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Zusammenfassung von How to Measure Anything

Name: How to Measure Anything
Rating: 4.1 (37 reviews)
Author: Douglas W. Hubbard

Douglas W. Hubbard

Finding the Value of "Intangibles" in Business

4.1 (37 Bewertungen)

16 Min.

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How to Measure Anything by Douglas W. Hubbard is a book that challenges the notion that some things cannot be measured. It provides practical methods for quantifying and managing uncertainty in decision-making processes.

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Entscheidungsfindung Kritisches Denken & Problemlösen Mathematik Unternehmertum

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How to Measure Anything
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What’s in it for me? Learn how to measure the seemingly immeasurable.

Imagine you’re faced with a critical business decision. You want to make the best choice, but the factors you need to consider seem elusive, intangible, even immeasurable. Now, what if you were told that everything you needed to inform your decision, no matter how fuzzy or abstract, could indeed be measured?

In this Blink, we delve into a groundbreaking approach that has transformed decision-making processes across industries. It’ll challenge your perception of what is quantifiable, taking you from a state of guessing and assuming to a place of knowing and understanding. It unveils methods that have been honed over decades, showing you how to measure the so-called “immeasurables” in surprisingly simple ways.

Estimating with intelligence

Picture yourself at the historic Trinity Test site, the birthplace of the atomic age. Amid the suspenseful countdown to the first atomic bomb detonation in 1945, you notice Enrico Fermi, Nobel laureate in physics, casually tearing a notebook paper into confetti. As the initial shockwave rushes past, he releases the confetti and observes how far the blast scatters it, predicting the yield of the atomic explosion with a margin of error that would make many sophisticated instruments blush. Fermi showed that simple observations, if used in the right way, can have great informative value.

Here’s another example. Imagine Fermi in a classroom, challenging his students to estimate the number of piano tuners in Chicago. Sound like an impossible task? That’s what his students thought too. But Fermi encouraged them to approach the problem by breaking it down into smaller, more manageable questions. How many people live in Chicago? How often do pianos need tuning? How many pianos can a tuner service in a day? By combining these approximations, students could calculate an estimate that was often closer to the actual number than they ever thought possible.

Here’s the good news: you can do this with your business challenges too.

Consider Chuck McKay, a consultant with Wizard of Ads. At one point he was approached by an insurance agent considering the idea of opening a new office in Wichita Falls, Texas. The town was already saturated with insurance carriers and the newcomer wasn't certain about the potential profitability. To tackle this uncertainty, McKay decided to employ Fermi’s method, breaking down the task of estimating market size into a series of manageable questions.

McKay began by identifying the total number of cars in Wichita Falls and the average annual car insurance premium in Texas. By multiplying these figures, he calculated a rough estimate of the gross insurance revenue in town. Considering the average commission rate, he then determined the total commission pool. After finding out the number of existing insurance agencies in town, he divided the total commission by this number to estimate the average annual commission per agency.

However, the declining population and potential market dominance by larger firms suggested the actual revenue per agency could be lower than anticipated. Armed with these insights, McKay advised the insurance agent against the venture.

This real-life example shows how Fermi’s method can effectively tackle seemingly immeasurable business challenges. Next, we’re going to drill deeper into how to break down so-called intangibles into much-more-tangible numbers.

Making confident predictions

In the business world, it’s common to have uncertainties about certain values. Be it the time spent on customer complaints or potential sales increase with a new campaign, predicting these numbers precisely can be challenging.

One way to express these uncertainties is to consider them as a range of possible values –⁠ a concept in statistics known as a confidence interval, or CI. For instance, when you predict that the number of prospects turning into customers will be between three and seven this quarter, with a 90 percent confidence level, this is considered a 90 percent CI. It doesn’t matter whether these estimates are computed through statistical inference or personal experience –⁠ they are still a reflection of your uncertainty.

Using calibration methods

Calibration methods can be utilized to assess both uncertainties and risks. Regrettably, many organizations use oversimplified risk measurements such as “high,” “medium,” or “low” ratings, which can lead to confusion. Instead, using specific figures, like the percentage chance of a certain loss, is far more effective.

Now let’s talk about a problem many organizations face: How to perform calculations when the available data are in ranges rather than precise points? The solution lies in Monte Carlo simulations. This method, devised by Enrico Fermi, uses a computer to generate large numbers of scenarios based on the probability ranges of different variables. Each scenario randomly generates a specific value for each unknown variable, which is then used to calculate a specific output.

Decomposing problems

The art of decomposition is a powerful tool in our measurement arsenal, even if it’s not a conventional “measurement” since it doesn’t require new observations. It’s like cracking open a complex question. Here’s the idea in a nutshell: define the problem, break it into measurable elements, quantify these elements, and recombine them to provide a comprehensive measure of the original problem.

Imagine you’re trying to measure a potential productivity improvement. You might be given an estimate that a process or technology could improve productivity by 5 to 40 percent for a set of employees. “5 to 40 percent” – that’s quite a wide range! Technically speaking, the uncertainty stems from approximating several unknown variables. But this is where the magic of decomposition comes in.

Using Bayesian analysis

You might have heard of Bayesian statistics. It’s a branch of statistics based on Bayes’ theorem, named after Thomas Bayes, an 18th-century English statistician and minister. This theorem provides a mathematical framework for updating probabilities based on new data. At the heart of Bayesian statistics is the concept of a probability distribution for a parameter of interest. For example, if you’re trying to estimate the average height of men in a certain city, a Bayesian approach would not just give you a single best guess but rather a distribution of plausible values, each assigned a probability.

Now, in the uncertain world of business, the ability to adjust your strategies based on new information is a vital skill. This is where Bayesian analysis comes in. It embodies this principle of adaptability and continuous learning, refining its probability estimates as fresh data enters the picture.

Understanding choices and preferences

Surveys are also a powerful tool for gathering data about people’s preferences. There are several ways of designing surveys. One example is the Likert scale, where respondents say how they feel about something on a scale from “strongly disagree or dislike” to “strongly agree or like.” Another example is an ordinal survey, where respondents rank several things on a scale –⁠ say, from 1, least preferred, to 8, most preferred.

In what scenarios might quantifying sentiments or subjective assessments come into play? Consider survey respondents stating they strongly agree with opinions like “Retail stores tend to put up Christmas decorations too early.” You might determine what percentage of shoppers agree with this sentiment and how this perception might impact your business decisions.

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Worum geht es in How to Measure Anything?

How to Measure Anything (2007) challenges the notion that certain things can’t be measured, arguing instead that with the right tools and perspectives, everything is quantifiable. It provides insightful methodologies and real-world examples to guide readers on how to turn seemingly immeasurable concepts into tangible data, ultimately helping to make more informed decisions.

Wer How to Measure Anything lesen sollte

Business executives and entrepreneurs looking to better quantify their company’s intangibles
Students studying business or economics
Anyone searching for insights on how to enhance their risk assessment techniques

Über den Autor

Douglas W. Hubbard is an acclaimed innovator, recognized globally for developing the Applied Information Economics (AIE) method and founding Hubbard Decision Research. His AIE method has been instrumental in analyzing risks and returns of critical projects across industries, from Fortune 500 IT investments to federal and state government operations. He’s also the author of The Failure of Risk Management and Pulse.

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