You're reading from API Analytics for Product Managers Understand key API metrics that can help you grow your business

Product type Paperback

Published in Feb 2023

Publisher Packt

ISBN-13 9781803247656

Length 344 pages

Edition 1st Edition

Concepts

REST API

Author (1):

Deepa Goyal

View More author details

Table of Contents (24) Chapters

Preface

1. Part 1:The API Landscape

2. Chapter 1: API as a Product FREE CHAPTER

3. Chapter 2: API Product Management

4. Chapter 3: API Life Cycle and Maturity

5. Chapter 4: Building and Managing API Products

6. Chapter 5: Growth for API Products

7. Chapter 6: Support Models for API Products

8. Part 2: Understanding the Developer

9. Chapter 7: Walking in the Customer’s Shoes

10. Chapter 8: Customer Expectations and Goals

11. Chapter 9: Components of API Experience

12. Part 3: Deep Dive into Key Metrics for API Products

13. Chapter 10: Infrastructure Metrics

14. Chapter 11: API Product Metrics

15. Chapter 12: Business Metrics

16. Part 4: Setting a Cohesive Analytics Strategy

17. Chapter 13: Drawing the Big Picture with Data

18. Chapter 14: Keeping Metrics Honest

19. Chapter 15: Counter Metrics to Avoid Blind Spots

20. Chapter 16: Decision-Making with Data

21. The API Analytics Cheat Sheet

22. Index

Why subscribe?

23. Other Books You May Enjoy

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...