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Alan Klement
I help businesses become great at making and selling products that people will buy.

Be suspicious of any method that claims to count (quantify)

Innovation is wrought with uncertainty. As a result, anything that promises certainty is attractive to designers and innovators. One such promise is that customers’ preferences and desired outcomes can be quantified in a reliable and valid way. Can it be done? The answer from statistical theory and psychology is clear: no .

This article will equip you with knowledge and understanding so you don’t fall into the trap of fooling yourself into quantifying what customers want.

Few have changed the world more than Dr. Deming. His knowledge of data and statistical theory helped transform the United States from agricultural backwater into the world’s breadbasket. He was pivotal in reshaping Japan’s economy from post WW2 disaster into to an economic powerhouse. Quantitative data based methodologies much as TQM, QFD, Six Sigma, and Lean can all be traced back to him.

Yet, what made Dr. Deming both a master and pioneer of statistical theory wasn’t only his proficiency in measuring things, it was also his understanding of what and be measured. For example, do you think it’s possible to quantify how much you love someone? Or rank the importance of each of your friends?

Unfortunately, few people heed lessons from those like Deming. As a result, we’ve picked up a bad habit as we’ve journeyed from the industrial age through the information ageAs a result, many suggest that “what customers want” can and should be measured.

However, people like Dr. Deming and anyone else with a basic understanding of statistical theory and psychology knows that any measure of customer preference (desired outcomes) will always be invalid and unreliable. You simply cannot count (i.e. quantify) what’s inside of someone’s head. Those who claim otherwise either don’t know any better, or are trying to sell you something.

This article covers the three biggest reasons why what customers want cannot be measured:

Equipped with this understanding, you will not only become a better innovator, designer and entrepreneur, you will also increase the likelihood of innovation success.

Putting a number on something, doesn’t make it quantitative

We see them all the time. In fact, just the other day I saw this while passing through Helsinki airport (figure 1).

Surveys such as these and the use of Likert scales form the basis for various innovation and customer satisfaction methodologies. However, there’s something to be known about such surveys and the data they collect. Something those well versed in statistical and number theory know that others don’t:

It’s the same thing with how we determine the winners of a race. We attach categorical and ordinal (ordered) descriptions to the people who cross the finish line (figure 2). If no one came before me, I came in 1st place. If two people came before me, I came in 3rd place. The fact that these are categorical data is why it’s entirely appropriate to exchange “1” with “Gold” and “3” with “Bronze”.

The other point to be aware of with categorical and ordinal data is that the distance between them is not part of the data. For example, you may know who came in 1st, 2nd, and 3rd place — but you don’t know the distance in-between them (figure 3). The person who got 1st place may have finished the race in 60 min, while the 2nd and 3rd place winners placed at 90 and 91 min respectively.

The fact that these numbers represent neither quantitative measures nor denote any distance between them, is why some surveys skip numbers all together. Instead they use facial expressions as categories (figure 4). This approach is closer to how we actually think about the products we use.

Now, there’s nothing wrong with using numbers to describe customer preference. It’s just necessary to know that these data are qualitative, not quantitative. They are not values themselves, they are descriptions of value. However, too many people either forget this or don’t know. So they end up doing things with them that they shouldn’t.

A perfect example of doing statistics wrong comes from a method developed by Anthony Ulwick of Strategyn called Outcome Driven Innovation. Part of this method includes an “Opportunity Algorithm”.

One big error this formula commits is to subtract between two different categories of data. This cannot be done. It’s equivalent to this:

In the Journal of Product Innovation Management , Jeffery Pinegar commented on this formula by saying:

In the paper A Critique of Outcome-Driven Innovation , author Gerry Katz sums it up nicely with a quote from MIT professor John Hauser:

This mistake is also echoed by author, Sarah Boslaugh:

In other words, because there’s no way to create objective, countable units from attitudes and opinions, you cannot subtract or add them. It’s like thinking you can do this:

Measures of customer’s desired outcomes (preferences) change continually and are easily manipulated

Some people may insist still that quantitative measures can be attached to customer’s desired outcomes. Or that since these data are categorical and ordinal, you can at least find the median and mode of a data set. However, even if you go down this route you still must contend with issue #2:

A good researcher and statistician knows that it’s not enough to just take a measurement of something. You must understand the system that is generating those data. How you collect data from a system is dependent on the type of system it is. For example:

The people in these environments understand that taking a snapshot of their data won’t work. Why? Because the things they are measuring are always in flux and are susceptible to outside influences.

