Foundations of Data Analysis: Cluster, Cohort, and Regression

Different types of data need to be analyzed differently. For example, usage metrics need to be analyzed in relation to a time period where we can track how customers use our product over time. But when we start to dive into customer behavior, we should segment the user base and understand how the different clusters of customers behave in contrast with each other.

In this lesson, we’ll learn the most important methods for analyzing data and how to use them to set up API product analytics.

Cluster analysis

Cluster analysis is a way to use statistics to find groups of similar observations in a set of data. It is a way to break up a large set of different data into smaller, more similar groups based on patterns and relationships in the data. The following illustration shows the plot of customers across the number of developers on their team on the x-axis and the time to the first Hello World metric on the y-axis. In this example, we can see that there are clusters forming in the plot, showing that Small and Medium-sized Businesses (SMBs) tend to be in a similar range for these two variables.

Cluster analysis can be used to find groups of customers with similar traits or patterns of behavior when API data is being looked at to understand customer segmentation. For example, the API data might include information about the types of products or services customers use, their geographic location, demographic characteristics, or other factors relevant to understanding customer behavior. By applying cluster analysis to this data, it is possible to identify customers with similar characteristics or behavior patterns and understand how these groups differ.

Cluster analysis can be used on API data to understand customer segmentation. One way to do this is to use a clustering algorithm to find groups of customers with similar traits or patterns of behavior. Many different clustering algorithms can be used for this purpose, each with its own strengths and limitations.

There are many factors to consider when deciding which clustering algorithm to use to understand customer behavior. Some of the factors that might influence our choice are:

• The size of the data: If we have a huge dataset, ...