Once you get to bring in users (free or paid) to use your product, your next concern is related to the engagement from these users, that is, are they using the product? Are they solving the problem that the product is supposed to solve? How many times a day (or a week, or a month) your product is being used? For how long? How is it being used?
It is very important to find metrics to measure engagement. For instance, for a product to send e-mail marketing, some engagement and usage metrics are:
Note that each product has different engagement and usage metrics. Each product manager must select metrics to track his/her specific product.
Have you ever stopped and thought about how many times a day you use your cell phone? What do you use to access it? WhatsApp? Facebook? Instagram? Can you tell that you are deeply engaged with these applications?
Promoting engagement should be one of the concerns of the product manager. In 2013, Nir Eyal launched his book called Hooked: How to Build Habit-Forming Products, in which he explains the theory behind these products that just end up entering our daily lives. It is a great book to understand more about this theme.
There are some strategies that can help you to increase your product’s engagement and usage. These techniques are called lock-in.
Despite the fact that churn was greater than 20% every month, growth in the year was 73 new customers.
Why is it possible to grow even with a high monthly churn?
There are two reasons. The first, which I have already mentioned, is that it is necessary to have a greater inflow of customers than the amount that goes out.
The second is that churn varies based on the age of the customer. It is common to have cases where churn is high in the first month, because the customer did not like the service and decided to cancel right away. Or in the third or sixth month if your billing is quarterly or semi-annually. Some people call it premature churn.
Premature churn, despite being common, is something that can and should be reduced. You do this:
The concepts of churn and engagement go hand in hand, because the more engaged a user is, the less likely they are to cancel the service. So, a good way to predict the churn of a given customer is to track their engagement.
For example, if you’ve launched a distance learning product and track the usage of that product, you’ll likely see that the churn rate is higher for customers who have never attended the class. Review the previous lock-in thread for tactics to increase engagement and decrease churn.
In recent years, the terms data science, machine learning, and artificial intelligence have appeared recurrently and abundantly. These terms are quite important for product managers. No wonder I dedicate 5 chapters of the book to subjects related to data and metrics.
As I mentioned in the previous chapter, the product manager must be a data geek, that is, a person who is always thinking about how to learn more from data. What is a person’s behavior in the months and days before unsubscribing to your product? And the behavior of a person who upgrades? What is the behavior of a user who says he is satisfied with your product? And what do you say very satisfied? If your product has multiple features, which is the most popular? Which generates greater satisfaction? What is the typical usage pattern for your product? If an atypical usage pattern appears, what does it 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, it is very likely that the product manager will 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 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 can hinder the evolution of the product.
As this learning from the data takes place, it is likely that the product manager will begin to see opportunities to embed these learnings into the product. For example, a product manager of a CRM software may notice, after analyzing the usage and engagement data of the product, that customers end up canceling less when they are using the commercial proposal generation functionality. Once that discovery is made, he can make a change to his product to make it easier and more immediate to use this functionality and, in doing so, decrease customer churn by making them more engaged. This is a way to infuse data science into your product.
Machine learning, which is nothing more than a way of implementing artificial intelligence, is when we program machines to learn from data, and the more data the machine has in its hand, the more it will learn. In other words, it’s a way to insert data science into the product to make it better. The more you use a particular product, the more data is available to the team that develops the product to get to know your user and how they use that product. For example, the more purchases you make at 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”.
That’s 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 your chances of building a successful product.
In the next chapter, we will continue with the topic of metrics, focusing on financial and long-term metrics. Let’s also understand the concept of negative churn, the “Holy Grail” of products with a subscription-based business model.
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