Companies must innovate to survive and grow. Yet the innovation process continues to be the achilles heel of companies worldwide. Our discoveries over the past 21 years have led to a very different and a very effective way for companies to innovate and grow. Using, this process, Strategyn has helped over a third of Fortune companies, and hundreds other firms worldwide, create new, breakthrough products and generate new growth. We are confident it will work for you too.

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Innovation is wrought with uncertainty. As a result, anything that promises certainty is attractive to designers and innovators. Can it be done? The answer from statistical theory and psychology is clear: no. The most important figures that one needs for management are unknown or unknowable, but successful management must nevertheless take account of them. Few have changed the world more than Dr. Yet, what made Dr. Or rank the importance of each of your friends? Not everything that can be counted counts, and not everything that counts can be counted.

Unfortunately, few people heed lessons from those like Deming. 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. 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.

The simple fact is that all of the time and money and skill poured into consumer research on a new Coca-Cola could not measure or reveal the depth and abiding emotional attachment to original Coca-Cola felt by so many people.

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.

Those numbers are not quantitative values. Rather, they are categorical descriptions of quality. 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 other point to be aware of with categorical and ordinal data is that the distance between them is not part of the data.

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.

They are not values themselves, they are descriptions of value. A perfect example of doing statistics wrong comes from a method developed by Anthony Ulwick of Strategyn called Outcome Driven Innovation.

One big error this formula commits is to subtract between two different categories of data. This cannot be done. Technically, there are two problems with this formula. First, satisfaction is subtracted from importance; this is like subtracting apples from broccoli.

It mixes units of measure. No self-respecting engineer would ever do such a thing. This mistake is also echoed by Statistics in a Nutshell author, Sarah Boslaugh:. Problem What is the argument against analyzing Likert and similar attitude scales as interval data? Solution There is no natural metric for constructs such as attitudes and opinions. We can devise scales that are ordinal the responses can be ranked in order of strength of agreement, for instance to measure such constructs, but it is impossible to determine whether the intervals among points on such scales are equally spaced.

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 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:. Because the things they are measuring are always in flux and are susceptible to outside influences. Any metrics associated with either are always in flux and susceptible to outside influences.

The waitress comes over to you. Upon hearing your friend, you decide to change your order. 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 Preference Reversal. Another example is grocery shopping. 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.

Their conclusion was simply:. People are just not good at guessing how their tastes in particular will change over a period of time. 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 Context-Dependent Preferences. Since the early days of opinion polls Cantril, ; Payne, , survey researchers observed that minor variations in question wording, format and order can profoundly affect the obtained answers.

Schwarz continues:. Respondents first need to interpret the question to understand what is meant and to determine which information they ought to provide.

If the question is an attitude question, they may either retrieve a previously formed attitude judgment from memory, or they may form a judgment on the spot, based on whatever relevant information is accessible at that point in time.

While survey researchers have typically hoped for the former, the latter is far more likely. 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:. 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.

Suppose you do choose to believe that you can attach a quantitative number to preference. And you believe such a measure would be reliable. Value is non-linear. 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.

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 Cumulative Prospect Theory. This means that to humans, value is non-linear. There are countless books and academic articles written about this topic. 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? A visual representation of a Likert scale within the context of measuring customer preference is depicted in figure 8.

What does this mean? Design would be simple. The realization of such facts are why, for example, Facebook offers simple options for rating preference. Google and the YouTube also realized the futility of quantifying preference several years ago:.

Great videos prompt action; anything less prompts indifference. Netflix vice president of product Todd Yellin commented:.


What Customers Want Quotes

Reading any book is incredibly contingent upon what the reader brings to the table no secret there. What is a moment of serendipity for me may be another part of the plodding you had to accomplish What Customers Want. Based on more than studies spanning more than seventy companies and twenty-five industries, Ulwick contends that, when it comes to innovation, the traditional methods companies use to communicate with customers are the root cause of chronic waste and missed opportunity. In What Customers Want, Ulwick demonstrates that all popular qualitative research methods yield well-intentioned but unfitting and dreadfully misleading information that serves to derail the innovation process.


What Customers Want

A world-renowned innovation guru explains practices that result in breakthrough innovations. For years, companies have accepted the underlying principles that define the customer-driven paradigm--that is, using customer "requirements" to guide growth and innovation. But twenty years into this movement, breakthrough innovations are still rare, and most companies find that 50 to 90 percent of their innovation initiatives flop. The cost of these failures to U. In a book that challenges everything you have learned about being customer driven, internationally acclaimed innovation leader Anthony Ulwick reveals the secret weapon behind some of the most successful companies of recent years. Known as "outcome-driven" innovation, this revolutionary approach to new product and service creation transforms innovation from a nebulous art into a rigorous science from which randomness and uncertainty are eliminated.

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The Illusion of Measuring What Customers Want

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This means that we work with our clients to:. This is recognised by Product Management and Innovation experts globally as best practice for creating breakthrough products. However, one of the challenges of this approach is a lack of repeatable structure around how these customer problems are stated and subsequently tested. This can lead to ambiguous market requirements that can reduce the chances of product success in the market place. There are 3 key components that make up this methodology: 1 Customers buy products and services to help them get jobs done 2 Customers use a set of metrics to judge how well a job is getting done and how a product performs 3 The customer metrics make possible the systematic and predictable creation of breakthrough products and services.

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