Product Recommendation Examples and Benefits You Haven’t Thought of

Are you wondering about implementing a recommender system, but you’re not sure how it performs? Today, we’ll show you some cases of such a solution in action.

But before learning more about recommender system examples, let’s dig deeper into what recommender systems actually are.

Purposes of recommender systems

Check out the most prominent purposes of recommendation systems 👇🏻

Encouraging users to stay with you

The primary purpose of recommendation systems is to propose to customers the content they might like. If you use a proper tool, you can provide users with personalized suggestions, which help them discover the items related to the products they are interested in. And foremost, the system aims to keep shoppers on your website and encourage them to purchase at your online store.

Showing the relevant content types

The other purpose is to reduce time spent searching and provide users with useful content quickly. When you deliver relevant results, the chances that a visitor leaves your website and looks for your competitors’ content instead are dramatically lowered. With machine learning services, giving visitors what they are searching for has never been easier.

Increasing engagement

When you show the customers the items they might need or like, they may browse through your website to look for more. And this is how you increase visitor engagement. Besides, visitor engagement on a website is a crucial metric for measuring the success of any online business.

Additionally, the metric shows how well your website attracts visitors and converts them into customers. Making business sales and content strategy decisions is much easier when you know the engagement levels.

Types of recommender systems solutions

Here are the most common product suggestion systems.

Content-based filtering

Content-based filtering is a type of recommender system that finds the similarity between preferred products based on their context, attributes, or description.

A user’s past preferences and behaviors, such as what they liked, bought, browsed, and their personal information, are also considered by the system to present accurate results. If you run an e‑commerce shop, the content-based recommender system works as a “personal assistant” who knows what a buyer likes and dislikes.

Collaborative filtering

A collaborative filtering recommender presents items or content to a user considering the preferences of other, similar users.

It is quite different than a content-based system. Collaborative filtering determines which users might have similar tastes. So it does not focus on the items’ attributes but instead examines how users have rated an item and finds other users who liked a product as well.

Based on the user profile and preferences, the recommender looks for the content that the other person enjoys and offers it to a user with similar interests.

Hybrid approach

This type of recommender system combines two or more recommendation methods to make better user suggestions.

For example, it takes advantage of different systems’ strengths to overcome individual methods’ limitations and give more relevant and diverse suggestions. It might be a combination of content-based and collaborative filtering together.

The hybrid recommender system might be more effective in some cases – thanks to combining collaborative and content-based capabilities, some companies may achieve better results.

Session-based

A session-based type of recommender system is an algorithm that uses data from a user’s current session to make personalized recommendations.

System recommendations are based on users’ current activities, such as what they’ve searched for, clicked on, and purchased. The session-based recommender system aims to provide users with content tailored to their specific needs and interests and a more personalized experience.

Guidelines for recommender system application

It is possible to build a recommender system internally by working with the IT department. However, it might be time-consuming and complicated. Luckily, much software on the market works perfectly, and no IT skills are required to deliver accurate recommendations.

Recommender system examples

Let’s have a look at the product recommendation examples.

1. Answear

If you want to see a specific example of a recommender system in an e‑commerce fashion industry, look no further than Answear. It is an online fashion company that uses product recommendations to suggest items to customers. The company’s catalog includes 90 thousand products from 350 fashion brands.

That’s a lot, isn’t it? Because of that, it’s worth implementing a recommendation system to enable buyers to find what they are looking for without any hassle.

Let’s talk numbers:

• 4 million users visit Answear per month
• 120 million requests generated per month
• 1.5 billion requests per year
• 40% increase in conversion rates
• 2x higher search usage

How Luigi’s Box helped Answear?

Answear uses Luigi’s Box recommendation engine to display relevant products. Moreover, some features were developed to meet Anwear’s needs and requirements. It enabled the company to simplify the integration process and reduce workload.

Integrating Luigi’s Box Recommender engine turned out to be a big success due to an increased search conversion rate, maintained even during the busy periods of the year, which are Black Friday and Christmas.

2. NAUREUS

It is an e‑commerce shop that sells healthy and natural products such as snacks, supplements, or cosmetics. Thanks to Luigi’s Box, it was possible to conduct A/B testing to monitor the performance of the Recommender.

Let’s talk numbers:

• 50.8% increase in CVR
• 2.6% increase in CTR
• 1.8x more conversions

How Luigi’s Box helped NAUREUS?

