Estimated to reach a whopping 6.54 trillion US dollars in 2022, the global retail e-commerce industry has grown leaps and bounds in the last few years. With multiple players competing for buyer’s attention, one of the most useful features that help attract customers and ensure a constant repeat business flow is product recommendation.
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Product recommendation engines are an excellent way to deliver customers with an improved user experience. Leveraging advanced algorithms such as machine learning and AI, a recommendation system can help bring customers the relevant products they want or need.
Here, we will explore various aspects of a recommender system, including its types, advantages, challenges involved, and applications across industries.
A product recommendation engine is essentially a solution that allows marketers to offer their customers relevant product recommendations in real-time. As powerful data filtering tools, recommendation systems use algorithms and data analysis techniques to recommend the most relevant product/items to a particular user.
The main aim of any recommendation engine is to stimulate demand and actively engage users. Primarily a component of an eCommerce personalization strategy, recommendation engines dynamically populate various products onto websites, apps, or emails, thus enhancing the customer experience. These kinds of varied and omnichannel recommendations are made based on multiple data points such as customer preferences, past transaction history, attributes, or situational context.
Recommender systems can be used across multiple verticals such as e-commerce, entertainment, mobile apps, education, and more (discussed in detail later). In general, a recommendation engine can be helpful in any situation where there is a need to give users personalized suggestions and advice.
One of the crucial components behind the working of a product recommendation engine is the recommender function, which considers specific information about the user and predicts the rating that the user might assign to a product.
Having the ability to predict user ratings, even before the user has provided one, makes recommender systems a powerful tool.
It uses specialized algorithms and techniques that can support even the largest of product catalogs. Driven by an orchestration layer, the recommendation engine can intelligently select which filters and algorithms to apply in any given situation for a specific customer. It allows marketers to maximize conversions and also their average order value.
Typically, a recommendation engine processes data through the below four phases-
Data collected here can be either explicit such as data fed by users (ratings and comments on products) or implicit such as page views, order history/return history, and cart events.
The type of data you use to create recommendations can help you decide the kind of storage you should use, like the NoSQL database, a standard SQL database, or object storage.
The recommender system analyzes and finds items with similar user engagement data by filtering it using different analysis methods such as batch analysis, real-time analysis, or near-real-time system analysis.
The last step is to filter the data to get the relevant information required to provide recommendations to the user. And for enabling this, you will need to choose an algorithm suiting the recommendation engine from the list of algorithms explained in the next section.
There are many problems solved by machine learning, but making product recommendations is a widely recognized application of machine learning. There are mainly three essential types of recommendation engines –
The collaborative filtering method is based on collecting and analyzing information based on behaviors, activities, or user preferences and predicting what they will like based on the similarity with other users. The prediction is done using various predictive maintenance machine learning techniques.
For example, if user X likes Tennis, Badminton, and Golf while user Y likes Tennis, Badminton, and Hockey, they have similar interests. So, there is a high probability that X would like Hockey and Y would enjoy Golf. It is how collaborative filtering is done.
The two types of collaborative filtering techniques are –
One of the main advantages of the collaborative filtering approach is that it can recommend complex items accurately, such as movies, without requiring an understanding of the item itself as it does not depend on machine analyzable content.
Content-based filtering methods are mainly based on the description of an item and a profile of the user’s preferred choices. In content-based filtering, keywords are used to describe the items, whereas a user profile is built to state the type of item this user likes.
For example, if a user likes to watch movies such as Mission Impossible, then the recommender system recommends movies of the action genre or movies of Tom Cruise.
The critical premise of content-based filtering is that if you like an item, you will also like a similar item. This approach has its roots mainly in information retrieval and information filtering research.
Hybrid Recommendation engines are essentially the combination of diverse rating and sorting algorithms. For instance, a hybrid recommendation engine could use collaborative filtering and product-based filtering in tandem to recommend a broader range of products to customers with accurate precision.
Netflix is an excellent example of a hybrid recommendation system as they make recommendations by:
Compared to pure collaborative and content-based methods, hybrid methods can provide more accurate recommendations. They can also overcome the common issues in recommendation systems such as cold start and the data paucity troubles.
Although recommendation engines produce a lot of revenue for e-commerce giants such as Amazon and Netflix, they do have various challenges. Some of these are discussed below –
The challenge of synonymy arises when a single product or item is represented with two or more different names or listings of items (for instance, action movie or action film) having a similar meaning. In such a case, the recommendation system is not capable of recognizing whether the terms show various items or the same item.
One of the other issues with recommendation systems is the scalability of algorithms having real-world datasets. In most cases, the traditional approach has become overwhelmed by the multiplicity of products and clients, leading to dataset challenges and performance reduction.
Latency issues arise when new products are added more frequently to the database of a recommendation engine. Still, already existing products are recommended to users since newly added products are not rated. Companies can use either a collaborative filtering method or the category-based approach in combination with user-item interaction to deal with the issue.
In most cases, customers need to feed their personal information to the recommendation system for tailor-made and beneficial services. However, it causes various data privacy and security issues, making the customers feel hesitant to feed their personal data into recommendation systems.
But since the recommendation system is bound to have the customer’s personal information and use it to the fullest to offer personalized recommendation services, they must navigate the situation with extra care and ensure trust among their users.
There are instances when users do not give ratings or reviews to the products they purchased, making the rating and review model relatively sparse leading to data sparsity issues. It leads to a decrease in the possibility of finding a set of customers with similar ratings or interests.
Now that we have discussed the challenges involved in recommended systems let us come to the advantages and applications.
Among the key advantages of recommendation, engines include –
One of the excellent methods to increase your revenue and average order value (AOV) is to encourage your website visitors to add recommended products and offerings at the checkout page.
