In recent years, the terms data science, machine learning and artificial intelligence have appeared recurrently and abundantly. These terms are very important for product managers. It’s no surprise that I dedicate 5 chapters of the Product Management book to issues related to data and metrics.

As I mentioned in this article, the product manager must be a data geek, that is, a person who is always thinking about how to learn more with data. What is a person’s behavior in the months and days before they unsubscribe from your product? What about the behavior of a person who upgrades? What is the behavior of a user who says they are satisfied with your product? And what do you say you are very satisfied with? If your product has several features, which is the most popular? Which generates greater satisfaction? What is the typical usage pattern of your product? If an atypical usage pattern appears, what does that mean? These are examples of some questions that the product manager can ask and that will have their answers in the product metrics. And with each new answer obtained, the product manager will likely want to ask more questions.

To find the answers to your questions, it is important that the product manager knows data science techniques and knows how to extract the answers to his questions himself, whether through data extraction and visualization tools or by running data queries. SQL in the product database. If the product manager does not have this independence and needs other people to extract the data for him, this could hinder the product’s evolution.

As this learning from data takes place, the product manager will likely begin to notice opportunities to insert these learnings into the product. For example, after analyzing product usage and engagement data, a CRM software product manager may notice that customers end up canceling less when using the commercial proposal generation functionality. Once this discovery is made, he can promote a change in his product to make the use of this functionality easier and more immediate and, therefore, reduce customer churn by making them more engaged. This is a way to insert data science into your product.

Machine learning, which is nothing more than a form of implementing artificial intelligence, is when we program machines to learn from data, and, the more data the machine has in its hands, the more it will learn. It’s a way to insert data science into the product to make it better. The more you use a given product, the more data is available to the team that develops the product to learn about its users and how they use that product. For example, the more purchases you make in an online store, the more it learns about your shopping habits and the easier it is for the store’s software to make recommendations that interest you. The same goes for Netflix and Spotify suggestions. In these cases, it is common for the store to compare its use with the use of people who show similar behavior to make suggestions such as “whoever bought this item also bought these other items”.

This is why the product manager and the entire team that develops the product must know and know how to use data science, machine learning, and artificial intelligence in their daily lives. They are powerful tools to help you increase the chances of building a successful product.