Tag Archives: communication

Imperfect Action is Better Than Perfect Inaction: What Harry Truman Can Teach Us About Loss Functions (with an intro to ggplot)

One of the heuristics we use at Intelex to guide decision making is former US President Truman’s advice that “imperfect action is better than perfect inaction.” What it means is — don’t wait too long to take action, because you don’t want to miss opportunities. Good advice, right?

When I share this with colleagues, I often hear a response like: “that’s dangerous!” To which my answer is “well sure, sometimes, but it can be really valuable depending on how you apply it!” The trick is: knowing how and when.

Here’s how it can be dangerous. For example, statistical process control (SPC) exists to keep us from tampering with processes — from taking imperfect action based on random variation, which will not only get us nowhere, but can exacerbate the problem we were trying to solve. The secret is to apply Truman’s heuristic based on an understanding of exactly how imperfect is OK with your organization, based on your risk appetite. And this is where loss functions can help.

Along the way, we’ll demonstrate how to do a few important things related to plotting with the ggplot package in R, gradually adding in new elements to the plot so you can see how it’s layered, including:

  • Plot a function based on its equation
  • Add text annotations to specific locations on a ggplot
  • Draw horizontal and vertical lines on a ggplot
  • Draw arrows on a ggplot
  • Add extra dots to a ggplot
  • Eliminate axis text and axis tick marks

What is a Loss Function?

A loss function quantifies how unhappy you’ll be based on the accuracy or effectiveness of a prediction or decision. In the simplest case, you control one variable (x) which leads to some cost or loss (y). For the case we’ll examine in this post, the variables are:

  • How much time and effort you put in to scoping and characterizing the problem (x); we assume that time+effort invested leads to real understanding
  • How much it will cost you (y); can be expressed in terms of direct costs (e.g. capex + opex) as well as opportunity costs or intangible costs (e.g. damage to reputation)

Here is an example of what this might look like, if you have a situation where overestimating (putting in too much x) OR underestimating (putting in too little x) are both equally bad. In this case, x=10 is the best (least costly) decision or prediction:

plot of a typical squared loss function
# describe the equation we want to plot
parabola <- function(x) ((x-10)^2)+10  

# initialize ggplot with a dummy dataset
library(ggplot)
p <- ggplot(data = data.frame(x=0), mapping = aes(x=x)) 

p + stat_function(fun=parabola) + xlim(-2,23) + ylim(-2,100) +
     xlab("x = the variable you can control") + 
     ylab("y = cost of loss ($$)")

In regression (and other techniques where you’re trying to build a model to predict a quantitative dependent variable), mean square error is a squared loss function that helps you quantify error. It captures two facts: the farther away you are from the correct answer the worse the error is — and both overestimating and underestimating is bad (which is why you square the values). Across this and related techniques, the loss function captures these characteristics:

From http://www.cs.cornell.edu/courses/cs4780/2015fa/web/lecturenotes/lecturenote10.html

Not all loss functions have that general shape. For classification, for example, the 0-1 loss function tells the story that if you get a classification wrong (x < 0) you incur all the penalty or loss (y=1), whereas if you get it right (x > 0) there is no penalty or loss (y=0):

# set up data frame of red points
d.step <- data.frame(x=c(-3,0,0,3), y=c(1,1,0,0))

# note that the loss function really extends to x=-Inf and x=+Inf
ggplot(d.step) + geom_step(mapping=aes(x=x, y=y), direction="hv") +
     geom_point(mapping=aes(x=x, y=y), color="red") + 
     xlab("y* f(x)") + ylab("Loss (Cost)") +  
     ggtitle("0-1 Loss Function for Classification")

Use the Loss Function to Make Strategic Decisions

So let’s get back to Truman’s advice. Ideally, we want to choose the x (the amount of time and effort to invest into project planning) that results in the lowest possible cost or loss. That’s the green point at the nadir of the parabola:

p + stat_function(fun=parabola) + xlim(-2,23) + ylim(-2,100) + 
     xlab("Time Spent and Information Gained (e.g. person-weeks)") + ylab("$$ COST $$") +
     annotate(geom="text", x=10, y=5, label="Some Effort, Lowest Cost!!", color="darkgreen") +
     geom_point(aes(x=10, y=10), colour="darkgreen")

Costs get higher as we move up the x-axis:

p + stat_function(fun=parabola) + xlim(-2,23) + ylim(-2,100) + 
     xlab("Time Spent and Information Gained (e.g. person-weeks)") + ylab("$$ COST $$") +
     annotate(geom="text", x=10, y=5, label="Some Effort, Lowest Cost!!", color="darkgreen") +
     geom_point(aes(x=10, y=10), colour="darkgreen") +
     annotate(geom="text", x=0, y=100, label="$$$$$", color="green") +
     annotate(geom="text", x=0, y=75, label="$$$$", color="green") +
     annotate(geom="text", x=0, y=50, label="$$$", color="green") +
     annotate(geom="text", x=0, y=25, label="$$", color="green") +
     annotate(geom="text", x=0, y=0, label="$ 0", color="green")

