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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Product recommendations can follow customers throughout their journey in an online shop. This usually includes the homepage, category and product detail pages, and shopping cart. It also extends to e-mail marketing.

In this article, we will show you where product recommendations make sense for e-commerce along with special features to consider. This is relevant for displaying recommendations at the right moments in an online shopping environment.

Ensuring relevant product recommendations for your e-commerce

In e-commerce, product recommendations are displayed through recommendation “reco” engines. As you might imagine, these deliver relevant recommendations tailored to the visitor. While customers are typically suggested products, recommendation engines can also be used for related content.

Recommendations are presented using widgets which provide the framework for relevant product or content suggestions. These can be customized to match the design and branding of your e-commerce platform.

Differentiate between new and existing customers

With the above information, you might be wondering how to effectively use an e-commerce product recommendation widget. You’ll first need to make a distinction between new and existing customers. This is important because you’ll need to tailor your recommendation engine to different audiences, based on shopping behavior.

In this context, it’s useful to know whether a customer has already visited your online shop, clicked through various categories, and completed a purchase. All of this is considered an online shopping history and only applies to existing customers.

Existing customers: online shoppers with history

If your customers have a history with your store, you can use data based on their previous habits or purchases to display suitable recommendations. You’ll want to analyze click and purchase behavior to personalize relevant product suggestions. In doing so, you’ll likely inspire more sales.

New customers: online shoppers without purchase history

While you can’t offer recommendations based on the personal interests of customers new to your shop, you still can make suggestions to inspire their purchase journey. These include using recommendation algorithms like top sellers, sale items, or new products.

Where to position product recommendations on your website?

There are many ways to position product recommendations in an online shop. You can add them to the homepage, product detail page(s), and directly in the shopping cart. It’s important to strategically place these at every step throughout a customer’s journey.

To help you keep track of the various options, we have provided an overview of suitable positions for online shop recommendations:

1. Homepage
2. Category page(s)
3. Product detail
4. Zero results page
5. Shopping cart
6. Wishlist
7. Thank you / confirmation page
8. Content page
9. Personal shopping area
10. Newsletter

It should be noted that recommendations should be displayed in accordance with the best user experience for each page. To illustrate this better, we will discuss a few pages in detail below. Below we’ll show you what to look for on each page regarding optimal recommendation placement.

1. A dynamic homepage

As mentioned above, you’ll need to build different shopping environments for existing and new customers. If executed properly, this will change the look and feel of your homepage. Just like in a brick-and-mortar store where a salesperson knows the behaviors of regular customers, you can show users their preferences are understood.

For example, an online shop for pet products should know who is purchasing for cats vs dogs. If a cat owner is recommended dog food, they will find the shopping experience counterintuitive. The right products should be presented the moment a customer lands on your homepage, and can be as detailed or broad as needed.

However, as previously explained, if a new customer lands on your homepage, past purchasing data won’t be available. In this scenario, it’s advised to showcase top sellers, sale items, or new products.

2. Category pages

On product category pages, you can use personalized recommendations by analyzing online shoppers’ browsing and purchasing behavior. This approach will allow you to highlight products that are relevant to their habits and align with their interests.

Pro tip: Read more about product category marketing in our new guide!

3. Product detail page recommendations

The product detail page contains more specific information than a homepage. Since a customer is looking at one item, this allows for highly relevant product suggestions. With a product detail page, two types of recommendations make the most sense. These are similar and complementary items.

• Similar products

An online shopper navigates to the product detail page because they find a particular item interesting. If they like the selected item, but are still unsure, similar products can help them make a decision. This is where the customer can become aware of products that may better meet expectations. It helps them feel informed about their purchase, leading to a sale.

• Cross-selling complementary items

Complementary items are relevant for the product detail page. If the customer is convinced they need the selected product, it’s worth showing them matching items. With this, you can draw their attention to other products that may interest the customer. It might inspire them to continue shopping.

When doing this, you’ll want to take the customer’s preferences into account. These include sizing, brands, preferred materials, and dietary needs. The latter is particularly valuable if a customer doesn’t purchase items containing allergens or is vegetarian/vegan. Of course, with new customers, this isn’t possible.