This is true of customer’s desired outcomes and preferences. Any metrics associated with either are always in flux and susceptible to outside influences.

For example, you’re at a restaurant with some friends. The waitress comes over to you. “I’ll take the steak with mashed potatoes” you say. Then your friend orders, “I’ll have the steak with grilled vegetables”. Upon hearing your friend, you decide to change your order. “Actually, instead of the mashed potatoes I’ll have the grilled vegetables as well.” Even though you knew grilled vegetables were on the menu, you first chose mashed potatoes. But for some reason, after hearing your friend order vegetables, you switched.

This happens all the time. So often in fact, behavioral economists call it a .

Another example is grocery shopping. Many studies have proven time and again that people who shop while hungry make poor shopping decisions and almost always buy food they don’t need or want. Such phenomena are called and . This happens because what we want today is highly influenced by what we feel at the moment. This makes predicting what we will want in the future extremely unreliable.

A recurring example of this in action is the outrage customers have against Apple from time to time. When Apple removed the floppy drive from their PCs, people called the company crazy. The same is true for removing optical drives — and more recently — the removal of the headphone jack from their iPhone. Customers are immediately indignant about the change, but over time they forget about it and even begin to appreciate the new way.

The fact that we are so terrible at predicting what we will like was outlined in Kahneman’s and Snell’s article Their conclusion was simply:

Moreover, measures of customer preference can vary depending on what options you provide them. This is well known among those involved in pricing products. Rarely do you see just one or two price options presented at once. Often there are low, middle, and high priced options presented together. The idea is that you make the middle-priced option more attractive simply by adding next to it a high priced option. This well documented phenomenon is called .

And if that wasn’t tricky enough, data gathered about “what customers want” can change during the survey. As Norbert Schwarz points out in his paper :

This happens because people don’t have a firm opinion on what they do and don’t like. Schwarz continues:

And even if you could tap into a customer previously formed judgment, it would be unreliable. As Nobel Prize winner Daniel Kahneman has pointed out:

This begs a question: when filling out a survey or responding in a interview, is the person retrieving a previously formed option about the outcome you’re asking about (remembered utility), or are they forming a judgment on the spot (moment utility)?

What does all this mean? Well, consumer preference — including the importance and satisfaction of desired outcomes — is always changing and highly malleable. This makes it difficult, and perhaps impossible, to measure them reliably.

Value is non-linear

OK. Suppose you do choose to believe that you can attach a quantitative number to preference. And you believe such a measure would be reliable. You’d still have one hurdle to clear:

For hundreds of years, value was believed to be linear. It makes sense to do because it makes the math way easier. An example of linear thinking would be to think that if I double my wealth, I double my happiness. But as we all know, that isn’t even close to being true. Why? Because value is distinctly non-linear. Moreover, gains are calculated differently than losses.

Figure 5 shows value as linear and nonlinear. The right image shows a model known as . It’s a combination of phenomena such as , , and . This means that to humans, value is non-linear.

This article won’t go into all the reasons why this is true. There are countless books and academic articles written about this topic. What will be pointed out is that anyone who tries to quantify customer’s desired outcomes must account for this phenomena.

I’ll illustrate what’s going on here with an example. Suppose I hand you a ruler where each value between each number is different (figure 7).

My question is this: how useful is this ruler when measuring something?

When you use something like a Likert scale to measure customer’s preferences, you’re using this unreliable ruler– whether you know it or not. In the customer’s mind, the distance between 1 and 2 is different than between 4 and 5. A visual representation of a Likert scale within the context of measuring customer preference is depicted in figure 8.

What does this mean? Even if you could assign a quantitative value to customer preference, and if those values didn’t change, you’d still have to account for where on the value spectrum the measurement falls, and then compensate for that.

Discussion and conclusion

It would be amazing if we could quantify customer’s desired outcomes reliably. Design would be simple. We’d just send off a survey to prospective customers, get back the results, and then build what they want. However, this isn’t even remotely the case.

The realization of such facts are why, for example, Facebook offers simple options for rating preference. Facebook offers a “Like” button as well as a collection of faces to express preference (figure 9).

Google and the YouTube also realized the futility of quantifying preference several years ago:

As a result, YouTube moved from offering a 5-star rating system, to a thumbs up / down model. The data told them that people didn’t logically rate their preference for a video. Instead they just rounded it up to a 5, labeled it a 1, or didn’t even care enough to vote (figure 10).

