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

Keeping Metrics Honest

In Chapter 13, Drawing the Big Picture with Data, you learned about analysis methods and goal setting with data. It is impossible to implement and track all possible metrics at all times. Setting a high-level strategy and goals allows you to identify and prioritize the right metrics for initiatives in the short term.

When we talk about data, we often only think about quantitative data and lose sight of qualitative data. Qualitative and quantitative data should be combined to form hypotheses and drive insights that may not be easily available without combining these two. Creating clusters of metrics and constantly validating hypotheses based on findings from one perspective with another set of metrics or qualitative insights allows you to remove biases from your metrics. The topics covered in this chapter include the following:

  • Mixing qualitative and quantitative feedback
  • Validating your insights
  • Defining the right product metrics
  • Framework...