It is also important to account for inventory. You don’t want to recommend products that aren’t in stock, as that creates a frustrating experience. The goal is to enable your customers to make additional purchases.

• Create complete looks with product sets

On the product detail page, showcasing a complete product set is popular. These are recommendations that present customers with an entire look based on one product. The idea is that they can easily add complementary items with minimal effort.

For example, customers can create a stylish outfit, or purchase necessary camping gear in an instant. If you’re selling cameras and photographic equipment, you can offer a set with matching lenses, memory cards, batteries, and a carrying bag. With this, you can allow customers to save or purchase these items at once.

4. Showing alternatives on a zero results page

Sometimes users will search for items that you don’t offer in your shop. This will usually display a page showing “zero results.” To prevent customers from clicking off your site, you have the option of using a product recommendation widget on your page. You don’t want visitors to feel discouraged.

Recommendations on the zero results page can be alternatives from your product range. For example, if a user is searching for a particular brand that you don’t offer, you can recommend similar brands or styles. This will inspire your customers to explore these items and potentially discover new products.

5. Recommendations on the shopping cart page

Even though a user is confirming their shopping cart, it’s not too late to recommend other items. You can still increase sales at this stage of the process.

• Add to cart recommendations

As customers click “add to cart,” you can provide quick personalized recommendations with a cart layer widget. This will pop up and show other products of interest.

• Recommendations on the shopping cart page

The shopping cart page itself can display product recommendations. With this, it’s recommended that you don’t show similar products here as that can confuse the customer. Personalized complementary items make the most sense at this stage.

Additional products that complete the original product or relevant low-cost items are suggested for maximum sales potential. You can set up a checkout zone in your shopping cart that encourages customers to purchase small, cheap items. Think of how a supermarket places snacks near a checkout line.

When recommending items, you have different options for what information to collect from the shopping cart. You can either take the entire order into consideration or you can suggest items based on the last product added. The right approach will depend on your particular shop and strategy.

6. Wishlist

What better place to recommend more products than in your shoppers’ wishlist? This page is already a place where buyers keep up with their future purchasing desires. Product recommendations on this page can encourage a higher order value in the future.

7. Thank you page: more than just an order confirmation

A thank you page is a great place for product recommendations. It doesn’t have to just contain information about the order, it can also encourage future purchases. With relevant recommendations, you can lead customers back to a product detail page. This keeps them engaged in your shop for a longer time.

8. Content page: Recommendations based on topics

By now, it’s clear to see how relevance is key when discussing product recommendations for your e-commerce. With certain campaigns, you may want to execute this manually with marketing and thematic landing pages. Combining content and topic-related recommendations, you can use digital storytelling to emotionally engage online shoppers.

The goal is to create a digital story on a topic and tie it to relevant products. For example, you can do this with inspiration for skiing or surfing vacations. The combination of content and products is a popular approach to e-commerce product recommendation strategy. It moves away from the purely functional aspect of sales, creating a much more personalized experience.

Below you can see what a content page in an online shop looks like. In this example, the topic is a surf trip. In addition to information and a story about the perfect vacation, the page offers thematically appropriate product recommendations for inspiration.

9. Personalized shopping: relevant recommendations and more

If you want to offer users the best experience, you can create a personalized shopping area in your online shop. This is a section that is dynamically, intelligently, and fully customized for each customer. It offers a central location in the shop of the customer’s favorite brands, categories, and items.

This personalized area allows users to browse their own product and brand world. Online shoppers can also receive relevant content suggestions, such as blog articles, and receive shopping news in real-time.

Relevant product recommendations also play a role here. For example, recommendations can be integrated in the form of product sets. Additionally, users can easily access desired information via clickable, interactive elements.

With personalized shopping, the site learns sizing and preferences to display available and on sale items. There is also the potential for embedding interactive content for a unique e-commerce environment.

As you can see, a personalized shopping area offers a particularly high level of inspiration and goes beyond product recommendation widgets.

10. Newsletter: recommendations in real time

To keep your customers fully engaged, you’ll want to provide recommendations through email newsletters. Like everything else discussed, it’s important these are personalized to the customer. You’ll want to account for consumer preferences.

