There are probably an infinite number of ways to track and measure success, whether that be on the personal, professional, or corporate level. Finding the right method or scheme for measuring success can actually actively contribute to said success, by helping you focus on what really matters and cutting out the fluff. Measure What Matters explains one of the best models for doing exactly that.
Measure What Matters was written in 2018 by John Doerr. As the chairman of the Silicon Valley venture capitalist firm, Kleiner Perkins, Doerr was an early and major investor in such titanic firms as Google, Amazon, and Intuit. But before that, he got his start as a salesman at Intel. It was from his experience working with and learning from legendary Intel CEO, Andy Grove, that the concepts in this book originated.
So, how then do you measure what matters? The answer is what Doerr calls “OKRs,” that is Objectives with Key Results. In this model, the “Objective” is the “What” while the “Key Results” are the “How.” Most simply put, OKR is a model for setting goals and outlining the essential means of accomplishing those goals.
There are several features that distinguish this model from the traditional MBO system developed in the mid 20th Century by Peter Drucker. One is that OKRs are developed from the bottom up. While the CEO and department heads must set objectives and key results for the firm as a whole, individuals and teams should also come up with their own OKRs. These are reviewed on a monthly or quarterly basis to make sure they are on track and contributing to the broader objectives of the company.
The OKR model is very flexible and dynamic. They can be changed on the fly, even in the middle of quarters, if they are not working. The focus is less on merely tracking a few key performance indicators, and more on trial and error, being willing to experiment with different workflows to see what works. It should be emphasized that while key results don’t necessarily have to be a conventional KPI, there can be absolutely no ambiguity as to whether or not they’ve been achieved.
Doerr learned about the OKR model from Andy Grove at Intel and then became a sort of apostle for the idea throughout Silicon Valley and beyond. He worked closely with Google in its early years as both a consultant and investor. Much of the book is dedicated to examining how OKR contributed to Google’s incredible success.
Towards the end of the book, Doerr explores some possible applications and expansions of the OKR concept in the modern workplace. One example is the replacement of the dinosaur that is the annual performance review with a more personal and dynamic process of Continual Feedback and Recognition (CFR). Such later chapters, as well as several guides and resources appended to the book give some idea of the versatility of OKRs.
That being said, it should be added the book is a bit lackluster in terms of practical and actionable guidance. It’s certainly there, but you feel like you have to dig for it sometimes. For instance, while there are anecdotes and case studies of OKR being applied in a wide variety of fields and contexts, there are few practical details on how to actually apply the OKR model.
On a similar note, a lot of the book reads like history. This is not a problem per se, as Doerr is an effective writer and the subject matter—the early years of Intel and Google, the management of the Gates Foundation—is fascinating. It should temper your expectations, however, as what you’re getting with Measure What Matters is not really a guide to measuring what matters so much as it is the story of how a lot of successful people have done so.
That caveat aside, it is still very much worth the read. If nothing else, it gives insight into the managerial mindset of one of the most influential people in the world of business and technology. The adaptability of the model also means that you can find some application of it, whether as a leader or simply in clarifying and accomplishing your personal goals.
Measure What Matters is available for purchase here.