Personalization is increasingly a key factor in the modern paradigm of the digital market. The theory is simple: every person is uniqueand 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, personalization in Data Science uses Data Science techniques facilitate the analysis of every customer’s behaviour in order to personalize products and make customer interactions with them as natural and satisfying as possible.
Why is personalization so useful to organizations?
If we look back a few years, we can see that online markets used the same sales techniques as traditional markets. The product website looked similar to every customer, and their interaction with the product was the same for all of them. The most recent innovations in Data Science have arrived to simplify the mass personalization of digital products.
Customers are more demanding than ever, and creating an innovative product that brings value to the customer is becoming harder and harder. That is why customizing a product so 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 minimizing 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 that is 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 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 so 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 reorganize each user’s app interface resulting in a unique, tailored user experience.
Microsoft Azure services:
Azure has various services that can speed up the development and productization 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 customer’s intention, in contrast to more traditional systems that use techniques such as keyword search. This uses a personalized search of different types of text documents, based onpertinent information such as name, location, language, and more.
Watch the video on personalization in Data Science and recommendation systems:
Conclusion
We hope this article was able to convince you of the importance of the personalization of digital products, a key factor in today’s market that gives every user a unique experience by improving customer satisfaction and 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.