The conversion rate increased because of the suggested items in the shopping cart. Also, NAUREUS implemented item ideas in product detail and category pages with personalized recommendations. As a result, all of the actions positively affected sales.

3. Košík

Košík is one of Czech’s e‑commerce grocery sellers that wanted to optimize their customer search experience. Luigi’s Box became their go-to solution. They aimed to improve the product search process, retain current customers, and attract new ones.

Let’s talk numbers:

• 15,000 products in Košík’s catalog
• 50% of customers used to search in the food segment (before using Luigi’s Box, there were 24%)
• 10.5% increase in conversation rates
• 40% of buyers add products to their cart directly after using a search solution

How Luigi’s Box helped Košík?

Košík was provided with an evaluation of their initial solution performance. Luigi’s Box Analytics helped to identify the problems and then focus on solving it. As a result, they increased search usage and lowered no-result searches.

Benefits of a product recommendation

The benefits list of recommender system evaluations is much longer!

Increased revenue

With a proper tool for personalized product recommendations, it is possible to increase the revenue of your business. How? Because of the data and machine learning algorithms. Users are exposed to tailored product suggestions and relevant recommendations, so they will likely continue purchasing at your place.

Also, a recommendation system can suggest complementary items to customers, making it easier and faster to do the shopping – they don’t have to search for items for hours! Instead, they’re just at their fingertips.

Improved retention

A company’s customer retention rate measures how frequently customers return to them and continue to purchase goods or services. In addition, it is about what businesses do to keep their customers coming back and staying connected to the brand. When visitors receive an excellent personalized experience, they are more likely to become repeat buyers.

Additionally, a reliable recommender system lets customers discover new products they weren’t aware of.

Boosted conversions

Conversion is about completing a specific action by an online user. It might be, for example, adding a product to a cart, making a purchase, or signing up. Relevant recommendations assist companies in keeping conversion rates high by offering appealing items. People are more likely to buy, add to a cart, or sign up for a newsletter if they find shopping convenient, fast, and enjoyable.

When conversion rates are high, customers find value in your products and services. It is a crucial metric to track, especially in e‑commerce.

Get a better understanding of key e‑commerce metrics.

Enhanced user experience

Nothing improves user experience more than personalization. That is why most businesses strive to personalize their experiences very often. And some good news here is that a product recommender system works amazingly well.

Thanks to personalized item recommendations for users, customers can find relevant products easily and in no time.

Additional advantages of personalization are building a relationship with your target market and increasing customer satisfaction.

Maximized AOV

Average Order Value (AOV) is a metric worth focusing on because it shows how much your customers spend on average during a single purchasing session. Thanks to machine learning algorithms, the system catches user profiles, as well as their preferences and interests.

Then, users can get recommended complementary products or ones they might like. And it encourages buyers to invest in other items as well.

Maximized Average Order Value is the massive benefit of a product recommender, and if you struggle with a low AOV rate, maybe it’s time to implement such a solution.

No idea what recommender system might work for you? Don’t worry. We’ve got you covered. Check out Luigi’s Box Recommender!

Use Luigi’s Box for more accurate recommendations

The Recommender tool is an AI-supported solution for displaying product suggestions (and their visual representations) according to visitors’ preferences and previous online behavior.

• It integrates seamlessly with every e‑commerce platform

Why hesitate? Recommender boxes can be displayed anywhere on the website. You don’t have to worry about the way it looks – they fit seamlessly with your website no matter what e‑commerce platform you use.

• It motivates prospective customers to make a purchase

Luigi’s Box Recommender suggests soon-to-sold-out products, products on sale, or top-selling products for individual users. Such offerings might motivate prospective customers to keep shopping.

• It provides a thorough analysis of the recommended products

The tool measures how many customers click on the suggestions and which are relevant to the customers. Additionally, it shows the trends. All the information is essential for making informed decisions about the sale strategy.

If you are looking for a reliable tool for boosting sales, increasing revenue, and gaining customer loyalty using recommender, look no further than Luigi’s Box.

It is software that helps companies to enhance their site’s search capabilities, but not only that. Luigi’s Box offers excellent tools for:

• Search
• Analytics
• Product Listing

All at your disposal at affordable prices. Request a quote for your online store for more details.

•  Conclusions on recommender system examples

A reliable recommender system is one of the essential components when you want to provide an excellent shopping experience at your e‑shop. Many product recommendation examples show that it is worth implementing this solution due to its excellent benefits.

Still hesitating? Try out Luigi’s Box and make the most of the recommender system. Get started with a free trial.

Product recommendation FAQ