Book SummaryHow to Measure Anything, by Douglas W. Hubbard

1-Page Summary1-Page Book Summary of How to Measure Anything

In How to Measure Anything (2010), management consultant Douglas W. Hubbard challenges conventional notions about measurement and provides practical insights on making informed decisions based on measurable data. An expert in the field of applied information economics, measurement, and decision analysis, Hubbard’s work focuses on helping organizations make better decisions by quantifying uncertainty and measuring things that many believe are unmeasurable.

In fact, Hubbard argues that **we can measure almost anything, and that...

How to Measure Anything Summary Part 1: Understand Why You’re Measuring

Hubbard writes that if something can be observed, it can be measured and quantified. By “quantified,” he means that it can be represented numerically—by assigning specific values, ranges, or probabilities to observable phenomena rather than relying on vague descriptors like “high risk” or “good quality.” This is crucial because the more we measure, the better our decisions and predictions in comparison with reliance on intuition.

While Hubbard’s principles of measurement apply across all fields—from scientific research to personal decisions—his book is primarily aimed at helping business professionals improve their measurement and decision-making processes. He notes that even modest reductions in uncertainty can translate into significant competitive advantages and financial gains, whether you’re gauging customer satisfaction, quantifying market risks, or estimating return on investment.

In this section, we’ll explain the purpose of measurement—to reduce uncertainty. We’ll also look at how measurement reduces uncertainty by defining something’s true value in probabilistic terms.

Balancing Data With Intuition

Although Hubbard writes that a measurement-based...

How to Measure Anything Summary Part 2: Know What to Measure

Now that we understand the purpose of measurement, we can look at what Hubbard says about deciding what to measure. This means defining the scope of your measurement—what will and won’t be included. He emphasizes that defining your scope properly is critical to measurement success. If you define your scope too broadly, you run the risk of collecting excessive data that doesn’t provide meaningful insights. Defining your scope too narrowly means you might miss important factors and fail to address the problem you’re trying to solve.

For example, let’s say a company wants to measure customer satisfaction to reduce customer loss. If they define their scope too broadly, they’ll survey customers about everything—product features, packaging, social media presence, company values, and more. This much data won’t clearly indicate what drives customers to leave. On the other hand, if they define their scope too narrowly, such as by using only product ratings, they might miss that customers are departing due to factors like poor customer service. The right scope focuses on the most relevant customer touchpoints: support interactions, billing processes, and core product...

How to Measure Anything Summary Part 3: Perform Your Measurement

In the first section, we explored why we measure. We followed that up by looking at what to measure and the purpose of measurement. In this section, we’ll conclude by examining some of the ways Hubbard says that we can perform measurements. We’ll cover techniques and tools like random sampling, controlled experiments, and Monte Carlo simulations.

Measurement Technique #1: Random Sampling

Hubbard writes that you can use random sampling to significantly reduce uncertainty. This is a method where you examine a small, randomly selected portion of something larger to learn about the whole thing. It’s particularly useful for measuring ongoing behaviors, activities, or characteristics across large groups—like determining what percentage of work time employees spend on different tasks, how often customers experience certain problems, or what proportion of your inventory has quality issues.

For the sample to be truly random—and therefore representative of the whole—every item in the sample has to have an equal probability of being selected. This is because if certain items have higher selection probabilities than others, those items will be overrepresented in your sample,...

Shortform Exercise: Strengthen Your Measurement Toolkit

Consider how Hubbard’s measurement principles might transform your decision-making in your personal and professional life.

Hubbard argues that if something can be observed, it can be measured. Think of something in your work or personal life that you’ve always considered “unmeasurable.” How might you break it down into observable, quantifiable components using Hubbard’s decomposition approach?