Lastly, Netflix has just killed off its 5-star rating system, also in favor of a thumbs up/down model. Netflix vice president of product Todd Yellin commented:

So, what should we do about quantifying what customers want? The answer is simple: don’t even try. Customers are humans, not robots. You can’t take measurements from them and build a product as if you were building kitchen cabinets.

What you can do, and what I have done for my own products, is to model customer behavior using qualitative data, and then use quantitative data to verify and adjust those models. And if you do use a self-report rating system, do what Google and Facebook did: either offer a up/down option or offer responses as emotions.

Learn more

In October 2016, I released the first book dedicated to Customer Jobs theory. Get a deeper understanding of what a customer Job to be Done is from my book

You can download it as a free PDF, or buy it in paperback kindle right here . You can also read it online .

If you have more questions about Jobs to be Done, or want help applying JTBD concepts to your business or startup, contact me .

[1] The phrase “desired outcome” is used to mean many different things ( example , example ). This article uses it to mean the result of an action, activity, or event. Similar to how the seminal paper described the desirably, or preference, for one type of bet over another.

Updated December 112017


A Critique of Outcome-Driven Innovation by Gerry Katz, Executive Vice President, Applied Marketing Science, Inc.

Pinegar, J. S. (2006). What Customers Want: Using Outcome‐Driven Innovation to Create Breakthrough Products and Services by Anthony W. Ulwick. , (5), 464–466.

Deming’s quotes are from his book, The New Economics for Industry, Government, Education, Second Edition.

Info on Tony Ulwick’s Outcome Driven Innovation and the Opportunity Algorithm can be found from his book,

Ulwick’s claim that importance and satisfaction are dimensionless quantities can be found .

Find the quote from CEO of Coca-Cola in the article , .

Learn about Netflix’s switch away from a Likert-scale .

Learn about Facebook’s reactions .

YouTube’s reason for switching away from Likert scales is found .

I. Elaine Allen and Christopher A. Seaman wrote a simple overview of Likert scales and what they can, and can’t do, can be found .

More good info on what Likert scales are, and are not, is found and .

Read Jeff Bezos’ shareholder letter .

Boslaugh, S. (2012). . “ O’Reilly Media, Inc.”

Hsee, Christopher K., and Yuval Rottenstreich. “Music, pandas, and muggers: on the affective psychology of value.” Journal of Experimental Psychology: General 133.1 (2004): 23.

Jamieson, Susan. “Likert scales: how to (ab) use them.” Medical education 38.12 (2004): 1217–1218.

Kahneman, Daniel, and Jackie Snell. “Predicting a changing taste: Do people know what they will like?.” Journal of Behavioral Decision Making 5.3 (1992): 187–200.

Kahneman, Daniel, and Richard H. Thaler. “Anomalies: Utility maximization and experienced utility.” The Journal of Economic Perspectives 20.1 (2006): 221–234.

Lichtenstein, S., Slovic, P. (1971). Reversals of preference between bids and choices in gambling decisions. , (1), 46.

Loewenstein, George. “Hot-cold empathy gaps and medical decision making.” Health Psychology 24.4S (2005): S49.

Loewenstein, George, Ted O’Donoghue, and Matthew Rabin. “Projection bias in predicting future utility.” The Quarterly Journal of Economics 118.4 (2003): 1209–1248.

Riquelme, Hernan. “Do consumers know what they want?.” Journal of consumer marketing 18.5 (2001): 437–448.

Schwarz, Norbert. “Cognitive aspects of survey methodology.” (2007).

Stevens, Stanley Smith. “On the theory of scales of measurement.” (1946): 677–680.

Tversky, Amos. “Intransitivity of Preferences.” Preference, Belief, and Similarity (1969): 433.

Tversky, Amos, Paul Slovic, and Daniel Kahneman. “The causes of preference reversal.” The American Economic Review (1990): 204–217.

Tversky, Amos, and Itamar Simonson. “Context-dependent preferences.” Management science 39.10 (1993): 1179–1189.

Tversky, Amos, and Daniel Kahneman. “Advances in prospect theory: Cumulative representation of uncertainty.” Journal of Risk and uncertainty 5.4 (1992): 297–323.


But this earnings season Wall Street is on guard for profit warnings and words of caution linked to Trump's tariffs.

