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

Avoiding cognitive biases

Cognitive biases are mental shortcuts that people use to make sense of the world around them. These biases can influence how people perceive and interpret information and can lead to inaccurate or distorted judgments. There are many different types of cognitive biases, and they can affect various aspects of decision-making.

In the context of setting product analytics, cognitive biases can play a role in how metrics and goals are chosen and how progress toward those goals is evaluated. For example, if a team is setting goals for a product and they are affected by confirmation bias, they may only consider information that supports their preconceived ideas about the product and ignore information that contradicts those ideas. This can lead to the selection of metrics that are not representative of the true success of the product and may not accurately reflect the needs of the users.

To avoid the influence of cognitive biases in setting product analytics...