How to Measure Anything: Finding the Value of Intangibles in Business

Douglas W. Hubbard

Language: English

Pages: 432

ISBN: 1118539273

Format: PDF / Kindle (mobi) / ePub

Now updated with new measurement methods and new examples, How to Measure Anything shows managers how to inform themselves in order to make less risky, more profitable business decisions

This insightful and eloquent book will show you how to measure those things in your own business, government agency or other organization that, until now, you may have considered "immeasurable," including customer satisfaction, organizational flexibility, technology risk, and technology ROI.

  • Adds new measurement methods, showing how they can be applied to a variety of areas such as risk management and customer satisfaction
  • Simplifies overall content while still making the more technical applications available to those readers who want to dig deeper
  • Continues to boldly assert that any perception of "immeasurability" is based on certain popular misconceptions about measurement and measurement methods
  • Shows the common reasoning for calling something immeasurable, and sets out to correct those ideas
  • Offers practical methods for measuring a variety of "intangibles"
  • Provides an online database ( of downloadable, practical examples worked out in detailed spreadsheets

Written by recognized expert Douglas Hubbard—creator of Applied Information Economics—How to Measure Anything, Third Edition illustrates how the author has used his approach across various industries and how any problem, no matter how difficult, ill defined, or uncertain can lend itself to measurement using proven methods.

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linear regression, do that. If you don’t feel comfortable using regression at all, stick with Dawes’s equally weighted z-scores. Each method is an improvement on the simpler method, and all improve on unaided experts. Panacea or Placebo?: Questionable Methods of Measurement The Big Measurement “Don’t” Above all else, don’t use a method that adds more error to the initial estimate. Some readers might think that, so far, my approach has been to lower the bar for what counts as

industry. I started writing the book well before the financial crisis started. I wanted to make it just as relevant to another Katrina or 9/11 as to a financial crisis. I’ve also written several more articles, and the combined research from them, my second book, and comments from readers on the book’s Web site gave me plenty of new material to add to this second edition. But the basic message is still the same. I wrote this book to correct a costly myth that permeates many organizations today:

Measurement Inversion In a business case, the economic value of measuring a variable is usually inversely proportional to how much measurement attention it usually gets. Apparently, our intuition about what to measure fails us more often than not. Because most organizations lack a method for measuring the value of conducting a measurement, they are almost guaranteed to measure all the wrong things. It is not that things like project costs and hours per week on some activity should not be

solution to 347 times 79 in particular but you knew that the same procedures applied to any combination of numbers and operations. So, if your problem happens to be something that isn’t specifically analyzed in this book—such as measuring the value of better product labeling laws, the quality of a movie script, or effectiveness of motivational seminars—don’t be dismayed. Just read the entire book and apply the steps described. Your immeasurable will turn out to be entirely measurable. The

you don’t know what, in fact, you do know and that you do know what, in fact, you don’t know. A Bayesian analysis was the basis of some of the charts I provided in Chapter 9. For example, in the “population proportion” table, I started with the prior knowledge that, without information to the contrary, the proportion in the subgroup was uniformly distributed between 0% and 100%. Again, in the “Threshold Probability Calculator,” I started with the prior knowledge that there was a 50/50 chance

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