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API Analytics for Product Managers

API Analytics for Product Managers

By : Deepa Goyal
4.1 (7)
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API Analytics for Product Managers

API Analytics for Product Managers

4.1 (7)
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)
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21
The API Analytics Cheat Sheet

Interpreting data

Data interpretation is the process of understanding and making sense of data. It involves examining and drawing conclusions based on the data. The data analysis and visualization process involves gathering data, creating visualizations, performing analysis, and drawing actionable insights, as shown in the following diagram:

Figure 13.3 – The process of interpreting data to form insights

Figure 13.3 – The process of interpreting data to form insights

  1. Summarize and process the data: The first step in the data interpretation process is to summarize and process the data in a way that makes it easier to understand. This might involve organizing the data into a tabular format, calculating summary statistics, or creating pivot tables.
  2. Create visualizations: Once the data has been summarized and processed, it is often helpful to create visualizations to help understand the data and identify patterns and trends. Visualizations can include charts, graphs, maps, or other types of diagrams.
  3. ...
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API Analytics for Product Managers
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