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Most online shops implement e-commerce product recommendation systems. But if you take a look at how these recommendations are displayed, you’ll start to notice some big differences. Depending on how the recommendation system works, different variables impact which products are prioritized for personalization.

Since recommendations depend on personalization and user data, it’s possible to choose how to suggest items. In the following article, you’ll learn about various recommendation systems and how to choose the right one for your shop.

Relevant recommendations through personalization

Product recommendations are most impactful when they are relevant to the customer. It is possible to achieve maximum results by ensuring that the recommendations are personalized and account for the individual preferences of customers.

With this, it should be noted that the most effective type of personalization depends on the e-commerce product recommendation system and strategy used.

Personalization requires dialog

Personalization is very complex. It can be used at various touchpoints and implemented using different systems. It means presenting online visitors with product recommendations that are as close as possible to their preferences.

To do this, you first need data on the user’s click and purchase behavior. This can be collected through a dialog with the customer, which is established via their engagement with the online shop. Once data is collected, it can be combined with product data and expert knowledge in a knowledge base.

In this knowledge base, all data is processed and evaluated. One approach is to use artificial intelligence (AI). This allows data (click and purchase behavior) to be processed into information by experts via data mining. The information is then turned into knowledge using algorithms (reinforcement learning).

This knowledge is ultimately used to provide customers with relevant recommendations. Product data, click and purchase behavior, and expert knowledge all converge in the knowledge base.

Dialog-based AI

To provide customers with the best results, an algorithm must first determine what the customer needs. The term dialog-based AI is used for such processes.

A so-called response engine, which uses sensors to record and analyze click and purchase behavior, plays a crucial role here. This takes on the task of identifying a customer’s goals and interests from their engagement with the online store.

Various possibilities for dialog

There are many ways to track customer engagement with an online shop. These are distinguished in the form of:

• Reactions

When a customer browses an online store, numerous reactions can occur. Based on user engagement with the content (clicks, product selection, purchases, etc.), you can find out what they want. Once you have this information, you can present customized content to customers.

• Language

We naturally think of language when we hear the word “dialog.” That said, a dialog between humans and machines based on language has pitfalls. Many people who use voice-activated assistants understand this.

When discussing online stores, human-to-machine dialog occurs through search terms. This allows language to be used for a personalized search engine. Therefore, a product search can be understood as a personalized search system triggered by linguistic input.

With this, the following question arises. How can e-commerce operators use customer-driven engagement and dialog to provide suitable recommendations? There are various systems for achieving this, which are discussed in more detail in the following sections.

Classic recommendation systems (static)

There is a clear distinction between different e-commerce recommendation systems. They are primarily distinguished by the data and methods used to determine relevant suggestions to customers.

With this, there are two classic variants: collaborative and content-based systems. In addition, recommendation systems can incorporate other elements, including demographic data and time spent shopping. We’ll look into two “classic” recommendation systems below.

Collaborative recommendation systems

When online shop customers share click and purchase behavior, a collaborative recommendation system can be used. This analyzes data from different customers and finds similarities to suggest relevant products for a consumer group.

Recommendations generated by this system can follow a headline like “customers who were interested in this product found these relevant.” This is because the system will recommend items to multiple customers with similar patterns. On a related note, algorithms that deliver product lists with this approach are known as collaborative filtering systems.

Collaborative recommendation systems are used by major retailers like Amazon, among others. It is the method of choice when little or no personalization information is available for a customer. It’s also good when the product catalog contains minimal characteristics.

• Advantages of collaborative systems

In e-commerce, the advantage of collaborative product recommendation systems is they can reveal relationships between users and items that aren’t explicitly apparent. Additionally. collaborative filtering can show customers products that differ from previous preferences, but may still be of interest. This means you can surprise your customers.

• Disadvantage of collaborative systems

However, there is a disadvantage. It is referred to as the “cold start problem,” and occurs primarily with new users and products. With this type of e-commerce product recommendation system, it is necessary that there is a large number of customers with similar behaviors. If there is minimal customer engagement, it can be difficult to generate recommendations.

Content-based recommendation systems

Unlike the above type, content-based recommendation systems do not work on the basis of users with similar engagement patterns. Instead, product attribute commonalities are used as a basis. In addition, individual customer engagement plays a role.

Content-based recommendation systems suggest items that are relevant to products a customer expressed interest in purchasing. To calculate recommendations of this kind, content analysis is required to determine product similarities.

When providing such recommendations, you can preface them with something like “similar products from your favorite brand.”

• Advantages of content-based systems

Content-based recommendation systems have both advantages and disadvantages. One major advantage is that content-based recommendation systems can suggest items even if there are no clicks or purchases on the site. This counteracts the “cold start problem” of collaborative recommendation systems.

• Disadvantages of content-based systems

A disadvantage of a content-based e-commerce product recommendation system is that it can be overly specialized. There’s no element of surprise regarding product recommendations. They are only based on the preferences of the individual customer.

If we look back at the example of “similar products from your favorite brand,” we can see another problem with content-based recommendation systems. The customer may wish to see products that have their favorite color, for example.

This makes it clear how important it is to understand preferences. In other words, a customer should be presented with a wide range of recommendation factors. It’s the best way to determine which, of the similar products, the customer actually wants.

