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

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In most cases, we want to compute the VoI for a range of values rather than a binary succeed/fail. So let’s tweak the advertising campaign example and say that a calibrated marketing expert’s 90% CI for sales resulting from the campaign was from 100,000 units to 1 million units. The risk is that we don’t sell enough units from this campaign to break even. And if the distribution is symmetrical, then the mathless table gives us a 90% CI for the mean as well as for the median. Very few experts actually measure their performance over time, and they tend to summarize their memories with anecdotes. They are right sometimes and wrong sometimes, but the anecdotes they remember tend to be more flattering to them.” By 1999, I had completed the… Applied Information Economics analysis on about 20 major [IT] investments… Each of these business cases had 40 to 80 variables, such as initial development costs, adoption rate, productivity improvement, revenue growth, and so on. For each of these business cases, I ran a macro in Excel that computed the information value for each variable… [and] I began to see this pattern: * The vast majority of variables had an information value of zero… * The variables that had high information values were routinely those that the client had never measured… * The variables that clients [spent] the most time measuring were usually those with a very low (even zero) information value… …since then, I’ve applied this same test to another 40 projects, and… [I’ve] noticed the same phenomena arise in projects relating to research and development, military logistics, the environment, venture capital, and facilities expansion.

How to Measure Anything — LessWrong How to Measure Anything — LessWrong

When measuring risk, we don’t just want to know the “average” risk or benefit. We want to know the probability of a huge loss, the probability of a small loss, the probability of a huge savings, and so on. That’s what Monte Carlo can tell us.

But you actually have more data than you think. Whatever you’re measuring has probably been measured before. And, you have historical data that can be useful, even if you think it’s not enough.

How to Measure Anything by Douglas W. Hubbard | Perlego [PDF] How to Measure Anything by Douglas W. Hubbard | Perlego

As far as the propositions of mathematics refer to reality, they are not certain; and as far as they are certain, they do not refer to reality. —Albert Einstein (1879–1955)” Hubbard’s book includes two case studies in which Hubbard describes how he led two fairly different clients (the EPA and U.S. Marine Corps) through each phase of the AIE process. Then, he closes the book with the following summary:The second form of the question is useful because the answer is often more straightforward and it leads to the answer to the other question. It also forces us to think about the likelihood of different observations given a particular hypothesis and what that means for interpreting an observation. So, I agree that you accomplished these desired things. However, before you accomplished them, how accurately did you know how much time they would take, or how useful they would be? This is a very important concern that I have too. I have not read the book, and it might be a very interesting read, but when it starts with:

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