Remember how helpful a shopping assistant in a real-life outlet can be. These specialists listen to what customers need, suggest similar products, alert shoppers to special deals, and point out best-selling or trending items. They build trustful relationships with customers and often increase the size of the customer’s checkout list. E-commerce recommendation engines act as these caring assistants in the world of online shopping. They offer customers new shopping options, help keep them loyal, and drive more sales for your e-commerce app.
In this article, we’ll provide a step-by-step guide on how to build recommendation systems and answer the question of whether all such custom software development solutions need to be AI-powered.
What is an e-commerce recommendation engine, and how does it work?
An e-commerce recommendation engine is a system used by online retailers to suggest products to customers based on their preferences and past interactions with the platform. These engines analyze customer data like browsing history and purchase history to predict what users might be interested in, thereby offering relevant product suggestions.
A recommendation engine is typically a separate system that integrates with your existing e-commerce app. These systems follow a standard workflow: they first collect customer data, then analyze it to identify patterns. Afterward, they use algorithms to generate recommendations and display them to users. Such an integration allows businesses to offer customers more relevant options and make their shopping and user experiences better.
When businesses implement product recommendations, they often experience significant benefits. Multiple surveys support this, such as McKinsey research showing that personalization can lift revenues by 5 to 15% while reducing customer acquisition costs by 50%. Additionally, Salesforce found that customers who interact with AI-powered product recommendations have a 26% higher average order value. Overall, integrating a recommendation engine into your e-commerce development solution can make your business more successful, drive better sales, and improve customer loyalty.
Examples of recommendation systems in world-renowned apps
Examples and statistics show that a recommendation system is an invaluable tool not only for businesses in the e-commerce sector and those focused on custom marketplace development, but also for a wide range of apps looking to boost user engagement. Here are a few stories of the businesses that have made a step towards a better understanding of what their customers desire.
Amazon
One of the notable examples of an e-commerce recommendation engine is Amazon. The company's algorithm analyzes multiple factors, including past purchases, search history, ratings, reviews, and other user interactions. Amazon offers 45 different recommendation widgets and tailors suggestions based on the user's location, trending items in their area, season, bargains, discounts, and also provides curated picks for customers. This focus on developing a robust recommendation system has proven fruitful. According to the studies, Amazon gains 35% of its revenue from such widgets.
Best Buy
Another successful example is Best Buy. The company is a direct competitor to Amazon and has developed its own e-commerce recommendation system. This system provides recommendations based on user queries and compiles similar products that users search for. The recommendation engine managed to boost Best Buy's online sales by 24% in the second quarter after the launch. Today, Best Buy uses an AI recommendation system that features curated lists of trending items, buying guides for specific categories, and personalized recommendations based on customers browsing and purchase history.
Airbnb
Airbnb uses machine learning algorithms to power a unique recommendation system aimed at apartment owners rather than app clients. This recommender engine analyzes large volumes of unstructured text, including in-app conversations between hosts and travelers, customer reviews for properties, and customer support requests made during stays. The goal is to assist property owners in creating the most attractive listings. By providing recommendations on what details to include, the system helps improve property descriptions and the overall appeal of listings.
Uber Eats
Another mobile app development product that effectively uses an e-commerce recommendation engine is Uber Eats. Equipped with machine learning algorithms, Uber Eats employs a system known as two-tower embeddings. One tower focuses on data about user preferences, such as past orders, current location, and potential cravings. The other tower encompasses data about restaurants, including their menu items, location, prices, and ratings. These two towers enable the product recommendation system to first gather information about all possible restaurants and then sort the offerings based on your preferences. This approach greatly improves the customer experience, allowing Uber Eats to accurately understand and cater to your unique tastes and provide exactly what you’re looking for.
All in all, recommender systems offer benefits across various app formats. Video and music streaming services can suggest new content by analyzing users' listening habits. Social media personalizes content based on users’ interactions and interests to get sticky. News aggregators like Flipboard deliver curated stories by examining users' reading behaviors. By tailoring experiences to individual preferences, they not only boost revenue but also foster a dedicated community around your app.
Should an e-commerce recommendation system always be AI-powered?
