Product recommendations in e-commerce: the ultimate guide

In this guide, I explore the world of product recommendations. I discuss the technology behind product recommendations, as well as the best strategies. Want to know the secrets behind successful product recommendations? Then read on!

What are product recommendations in e-commerce?

Product recommendations are personalized suggestions of products to customers based on their behavior, preferences and interests. This is done based on data such as browsing history, previous purchases and searches. These recommendations often appear as carousels, pop-ups or sections on the webshop.

Examples include “Recommended for you” or “Other customers also bought”. Product recommendations have various goals:

• To help customers find products that interest them;
• To increase conversion rates;
• To increase customer satisfaction;
• To increase average order value.

In addition, recommendations in e-commerce help to reduce choice overload. Customers can sometimes get confused by the wide range of products on offer. Which product is exactly the one they are looking for?

Using e-commerce recommendations is therefore certainly useful. It helps your customers, but also your webshop to learn more about the customer. Product recommendations therefore also play an important role in the marketing strategy.

Below you will find an example of product recommendations from kappersshop.com:

What are the advantages of product recommendations in e-commerce?

Customers expect online shops to understand their needs and respond to them. One of the most effective ways to do this is to use product recommendations.

A few facts:

• Research shows that recommendations increase conversion by 31%.
• The conversion rate of visitors who clicked on a recommendation is five and a half times higher than customers who did not click on it.
• Customers with product recommendations in their shopping cart are more likely to checkout than customers without product recommendations.
• As many as 81% of online shoppers said they only wanted to make a purchase after receiving personalized recommendations in the email.
• 74% of customers find it irritating when a homepage is not personalized at all.

Are you concerned about what your customer thinks about this?

More than half of consumers (57%) are fine with providing personal information (on a website) as long as it is to their advantage and used responsibly.

8 examples of product recommendations

Are you not (yet) convinced of the power of product recommendations? I would like to explain eight benefits of product recommendations in e-commerce.

1. Personalization is key

Product recommendations offer customers a personalized shopping experience by showing them relevant products based on previous purchases, search behavior or interests. When customers feel that a webshop understands them, they are more likely to return. Personalization makes customers feel more connected to your brand, encouraging them to return more often. It creates a feeling of exclusivity.

2. Increased order value and sales

Product recommendations are able to increase the average order value. This is because customers are inspired to place more products in a single order. This can vary from recommending accessories to offering bundles and discounts.

Or think about pushing the right products, such as seasonal purchases. Customers looking for summer clothing will automatically receive tips for sunscreen, accessories or summer activities. This ensures that they not only buy the products they need, but also other additional products. How handy is that?

In addition, product recommendations play on the psychology of customers. Just think about creating urgency. For example, a customer looks at a popular product and is then shown a recommendation for a similar product that is almost sold out.

This encourages them to make a purchase more quickly. After all, the product is almost sold out.

3. Better customer retention

With product recommendations, you ultimately strengthen the bond with your customer. When a website is completely tailored to their needs, they feel understood and appreciated. This increases their loyalty, making them return to your website more often. This is especially important in a market where customers easily switch to a competitor. Customers who regularly receive personalized recommendations are more likely to consider your webshop a trusted source. And this in turn is good for retention rates.

4. Upselling

Recommendations are a powerful tool for cross-selling, as demonstrated in point 2. But also for upselling. By promoting related or more expensive products, the average order value increases.

Upselling, offering more expensive options, works best when customers recognize the value of the products. Consider better quality or extra features. This makes it easier to convince the customer.

5. More efficient marketing

Product recommendations naturally also generate a lot of data. This allows you to quickly see a connection between products that are often bought together. Or you can quickly see which target group is receptive to which marketing. This allows you to create campaigns that are tailored to the interests and buying behavior of customers.

6. Longer time on your website

By showing relevant products, product recommendations ensure that visitors spend more time on your website. They explore additional products that they have not come across before.