Recommendation systems allow you to drive much higher conversions and enhance average order value. You can bring multiple data sets (historical data, real-time visitor behavior, and third-party insights) into a recommendation algorithm using a recommendations engine. These data sets can then deliver relevant recommendations in real-time and allow customers to engage with your brand in real-time.
This kind and level of relevancy give a definite boost to your sales and average order value by exposing your customers to a higher volume of products that are likely to pique their interest.
Further, by leveraging various data algorithms and inferences about what the customer will like based on their past preferences or what has been purchased by similar customers, the recommender systems can systematically encourage additional spending while offering a much more engaging user experience.
One of the most efficient ways for any brand to meet customer expectations is to build customized and relevant content. Recommended system allows brands to personalize the customer experience and make suggestions for the items that make the most sense to them.
A recommendation engine also allows you to analyze the customer’s current website usage and their previous browsing history to be able to deliver relevant product recommendations. The best part is that all this data is collected in real-time so the software can react as the consumer preference or shopping habits alters.
Recommendation engine AI can be key to creating a consistent brand experience by simply drawing data from various channels. It allows you to optimize your omnichannel customer experience and make customers feel part of an ongoing journey instead of starting afresh with each interaction.
Serving your customers with more personalized recommendations across various channels they interact with you, including email, web, and the in-store app, can also allow you to build a long-lasting and robust connection with them.
Using a recommendation engine allows you to bring targeted traffic to your website. The recommender system can achieve this with specifically targeted blasts and personalized email messages.
Let us explore the use cases and recommendation engine examples across large and well-known organizations –
The most common usage of recommendation systems is in the e-commerce sector. Companies and e-commerce stores use modern recommendation systems with sophisticated algorithms to filter data based on the customer’s buying choices.
Amazon.com is an excellent example that uses item-to-item collaborative filtering recommendations in most of the pages on their website and email campaigns. A report by McKinsey suggests that 35% of Amazon purchases are based on recommendations systems.
Amazon uses DSSTNE (Deep Scalable Sparse Tensor Network Engine), open-source deep-learning software for driving product recommendations to its e-commerce site.
Recommendations here are used as a marketing tool, and the company uses various types of recommender systems to achieve this such as-
a. Top Categories for You tab, which lists all broad categories like electronics, furniture, or
home decor based on your browsing history.
b. Items lying in your Amazon cart to suggest related items that are frequently bought together.
Another e-commerce giant, Alibaba.com, uses a trusted recommendation system to dominate the e-commerce market. The company leverages AI-powered recommender systems capable of receiving data points from a new customer’s purchase history somewhere else and matching it with similar kinds of products available in their platform.
Alibaba makes various product recommendations to its customers based on past transactions, browsing history, product feedback, and geographic location.
Video streaming is another area where recommendation engines are used widely. Some of the examples of these are listed below –
Netflix is a popular name that leverages recommendation systems to boost customer satisfaction. The video streaming giant uses robust predictive knowledge about which genre of movies/ series customers are likely to watch next, ensuring that their customers remain loyal and do not switch over to the competitors.
The main objective of Netflix is to use its recommendation system to bank on customer retention and ensure a regular flow of a recurring subscription model.
What’s unique about the Netflix recommender system is that instead of looking at broad genres, it focuses on nuanced threads within the content to help viewers break preconceived notions and find movies or shows that they might have selected initially.
YouTube is another video streaming giant with a powerful recommendation system with sophisticated algorithms to filter content based on customer’s browsing history, age of videos, search terms, and more.
However, unlike Netflix’s subscription model, YouTube relies primarily on advertisements and hence uses a powerful recommender system that matches relevant ads based on historical data.
When it comes to audio streaming platforms with a massive amount of content available, narrowing down to just one from the entire collection with songs from all existing genres can be an overwhelming task.
An efficient audio streaming platform integrates a smart AI-driven recommendation engine that keeps a close track of customer’s listening trends. It then recommends tailor-made playlists every week or month that they are most likely to listen to based on their individual music preferences.
The music streaming giant, Spotify, uses an AI-powered recommendation engine that regularly updates a personal discovery weekly playlist for users to help them not miss any updates on newly released tracks by their favorite artists.
The company has also acquired The Echo Nest, a music intelligence and data-analytics startup that enables them to systematically create a music recommendation engine with three different recommendation models, including collaborative filtering, audio file analysis, and natural language processing.
Apart from these, the recommendation system has a wide range of applications in the below areas/fields as well-
5. Machine learning in healthcare for better analysis and deeper insights
6. Recommendations in financial services and insurance products
7. Game recommendations in Xbox
Recommendation engines today serve as the key to the success of any online business. But, for a sound recommendation system to make relevant recommendations in real-time requires powerful abilities to correlate not just the product but also customer, inventory, logistics, and social sentiment data.
All in all, recommender systems can be a powerful tool for any e-commerce business, and rapid future developments in the field will increase their business value even further.
With a wide range of business applications, including anticipating seasonal purchases based on recommendations, determining essential purchases, and offering better suggestions to customers, brands can leverage recommender systems for two key areas – brand loyalty and enhanced customer retention.
The first step to having great product recommendations for your customers is really just having the courage to dive into better conversions. And remember – the only way to truly engage with customers is to communicate with each as an individual.
However, developing a recommendation engine takes a great deal of data expertise. Your recommendation engine is only as effective as it is built to be. At Maruti Techlabs, our machine learning experts are well-versed with techniques like deep learning, supervised learning, unsupervised learning, reinforcement learning, etc.
If you wish to improve your recommender systems with these cognitive computing methods or simply want to learn more about how machine learning solutions can resolve your business challenges, get in touch with us today.
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