Since emails are read at various times, you want to make sure the recommendations are relevant to when the newsletter is opened. This ensures that all information (including inventory) is up to date. There are also various ways of showcasing recommendations.

• Complementary items

With order confirmation emails you can suggest complementary items. This is a similar strategy to what was discussed on the thank you page.

• Alternative products

If you want to remind inactive customers of products abandoned in their shopping cart, you can suggest both similar and alternative items.

• Topic-related recommendations

Newsletters can focus on a specific topic and showcase thematically related items. With this, you can send topic-specific emails to ensure customers receive highly relevant recommendations.

Why recommendation visibility matters

Proper positioning is essential for product recommendations to perform optimally. As an example, it is important to not add suggestions “below the fold.” You don’t want customers to have to search for recommended items. There’s data to suggest that only 20% of content “below the fold” is seen by viewers.

This means that even if the right products are recommended, 80% of consumers would miss them if improperly placed. As you can see, this creates a drop-off in potential sales. It also means, since there was no engagement, less data is collected to inform better recommendations in the future. This insight is crucial for optimization.

If your recommendation engine is well set up, but recommendations aren’t seen then you’re missing valuable sales potential. Due to this, recommendation positioning on all pages of your website is important. This is particularly true for product descriptions where there’s a high chance of interaction.

Optimal e-commerce product recommendation placement for maximum potential

As you can see in this article, an effective e-commerce product recommendation strategy is a win-win. Your users become inspired while shopping and discover new products. As a shop owner, you can increase engagement and sales.

To fully benefit from the potential of a recommendation engine in your online shop, you should place product suggestions on the various stages mentioned. With the right data, you can anticipate the needs of your customers.

Also, make sure online shoppers actually see your recommendations and remain informed after they leave your site. It’s important to send personalized emails that contain real-time data.

This article shows the potential and power of useful product recommendations. Now it’s your turn to implement the best strategy on your site.

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In this article, you’ll learn more about what’s involved in achieving an effective approach. You’ll also discover how digital marketing plays a role.

We’ll go over how to implement an easy-to-follow 8-step strategy for maximizing engagement and sales. The key is to balance accessibility, unique merchandising, and customer behaviors. This can all be achieved through understanding and implementing data and analytics. 

Read on to learn more, starting with the fundamentals of category management.

What is category management?

With category management, products are grouped together. The focus is placed on the needs of consumers. This structure allows for more efficient purchasing by anticipating customer demand.

The goal is to not cluster similar products together, but instead to group them by complementary items. That said, category management has been standard in physical retail for decades.

For example, you’ll find pasta alongside sauces in a supermarket. Similarly, hiking gear, clothes, shoes, and backpacks are usually in the same aisle of a sporting equipment store. This concept applies to e-commerce environments.

Cooperation between retailers and manufacturers

Optimal category management is based on functional cooperation between retailers and manufacturers. It works when both parties want to achieve a triple-win situation for satisfied customers, higher profits, and more sales. Within this context, product groups or service categories are viewed as strategic business units.

To be effective, a holistic approach that engages various departments in the company is needed. As mentioned above, this also involves input from manufacturers. Retailers control the product groups, while manufacturers provide detailed knowledge of categories. In the end, both sides are interested in finding the best possible solution for customers.

Tasks of a category manager

Typically, an effective strategy is managed by a dedicated person or team. They are usually responsible for the following tasks:

• Analysis of shopping baskets
• Definition, structure, and optimization of product groups (category management)
• Strategic planning of featured products
• Merchandising and purchasing for the respective product segment

Goals of category management

Category management is a strategic approach designed for long-term goals. The aim is to achieve higher customer satisfaction with an easy-to-navigate shopping experience. This generates more sales and improves the overall brand image.

Also, effective category management provides a competitive advantage. If done properly, consumers will come to trust a store for its expertise in certain product groups.

E-commerce category management

As you might imagine, category management not only applies to physical stores, but also to e-commerce. That said, it’s fairly new to the digital environment and there is room for development. Similar to brick-and-mortar retail, an online store can meet customer needs with structured product categories. This, along with an optimal user experience, can increase customer satisfaction and boost sales.