This ensures that customers are first of all captivated and remain captivated. Secondly, it is good for SEO. The page on time is increased, as a result of which Google will see your webshop as relevant. This will improve your score.

7. Lower bounce rates

Recommendation engines lower the bounce rate by showing customers recommendations that match their interests and preferences. This is because customers are encouraged to explore the website more.

8. Product recommendations save time

Does it also seem like you have less and less time? And want to do more in less time? That's why I really like being able to find my product directly in a webshop. Product recommendations help with this. They immediately present customers with the products they are looking for. This saves time spent on lengthy searches.

In addition, the customer journey is further optimized. You make recommendations at the right moment in the buying process.

In short, product recommendations are an indispensable part of the web shop. Because the more personalized it is, the easier the shopping experience becomes for the customer. And the easier it is for the customer to remember your web shop. In addition, the customer journey is further optimized. You make recommendations at the right moment in the buying process.

How do product recommendations work?

Most product e-commerce recommendation software uses algorithms. An algorithm is a set of rules that are followed. The rules can be adjusted, resulting in different types of algorithms. What is the difference in each algorithm? And what are the most commonly used algorithms for product recommendations? I would like to explain a number of them.

1. Collaborative Filtering This involves analyzing two groups to see if they have similarities in their buying behavior. If two customers have bought similar products, a product that one customer has bought is recommended to the other. This type of filtering is effective in discovering new products. A downside to this algorithm is that it is less suitable for webshops with little purchase history. In that case, there is actually no data on which the algorithm can base itself.

When is this algorithm useful?

Are you a new web shop? Then this algorithm is not (yet) that effective. In that case, there is actually too little data for the algorithm to show a correct recommendation.

Do you have a web shop with a wide range of purchase history data? Then this algorithm is perfect for your web shop.

2. Content-Based Filtering

This method looks at, surprise: content. By this I mean that it looks at the characteristics of certain products. Similar products are then recommended based on these characteristics. For example, if a customer has previously bought a book on cooking, other cookbooks will be recommended. This is useful in product categories with rich descriptions, such as books, electronics and fashion.

When is this algorithm useful?

Do you have a webshop in a niche? Then this algorithm is useful for your webshop. Customers are shown specific products based on previously purchased categories. Examples include fan webshops and Lego webshops. The algorithm limits itself because recommendations are based on a small amount of data. There is a risk that customers will get stuck in a “filter bubble” where only products that are very similar to what they already know are recommended.

3. Hybrid Systems

A combination of collaborative filtering and content-based filtering are hybrid systems. Hybrid systems often offer the most versatile solutions. This is because they are able to recognize both broad and specific patterns in customer data. They can therefore handle both small and large pieces of data. This type of recommendation system is widely used by major players such as Amazon and Netflix.

4. Machine Learning and AI

Of course, AI cannot be left off this list. AI and machine learning make predictions based on the data available to them. This includes purchase history, browsing history and search queries. If you have searched for brand A before, the next time you search you will see a lot of brand A thanks to machine learning. This allows for predictions to be made about what a customer is likely to want to buy. Are you looking for real-time recommendations? Then this is recommended for you.

When is this algorithm useful?

This algorithm is increasingly integrated into omnichannel strategies, where recommendations are synchronized between physical stores, websites and mobile apps.

5. Contextual recommendations

Contextual recommendation systems not only look at historical data, but also at the customer's context, such as time, location and device. For example, a customer who shops on their phone in the morning will receive different recommendations than if that same customer shops on a laptop in the evening. Seasonal products and trends also play a role in contextual recommendations. Think of holiday items, sunscreen or an ice scraper. By using location data, these systems are constantly optimizing their recommendations.

When is this algorithm useful?

This algorithm is increasingly used in omnichannel strategies.

6. Social proof and popularity

Recommendations based on social proof and trends also work well. Sections such as “Top rated products” or “Trending now” make use of this wisdom. This approach is effective because customers often rely on the choices and opinions of others.

