Book Image

API Analytics for Product Managers

By : Deepa Goyal
Book Image

API Analytics for Product Managers

By: Deepa Goyal

Overview of this book

APIs are crucial in the modern market as they allow faster innovation. But have you ever considered your APIs as products for revenue generation? API Analytics for Product Managers takes you through the benefits of efficient researching, strategizing, marketing, and continuously measuring the effectiveness of your APIs to help grow both B2B and B2C SaaS companies. Once you've been introduced to the concept of an API as a product, this fast-paced guide will show you how to establish metrics for activation, retention, engagement, and usage of your API products, as well as metrics to measure the reach and effectiveness of documentation—an often-overlooked aspect of development. Of course, it's not all about the product—as any good product manager knows; you need to understand your customers’ needs, expectations, and satisfaction too. Once you've gathered your data, you’ll need to be able to derive actionable insights from it. This is where the book covers the advanced concepts of leading and lagging metrics, removing bias from the metric-setting process, and bringing metrics together to establish long- and short-term goals. By the end of this book, you'll be perfectly placed to apply product management methodologies to the building and scaling of revenue-generating APIs.
Table of Contents (24 chapters)
21
The API Analytics Cheat Sheet

Methods for analyzing data

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

In this section, you’ll learn the 10 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 screenshot 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, you can...