Most of the e-commerce recommendation engines mentioned above are powered by machine learning. These custom AI agent solutions with recommender functions can provide highly personalized recommendations, but implementing them is often an expensive and complex procedure. They require substantial investment and the expertise of specialists to deploy and maintain such sophisticated solutions. However, AI recommendation systems aren’t the only option. There are other simpler algorithms that can serve as the basis for recommendation systems.
Rule-based recommendations
One such option is implementing a rule-based recommendation system. These systems suggest products or content to users based on a set of predefined rules set by the business owners themselves and operate based on clear if-then conditions. For example, they might dictate, “If a person buys toothpaste, offer them a toothbrush as well.” This approach is convenient because it provides clarity and gives product stakeholders control over the rules, allowing them to change them whenever needed.
Tagging
Another non-AI recommendation strategy you can implement is based on tagging. In this process, each product in the catalog is tagged using different properties. For instance, a skirt in a summer sale might be tagged as "summer season," "casual style," "denim," "blue," "mini," and "price." When a user selects this item, the algorithm will show them 2–3 more items matching these properties. This approach requires preparatory work to label all catalog items with relevant properties. Additionally, you'll need to decide which properties to match on and the relative importance of each. This method is particularly suitable for a recommendation system if your business’s catalog doesn’t expand too quickly, as each new item must be tagged before being added to the system.
Using sales history
Another non-AI recommendation algorithm uses sales history to generate suggestions. This method operates on the assumption that users who purchase similar items have similar needs. When a person buys an item, they receive personalized product recommendations for additional items purchased by others who bought the same initial item. However, identifying these product-buyer-product relationships can be complex and time-consuming, making this approach less suitable for companies with large product catalogs. Additionally, this method might recommend popular products that aren't necessarily related to a specific user’s interests or the initial product, potentially limiting its effectiveness in delivering truly personalized suggestions.
These recommendation algorithms provide a structured, transparent, and efficient approach to personalization while remaining cost-effective and easy to develop. There are specific scenarios where they can bring the most benefit:
- Limited dataset: If your dataset is limited, a machine learning algorithm may struggle, much like a car without enough fuel. For a small online shop lacking substantial data, AI recommendation systems may rely more on guessing user preferences than identifying patterns. Here, simpler algorithms, such as rule-based systems, are an excellent alternative.
- Budget constraints: Implementing and maintaining AI-powered recommendation systems can be costly, requiring significant technical expertise. Simpler algorithms are typically more cost-effective and don't require extensive investment in resources or personnel.
- Need for quick deployment: If you need to launch or update your e-commerce recommendation engine rapidly, simpler algorithms allow for faster implementation and less time-consuming processes, making them ideal for quick iteration.
- Control over recommendations: Rule-based systems provide tight control over recommendations with clear, predefined rules. This can be beneficial when businesses want to maintain strict oversight of the recommendation process and align it with specific business goals or strategies.
- Evolving business needs: For businesses in a state of flux, where requirements change frequently, simpler algorithms offer flexibility. They can be easily modified or updated to accommodate new needs without the need for retraining associated with machine learning models.
- Transparent operations: When there's a need for transparent and easily understandable decision-making processes, non-AI algorithms provide clarity. They allow stakeholders to see exactly how recommendations are being generated and make adjustments as necessary.
These simplified algorithms assume that users always know what they want to buy and can clearly articulate it. However, this is not always the case. Sometimes, the most effective recommendation is the one users haven't considered or cannot find on their own. In such instances, an AI-powered recommendation system can assist with these unexpected discoveries. For example, shoppers searching for a gardening shovel might also need seeds, soil, and a watering can. Their interests may extend to house or landscape design, caring for kids or animals, cooking, traveling, and many other unpredictable areas.
With the help of seamless AI integration, businesses can leave the prediction of such interests to the machine learning algorithm and conduct deeper research of customer behavior. This system scans beyond single clicks and analyzes the sequence of user actions to identify patterns, uncovering unobvious options to offer users. To start recognizing these patterns, businesses begin by collecting user events that can train an ML solution. This solution also provides scalability, allowing the recommendation system to adjust as more data is gathered and the user base grows, thereby providing more accurate and broad-reaching recommendations.