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For its part, Wall Street has learned to tune out these presidential attacks as mostly noise. Pfizer shares closed slightly higher on Monday.

"Almost on a daily basis," Yardeni said, "Trump generates news that's both bullish and bearish."

CNNMoney (New York) First published July 10, 2018: 12:58 PM ET
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Tracy Moore

How exactly does chemical castration work, anyway? And knowing Jackson’s unusual childhood, is it actually possible?

Don’t be fooled by the “new report” that Michael Jackson’s father, Joe, had his preteen son chemically castrated to preserve that angelic high tenor , often described as “childlike” and impressive for Staud Mini pink Moreau Bucket Bag Manchester Cheap Price HuFb2ttCZU
. This conspiracy theory has been bouncing around for several years now, and it’s taken more twists and turns than the origins of Jacko’s posthumous albums . Now, nine years after MJ’s death, that pesky rumor just won’t, uh, beat it.

But how exactly does chemical castration work, anyway? And knowing Jackson’s unusual childhood, is it actually possible?

Here’s a brief rundown of the theory’s many appearances over the years — and whether there’s any truth to it at all.

A warning to the truthers: Don’t get your hopes up.

Why is this in the news rightnow?

New video obtained by a site called The Blast shows MJ’s doctor, cardiologist Conrad Murray (yes, the guy who spent two years in prison for the fatal dose of propofol that killed Jackson), reiterating that MJ was “chemically castrated to maintain his high-pitched voice” by father Joe, as part of Joe’s longstanding abuse of his children.

Is there any new information?

No. In 2016, Murray noted the same thing in his book, This Is It! The Secret Lives of Dr. Conrad Murray and Michael Jackson , wherein Outlet Footlocker Pictures R13 oversized stripe mechanic jumpsuit Clearance Professional DeDdnL
that Joe Jackson had forced MJ to get hormone injections at age 12, using a chemical castration drug. The alleged injections were supposedly for acne — and to prevent Michael’s voice from changing during puberty.

What is chemical castration?

Castration generally refers to the surgical removal of the testicles, also called orchidectomy. As a result, 95 percent of testosterone production halts, according to Slate . Chemical castration on the other hand lets you keep your balls, and instead just plays tricks on them with hormones that confuse or alter testosterone production. Slate explains that giving a man progesterone (a female hormone), for instance, will counteract testosterone’s effect in his bloodstream. Other drugs (Lupron or Zolodex) are also anti-male hormones that tell the pituitary gland to stop making testosterone altogether.

"My goal is simple,” Hawking once said. “It is a complete understanding of the universe, why it is as it is and why it exists at all."

His 1988 book "A Brief History of Time" became an international bestseller and brought him widespread fame.

Hawking was also recognized for his successful research on black holes. He was able to prove that small amounts of radiation could escape black hole gravitational pull. His work led the discovery to become known as Hawking radiation.


A sign of his great popularity came in October 2017, when Cambridge put Hawking's 1966 thesis on the internet for the first time. Demand for the thesis was so high that it caused the university's website Tory Burch Fleming Convertible shoulder bag Sale Huge Surprise VsP6nrGcO


Hawking said belief in a God who intervenes in the universe “to make sure the good guys win or get rewarded in the next life” was wishful thinking. “But one can’t help asking the question: Why does the universe exist?” he said in 1991. “I don’t know an operational way to give the question or the answer, if there is one, a meaning. But it bothers me.”


Hawking was a big supporter of human space travel to the Moon and Mars. He said such missions would help unite humanity in a shared purpose of spreading the human race beyond Earth.

"We are running out of space and the only places to go to are other worlds. It is time to explore other solar systems . Spreading out may be the only thing that saves us from ourselves. I am convinced that humans need to leave the Earth," he said last year.


I’m Bryan Lynn.

Bryan Lynn wrote this story for VOA Learning English, based on reports from VOA News, the Associated Press and Reuters. Mario Ritter was the editor.

We want to hear from you. Write to us in the Comments section, and visit our Facebook page .

_____ __________________________________________________________

__________________________________________________________ Words in This Story

synthesizer n. electronic machine that produces and controls sound and can be used for reproducing speech

potential n. the unrealized possibility of doing something or reaching some goal

particles n. very small pieces of something

radiation n. type of dangerous and powerful energy produced by radioactive substances

thesis n. a long piece of writing completed as part of an advanced university course

bother v. cause (someone) to feel troubled, worried, or concerned



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