Improve your e-commerce sales with recommendation algorithms
In e-commerce, the competition for customers' attention is a great challenge for SMEs and freelancers. One of the best allies to achieve more sales and improve the user experience is the use of recommendation algorithms.
If you have an e-commerce and want to know how these algorithms can help you improve your sales, here we explain everything you need to know to get started.

What are recommendation algorithms?
Recommendation algorithms are systems based on artificial intelligence and machine learning that analyse users' behaviour on a digital platform to predict and suggest products or content that might be of interest to them. This type of algorithm processes large volumes of data - such as previous purchases, products viewed, and searches performed - to identify individual patterns and preferences. In this way, algorithms can provide personalised recommendations that align with each customer's needs and tastes.
To achieve this, recommendation algorithms are often based on different models, the most common of which are the following:
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Collaborative filtering: based on the behaviour of users with similar tastes.
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Content-based filtering: based on the characteristics of the product or content consumed.
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Hybrid systems: combine the two previous models to offer more accurate recommendations.
What are the benefits for online businesses?
Implementing recommendation algorithms in e-commerce offers multiple benefits for both the business and its customers. Among the main advantages are:
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Increased conversion rate: by displaying relevant products or services, the likelihood of purchase increases significantly.
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Improved user experience: personalised recommendations make the shopping experience smoother and more engaging, as the customer feels that the shop understands their interests.
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Increased time spent on the website: by displaying content of interest, the user tends to browse more, which can translate into more product visits.
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Increased average order value: algorithms can also suggest complementary products or accessories that the customer might want to add to their purchase.
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Customer loyalty: personalisation creates a stronger bond with the customer, which can keep them coming back to the shop and developing brand loyalty.
How to implement recommendation algorithms step by step
If you are considering integrating this technology into your e-commerce, here is a step-by-step guide to implementing it and making the most of its benefits.
1. Define your objectives
Before you begin, it is critical that you determine what you want to achieve with recommendation algorithms: are you looking to increase conversion, increase average order value, or perhaps improve customer retention? Defining your objectives will help the system align with your business goals and make it easier to measure results.
2. Analyse your database
The key to success in any referral system is quality data. Review and organise the information you have about your customers, their behaviours and your products. Variables such as purchase history, favourite products, dwell times, searches and demographics can help you generate more accurate recommendations.
3. Choose the right recommendation model
As mentioned, there are different types of recommendation algorithms. Depending on the type of business and the data available, choose the model that best suits you. For example:
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If you have a large and diversified catalogue, collaborative filtering can help you detect patterns among similar users.
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If you have products with detailed descriptions and attributes, content-based filtering may be ideal.
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If you are looking for greater precision and have the resources, opt for a hybrid model that combines both approaches.
4. Select the implementation tool
Fortunately, today there are affordable and effective tools to implement referral systems in your online shop. Some of the most popular are:
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Google Recommendations AI: an advanced option that uses machine learning to recommend products based on purchase patterns.
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Nosto: a platform specialised in e-commerce that allows you to personalise product recommendations and adapt them to different customer segments.
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Amazon Personalize: an Amazon Web Services (AWS) solution that offers highly customisable machine learning-based recommendations.
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Recombee: an affordable option that allows you to implement recommendation algorithms based on collaborative filtering and content.
Select the tool that best suits your budget and needs, and make sure it is compatible with your e-commerce platform.
5. Configure, test and optimise
Once the tool has been chosen, it is time to configure it. Set up the recommendation rules and configure the algorithm parameters according to the objectives defined at the start. Then, test in development environments or with small groups of customers to verify that the recommendations are accurate and relevant.
Remember that recommender systems need constant optimisation. Analyse the results of the recommendations and adjust the algorithm parameters if you notice that performance can be improved.
6. Monitor results
The last step is to monitor the results of your referral algorithms. Look at key metrics such as conversion rate, time spent on the site, and average purchase value. This will allow you to understand the real impact on your business and adjust as needed.
As you can see, integrating recommendation algorithms into your e-commerce not only improves your customers' experience, but also boosts your sales, builds shopper loyalty and optimises the profitability of your online shop. It is an essential tool in an environment where personalisation is increasingly important to gain a competitive advantage.
If you are looking for more resources and tools to digitise your SME and improve its performance, be sure to visit our resources on digital transformation.