When is this algorithm interesting?

Platforms such as Instagram and TikTok have reinforced this trend by integrating social recommendations with direct purchasing options. This has narrowed the gap between inspiration and purchase.

7. Visual recommendations

A more recent development is the use of image recognition to identify similarities between products. This technique uses AI to identify people, places and objects. This is very useful in categories such as fashion and interior design, where style plays a major role. A concrete example of this is when customers upload a photo of a product they have at home. The system then recommends similar items based on what it sees. For example, the same color, pattern or design.

This technology is increasingly being applied to mobile apps, where users can easily take photos and upload them for recommendations. Examples include make-up, skincare routines or what new glasses might look like.

8. Personality and emotion-based recommendations

To make it ultra-personal, some systems take personality and emotions into account. They do this by using sentiment analysis and psychographic data. This enables the systems to recommend products that match a user's mood or lifestyle. For example, a customer looking for relaxation products should receive suggestions for aromatherapy or meditation apps. This level of personalization makes it possible to create a deeper connection between the customer and the brand.

When is this algorithm useful?

This approach is particularly valuable in markets where brand experience and customer loyalty play a crucial role, such as luxury products and lifestyle segments.

12 tips for e-commerce product recommendations for direct conversion

Looking for tips for product recommendations that directly increase sales? Look no further! I have listed 12 tips for you.

1. Product recommendations based on browsing history

Make smart use of the data that your website visitor leaves behind. Think of the visitor's browsing history. This strategy takes advantage of the fact that the customer has looked at specific products before. This works well for returning customers.

For example, when a customer has previously visited the web shop and returns later. In that case, the product recommendation block will suddenly say “products recommended for you”. It feels personal because it is personal.

A good example of this is Wehkamp. When you log in to Wehkamp, everything is suddenly personalized. This user has often viewed makeup, perfume and underwear on Wehkamp in recent weeks. And this is immediately recommended on the homepage.

2. Product recommendations based on bestsellers

Do you have certain bestsellers in your assortment? Then it makes sense to recommend these products. Emphasizing popular products can serve as social proof. By showing that many other people like these products, a new visitor will feel the need for them as well.

For example, Intertoys shows their bestsellers right on the homepage.

3. Product recommendations based on “often bought together”

This recommendation is purely data-driven. The data is used to determine which products are often bought together. A recommendation block is then shown to the customer with product B, which is often bought with product A. Amazon seems to be particularly good at this:

4. Product recommendations based on new product range

Has a particular brand launched a new product range? If so, it is advisable to show customers who like this brand recommendation blocks for this new product range. Or do you want to make a new product range known to your entire shopping public? In that case, it might be a good idea to place such a block on the homepage. AboutYou is a good example of this:

5. Product recommendations based on bestsellers from a category

Has the website visitor been searching for a long time in a certain category? Then it is useful to help the customer a little. You can do this by means of e-commerce recommendations based on bestsellers. ZooPlus makes it very visual. Within their cat snacks category, they offer popular subcategories:

6. Product recommendations based on bestsellers from a specific brand

If the website visitor has previously shown interest in a specific brand, it is best to show recommendations from that brand. This comes across as personalized and the visitor may be more inclined to click on them.

7. Product recommendations based on trends/seasons

When it is winter, you want to recommend scarves, gloves and snow boots. And when it is summer, you want to recommend sunscreen. Preferably all products with a high margin. Or perhaps you would prefer to prioritize your own brand in the recommendations. Think about this carefully. For example, feestbazaar has the best choices for you right on their homepage, specifically geared to the season that goes with December 5:

Except for the lederhosen, of course. They belong at the Oktoberfest. So there is still room for improvement.

8. Product recommendations based on reviews

Do you have certain products that have good reviews? Push those products in a recommendation feed for e-commerce. For example, it is quite a coincidence that all these products in the feed at Notino have all been rated well. Or have they?

