Don’t find customers for your products, find products for your customers.
Every interaction online is now a data point, feeding algorithms like those behind the “Because you watched…” and “You may also like…” prompts that drive 80% of Netflix’s revenue. Recommendation engines like these are working hard to get you to watch (and buy) more.
There are dozens of choices for most anything, and recommendation engines narrow the selection for each customer, steering them to specific products. Their role in the success of an eCommerce business cannot be overestimated. Recommendation engines work at every stage of the famous conversion funnel, from awareness to action, engaging shoppers and guiding them along the customer journey.
Connecting a customer with the right product is among the primary goals for any retailer. This is why many are setting their sights on recommendation technologies. The market for recommendation engines is growing fast: we can see a sharp increase in adoption across multiple industries.
Size of recommendation engine market by end-use industry, $US billion
In this post, we look into how recommendation engines work and how to build one for your eCommerce business — because you do need one if you want to increase your sales!
What is an eCommerce recommendation engine?
A recommendation engine for eCommerce is software that suggests products to customers. While this sounds rather simple, recommendation engines are in fact complex systems powered by artificial intelligence (AI), machine learning (ML), and big data analytics. Using ML algorithms, recommendation engines identify patterns in a customer’s behavior and recommend products that may be relevant.
The concept of product recommendations is not new — traditional recommender solutions that use historical data and look for predefined patterns in customer behavior have been around for quite some time. However, the arrival of AI and ML has heralded a major shift in the capabilities of eCommerce recommendation systems. In addition to analyzing volumes of historical data, AI-powered eCommerce recommendation engines use real-time contextual data to identify customer habits and preferences and generate highly personalized product suggestions.
Benefits of recommendation engines for eCommerce
- Increased sales
Users are more likely to purchase items that correspond to their preferences and shopping habits. - Improved customer retention
The feeling of being understood and cared for drives loyalty and increases the repeat purchase rate. - Enhanced customer experience
Personalized recommendations make shopping smoother, more intuitive, and more enjoyable. - Better inventory management
Retailers can configure recommendation engines to support strategic objectives — such as promoting overstocked or seasonal items — by blending personalization with business logic. - Higher marketing efficiency
Accurate product targeting helps retailers reduce advertising waste and focus on high-intent customers. - Reduced employee workload
AI-powered recommendation engines automate personalized suggestions, allowing employees to focus on other tasks.
For eCommerce businesses, these benefits are quite tangible. For example, Salesforce has calculated that customers who interact with AI-powered product recommendations have a 26% higher average order value (AOV). McKinsey’s research also stresses the importance of personalization in eCommerce, showing that it can reduce customer acquisition costs by up to 50% while increasing revenue by 5–15%.
Types of eCommerce recommendation engines
AI-powered recommendation engines rely on similarities in user behavior, user preferences, and product characteristics to suggest items to purchase. We can define two major recommendation techniques based on the types of interactions that are used in making recommendations:
| Collaborative filtering | Content-based filtering |
|---|---|
Recommendations are based on what similar users prefer. If users A and B have purchased similar items, items purchased by user A can be suggested to user B (and vice versa). |
Recommendations are based on similarities in product attributes. If a user has purchased item A, item B (with similar characteristics) can be suggested to them. |
| Pros: Collaborative filtering is effective in recommending niche or novel items that users may not know about. Based on preferences of the identified user group, this method can recommend items that do not share common features with those the target user has chosen before.
Cons: As collaborative filtering relies on user data, it is not especially effective for new users. |
Pros: Content-based filtering can be effective with new users, as it requires little or no user data. It works well for introducing new items based on similarities in item characteristics.
Cons: This approach may miss some features that are important to the user while focusing on others. For example, it may recommend films with the same actors while ignoring the plot or genre that the user may be looking for. |
The most robust systems use a hybrid approach, blending collaborative and content-based methods — often through a weighted or ensemble model — to make recommendations more relevant.
How eCommerce recommendation engines work
In generating targeted product suggestions, recommendation engines leverage data science, and AI and ML. Product recommendations usually follow a five-step process:
- Collect data. Data from multiple sources — purchase and browsing history, cart activity, user reviews, social media comments — is gathered for processing. At the same time, the recommendation engine also uses demographic data such as age, gender, and lifestyle preferences to group users and define products relevant for each group.
