Recommendation engines: how to increase your sales with intelligence

12 de September de 2023

We live in a digital age where the volume of information and content available far exceeds our ability to consume it. Whether it’s looking for a movie to watch on a rainy afternoon or simply browsing the endless lists of products in online shops. This is where recommendation engines come into play. These tools personalise our online experience, presenting options tailored to our tastes and interests.

Find out what recommendation engines are and how they can benefit your business.

What is a recommendation engine?

It is a technology that uses Artificial Intelligence and machine learning to analyse user behaviour and suggest products, services or information to users based on their preferences. An effective recommendation engine analyses data to create individualised profiles.

If you have ever shopped online and got a “you might be interested in…” box, then you have interacted with a recommendation engine!

To achieve these results, it draws on previously collected data such as:

  • Browser history.
  • Current shopping behaviour.
  • Most viewed products.
  • Shopping cart
  • Wishlist.

Types of recommendation engines

There are different approaches to recommendation engines:

Collaborative filtering systems: it analyses the behaviour of different customers to predict the preferences of a specific person. It will then provide recommendations based on the user’s preferences. For example, if two users have bought the same three products, and one of them buys a fourth product, it is likely that the other user will also be interested in that fourth product.

Content-based filtering systems: Analyses user behaviour and provides recommendations based on user preferences. They are based on the item description and the user’s profile. If a customer has shown interest in a particular product, similar products will be recommended based on descriptions, categories or features.

Hybrid recommendation systems: Combining both techniques, it offers suggestions based on the preferences of similar users and content. This type of recommendation system has grown in popularity due to the highly personalised experience.

If you want to find out how content appears on your favourite websites, some companies have a section explaining the filters they add to make the user experience as personalised as possible. For example, Netflix and Asos provide users with a brief explanation of how their recommendation systems work.

The case of Spotify

Let’s look at an example of a hybrid recommendation system. This system is one of the most popular thanks to its great capacity for analysis and personalisation.

Spotify has a clear objective: to offer the best selection of content for each user. In this way, it meticulously analyses each song that is added to its platform, reviewing not only the basic metadata, such as title, artist, producer and genre, but also details of the audio content. The system identifies characteristics such as style, perception (energetic, calm), and emotion evoked. An additional analysis is done with “natural language”, interpreting the content of lyrics and external references about the song.

However, it is not only based on content. User behaviour is vital to the recommendation algorithm. Spotify tracks direct actions, such as saved tracks and “likes”, as well as indirect actions, such as listening duration and skipped tracks. In addition, it considers contexts such as location and device used.

All this feeds into BaRT (Bandits for recommendations as treatments), an advanced technique in the field of machine learning and artificial intelligence, which integrates information from songs and users. It learns from each interaction and validates its recommendations by considering click-through rates and playback probabilities. It combines user data with patterns observed in the community to make suggestions, especially useful for new or less active users.

Benefits of implementing a recommendation engine in your business

  • Increased sales: Personalised recommendation motivates customers to consider products they might not otherwise have discovered. This “cross-selling” can mean a substantial increase in shopping cart value.
  • Optimisation of own actions thanks to analysed data: Recommendation engines collect and analyse large amounts of data. This data can be used to better understand customers, their preferences and behaviours. This can help companies make informed decisions about inventory, marketing and other critical areas.
  • Increased loyalty: when customers feel that a business understands their needs and offers relevant products, they are more likely to return. A recommendation engine personalises the shopping experience, which can lead to increased customer brand loyalty.
  • Increased customer satisfaction: by receiving relevant recommendations, customers can feel that their time is valued and that they do not have to search through countless options. This will lead to a smoother and more satisfying shopping experience.
  • Personalisation of the shopping experience: as the recommendation engine learns more about a customer’s preferences and behaviours, it can offer increasingly accurate suggestions.

How to implement a recommendation engine

Before implementing a recommendation engine in your business, it is essential to analyse whether it is necessary. Examine factors such as the needs of the business, the number of current customers or the volume of products in the catalogue. If the numbers are low, it is probably not necessary to implement such a system.

  1. Gather data: For a recommendation engine to work properly, it is essential to have quality data. There is no one-size-fits-all data, it depends on your objectives and the level of personalisation desired. Some of the data that can be taken into account are: information on previous purchases, web browsing, interactions on social networks, average active time…
  2. Choose the type of engine: depending on the type of business and the amount of data available, you can opt for a content-based approach, collaborative filtering or a combination of both.
  3. Testing and tuning: Like any tool, recommendation engines need tuning and optimisation. It is vital to analyse the results, measure the impact on sales and customer satisfaction, and make improvements as needed.
  4. Stay up-to-date: Customer preferences change, as do market trends. It is essential to regularly review and update the system to ensure that recommendations remain relevant.

Which brands offer recommendation engines?

If you are considering incorporating a recommendation engine into your business and are unsure which platforms to use, here are two options that can help you start personalising your customers’ experience with your business.


Recommendation engine based on Visual AI and Natural Language Processing. This system can personalise suggestions without the need for behavioural data, mimicking the physical shop experience in the online shop.

It uses deep learning to connect products by visual similarity, infer relationships through textual analysis, and improve recommendations based on specific shopper needs and preferences.

Dynamic yield

Focuses on “personalisation anywhere”, helping brands increase revenue by individualising every user interaction across web, mobile and email.

Its recommendation engine uses AI and machine learning to analyse user behaviour patterns, enabling businesses to deliver personalised and meaningful experiences to their customers in real time.

Shall we talk?

If you want to implement a recommendation engine in your business, you’re in the right place. Cognodata is a leading Algonomy partner, so we have certified consultants, experts in the implementation and management of this platform. Are you ready to transform your ecommerce experience?