify patterns and relationships with machine learning algorithms to make recommendations about e-commerce products. For this, they take into account factors such as the customer's search history, browsing behavior, and previous purchases. By understanding these patterns, recommendation engines can make highly personalized suggestions that are much more likely to lead to a sale.

By understanding what products users are interested in and connecting them with similar or complementary items, recommendation engines can help drive increased engagement and conversions. For instance, if a shopper searches for the query ‘LED TV’, an AI-powered site search engine will not only display the most relevant recommendations for LED TVs, but will also recommend ‘products bought together’ such as a stabilizer or a firestick. This offers consumers a peak online retail experience while helping them choose the best options. 

How do Ai powered Recommender system works? 

Many e-commerce platforms use some form of AI to personalize the visitor’s experience and offer products that may interest them. AI recommender systems are advanced data-filtering software that use machine learning algorithms to sift through products using past user data and preferences. The self learning algorithms allow recommender systems to parse hundreds of thousands of suggestions and narrow down the user's choices tailored to the users' specific needs, search behavior, geolocation, and past purchases. 

The first step is gathering relevant data so that the recommender has enough to know the user's buying needs. Some common forms of data that product recommendation engines use to self-learn and improve are - 

• Product attributes. This includes ratings, likes, and clicks. It tells the system what the user likes and doesn't like.

• User behavior data. This captures user behavior such as time spent on a page, average cart values, or product purchase history. It can tell the system what the user is interested in, even if they haven't explicitly rated anything yet.

• Demographic information. This can include age, gender, user's location, economic status, average cart value, and a lot more easy-to-analyze data. It helps the system understand who the user is and what kinds of products they might be interested in. 

Once the data is stored, the system then analyses and filters the data using batch, real-time, or near-real time filtering methods. This allows the recommender to establish relationships and associations between the different data points, and extract information about user engagement on similar products. 

The content-based and collaborative filtering algorithms are used to then recommend similar products. Cluster filtering also takes into account data from other users to recommend products that are more widely liked and preferred. 

Why is it important to build a robust product recommendation system for your business?  

Product recommendation engines are important  because they can help you increase customer lifetime value, improve customer satisfaction, and drive more sales. Here's how - 

• By increasing the visibility of certain products, you encourage customers to purchase items that they may not have otherwise known about or considered purchasing. A well-built product recommendation system can also help boost customer loyalty and satisfaction. By providing customers with tailored recommendations, you signal to your potential customers that you take note of their needs, and are proactive about meeting them. And consumers respond in higher sales, cart values, and conversions.
• Product recommendations have also shown to drastically improve customer retention rates – a critical metric for your business' performance. 
• You can also successfully cross-sell and upsell products by offering intelligent recommendations. By understanding the relationship between different products and how users engage with them, you can make suggestions for other products that complement their product choices. This not only serves as an opportunity to increase your average order value, but boosts the overall user experience and satisfaction.

Types of product recommendation engines 

Below are the three main types of product recommendation engines.

1. Content-based filtering

Content-based filtering relies on the data within the products themselves to make recommendations. This data can include things like product descriptions, category tags, user ratings, reviews, and other metadata. The algorithm looks at the attributes of these items and compares them to other products in the same category to find similar items.

One advantage of content-based filtering is that it does not require any user input; all that is needed is some sort of product data. This makes it possible to generate recommendations for new users or items with no ratings history. However, this approach can suffer from the cold start problem, where new users or products don't have enough data for the algorithm to make good recommendations.

2. Collaborative filtering

Collaborative filtering method takes into account the past interactions of all the users. It then maps their tastes and preferences to similar user profiles to make relevant e-commerce product recommendations. This approach uses past user data (and not product data) to find patterns and trends, which are then used to make recommendations to new users. For example, if two users have similar purchase history, the system might recommend the same products to both of them.

3. Hybrid filtering

As the name suggests, hybrid filtering combines the content-based and collaborative filtering approaches to make e-commerce product recommendations. With hybrid filtering, the system looks at the user's past behavior and preferences as well as the characteristics of the items being recommended. This allows for a more personalized experience that takes into account not only what the user likes, but also what they are likely to be interested in.