- Store data. Before data is processed, it is stored in a data warehouse or data lake. Such storage systems can receive structured and unstructured data from multiple sources.
- Process and analyze data. ML models structure and analyze user data to determine patterns and identify their weight. Models can process large datasets to detect fine patterns and generate targeted recommendations.
- Filter data. After analysis, recommendation engines apply algorithms to identify the most relevant data patterns to be used in creating product suggestions.
- Generate and display recommendations. Using the results of data analysis and filtering, the recommendation engine produces personalized product suggestions and displays them to users via the selected channels: eCommerce website, email, push notifications.
eCommerce recommendation engine use cases
Businesses in different industries are leveraging AI-powered recommendations to increase sales and attract more customers. Today’s technology supports a variety of use cases.
Personalized product recommendations
Based on individual user behavior, browsing patterns, and purchase history, recommendation engines can suggest products with similar features. Netflix uses this approach to build rich and diverse suggestions of films and shows for each user. Its “Because you watched…” and “We think you’ll love these…” lists are carefully selected based on a user’s previous interactions with the streaming service.
During the purchase process, online stores can suggest products that complement those that a buyer has selected. Using this technique, iHerb, a global retailer of cosmetics and wellness products, shows a “Frequently purchased together” section for each item. For example, if you’re looking at a face cream, iHerb may suggest a serum and a cleansing foam for a complete skincare routine.
Real-time recommendations
Responding to a user’s behavior on the website, a recommendation engine can adjust its suggestions to promote trending or time-sensitive products. This method is at the core of Hopper, an AI-powered flight recommendation startup supported by Lufthansa. Hopper analyzes massive volumes of flight and hotel data and generates price predictions together with recommendations for the cheapest trip.
Homepage personalization
Based on a customer’s shopping behavior, AI- and ML-powered eCommerce recommendation engines adjust product descriptions on the fly to present information that is most relevant to the individual customer. Amazon reportedly uses an LLM to analyze each customer’s activity, generate personalized recommendations, and adjust page layouts .
How recommendation engines work across different retail verticals
While the overall principle of generating personalized recommendations is the same across retail verticals, the implementation and focus may vary. Different product groups have different attributes and purchasing cycles, shaping unique customer behaviors.
| Vertical | Key characteristics | Recommendation strategies |
|---|---|---|
| Groceries |
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| Fashion |
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| Travel and hospitality |
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| DIY and home improvement |
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How to build your own product recommendation engine for eCommerce
There are several ways that you can leverage AI-powered product recommendations:
- Plug-and-play recommendation engines. Such solutions are rather straightforward in terms of integration with existing eCommerce infrastructure. They are intuitive and user-friendly ; however, customization options are limited. Still, if you need convenience and speedy deployment, check out Recombee or Seldon.
- Pre-trained cloud-based recommendation services. Solutions by leading cloud providers such as Vertex AI Search by Google, Amazon Personalize, or Azure AI Personalizer come with APIs that allow for seamless integration with your application. They are dynamically scalable, which ensures high performance under heavy loads. However, you may experience vendor lock-in and face certain customization limitations.
- Custom recommendation engine. Building an AI-powered recommendation system for eCommerce is a daunting project, as it requires extensive knowledge and expertise in such fields as AI & ML and big data. However, you can simplify the process by partnering with an expert provider of eCommerce development services that can create a custom recommendation system for you.
If you choose to build your own AI recommendation engine for eCommerce, here’s a rough outline of the steps to take and things to consider:
- Define your objectives. List the business goals you expect to achieve with the recommendation engine. Clear goals allow you to estimate the project scope. For example, if you’re looking to increase the average order value, you may focus on existing customers and use content-based filtering.
- Collect data. Prepare the data you will use to train your machine learning model. Depending on the selected recommendation method, you might focus on different types of data: user behavior data for collaborative filtering, data on product features for content-based filtering. Store the data in a relational database or data warehouse.
- Preprocess the data. Normalize the data to optimize model performance, removing outdated and irrelevant entries. Keep the data that is most likely to describe actual user preferences and behavior and/or current product features.