9. Product recommendations based on previously purchased products

Wehkamp makes clever use of recommended products based on previously purchased products. When browsing the webshop, it is immediately clear that this customer often buys Pampers. A smart tactic is to immediately give a discount on the brand that is purchased more often.

10. Product recommendations based on a big sale

Does your webshop have a great sale promotion? Then why not create a recommendation block based on this sale. This will make the sale items stand out even more, and increase the chance of a conversion on the sale items. Epplejack is a good example of this:

11. Product recommendations based on “complete your look”

You probably know the feeling. You see a nice pair of pants on a website. But you actually like the entire outfit and want to buy that exact outfit. A frequently used product recommendation block is then “complete the look” or “shop the look”. In this block you will find all the products that complete the look. A webshop that always does this well is Guts & Gusto.

12. Product recommendations based on “recently viewed”

Perhaps one of the most frequently used blocks. The recommendations block with “last viewed”. Perhaps the visitor scrolls a lot over the website and clicks on various products. How convenient it is then that the visitor finds the clicked products again in a “recently viewed” block. This way, the visitor can easily return to products he was previously interested in. And the chance of conversion increases. A good example of this is kerstland:

What are the 10 best product recommendation tools in e-commerce?

The big question, of course, is: How do I add product recommendations to my website? This is done with a product recommendation tool. There are now quite a few product recommendation tools for e-commerce companies. Which one is the best? That depends entirely on what you are looking for, how your business is structured and perhaps the price. Below you will find 10 product recommendation tools with their pros and cons. This will help you make an informed choice about which tool best suits your webshop.

1. Reloadify

Reloadify is an all-in-one retention platform. From landing pages to emails, pop-ups, flows and other automated emails (such as abandoned shopping cart emails). We also offer product recommendations based on AI and machine learning. Not only that, but all customer data collected is used to show the right products. This includes order history, customer type and browsing behavior. Together, these factors make it possible for this product recommendation engine to always be spot on. Our support team is available on weekdays between 9:00 a.m. and 5:00 p.m. via email, chat and telephone (in Dutch, English and German).

Pro's:

• Dutch, fully GDPR-proof.
• Recommendations possible on the website and in the email.

Cons:

• Fewer data in your webshop means less accurate recommendations.

How does Reloadify integrate with other platforms?

• Shopify: Has a ready-made plugin in the Shopify App Store.
• Magento: Plugin available via Github.
• Lightspeed: Has a ready-made plugin in the Lightspeed App Store.
• WooCommerce: Plugin available; easy installation.
• Shopware: Has a ready-made plugin in the Shopware App Store

2. Algolia

Algolia actually consists of two parts: search implementation and search analysis. This is the basis of the package. This was later expanded with Algolia Recommend. This expansion allows recommendations to be displayed on the website. Algolia uses collaborative filtering and content-based filtering. They also support cross-selling and upselling and have an integration with Shopify and Magento. Need help setting up Algolia? They offer an extensive knowledge base. If you need someone to set everything up for you, they will refer you to your own development team. The support team can be reached by email, but not by chat or telephone.

Pro's:

• Extremely fast, ideal for dynamic environments.
• Advanced personalization options.

Cons:

• Expensive for small businesses.

How does Algolia integrate with other platforms?

• Shopify: Has a ready-made plugin in the Shopify App Store.
• Magento: Native integration with an extension in the Magento Marketplace.
• Lightspeed: No native support, possibly via API.
• WooCommerce: Plugin available; relatively simple installation.
• Shopware: Available as an extension via the Shopware Marketplace.

3. Nosto

Nosto is a platform that focuses on merchandise, personalization and customer engagement. They characterize themselves as an e-commerce Experience Platform (CXP). Their focus is on search and merchandise. They also offer pop-ups. And through the integration with Klaviyo, you can send emails via Nosto. So you pay both Klaviyo and Nosto for emailing. They integrate easily with most e-commerce platforms. Nosto has an extensive help center with help articles for all your questions (English, French, German). Still can't figure it out? Then their chat is ready and waiting for you.