8 step strategy for E-commerce Category Management

Below, you’ll find an 8-step process that applies to both physical and online retail environments. This involves a systematic and structured approach. It’s encouraged to repeat the steps as much as necessary for implementing optimization and growth potential.

The basis for decision-making will come from data related to online marketing analytics. Therefore, close coordination with relevant teams and departments is essential.

Strategic coordination

Before the process begins, you should discuss strategies and goals with manufacturers for your future product groups. This will help avoid potential conflicts.

1. Understanding consumer behavior

This stage is about learning about your customers through purchasing patterns. You’ll want to pay attention to which products or services they choose. To do this, it’s helpful to analyze quantitative and qualitative data provided by market research.

2. Category importance

This step is where you’ll determine how important the selected product category is in your store’s overall portfolio.

3. Category evaluation

As soon as you have defined a product group, you’ll want to analyze its performance. Sales and various key figures on purchasing behavior will help assess where there is potential for development, particularly in comparison to competitors.

4. Category goals

To monitor the success of your product category, you’ll define specific and measurable targets. These relate to customer profiles, financial reports, market share, and performance. This is important for reaching specific audiences, achieving a certain turnover, and determining featured product categories.

5. Strategy development

At this stage, you’ll develop strategies to help achieve the previously defined goals. These are handled through marketing and procurement. Marketing involves conversion rate, average basket value, and profit. Procurement, on the other hand, focuses on more efficient processes.

6. Tactics for your category management

It is recommended that you and your team define a concrete action plan for the product range, placement, and visibility in your online store. With this, you’ll want to implement effective pricing, communication, and advertising strategies. If executed properly, these tactics will all help boost performance.

7. Implementation of your action plan

Now that you’ve defined goals, strategies, and measures, it’s time to implement your plan. For successful e-commerce category management, you’ll want to assign responsibilities and set deadlines.

8. Review

With your plan set, you’ll want to regularly review product group performance. This will help identify potential for growth and optimization. It’s important to implement improvements regularly. The aim is to design your product range as efficiently as possible.

E-commerce category management and digital marketing

There are many overlaps between e-commerce category management and digital marketing. You can use data provided by both areas to inform overall strategies. This will help determine the right campaigns to run to boost sales and performance.

Product allocation using keywords and clickstream data

As mentioned above, useful data is collected by digital marketing efforts. In addition, it’s important to pay attention to user behavior involving clickstream data and UX design.

This information helps determine which products belong to certain categories. It also is useful for identifying product range gaps so you can add relevant items to respective groups. Additionally, you’ll identify opportunities for cross-selling or upselling strategies.

Optimal website category structure

In addition to what’s already discussed, category managers can use data from digital marketing to identify optimal paths for product selection. This helps create an easy-to-navigate website for customers.

If your visitors are shopping through specific products or brands, you’ll want to make these entry points accessible. It should be simple to select paths through corresponding filter attributes in the search function of your website.

In some cases, themed landing pages, personalized product recommendations, or a brand landing page are worth pursuing. The aim is to offer visitors on your site an outstanding customer experience through an optimal website structure.

For example, category management can be used to structure “barbecue” products such as aprons, spices, charcoal, etc. This would be considered a product group. You may also want to create a themed landing page for barbecue season, which would not only feature products but also content for party inspiration.

Growth potential based on conversion rate and consumer behavior

With the help of digital marketing data, including website traffic, conversion rate, bounce rate, and average shopping basket value, there’s potential for growing sales.

For example, if you notice a category isn’t performing so well, you can improve discoverability in your e-commerce environment. With this, it’s possible to improve overall success.

E-commerce category management offers growth potential

As explained, you can increase customer loyalty and generate higher sales with strategically thought-out e-commerce category management. A long-term approach allows for enormous growth potential for your store. All these efforts can improve loyalty and customer engagement.

Frequently asked questions about e-commerce category management

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An e-commerce recommendation engine is essentially a way of automatically pointing customers to suggested products beyond what they have currently searched. It can follow them throughout various stages of the shopping process. When it comes to machine learning, this typically uses user data and engagement (clicks, search, preferences, etc) to make informed recommendations. This is usually handled via an algorithm. 

