Personalisation in Data Science (and recommendation systems)

  • Data science techniques are useful for identifying every customer and tailoring offers for them, using, for example, recommendation systems;
  • Technologies such as artificial intelligence and machine learning have changed the way in which customers interact with brands;
  • Data science is great for the implementation of dynamic user interfaces in apps (they adapt according to the use).

Personalisation is increasingly a key factor in the modern paradigm of the digital market. The theory is simple: every person is unique, has their own taste and lifestyle and this is why everyone should be given a unique experience, suited to their individuality as much as possible.

In practice, Data Science techniques facilitate the analysis of every customer’s behaviour in order to personalise products and make customer interactions with them as natural and satisfying as possible.

Why is personalisation so useful to organisations?

If we think back a few years, we can see that online markets were using the same sales techniques as in a traditional market. The product website looked similar to every customer and their interaction with the product was also the same for all of them. The most recent innovations in data science, associated with big data technologies, have arrived to simplify the mass personalisation of digital products.

Customers are, more demanding than ever, and it is becoming harder and harder to create an innovative product that brings value to the customer. That is why is customising a product in such a way that the customer feels it was designed exclusively for them is so important. Currently, in the IT field, technologies such as artificial intelligence and machine learning are changing the way in which customers interact with brands, improving metrics such as user experience, conversion taxes or minimising the churn rate.

Recommendation systems:

All of us have already experienced that terrifying sensation of receiving an advertisement for a product we were thinking of. No, your phone is not (yet) able to read your mind, but this situation is generated by recommendation systems.

Data science techniques allow us to take advantage of your history of interactions with various products to build a picture of your tastes and interests, allowing the ability to suggest new content, different and unique for every customer. These systems are used for product recommendations in online stores, music and film recommendations on streaming platforms, advertisements we see on the Internet and even in new friend suggestions on social media platforms.

Dynamic user interface:

Nowadays, apps display many more tools and features. More features imply a more complex user interface, with more buttons. However, each user is unique and, therefore, their behaviour when using an app will also differ from person to person.

Data Science facilitates the extraction of these users’ behavioural patterns, making it possible to reorganise the app interface for each user, therefore resulting in a unique, tailored user experience.

Microsoft Azure services:

Azure has various services that can speed up the development and productisation of this type of product. One of those services is Azure Cognitive Search, a search service in the cloud, with integrated AI abilities.

Cognitive Search facilitates an intelligent search of structured and unstructured data, based on the intention of the customer, in contrast to more traditional systems that use techniques such as keyword search. This uses a personalised search of different types of a text documents, on the basis of pertinent information such as name, location, language, and more.

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We hope this article was able to convince you of the importance of the personalisation of digital products, a key factor in today’s market, that gives every user a unique experience, by improving customer satisfaction and their retention time.

Some examples of using Data Science in this area are recommendation systems, frequently used in online shopping and streaming platforms, and the development of dynamic interfaces in apps, where changes are carried out based on user behaviour in order to allow a more comfortable interaction.

João VarelaPersonalisation in Data Science (and recommendation systems)


Data Science

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