Pro's:

• Simple integration and user-friendly interface.
• Specially designed for e-commerce.

Cons:

• To send emails, you also need a Klaviyo subscription.

How does Nosto integrate with other platforms?

• Shopify: app available in the Shopify App Store.
• Magento: Fully integrated with an extension in the Magento Marketplace.
• Lightspeed: No direct support, possible via custom integration.
• WooCommerce: Plugin available with simple installation.
• Shopware: Available as an extension.

4. Dynamic Yield

Dynamic Yield is a Mastercard platform. They create machine-learning recommendations for products, content and offers. Dynamic Yield is suitable for larger parties. Think of parties that are internationally known with online and offline shops, such as Bershka or Zara. It is not a plug and play platform. A developer will have to be involved to implement everything. Dynamic Yield has an extensive help center with clear help articles. They have also built up a community where you can ask questions.

Pro's:

• Very good personalization functions for large companies.
• Versatile integration options.

Cons:

• Requires technical knowledge to make optimal use of it.

How does dynamic yield integrate with other platforms?

• Shopify: Requires customized implementation via API. No native app.
• Magento: Advanced extension available, ideal for larger companies.
• Lightspeed: No direct support, only possible via API.
• WooCommerce: API-based integration required.
• Shopware: No direct extension; integration via API.

5. Clerk.io

Clerk comes with a search function, chat function, an audience builder and an e-mail system in one. They use the data of visitors and customers to offer a customized experience. AI tracks purchasing, clicking and searching behavior. Based on this, product recommendations are shown on the one hand and the search results are optimized on the other. The platform is fairly self-explanatory and you can get started right away. You purchase each module separately, so you pay for search, recommendations, chat, email and the audience builder. Clerk also has an extensive help center with many help articles. An AI chat can also help you get started.

Pro's:

• Good support and simple setup.

Cons:

• Less suitable for very large, complex e-commerce environments.

How does Clerk integrate with other platforms?

• Shopify: Native app available in the Shopify App Store.
• Magento: Has an extension in the Magento Marketplace.
• Lightspeed: Direct integration via Lightspeed's API.
• WooCommerce: Plugin available.
• Shopware: Native extension available in the Shopware Marketplace

6. Emarsys

Emarsys offers product recommendations in most e-commerce communication channels: not only via the website, but also in emails, mobile apps, mobile push notifications, text messages or directly in emails. They use ready-made templates to ensure that your message is always sent consistently across different channels. It is a cloud-based SaaS platform. You do not have to host anything yourself and you receive automatic updates. It is an API that you need to have a developer install. Emarsys also offers a wide range of help articles. They also have an AI chatbot that can help you (German, English, French, Turkish).

Pro's:

• Strong focus on customer focus and automation.

Cons:

• More expensive and complex for smaller companies.
• The learning curve is steep.
• The dashboard is considered complex.

How does Emarsys integrate with other platforms?

• Shopify: integration possible via Emarsys' connector.
• Magento: extension.
• Lightspeed: integration requires an API solution.
• WooCommerce: custom integration possible via API.
• Shopware: extension.

7. Monetate

One part of Monetate is product recommendations. Thanks to AI, the entire website can be improved. Think of recommendations, but also searches. Well-known customers of Monetate are The North Face and National Geographic. They did not limit themselves to e-commerce. Both the content and product recommendations and searches can be personalized. They have extensive analytics. Which can result in you being presented with an abundance of data. Paying this company a visit is rather difficult. They are based in Dallas, Texas. They do provide technical support via a ticket system. And a developer can install Monetate for you via extensive documentation.

Pro's:

• Suitable for companies that want to create personalized experiences.
• Flexible integrations and extensive analytics.

