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API Analytics for Product Managers

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

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Product type Paperback
Published in Feb 2023
Publisher Packt
ISBN-13 9781803247656
Length 344 pages
Edition 1st Edition
Concepts
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Author (1):
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Deepa Goyal Deepa Goyal
Author Profile Icon Deepa Goyal
Deepa Goyal
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Table of Contents (24) Chapters Close

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 23. Other Books You May Enjoy

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...

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