Let’s discuss each type of recommendation engine along with different technologies for recommendation systems, and the best context to use them in. As with anything, there are advantages and disadvantages to the solutions offered, which will be briefly covered below. Read on to learn more.

Category management

To improve the relevancy of product recommendations, it’s advised to group products together by categories. With this, it’s worth mentioning that creating complementary clusters is more impactful than grouping similar items together. A good product recommendation strategy is built upon knowing your customers’ needs. To learn more about this topic, consult our related article on e-commerce category management.

Product recommendation engine types

As mentioned above, there are different types of product recommendation engine approaches. Regardless of which one is used, historical customer data is highly suggested to make sure your website visitors have personalized recommendations. The more data and behavior that you have, the more meaningful the suggestions will be.

The idea is to make relevant predictions on what users want outside of search-based methods. With that, the amount of information available will determine the best approach to integrate. Each type is briefly explained below.

Collaborative filtering

To enable effective collaborative filtering on an e-commerce website, it’s necessary to have pre-existing data. In fact, the more historical insight, the better. This is because collaborative filtering uses past behaviors to suggest what a similar customer will need. In general, this type uses product ratings to highlight relevant items.

Content-based filtering

Content-based filtering differs from collaborative filtering in that it’s more personalized. It also doesn’t depend on previous user behavior and makes suggestions based on preferences. These are indicated by customers when setting up an online account with your brand.

In addition, this type continues to fine-tune suggestions based on website interactions – typically handled with an algorithm. That said, it’s advised to pull in preferences from other data sources, aside from shopping behavior. For example, a profile could also include entertainment-based and news article engagement.

This requires a hybrid approach, which is further explained in the following section.

Hybrid filtering

When discussing hybrid filtering, these methods are usually a combination of both collaborative and content-based approaches. Also, other techniques may be incorporated. Hybrid filtering can use different types together or independent of one another. This type of recommendation engine is good for e-commerce websites with minimal historical data.

Of course, there are advantages and disadvantages to each type of product recommendation engine. Collaborative, content-based, and hybrid filtering are all good at suggesting items in different ways.

Advantages vs. disadvantages

Essentially, the advantages and disadvantages of different product recommendation engine types come down to how much data is available, and suggestion specificity. If there is minimal customer information, it’s best to use content-based filtering. This is because of its highly specific nature.

That said, if data is available, collaborative filtering might be better. Ideally, the products suggested should be similar enough to the customers’ interests while allowing for some flexibility. Since collaborative filtering is less based on the inclinations of a particular user, it can make suggestions outside the realm of content-based filtering.

The right approach will depend on the type of products being suggested and the availability of historical information. In general, it’s good to A/B test the best model, which will be explained following information on product recommendation engine technologies. There is no one-size-fits-all approach.

Context-aware recommendations

In addition to the types of recommendation engines listed above, it’s important to consider the context of where the customer is engaged. This means different suggestions should be used for a product detail page vs a homepage, for example. Additionally, recommendations increasingly require more personalization, through related content beyond products.

Product recommendation technologies

To support the different types of recommendation engines, there are different technologies utilized. These include session-based, reinforcement learning, multicriteria, risk-aware, mobile, and artificial intelligence (AI). They are outlined below with a brief description of each type of technology.

• Session-based

With session-based, the suggestions are given based on individual browsing behavior. This technology doesn’t use past data and is only relevant to the interactions within a single session. It’s good for sites that lack prior user knowledge.

• Reinforcement learning

As the name suggests, reinforcement learning is based on positive reinforcement to make informed decisions for users. The recommendation agent is rewarded for certain interactions and uses this insight to optimize website performance.

• Multicriteria

Using multiple criteria to make recommendations, multicriteria technology understands users across a set number of preferences. This is based on how a customer will rate various aspects of a product. Examples include sizing, color, fabric, and price.

• Risk-aware

As customers are more responsive to recommendations based on different contexts, risk-aware technology understands when it’s best to suggest products. The goal is to not turn off customers by interrupting them at inopportune moments.

• Mobile

This recommendation technology uses mobile data to provide location-based suggestions. Since mobile phones contain GPS and other functionalities not found on computers, they can recommend products differently.