- Build and train the model. Using machine learning tools like Pandas, Scikit-learn, LightFM, and TensorFlow, create your recommendation engine and train the ML model on your data. Use historical data as a training set to teach the model to identify patterns.
- Deploy the recommendation engine. Integrate your recommender into your eCommerce platform via APIs or embedded widgets. Test the response speed to ensure low latency.
- Monitor and optimize. Track your recommendation engine’s performance through KPIs such as conversion rate and click-through rate. If you notice that recommendation accuracy is lower than expected, fine-tune the filtering algorithms until you reach the desired level.
- Do A/B testing. Compare different recommendation algorithms and strategies to identify the best-performing approach.
How to integrate a recommendation engine into existing infrastructure
Integrating a recommendation engine with existing Enterprise Resource Planning (ERP), Customer Data Platform (CDP), Product Information Management (PIM), and other enterprise systems allows eCommerce businesses to create hyper-personalized product recommendations. However, such integrations present certain implementation challenges.
| Challenge | Solution |
|---|---|
| Data silos. Enterprise systems often exist autonomously, using different data formats and going through different update cycles. |
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| Lack of API readiness. Legacy systems may lack APIs that support modern ML applications. |
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| Integration with headless architectures. In headless commerce solutions, frontend and backend systems are decoupled, making delivery of recommendations to various touchpoints more complex. |
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Addressing data and technology challenges
When building your own product recommendation system for eCommerce, keep in mind the most common challenges that you need to resolve:
| Data integration. Your eCommerce platform receives data from multiple sources: websites, mobile apps, email, social media. For your recommendation engine to work properly, you need to integrate disparate data into a unified repository. | Solution: Implement data pipelines and ETL (extract, transform, load) tools to unify data. Technologies like Apache Kafka, AWS Glue, and Google Cloud Dataflow can automate and streamline this process. |
| Scalability. Your recommendation engine needs to grow together with your user base and be able to maintain processing speed as traffic increases. | Solution: Use cloud-based recommendation solutions to leverage dynamic scaling and implement distributed computing frameworks such as Apache Spark to ensure performance under heavy loads. |
| Data privacy. When you collect and process user data, you are subject to data protection requirements of the GDPR, HIPAA, and other laws and regulations. | Solution: Implement data anonymization and user consent management techniques. Ensure end-to-end data security to protect user data and maintain compliance legal and regulatory compliance. |
| Algorithm bias. Biased data used in ML model training may affect product recommendations. | Solution: Review recommendations for bias and use fairness-aware algorithms to ensure diversity and equality in product suggestions. |
| Ethical AI and explainability. Non-transparent use of customers’ data and unclear recommendations may result in a lack of trust in the brand. | Solution: Explain how recommendations are created and always obtain customers’ consent to use their data. |
Intellias’s experience building eCommerce recommendation engines
At Intellias, we use our extensive expertise in data and analytics and AI and ML to help businesses increase sales and revenue with eCommerce product recommendation engines. We build recommendation solutions not only for retail businesses but also for customer-serving companies in other industries including education and hospitality.
AI-powered personalization solution for an eLearning platform
For a provider of B2C eLearning services, we designed a SaaS educational platform that improved the user experience. Targeting corporate customers, our client was focused on offering learning services maximally adapted to users’ busy schedules. The solution we came up with is a comfortable learning space that allows users to choose and customize educational programs based on their requirements and build curated educational journeys.
Personalized trip planning and booking engine
To assist our client in expanding to new markets and attracting new customers, we built a cloud-based transportation booking platform that supports third-party service integrations, which extends the choice of options provided to travelers . The intelligent algorithm behind the booking engine helps users find and book tours that meet their expectations.
Are you ready to adopt AI as your product recommender?
AI-powered recommendations have moved from innovation to mainstream, accounting for a significant share of revenue among leading digital retailers. As digital commerce continues to gain momentum, personalized recommendations are becoming one of the main drivers of growth — increasing sales, encouraging loyalty, and attracting new customers.
Don’t miss your opportunity to lead the competition — adopt intelligent recommendations and build a dynamic data-driven eCommerce ecosystem. Let’s discuss how AI-powered personalization can give you a competitive edge. To get started, schedule a free architecture consultation.
