Python Tutorial : Identifying and understanding KPIs

Want to learn more? Take the full course at https://learn.datacamp.com/courses/customer-analytics-ab-testing-in-python at your own pace. More than a video, you'll learn hands-on coding & quickly apply skills to your daily work. --- Great work on the exercises! Now let’s dive into KPIs! The example we will use throughout this course is that of a mobile app that offers meditation services for a paid subscription as well as one-off in-app purchases. The app is growing quickly and we are motivated to maintain a strong free-trial to paying user conversion rate. Additionally, we want to maintain strength in a variety of other business areas as we will see. While this is a very specific example, we can imagine interchanging users, meditation-app, and purchases with other nouns and KPIs, and the same mathematical techniques would still apply. We have two data-sets related to our app. First is a set of user demographics, tied to a unique user id number. Let's import this file, customer_demographics dot csv with the Pandas dot read_csv() method. As we can see, it includes a broad set of demographic information. The second is a set of user actions called customer_subscriptions dot csv, containing the date the trial period ended, the date of purchase if they purchased, and the price they paid upon subscribing (in cents). For now, let’s consider the KPI of conversion rate. We will consider a variety of others throughout the course. One question in defining our KPI is over what interval should we consider the conversion rate? The conversion immediately after a lapse? one week after? One month? One way to decide this is to see the generalizability of these statistics across different demographic groups. Stability in this way is desired so we don't need custom KPIs for each breakdown. A second is to see if one is more correlated with important factors like retention or spending than the others. To begin answering these questions, we must match our demographics data to our