• AI

The most recent type of technology, AI uses past data to automatically calibrate product recommendations. This is based on machine learning and relies less on the interactions of customers.

Looking for an AI-powered recommendation engine to create new revenue opportunities and higher your AOV? Inspire the right customers at the right time with AB Tasty. From A/B testing to product recommendations, AB Tasty is the only platform your experience optimization strategy needs.

How to A/B test with product recommendation engines

When A/B testing a product recommendation engine, it’s important that conversions increase. This will indicate the right type to use. There are various ways to A/B test, including algorithm vs curated, placement, personalization, format, social proof, and dynamic vs. static.

Of course, when choosing the right method, it’s important to consider the resources available. For example, hand-curation will take more time than automatically populating recommendations with an algorithm. Similarly, researching social proof, including reviews, and testimonials takes time. The goal is to find the right A/B testing approach while optimizing operations.

Conclusion

We hope this page provides a good starting point for learning more about what a product recommendation entails. As you implement your strategy, we want you to feel confident in choosing the best approach.

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AB Tasty’s acquisition of Epoq in October 2022 realized a shared vision of empowering digital teams to deliver relevant and engaging shopping experiences along the consumer journey and brought search and product recommendations to our best-in-class experience optimization platform. 

Placing Epoq within the AB Tasty brand represents an exciting next step for AB Tasty as we consolidate all our solutions under one place and one name. 

The AB Tasty and Epoq websites are now one. All resources and landing pages previously hosted on Epoq’s website (epoq.de) can be found in one location on the AB Tasty website (abtasty.com).

If you have questions about what this means for you, you’ve come to the right place. Below we will dig into what is changing, helpful links and resources and some general FAQs.

As always, our team of AB Tasty magic makers are available to answer any additional questions that might pop up along the way. If you have any more questions after reading this, don’t hesitate to send us an email at hello@abtasty.com and we will update this page as needed.

How are AB Tasty and Epoq related?

AB Tasty acquired AI-powered personalization provider Epoq, ushering in a new era of experience optimization. Through recommendations and intelligent search the acquisition expands AB Tasty’s best-in-class offering to provide relevant and engaging customer experiences. Simplifying access for digital teams (from marketing to product to technology) by providing a single platform that delivers a 360-degree view to further optimize the digital customer experience.

What do you mean when you say merge? Will the Epoq website be gone for good?

By merging we mean all content around our leading Experience Optimization Platform will be available on one website. The Epoq website will no longer be available but all the search and recommendation content you have come to love will not disappear and will continue to be available on the AB Tasty website. New articles and insights to help you build your 1-1 personalization strategies will continue to be added. 

Why are we merging the Epoq and AB Tasty websites?

The website merge is aimed to make it easier for everyone to access all the information around AB Tasty’s EOP solutions in one place, including content around the products, technology and impact.  Grouping together the combined knowledge of Epoq and AB Tasty’s experts in one resource hub, giving marketing and product teams best practices and insights into experimentation and personalization strategy.

What will happen to all the resources (blog posts, guides, e-books, etc.) on epoq.de?

Epoq’s resources section will be moved to the AB Tasty website. All the Epoq content will be redirected to help customers find the content quickly and easily. 

How can I log into Epoq? And where can I access the documentation?

You can log into the Epoq Control Desk, the new AB Tasty Search & Recommendation Workspace, through a link on the AB Tasty website in the upper right corner. The documentation can be accessed through the menu of the workspace called “Developer Documentation” where you will be forwarded to our knowledge base.

Will there be any changes to the products or services offered?

The products and services offered will not be changed themselves. The joint product range will become a unique platform for optimizing the digital customer experience, offering our clients even more opportunities to differentiate and stand out in the market.

How will the merger affect customers who are new to Epoq? Where can I sign up for a demo for AB Tasty’s intelligent site search and recommendation solution?

If you’re new and you’d like to try out AB Tasty Search or Recommendations, click the banner below or click the “Get a demo” button on the top right-hand corner of the page to explore how AI-powered 1:1 personalization can help you deliver memorable digital experiences.

