
Measure What Matters: How Google, Bono, and the Gates Foundation Rock the World with OKRs
by John Doerr
4.5 stars from 9,961 reviews. One of the highest rated Self Help books we track.
Last checked: Jun 12, 2026
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Price range: $1.99 – $15.99
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Fans of these authors will like this...
- Jim Collins
- Peter Drucker
- Clayton Christensen
Fans of these books will like this...
- Good to Great
- The Goal
- Radical Candour
Perfect for you if...
- You're leading a team or company and frustrated by misaligned priorities or employees who don't know how their work matters.
- You work in tech or at a scaling start-up and want the playbook that's already proven at Google, Intel, and the Gates Foundation.
- You're curious how legendary managers like Andy Grove actually ran their companies, beyond the mythology.
Maybe not for you if...
- You want inspiration without practical frameworks. This is a how-to manual, not a motivational read.
- You work in a rigid, hierarchical environment where transparency and cross-functional collaboration aren't possible.
- You're looking for a quick productivity hack. OKRs require discipline and cultural commitment to work.
About this book
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Price History
Kindle price over time. Green zones = periods when the book was free.
Currently on sale at $1.99 — 88% off peak. Price has dropped 16 times over the last 6 years. Goes on sale roughly every 137 days. Typically stays discounted for 13 days.
Sales Rank
Amazon sales rank: lower number = better selling book. #1 is the bestseller.
Currently around #54K in the Kindle store — a steady mid-list seller. Sales have softened 63% recently.
Rating & Reviews
Purple line = star rating over time. Blue area = total review count growth.
Currently 4.5★ from 9,961 reviews
Gaining approximately 42 new reviews per month. Steady word of mouth growth. Rating has remained stable at 4.5 over the tracking period.
Sale Prediction
Actual Predicted
This book is currently discounted at $1.99. It typically returns to around $15.99.
Prices shown in USD from Amazon US. We continuously pull new data to improve tracking. If dates look old, it usually means there has been no significant change since the last data point.
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