Cons:

• Can be pricey and requires a certain level of technical expertise.
• High learning curve.

How does Monetate integrate with other platforms?

• Shopify: Only possible via API.
• Magento: Requires custom integration.
• Lightspeed: Not directly supported.
• WooCommerce: Only possible via API.
• Shopware: Requires custom integration.

8. LimeSpot

Looking for product suggestion software and nothing else? Then LimeSpot is for you. They use AI algorithms to make personalized recommendations on your website. Nothing more, nothing less. You can easily display trending items, new arrivals and other personalized blocks on your website. LimeSpot is plug and play: install the plugin and everything will work. You don't need a developer for this. LimeSpot also integrates with Shopify and Klaviyo. They have an extensive knowledge base and also a chat (both in English).

Pro's:

• Affordable solution for small to medium-sized businesses.

Cons:

• Less advanced than larger AI platforms such as Dynamic Yield

How does Limespot integrate with other platforms?

• Shopify: Native app available in the Shopify App Store.
• Magento: Custom integration required.
• Lightspeed: No support.
• WooCommerce: No direct plugin, integration via API possible.
• Shopware: No support.

9. Recombee

Recombee is a product recommendation engine that specializes in providing personalized product suggestions. It uses AI algorithms and machine learning to make relevant recommendations to customers. They offer two different blocks: Recombee public and Recombee Tracker. One block is able to identify anonymous users, while the other block collects data about logged-in customers. If a page is viewed, recommendation blocks are created based on customer IDs. These IDs are always forwarded via the recombee API. So this is not something you can do yourself. You need a developer for this. The head office is in Prague, so not exactly around the corner. You can, however, send the support department an email. Of course, there is also extensive documentation for developers to make Recombee work on the website.

Pro's:

• Flexible and scalable, suitable for both small and large companies.
• Can be adapted to very specific needs.

Cons:

• Requires more technical expertise to implement and manage.

How does Recombee integrate with other platforms?

• Shopify: Integration only possible via API.
• Magento: Integration only possible via API.
• Lightspeed: Integration only possible via API.
• WooCommerce: Integration only possible via API.
• Shopware: Integration only possible via API.

10. Tweakwise

Tweakwise is a platform that optimizes webshop recommendations, search bars and merchandising. The recommendations generated by Tweakwise can contain many dynamic components, such as certain brands or trends. Everything is aimed at optimizing the conversion of your website or webshop. You can make changes yourself using an online dashboard. They do not offer email. However, you can easily visit them. They are located in Zwolle, the Netherlands. Their helpdesk and support are also available to you. By telephone or email (Dutch or English).

Pro's: :

• Ideal for companies that want a highly centralized search and recommendation experience.

How does Tweakwise integrate with other platforms?

• Less suitable for companies that operate worldwide.
• Shopify: Custom integration possible via API.
• Magento: Available as an extension.
• Lightspeed: Available as an extension.
• WooCommerce: No direct plugin, but API integration possible.
• Shopware: Available as an extension.

What should you look for when choosing a product recommendation engine?

Which recommendation tool is right for your webshop? Some features are more important than others, depending on what you are looking for. Below are some points to help you make the right choice.

• Scalability: How well can the engine adapt recommendations based on individual user preferences or behavior? Can it handle complex data sets, such as multi-channel user interactions?
• Data integration: Can the recommendation engine be easily integrated with your existing tech stack (e.g. CMS, e-commerce platform, CRM)? Does the engine support APIs and other connection methods?
• User-friendliness: How simple is the implementation and configuration? Is there a user-friendly interface for marketing teams or data analysts? A data analyst works differently from a marketer. Is training needed in how to use it?
• Data and privacy: How does the engine handle data, privacy and security? This is important for compliance with European regulations such as GDPR. Does the engine have access to the right types of data to generate recommendations? You will almost always be in the right place with Dutch software. Pay particular attention to this for engines outside of Europe, such as America.
• Support and documentation: Is there good customer service or technical support available? Is the documentation complete and easy to understand?
• Reporting and analysis: Does the engine offer detailed insights into the performance of recommendations? Is this data easy to use to adjust strategies?
• Costs: Is the cost model (e.g. license, pay-per-use) feasible for your budget? Are there any hidden costs for implementation, maintenance or adjustments?
• Omnichannel possibilities: Where exactly can the product recommendation engine make recommendations? Via website, email or other channels?