Have any additional questions about Epoq and AB Tasty? Send us an email at hello@abtasty.com to let us know and stay tuned for more exciting updates and information still to come!

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From achieving higher click rate on their newsletter and product pages to increasing their sales, here are 5 examples to see how different e-commerce clients leverage product recommendation and intelligent search to personalize experiences for customers and increase their KPIs in the process.

Inside this e-book, you will find:

• How to increase newsletter click-through rate through personalized, category-related product recommendations
• How a combination of store operator know-how and a personalized recommendation strategy can help push sales
• How to achieve higher click-through rate on product overview pages with self-learning ranking
• How a combination of a high-performance recommendation engine and a custom-developed personalization strategy can lead to more sales
• How intelligent search can result in an increase in sales

The post 5 E-Commerce Tips for Product Recommendation and Site Search appeared first on abtasty.

With AB Tasty’s recent acquisition of Epoq, we can better serve brands on their mission to build optimized and personalized customer experiences.

This webinar will give you an overall view of the different features of Epoq’s best-in-class tools:

• Intelligent search – fast, powerful search engine that gets customers straight to the product they want to buy.
• Intelligent recommendation – an AI-powered recommendation engine that brings new revenue opportunities
• Personalized emailing – connect with your customers during and after purchase to boost traffic

Save your seat to find out more!

The post Deliver Intelligent Experiences with Search and Recommendations appeared first on abtasty.

Babymarkt.de regularly sends out category-based newsletters to customers 20 days after they made a purchase in the online shop. The corresponding products for the product recommendations in each category were manually assigned to the customer segments which was well-received but required a lot of effort.

In a bid to personalize their newsletter to the child’s age, the company turned to AB Tasty to help them generate a knowledge base based on artificial intelligence to make more relevant recommendations.

A comparison of the click rate and the email marketing revenue of the category-based newsletter showed a clear result.

The post Babymarkt.de boosts click rate with personalized product recommendations appeared first on abtasty.

They already had a recommendation engine in place to display recommendations on various shopping pages of hagebau.de.  To evaluate the performance of the recommendation engine and test out real-time personalization, Baumarkt Direkt decided to compare AB Tasty’s AI-powered engine against the other tool already in place.

The post Hagebaumarkt increases recommendation performance switching to AB Tasty appeared first on abtasty.

The Difference Between Recommendation and Personalization

A recommendation is a form of personalization, but personalization is not a form of recommendation. For example, YouTube might suggest related videos based on previous viewing habits, this is a recommendation based on what other YouTube users also watched. A restaurant, however, might suggest a table by the window based on a previous booking you have made. This is personalization, as it is based on the specific habits of the individual and not a broad algorithm. The more you know about a person, not just their viewing habits, the better. In other words, a recommendation is often built around items, whereas personalization is built around individuals.

There is of course much overlap, and the more informed and well designed a recommendation engine becomes, more on that in a moment, the closer to personalization such methods become. For now, though, it is important to separate the two categories and their techniques by definition and practice to understand fully their implications, potential, and use.

Recommendations

Recommendations are best known to most as algorithms that suggest further content on media websites. The previously mentioned YouTube relies heavily on this model in order to keep users on the site for as long as possible to generate ad revenue.

But the concept of recommendations is not confined to media companies and viewing habits.

There are three main recommendation concepts. Each one of these concepts has their own advantages for specific sectors. These are:

• Recommendation engines
• Product recommendation
• Rating recommendation

Recommendation Engines

Sometimes referred to as a recommender system, recommender engines are the previously mentioned algorithms that are primarily used for media sites. Netflix, for example, might use your previous viewing habits to recommend another series or film. If, for example, you have watched Star Trek, it stands to reason that it will recommend another Sci-Fi series. So far, so simple. However, by tracking the viewing habits of other customers, Netflix might well find that Star Trek viewers are also often interested in nature documentaries. What’s more, specific Star Trek releases, such as the original series, might correlate with specific nature documentaries, such as those related to large predators of the sea.

