What Are Product Recommendation Engines?

What Are Product Recommendation Engines?

Think about the last time you shopped on Amazon. Did anything in the “other customers also bought,” “just for you,” or “bought together” sections catch your eye? Amazon’s AI based recommendation system powers these suggestions. This algorithm identifies items customers may be interested in based on browsing history, past purchases, similarity to other users, and related items.

Although basic product recommendation technology has been around since the mid-1990s, Amazon changed the game by implementing a new algorithm that focused on similarities between products instead of customers.

For instance, say customers A and B each bought the same pair of safety goggles. A customer-to-customer algorithm would see that customer A and B have something in common and use customer A’s purchases to make suggestions to customer B, and vice versa. Although this works well in some cases, if the only thing these two customers have in common is their need for safety goggles, the customer-to-customer model won’t be very effective. On the other hand, an item-to-item model bases recommendations on the product, not the customer. So, if it sees that customer B bought safety goggles, the algorithm might recommend gloves or a dust mask.

Today, Amazon’s recommendation engine drives more than a third of the company’s sales. Customers prefer shopping with businesses that remember what they are interested in and make personalized recommendations. Relevant suggestions help buyers find what they are looking for more efficiently, reducing bounce rates, growing revenue, and improving long-term customer satisfaction.

In 2015, Amazon launched Amazon Business, its B2B marketplace. The company announced it had reached more than $10 billion in annualized sales in just three years. If distributors want to keep up with Amazon and other disruptive marketplaces, they must revolutionize how their customers shop with them. 

Recommendation Models and AI

Today, there are three core recommendation models: collaborative, content-based, and hybrid. Although collaborative filtering is the most common, many businesses are experimenting with content-based or hybrid approaches.

  • Collaborative filtering: Amazon’s item-to-item algorithm is a type of collaborative filtering. It compares set groups to find similarities, then uses those similarities to make recommendations. Although AI can compare various subcategories, the most common are item-to-item and user-to-user.
  • Content-based filtering: Content-based algorithms use keywords, item descriptions, and profile settings to make suggestions. For instance, a program may identify a book a user liked or saved to a wishlist and then use keywords in that book’s description to recommend similar items to the customer.
  • Hybrid filtering: This model combines collaborative and content-based filtering to gain a more holistic understanding of what customers are interested in. Although they are more complex, hybrid recommendation models (like Proton’s) are more effective and can provide highly accurate predictions about what customers are likely to buy.  

Amazon, Spotify, and Netflix: Driving Sales with Personalization

If you want to see how effective recommendation models are in real life, just look at Amazon, Spotify, and Netflix. These three businesses have all invested significantly in their recommendation models. While each is unique to its parent company, they all have the same end goal – pulling users into the system, increasing engagement, and enhancing lifetime customer value.

Amazon: Since the ’90s, Amazon has poured countless resources into developing its product recommendation technology with great success. The company attributes 35% of sales to its recommendation model, making this technology worth billions. Amazon’s recommendation strategy has also resulted in lower bounce rates and higher conversions. Amazon’s bounce rate is only 35%, much lower than its competitors, Target (45%) and Walmart (50%). While the average website converts 2% of customers, Amazon converts an astounding 13%.

Spotify: Spotify has over 422 million highly engaged users and 1.5 times more market share than Apple Music. In addition to its massive music library, Spotify also has an uncanny ability to know precisely what listeners are searching for. Spotify keeps listeners engaged with the help of three recommendation models: collaborative filtering, natural language processing (NLP), and audio models.

The company’s natural language processing model crawls through websites, news articles, social media posts, and blogs to learn about artists, albums, and genres. In contrast, the audio models dig into the raw data of each song to find similarities between lyrics, tone, instrumental variances, and other characteristics. In addition to the collaborative model, these algorithms identify what users are most likely to listen to keep them engaged by suggesting new music that aligns with their preferences.

Netflix: Netflix uses algorithmic predictions to identify when customers are likely to unsubscribe and sends proactive suggestions to keep them engaged. Netflix reported that its recommendations influence more than 75% of viewer activity – keeping a vast majority of users engaged and saving the company $1 billion a year from potential viewer loss.

Recommendation Models in Distribution

AI recommendations have the potential to drive billions of dollars in revenue. So, why don’t more distributors take advantage of this technology?

B2B businesses deal with massive quantities of data. Unlike B2C companies, many distributors have hundreds of thousands of SKUs and decades of purchasing history across various channels and touchpoints. Because of the complexity of information, distributors need deeper, more intricate AI models to find relevant patterns and similarities.

An AI model designed for B2C isn’t enough – distributors need to find a solution made specifically to handle the breadth and complexity of B2B product pairings and data. With the right technology partner, distributors can utilize recommendation technology to improve their shopping experience and provide customers with a higher level of care.

The benefits of AI based product recommendations include:

  • Product Knowledge: Deep learning models can develop a deeper understanding of products and their relevant pairings than even the most experienced sales reps. With training, systems can suggest products that naturally go well together (i.e., safety goggles and work gloves), recommend products that offer complementary features and benefits to what a customer has in their cart, and offer replacements if an item is out of stock.
  • Personalization: AI can also use customer purchases and browsing patterns to identify items they are likely to buy. This feature, combined with geo-targeting, enables the AI to recommend products that are a good fit for customers and are in stock or available to be shipped to their location.
  • Upsells and Cross-sells: With the help of AI-powered recommendations, customers will only see helpful or relevant suggestions to their needs. For example, if they purchased a vacuum cleaner, the recommendation engine may suggest an extension cord and extra bags. This not only helps customers find everything they need in one trip, but it also increases revenue for the company.
  • More Productive Sales Reps: The right AI model won’t just make suggestions to customers online but will also provide sales reps with product recommendations during customer interactions. For instance, while on a call with a customer, a sales rep will be able to see any items that are due to be reordered, similar products that the customer may need and replacements for out-of-stock products. Some solutions, like Proton, will even identify high-priority customers and alert sales reps when they need to reach out.

AI recommendation systems can help you improve the customer experience, make better upsell and cross-sell suggestions, be more consultative, and keep customers satisfied. Not all recommendation models are created equal. Some distributors have yielded triple- and even quadruple-digit ROI with the right AI-powered product recommendations.

For the best results, find a technology partner with experience in your industry and an AI solution explicitly designed to handle the complexities of B2B offerings. Contact one of our specialists today to learn more.  

Join our thought bubble

Get industry insights and Proton news.