How to develop an e-commerce recommendation engine?
There are several ways to develop a recommender system. You can use an out-of-the-box solution or develop a custom system. Popular out-of-the-box solutions for deploying a recommendation engine include Recombee, GetResponse AI Recommendations, Algolia, Clerk.io, and others. These systems are quicker to deploy than custom solutions and offer features such as recommendations, real-time analytics, and integration with popular e-commerce platforms. They require no extensive coding or deep tech expertise.
However, while these solutions can be integrated into your business, they come with challenges typical of ready-made solutions. A product recommendation engine created with their help might have limited customization options and may not meet the unique requirements of your business. As your business expands, these solutions might not adapt easily to new needs, such as integrating with other systems or supporting new types of data.
If you decide to build a custom AI-powered recommendation engine, you'll most likely need to follow a sequence of carefully planned steps to reach success.
Step 1: Define objectives
When you start developing any software, including an e-commerce recommendation engine, it's crucial to define the software's objectives to make sure that the product aligns perfectly with your business goals and is poised to help you achieve them.
One of the first steps is to determine what goals you aim to achieve with the recommendation engine. Whether it's increasing sales through product recommendations, improving customer retention, raising user engagement, or boosting customer satisfaction, having specific objectives will guide the development process effectively.
Next, you’ll need to outline the scope of the recommendation engine clearly. Decide which features and functionalities can help you achieve your goal and prioritize these based on their potential impact. Furthermore, understanding what customer data is available and how users interact with the system will shape the architecture of your future recommendation system.
Step 2: Collect data
If your e-commerce recommendation engine is based on machine learning, it will need training to effectively identify patterns and provide precise recommendations. Gathering the right data is essential for this training process, and should encompass several key areas:
Properties of goods: Create a database for all items in your catalog, including both in-stock and out-of-stock products, as they help identify similar users. Include item categories, especially if your catalog is multilayered and items can belong to multiple categories. Text product descriptions are valuable, particularly for items with limited interactions, and images can be collected to train models on visual similarity for product recommendations.
Properties of users: Collecting user data is crucial. Gather information on user location, search history, and profile interests. This information helps provide some recommendations even when users are new and haven't interacted with products yet.
Interactions between users and goods: One of the most critical datasets involves capturing user interactions with products. This knowledge should be extracted from interaction or rating matrices of your application and can include ratings, browsing history, clicks, interactions with content like videos and posts, purchase history, and more. The collected data from these interactions can then be used to build user profiles and model user behavior, forming the basis for personalized recommendations.
Once you gather the necessary data, it needs to be cleaned of inaccuracies and organized into standardized formats. This prepares datasets for training your recommendation engine.
Step 3: Choose a recommendation algorithm
Three key recommendation algorithms that you can use in your recommender system.
Collaborative filtering. Collaborative filtering technique suggests items to users based on the preferences of similar users in recommendation systems. It identifies users with shared tastes and recommends items liked by one user to others with similar preferences. This approach relies on user behavior and interactions without analyzing item content. Training requires a user-item interaction matrix, including data on ratings, clicks, and purchases. Platforms like Netflix use this collaborative filtering. For example, if user A and user B liked the same series, the system might suggest another series that user A enjoyed but user B hasn’t watched.
Content-based filtering. Content-based filtering suggests items to users based on attributes of items they've previously liked, focusing on item properties and user preferences. It builds a user profile and recommends items with similar features. To enable this recommendation algorithm, extensive data on item properties is needed. Netflix also uses this method to suggest a series of similar genres to users.
Hybrid filtering. This combines content-based and collaborative filtering techniques to offer hybrid recommendations, showing users suggestions based on both past interactions and item properties. It delivers tailored recommendations and appeals to large enterprises with the resources for complex systems. This balanced approach results in improved effectiveness of e-commerce recommendation engines.
Step 4: Develop and train the model
During this stage, tech specialists from your team will build an AI model using tools and frameworks designed to support machine learning development and deployment. These tools include TensorFlow, PyTorch, Scikit-learn, and others.