So how does Netflix find such seemingly unrelated links? By tracking every one of its many millions of viewers. In 2006 Netflix offered a reward of $1m to find the most effective algorithm in tracking and predicting user behavior. The original winners of the prize improved the system by 10%, which may not seem like a lot, but such enhancements are worth enormous sums of money. The better the system worked, the more people joined, the more people joined, the more data Netflix had to work with and the better the system became. This snowball effect has led to them becoming one of the most successful media companies in the world.

But it isn’t only media companies that use recommender engines. To some degree, search engines are recommender engines, filtering out unrelated data to make results more effective.

Product Recommendation

Product recommendations are simply an extension of the recommendation engine’s ability to filter out irrelevant items, but in this case, it is related to products. It requires it’s own category as it relates purchasing items rather than content.

E-Commerce, which also uses many other features of both recommendation and personalization, has always been an innovator in the field of recommendation engines. Most famously, Amazon uses the technique in various ways to increase its sales by a reported 35%. It should be noted that Amazon is notoriously secretive about such data, however, so this is something of an estimation.

The most successful product recommendation engines don’t just provide suggestions on site. Email conversion rates, sales garnered by links sent via emails, are known to be extraordinarily high for companies like Amazon. This is partly made possible by the data collected by recommendation engines and well-targeted campaigns.

Rating Recommendations

Rating recommendations work across all sectors, or at least they can in theory. The previously mentioned Netflix and Amazon both have rating systems that provide feedback from other customers. For Amazon, it is the ubiquitous star rating, where users rate each product out of five. For Netflix, this is a thumbs up or thumbs down rating, which also helps the recommendation engine filter out specific suggestions, making the algorithm more personalized.

Rating recommendations are sometimes referred to as “Implicit feedback” (which also includes comments). Surveys have shown that the vast majority of users, 88% according to a BrightLocal survey in 2014, are influenced as much by this feedback as a personal recommendation from a friend.

Personalization

This is partly because the more a company knows about a person, the more effective it becomes. What is sometimes referred to as one to one marketing, it’s ambition and scope could change the way we interact with technology forever.

In truth, right now the technology available isn’t capable of collating the individual data to reach anything like the potential businesses crave, and there are many issues relating to privacy that the Internet is still coming to terms within its relative infancy. It seems, however, that in the future businesses will likely be using some form of personalization.

It should be noted that there are two types of personalization, product, and website. These are two very different concepts.

Product Personalization

Product personalization is a much simpler concept and one that most of us have used, or at least been aware of, for some time. A common example would be choosing the color of a piece of clothing from a varied selection, such as a shoe. Sometimes product personalization can become quite detailed, allowing customers to construct a product almost from scratch.

Website Personalization

Age, gender, location, the shopping habits and ratings left on websites, social media likes, incentives that might have been successful from other marketing campaigns, the time of day and even how the weather is at that current moment all can be taken into consideration.

This all begs the question, how does personalization help? Firstly, people are bombarded with images and information every time they go online. Usually, at best, this vaguely relates to some interest or other, at worst it is an irrelevant distraction. As seen above, this information can help websites target offers, present the customer with the most relevant and helpful information and suggest the most likely products they might wish to purchase.

App personalization uses the same principles as website personalization. At this point, app personalization is quite some way behind website personalization in terms of its development, but the gap is narrowing as more and more businesses become aware of its advantages.

Optimization

The only way in which a business can be sure that their recommendation engine or personalization system is as effective as it can be is by A/B testing. This is part of the process of optimization called recommendation testing. AB Tasty provides recommendation tools that pinpoint optimization and filter out those that are ineffective. Every aspect of website interaction can be improved, leading to a higher return on investment. Server-side A/B testing also gives you more flexibility to test recommendation algorithms.

Advantages of optimization include:

• Better experience for the customer, cultivating customer retention
• Higher basket totals, more sales per visit
• Focused recommendation for better conversion rates
• Improved general content, better website ranking
• Greater return on investment
• Focused Email campaigns
• Improved leads and ranking
• Attracting newcomers
• Filtering out unwanted ads (SPAM)
• Increasing basket totals

Each and every aspect of optimization has the potential to focus the attention on what is working for a website and what can be improved upon. Several metrics, both simple and complex, can form a formidable and deeply insightful method for optimization, for all business when applies correctly.

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