Nowadays, e-commerce and retail businesses use different techniques and channels to promote shops and goods. Most of them keep the focus on advertisement. However, the power of e-commerce product recommendations and a product recommendation engine is often underutilized. In fact, relevant product recommendations can not only increase revenue but also can have positive effects on the user experience. To gain further insight into how these strategies impact customer lifetime value in retail and e-commerce, read more here.
In this article, we want to review product recommendation engines for e-commerce. How do these recommendation engine work? What value can they bring to a business? How can tailored e-commerce recommendations boost sales and marketing? What filters can be implemented within a product recommendation system? It may be relevant not only for e-commerce business owners looking to understand what is an online recommendation engine but also for sales analysts, business analysts, and marketing managers.
A product recommendation is a filtering system that tries to foresee and show the goods that a user may likely buy. Almost every person has seen such a recommendation while doing online shopping: when you view or add an item to your basket, you can see suggestions like "you may also like these products". Sometimes it can show inaccurate suggestions and become annoying for you. However, if it shows an appropriate item, it becomes a win-win situation where a client receives a needed product and a shop increases a revenue.
In offline shops, you may meet a shop assistant who is responsible for customer satisfaction and driving the company's upsales. E-commerce businesses don't have the benefit of having a friendly sales manager to assist your clients with each step of their shopping journey. In the digital realm, this pivotal role is performed by AI algorithms that develop product recommendation systems for each client.
Recommender systems, also known as online recommendation engines, have soared in popularity over recent years. They are now integrated across various sectors: filming, music streaming services, news platforms, bookstores and online libraries, research articles, and of course, E-commerce businesses. They can work as generators of playlists for video and music services like Netflix, YouTube, or Spotify, product recommenders for services such as Amazon or AliExpress, or content recommenders for social media such as Facebook and Instagram. Mostly used in the digital domain, the majority of today's E-commerce websites like eBay, Amazon, and Alibaba make use of their recommender systems to serve the customers better with the products they may need. For a broader perspective on how AI is being utilized in e-commerce, including various use cases, explore our article here.
The mechanics of recommender systems are straightforward:
In most cases for e-commerce businesses, product recommendations are made directly on the website while purchasing. Also, it can be done through email campaigns or on advertising banners. With advanced software, you can get more accurate predictions. As a result, the best e-commerce recommendation systems will have a significant impact on the conversion rate, sales flow and increase revenue.
A product recommendation system is a software or a tool. Usually, it's based on various machine learning algorithms that are used to conduct the data filtering process. There are a few different types of recommendation systems. Let's review some of them:
Collaborative filtering is based on the opinion that people who decided to make a purchase in the past will decide in the future, and that they will likely prefer similar kinds of items as they did in the past. The system generates recommendations using data about rating profiles for different users or goods.
The collaborative filtering approach has lots of advantages. One of them is that it is capable of accurately recommending complex items such as movies without requiring an "understanding" of the item itself. Many algorithms have been used in measuring user similarity or item similarity in recommender systems.
Further, there are several types of collaborative filtering algorithms:
Although the collaborative filtering method is clear enough, there may be some problems while implementing it. For example, a cold start can be an issue. For a new user or item, there isn't enough historical data to make accurate recommendations. There may be an issue of a product cold start or user cold start. The user cold start problem occurs when new users enter a website or app for the first time and the system has no information about them or their preferences. In this case, the system fails to recommend anything. Similarly for new products, as they have no reviews, likes, clicks, or other interactions among users, so no recommendations can be made.
One of the methods to deal with the issue is to recommend trending products to the new customer in the early stages. Here the selection can be narrowed down based on contextual information – their location, which site the visitor came from, a device used, etc. Behavioral information will be collected after a few clicks during that first visit, and start to build up from there.
Another way to deal with the problem of the cold start is by using metadata about the new product when creating recommendations.
Content-based filtering is another approach for recommendation systems. This filtering method is based on a description of the product and information from the users' profile. Content-based system's algorithms recommend products that are similar to the ones that a user has interacted with some time ago. The main idea of this kind of system is that if you like some product you will also like a 'similar' product.
To create a user profile, the system mostly focused on two types of information:
1. A model of the user's preference.
2. A history of the user's interaction with the recommender system.
Content-based filtering has issues in serving, too. If the system is able to learn user preferences from users' actions regarding one content source, it will use them across other content types. The system has less value when it's limited to the recommended content of the same type that the user is already using. For example, recommending news articles based on browsing of news is useful, but would be much more useful when music, videos, products, discussions etc. from different services can be recommended based on news browsing. To overcome this, most content-based recommender systems now use some form of the hybrid system.
Nowadays most of the used recommender systems are hybrid. Hybrid recommender systems combine collaborative filtering, content-based filtering, and other approaches in different ways. You can make content-based and collaborative-based predictions separately and then combine them; add content-based capabilities to a collaborative-based approach, or just unify the approaches into one model.
Hybrid methods usually can provide more accurate recommendations than simpler approaches. These methods can also be used to overcome some of the common problems in recommender systems such as the cold start.
One of the best examples of using the hybrid recommender system is Netflix. Let's review how Netflix's recommendation engine works. Whenever you access their service, the recommendation system combines the following data to make better suggestions:
- information about interactions with the service: viewing history, your ratings
- information about other users with similar preferences
- information about the titles, such as their genre, categories, actors, release year
- information about the time of day user usually watch something
- info about the devices used to watch films on Netflix
- time that the user spends on watching something.
Another successful usage of recommendation systems is shown by Amazon. 35% of the company's revenue is generated by the recommendation engine. There is information that the company uses to provide relevant recommendations to its customers:
The most obvious benefit of using recommender systems is that your company can increase revenue without dramatical changes in advertisement expenses. Amazon is an inspiring example of it. Along with the revenue, you can increase the number of users and the level of their satisfaction.
To develop the most effective filtering systems, various data types are essential. Alongside recommendation systems, effective inventory management plays a crucial role in e-commerce success. Learn more about the value of inventory management in retail businesses here. These systems can be integrated seamlessly with the right tools. If computational or storage capacity is a limiting factor, especially when handling vast user and product data, cloud services can be considered.
If implementing a product recommendation system is on your agenda, the Datrics team is ready to assist. Our specialists boast vast experience in recommendation system deployment. The Datrics platform simplifies analytics, offering diverse integrations with custom visualization and API access. To explore how recommendation systems can elevate your business, feel free to discuss further with our team.
Q1: What is a product recommendation engine?
A product recommendation engine is a sophisticated tool that analyzes customer data and behavior to predict and display products a customer is likely to purchase, enhancing the shopping experience and increasing sales.
Q2: How do product recommendation engines benefit e-commerce businesses?
These engines personalize the shopping experience, improve customer engagement, increase average order value, and boost sales by providing timely and relevant product suggestions to shoppers.
Q3: What are the main types of product recommendation systems?
The main types include collaborative filtering, which recommends products based on similar user interests; content-based filtering, which suggests items similar to what the user has liked in the past; and hybrid systems, which combine multiple recommendation approaches for more accurate suggestions.
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