Adding personalized product recommendations on your e-commerce store

Offering personalized recommendations on your e-commerce site can be an excellent strategy to delight customers with relevant suggestions and make their buying journey as intuitive and seamless as possible. Some ways you can explore personalized product recommendations include - 

- Product suggestions in search results

Shoppers who use e-commerce site search have a much higher intent of buying your products. This is why personalized recommendations can be an instrumental addition to the search results. Not only does this make things more simple (among a sea of choices), but it can also engage users in meaningful interactions with your site. 

In fact, 49% of consumers admit that they've bought a product that they initially didn't intend to buy because it was recommended to them. 

- Best selling products

Recommending best sellers is an effective strategy to boost products that are loved by other consumers. Many e-commerce sites including Amazon and ebay recommend best selling items to users based on product attributes and performance data. In fact 35% of Amazon's revenue can be attributed to its recommendation engine – which is a huge portion of their sales! 

- Deals/promotional offers

In addition to displaying products that perform well with users, e-commerce stores can also recommend discounted products or promotional offers to visitors. Not only can this increase the chances of boosting the cart values, but it can encourage users to spend a lot more time on your store looking for the best deals. Consider this – shoppers who clicked on product recommendations spent almost 10 more minutes on an online storefront than those who didn't explore recommendations. 

- During checkout to boost order values

The checkout page is not only the most important conversion touchpoint, but it also serves as a great opportunity to offer personalized recommendations. By offering relevant products at the checkout, brands can encourage customers to add more items to their cart, especially when the recommendations are too good to pass. This often leads to improved AOVs (Average Order Value). As a matter of fact, 54% of online retailers consider product recommendations as the key driver of improved average order values.

Where should you offer personalized product recommendations? 

There are a number of places you can offer product recommendations at, but their effectiveness largely depends on your target audience and where they spend their time. Let’s take a look at where you can and should offer e-commerce product recommendations - 

- On your website - Your e-commerce website is probably the most impactful place to offer product recommendations to users. Imagine online commerce giants like Amazon and ebay without recommendations. The shopping and browsing experience will be a lot less personalized. In short, passing on this opportunity to serve customers with tailored suggestions is leaving money on the table.

What’s more, displaying e-commerce product recommendations at different touchpoints on your website (including product pages, cart, or home page) can nudge purchases that would otherwise be unlikely to happen.

- On site search - Your site search engine is a goldmine for serving not only the most accurate and relevant search results, but also personalized product recommendations. For example, if a customer searches for "women's shoes size 9," you can recommend products that match those criteria while taking into consideration their purchase history and preferences. 

This is especially helpful if your site search isn't always accurate, as the recommendations can help guide shoppers to the right product even if their initial search query isn't perfect.

- In your email newsletter - If you send out regular email newsletters to your customers, consider adding personalized product recommendations to them as well. This is a great way to promote new or seasonal items that may be of interest to your subscribers, and it can also help encourage those who haven't shopped in a while to come back and check out what's new on your site.

- On social media - Social media platforms like Facebook, Instagram, and Twitter can also be a great way to offer e-commerce product recommendations to your users. For example, you could create ad campaigns with targeted product recommendations based on the interests and past behavior of your target audience. Additionally, you can leverage the option to invite collaborators from Instagram, allowing influencers or brand ambassadors to promote your products to their followers, further expanding your reach and increasing the effectiveness of your e-commerce initiatives.

Conclusion - AI-powered product recommendations are elevating shopping experiences 

Personalized recommendations can enhance the shopping experience by helping users find the perfect product more easily, and at the right time. In fact, 45% of online shoppers prefer to shop on an e-commerce site that offers personalized recommendations And because they're based on self-learning algorithms, they can be constantly fine-tuned to become more and more accurate as consumers spend more time on your website. 

Platforms like Zevi are leading the way with AI-based site search engines and recommendation systems to help businesses leverage data in the best way possible, and to optimize shopping experiences through state-of-the-art search capabilities. 

To experience this yourself, book a free demo today.