Once the model is established, developers will split your customer data into training, validation, and testing sets. After that, developers will feed the training data into the model to begin the learning process. Throughout this phase, they will leverage the resources provided by AI frameworks and fine-tune hyperparameters to optimize performance. This iterative process helps create a recommendation engine that can deliver accurate recommendations.
Step 5: Validating and testing
The AI model should be evaluated using a validation set. This step involves feeding the model a separate piece of data, distinct from what was used during the training phase. A validation dataset in machine learning is crucial for tuning model hyperparameters and preventing overfitting. The validation procedure checks if the recommendation engine generalizes well to new, unseen data. During this phase, pay attention to metrics such as precision, recall, F1 score, or RMSE, depending on the specific nature of the e-commerce recommendation engine.
After validation, use the test set to conduct final evaluations. This ensures the model provides accurate recommendations in real-world scenarios. Beyond addressing technical aspects, involve non-technical stakeholders to evaluate the model's performance. This collaborative assessment helps determine how relevant and practical the recommendations from the e-commerce engine are, enhancing customer satisfaction and overall system reliability.
Step 6: Integrate the recommendation engine into the app
Once the e-commerce recommendation engine is developed and trained, it must be integrated with the app or website. Developers will establish API endpoints for key functions like retrieving recommendations and updating product catalogs, using RESTful principles for smooth communication. To build the engine’s interface and integrate it into your ecommerce product, you’ll need a tech specialist capable of providing high-level backend development services. Additionally, they will integrate recommendation results into the frontend UI so that suggestions appear clearly on product pages, checkout pages, and personalized dashboards.
Step 7: Deploy and monitor
To deploy the product recommendation engine, tech specialists will establish scalable infrastructure, using cloud platforms such as AWS, Google Cloud, or Azure. Containerization technologies like Docker and orchestration tools like Kubernetes will be employed to simplify deployment and make sure the system can handle varying traffic loads.
Once the recommendation engine is deployed, continuous monitoring is essential to maintain optimal performance. Tech specialists will implement monitoring tools such as Prometheus and Grafana to track metrics like system latency, throughput, and error rates. These tools offer real-time insights that help identify and address issues proactively.
Regular performance reviews will be conducted to fine-tune the product recommendation engine. A/B testing and user feedback can be used to refine algorithms to make sure that the system offers relevant recommendations and functions correctly.
A remark about data security
When creating non-AI matching mechanisms for e-commerce recommendation engines, security issues may arise, as these approaches introduce users’ personal information directly into the matching algorithm. While it's possible to create security and privacy in these systems, using customer data in public-facing code increases the risk of exposing private information. Preventing data leaks in this case requires a commitment of time and specialized knowledge.
In contrast, a machine learning recommendation system offers significant advantages for user privacy and data security while also providing more effective product recommendations. These systems learn from the aggregated past behavior of many users and make predictions based on models, potentially reducing the need to analyze personal data directly.
To comply with regulations such as GDPR, it's important to balance privacy and personalization. Tech specialists can achieve this by employing data minimization strategies, pseudo-anonymizing user information, and using security features provided by the technologies and frameworks used during development.
Wrapping up
All in all, recommender systems are a valuable type of software that can elevate your business and increase profits. They can be implemented in your e-commerce app using either an out-of-the-box solution or custom development. Developing a custom product recommendation engine requires setting clear objectives, gathering and preparing customer data, selecting the right recommendation algorithm, building and training the AI model, integrating it into your existing platform, and continuously monitoring its performance.
Many of these steps involve complex technical tasks that require specialized expertise in machine learning, data processing, and software integration. For this reason, if your business for this reason, if your business — whether it’s a growing enterprise or focused on startup software development — plans to implement an e-commerce recommendation engine, it’s often wise to collaborate with an experienced tech partner or a third-party software development agency. This partnership can help you get a recommendation system that is set up securely, performs reliably, and delivers maximum value for your business and customers.
By the way, our team has experience integrating AI into a variety of businesses. We can analyze your workflow and identify the processes that are best suited for AI automation. In addition, we can design and develop an AI-powered app from scratch or enhance your existing solution with an AI recommendation system.
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