Take a good look at the above factors and consider what is important to you.

Where do you place product recommendations on the webshop?

The next question that might come to mind is: where do I place product recommendations? There are numerous possibilities. I would like to give you some tips on where product recommendations can be placed.

Product recommendations on the homepage

The most important starting point for visitors to the webshop. On the homepage you will often see the section “Most popular”. For new visitors, these are often popular products based on how often the product is purchased. For returning visitors, this section can be personalized by showing products in which they have previously shown interest. At feestbazaar.nl, for example, you can immediately see the trending products on the homepage:

It is clear that St. Nicholas and Christmas are just around the corner. These are the most popular products at that time. When you land on the page, the products immediately jump out at you.

Product recommendations on category pages

For the category page, the “Most popular in category” is a common choice for displaying products. This is done for both new and returning customers. I recommend always using personalized strategies for returning customers. Are you dealing with a new customer? Then fall back on the popular items in the category.

An example of this is Amazon. When you go to Amazon and go to the category “Home and Lighting”, the first thing you see is “bestsellers”. And then “top rated”. What is also noticeable is that you see many products twice. At Reloadify, this is easy to prevent by specifically excluding certain products from the feeds.

Product recommendations on the product page (PDP)

The product page, or product detail page, is an excellent opportunity for recommendations. On this page, the customer is already looking at a specific product. “Similar products” or “often bought together” are very suitable to use. It makes a difference what the goal is. Do you want to upsell the customer to a more expensive alternative? Then choose “similar products”. Do you want the customer to purchase a cross-sell item? Then choose “often bought together”.

The place on the PDP certainly matters. In practice, you see that this is handled differently.

For example, at kappersshop, you see that the choice is made to show both blocks below the product itself:

While another webshop, such as Boozyshop, specifically chooses to only show one block:

It is interesting to note that both products are positioned below the fold. Another example is MetOlijf. They have managed to place an upsell block above the fold:

This way, you can immediately see which product you should also purchase. Clever, because there is a CTA button to order both products.

Product recommendations on the shopping cart page

Have you ever noticed that products are still recommended in the check-out? Think of blocks such as “Purchased together” or “Checkout bargains”. The shopping cart page is perfect for something like an e-commerce recommendation system. The result is that an upsell is quickly chosen in the check-out.

This strategy actually works the same as in a supermarket. At the checkout you can often find that little something you were missing: a packet of chewing gum, a chocolate bar, cleaning wipes or something else. A good example of this is kerstland.nl:

These are small additions to the current order, such as extra batteries or an extra bauble. Everything is done to get the customer to spend more. Realistically, you should be able to place a recommendation on any page you want with the recommendation engine. Test carefully what works for which target group. You will automatically see an increase in sales. Top tip: set recommendations on pages without product search results. That way, the customer still has something to order on the page.

Start today with product recommendations

Strategic marketing plans are unique for each web shop. What works for one person may be less suitable for another. Nevertheless, a product recommendation system is a valuable tool that every e-commerce manager should consider. It is a simple and effective way to improve the personalization of the web shop. This not only increases the chance that customers will add more products to their shopping cart, but also offers them a personalized shopping experience. Relevant recommendations allow customers to see products they might otherwise miss, which increases overall customer satisfaction.

Are you curious to find out what e-commerce recommendations can do for your web shop? Then contact us and book a demo with no obligation.