Product Recommendations Engine: The Ultimate Guide 2024

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Product Recommendations have become a must-have for eCommerce businesses to deliver the best experience to their customers. In the digital age where users have access to the best customer experiences that help them build loyalty towards their favourite brands, not having a personalized recommendation system might be putting retailers at a disadvantage. 

Most eCommerce websites leverage data and Artificial Intelligence to deliver a personalized experience to their customers by giving them relevant product recommendations, increasing their engagement, conversions and revenue. Listed below are some real-world examples of success with product recommendations.

What are Product Recommendations?

Product Recommendations is an eCommerce personalization strategy that aims to populate a customer’s webpage with relevant products based on pre-existing data such as browsing history, customer attributes and product information. 

Product recommendations influence a customer’s choices and increase revenue, engagement and conversions for eCommerce retailers at scale. Ecommerce recommendations particularly come into play when a retailer has a diverse inventory of products.

How do Product Recommendations work?

A product recommendations system takes into account specific information about the users and predicts whether a specific product would be suited to their needs or not. Typically, a rating is used to determine the likelihood of the user picking a product based on the existing data provided to the recommendation engine.

A good product recommendation engine can make informed decisions about the users with the data provided even before the user interacts with the system. A product recommendation system usually comprises a combined mechanism of specialized algorithms and techniques that can score through large volumes of data(product catalogues, inventory, etc).
A product recommendation system processes data in four steps, as follows:

1. Data Collection – The product recommendation system collects data from the users – like product ratings and reviews, comments, etc(Explicit data) or page views, order and return history(implicit data)
2. Data Storage – The product recommendation system then stores the data and sorts it out in a database. This helps with the next step, which is analysis.
3. Data Analysis – Different analysis methods are run on the data that was collected and the product recommendation engine finds products with similar user engagement
4. Data Filtering – The product recommendation system then filters the relevant information based on the specific algorithms to provide recommendations to the user.

Here are some Product Recommendations examples:

• Bundling – Recommending products that are frequently brought together as a package to customers is called ‘Bundling’. Bundling helps you increase the value of your customer’s cart.
  
  For example – If they are buying a new smartphone, recommend a compatible case and other accessories.
  
• Upselling – Recommending higher-priced products based on similarity to tempt your customers to upgrade their purchase can help increase revenue-per-transaction. Upselling particularly works very well with high-intent browsers.
  
• Trending Products – Featuring products that are popular and gaining traction helps to maintain relevancy in the eyes of the customer. Popular products that are moving fast also lead to higher traffic.
  
  For example, many eCommerce sites listed COVID-19 Essentials such as masks and sanitisers on their homepages which significantly contributed to their traffic, engagement and sales.
  

Here’s how Vue.ai’s Product Recommendation Engine helps Retailers

Vue.ai’s Personalization platform combines Product Intelligence and Customer Intelligence to build individualized profiles tailor-made for each customer. Our product recommendation engine handles the tough tasks that come with personalizing eCommerce sites, enabling a seamless experience for both retailers and customers. 

Our eCommerce recommendation system aids retailers in growth, and here are some benefits of opting for Vue.ai’s product recommendation engine:

Increase Average Order Value(AOV)

With multiple product offerings to choose from, having a product recommendations engine can help you recommend the right products to your users and personalize their experience to suit their needs. This includes cross-selling, bundling and upselling additional products, driving direct revenue and profit. 

For example, recommending a model-specific phone case when the user purchases a new phone on your website will enable them to save time and effort by delivering everything they need for a complete shopping experience. 

Case Study: Vue.ai delivered a Dynamic Personalization engine to a resale customer, resulting in a 16.5% increase in AOV.

Reduces Bounce Rate and increases engagement

Bounce rates determine how happy your customers are with your website and your products. A personalized recommendation system ensures that your users are engaged and spend more time on the website because of the tailor-made experience that it can offer that caters to their specific needs. 

Case Study: Using Vue.ai helped this Japanese retailer increase their user engagement by 8x

Increases Customer Loyalty

Delivering a personalized user experience with a product recommendations can make shopping online convenient for customers. By providing these seamless experiences, you can ensure that your customers return to your website, thereby increasing your brand value and their loyalty to your brand.

Increases Revenue

A product recommendation engine enables you to increase your revenue by tapping into all the factors that make a user-experience delightful. It ensures that your customers open and stay on your website and reduces the average customer’s effort to convert by giving them what they want. It also goes a step further to deliver an all-rounded experience. 

As mentioned in the above case study, Vue.ai’s personalization engine helped a leading Japanese retailer achieve 5x more revenue per visitor.

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