Context-aware recommendation systems (dynamic)

Personalization goes beyond the matter of providing customers with desired content. Users increasingly expect content to be presented in the “right” context, one that’s familiar. This presents a challenge for personalization services.

To present customers with relevant recommendations in a context that’s suitable for them, dynamic information is required. This is in addition to static information (such as product similarities). Context-aware recommendation systems process this information.

Within this system, the context is another input for the recommendation system. It conveys what the customer is doing and where recommendations are displayed. The dynamic context-aware information and its interrelationships significantly improve recommendation quality.

Multiple recommendation contexts

When discussing e-commerce, multiple recommendation contexts refer to suggestions beyond products. This typically occurs in a personalized section of the website dedicated to the customer. It’s useful for keeping consumers engaged beyond their purchase(s).

It is possible to show a variety of recommendations in multiple contexts that are tailored to the customer’s preferences. This includes interactive elements and offers a mix of inspiration from similar products to content and entertainment.

If successful, this encourages customers to return to the online shop on their own, increasing consumer loyalty and website engagement. By discovering related content to their favorite brands, styles, etc, users may purchase more items.

This entertainment-driven approach is based on data already collected from the customer’s previous interactions with the website. The overall experience matches what consumers already know, creating a familiar environment for discovery.

Individual recommendation contexts

If your shop can’t host the content necessary for a multiple recommendation environment, you should ask what your customers specifically need. This will help you deliver the best individualized recommendations possible.

To illustrate how crucial it is to develop a suitable recommendation environment, let’s look at the following scenarios.

• Product detail page

An online shop visitor is looking at a product (for example, a pot) on a product detail page. The customer is currently researching information and wants to buy a pot. To best lead the customer in the right direction, you can display similar products or products customers have also purchased. This can be shown below the product information in a recommendation widget.

• Shopping cart layer

In this situation, a shop customer puts a product (for example, a bicycle) in the shopping cart and a pop-up appears with similar products. Here, the customer is one step away from completing the purchase.

They are about to buy a bicycle and have already added it to their shopping cart. When this happens, you should not display similar products under any circumstances. This will confuse the customer and delay the purchasing process.

To avoid them changing their mind at this stage, you should instead present complementary items like a helmet or bicycle lock. This is known as cross-selling and inspires customers to increase the value of their cart.

Compromises for individual recommendations

If only one or two recommendation methods can be presented on a product detail page, you have to select the best one. An example would be “similar products that you may also like.” It is important to take the term “similar” literally.

“Similar” here means products with the same characteristics as the product viewed are understood. This relates to a customer’s personal preferences. If done correctly, it will increase the quality of recommendations and drive sales.

For example: if a customer shows a particular interest in black items, other black items are considered to be very similar. Without this information about customer behavior, the product color would not help inform recommendations.

Hybrid recommendation systems

It may be necessary to mix or modulate recommendation systems. By combining content-based and collaborative recommendation systems, disadvantages can be minimized. This means that high-quality and relevant recommendations can be generated more quickly for online shop customers. If this occurs, this is called a hybrid recommendation system and ensures better results.

That said, truly relevant recommendations cannot be generated with a universal algorithm. They require the dynamic interconnection of a series of intelligent basic algorithms. The prerequisite for this is a modular software system that supports these basic algorithms in a compatible manner. It also requires experts to be able to configure such dynamic architectures with the right parameters.

Selecting the right recommendation system

We’ve covered a wide range of e-commerce product recommendation systems, alongside various methods and data uses. The final question remains. Which one is “right” for generating suitable recommendations?

Understanding your customer’s needs

The above question cannot be answered easily in general terms. The right recommendation system for your e-commerce platform depends on various factors. Recent developments in e-commerce reveal that previously static structures are becoming more dynamic.

In addition, the shopping environment is becoming increasingly important. With this, it goes without saying that it’s necessary to have a wide product and content selection available. The right recommendation strategy depends on the phase of the customer journey and product context. It’s best to explore a mixture of different systems for an optimal experience.

Expert knowledge as a prerequisite

To dynamically provide personalized recommendations in the right context, your website needs the right software architecture. It needs to dynamically combine a wide range of algorithms. With this, an understanding of the shopping environment context is necessary.

Configuring these architectures requires expert knowledge. This is because only trained individuals can identify the requirements of individual touchpoints for selecting the right recommendation system.

They will best know how to choose the best personalization type for the context. By using an expert, you’ll ensure individual recommendations are generated properly for customers.

Targeted combination of different recommendation systems

As you can see in this article, there are various recommendation systems. They each have their own advantages and disadvantages. The development of e-commerce shows that dynamic structures are becoming more important. Shop customers expect product recommendations in a familiar environment.

To meet such demands, different recommendation system processes can be combined with each other. It’s possible to facilitate this in a targeted manner, based on contextual information.

Since these hybrid systems are very complex, expert knowledge is crucial for success. This is because dynamic architectures need to be designed and personalized to generate relevant product recommendations.

With this, it’s important to understand customer preferences and ensure recommendations are appropriate for various stages of the journey. Following these recommendations can make a big difference in presenting optimal recommendations. All of this means more revenue from increased sales.

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