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

Reliability metrics

In the previous sections, you learned about the measurement of infrastructure performance and usage. Monitoring usage metrics helps to improve the reliability of an API by providing insights into how the API is being used and identifying potential issues or areas for improvement.

By tracking metrics such as the RPM, error rate, response time, and concurrent connections, you can identify patterns of usage and identify potential issues—such as high error rates, slow response times, or high concurrent connections—that may indicate that the API is not performing well or is being overloaded.

For example, if the error rate is high, it can indicate that there is a problem with the code, configuration, or infrastructure of the API. By identifying the cause of the errors, you can take steps to fix the problem and improve the reliability of the API.

By monitoring the response time, you can identify potential bottlenecks or issues with the API, such...