And time+effort grows as we move along the x-axis (we might spend minutes on a problem at the left of the plot, or weeks to years by the time we get to the right hand side):

p + stat_function(fun=parabola) + xlim(-2,23) + ylim(-2,100) + 
     xlab("Time Spent and Information Gained (e.g. person-weeks)") + ylab("$$ COST $$") +
     annotate(geom="text", x=10, y=5, label="Some Effort, Lowest Cost!!", color="darkgreen") +
     geom_point(aes(x=10, y=10), colour="darkgreen") +
     annotate(geom="text", x=0, y=100, label="$$$$$", color="green") +
     annotate(geom="text", x=0, y=75, label="$$$$", color="green") +
     annotate(geom="text", x=0, y=50, label="$$$", color="green") +
     annotate(geom="text", x=0, y=25, label="$$", color="green") +
     annotate(geom="text", x=0, y=0, label="$ 0", color="green") +
     annotate(geom="text", x=2, y=0, label="minutes\nof effort", size=3) +
     annotate(geom="text", x=20, y=0, label="months\nof effort", size=3)

Planning too Little = Planning too Much = Costly

What this means is — if we don’t plan, or we plan just a little bit, we incur high costs. We might make the wrong decision! Or miss critical opportunities! But if we plan too much — we’re going to spend too much time, money, and/or effort compared to the benefit of the solution we provide.


p + stat_function(fun=parabola) + xlim(-2,23) + ylim(-2,100) + 
     xlab("Time Spent and Information Gained (e.g. person-weeks)") + ylab("$$ COST $$") +
     annotate(geom="text", x=10, y=5, label="Some Effort, Lowest Cost!!", color="darkgreen") +
     geom_point(aes(x=10, y=10), colour="darkgreen") +
     annotate(geom="text", x=0, y=100, label="$$$$$", color="green") +
     annotate(geom="text", x=0, y=75, label="$$$$", color="green") +
     annotate(geom="text", x=0, y=50, label="$$$", color="green") +
     annotate(geom="text", x=0, y=25, label="$$", color="green") +
     annotate(geom="text", x=0, y=0, label="$ 0", color="green") +
     annotate(geom="text", x=2, y=0, label="minutes\nof effort", size=3) +
     annotate(geom="text", x=20, y=0, label="months\nof effort", size=3) +
     annotate(geom="text",x=3, y=85, label="Little (or no) Planning\nHIGH COST", color="red") +
     annotate(geom="text", x=18, y=85, label="Paralysis by Planning\nHIGH COST", color="red") +
     geom_vline(xintercept=0, linetype="dotted") + geom_hline(yintercept=0, linetype="dotted")

The trick is to FIND THAT CRITICAL LEVEL OF TIME and EFFORT invested to gain information and understanding about your problem… and then if you’re going to err, make sure you err towards the left — if you’re going to make a mistake, make the mistake that costs less and takes less time to make:

arrow.x <- c(10, 10, 10, 10)
arrow.y <- c(35, 50, 65, 80)
arrow.x.end <- c(6, 6, 6, 6)
arrow.y.end <- arrow.y
d <- data.frame(arrow.x, arrow.y, arrow.x.end, arrow.y.end)

p + stat_function(fun=parabola) + xlim(-2,23) + ylim(-2,100) + 
     xlab("Time Spent and Information Gained (e.g. person-weeks)") + ylab("$$ COST $$") +
     annotate(geom="text", x=10, y=5, label="Some Effort, Lowest Cost!!", color="darkgreen") +
     geom_point(aes(x=10, y=10), colour="darkgreen") +
     annotate(geom="text", x=0, y=100, label="$$$$$", color="green") +
     annotate(geom="text", x=0, y=75, label="$$$$", color="green") +
     annotate(geom="text", x=0, y=50, label="$$$", color="green") +
     annotate(geom="text", x=0, y=25, label="$$", color="green") +
     annotate(geom="text", x=0, y=0, label="$ 0", color="green") +
     annotate(geom="text", x=2, y=0, label="minutes\nof effort", size=3) +
     annotate(geom="text", x=20, y=0, label="months\nof effort", size=3) +
     annotate(geom="text",x=3, y=85, label="Little (or no) Planning\nHIGH COST", color="red") +
     annotate(geom="text", x=18, y=85, label="Paralysis by Planning\nHIGH COST", color="red") +
     geom_vline(xintercept=0, linetype="dotted") + 
     geom_hline(yintercept=0, linetype="dotted") +
     geom_vline(xintercept=10) +
     geom_segment(data=d, mapping=aes(x=arrow.x, y=arrow.y, xend=arrow.x.end, yend=arrow.y.end),
     arrow=arrow(), color="blue", size=2) +
     annotate(geom="text", x=8, y=95, size=2.3, color="blue",
     label="we prefer to be\non this side of the\nloss function")

Moral of the Story

The moral of the story is… imperfect action can be expensive, but perfect action is ALWAYS expensive. Spend less to make mistakes and learn from them, if you can! This is one of the value drivers for agile methodologies… agile practices can help improve communication and coordination so that the loss function is minimized.

## FULL CODE FOR THE COMPLETELY ANNOTATED CHART ##
# If you change the equation for the parabola, annotations may shift and be in the wrong place.
parabola <- function(x) ((x-10)^2)+10

my.title <- expression(paste("Imperfect Action Can Be Expensive. But Perfect Action is ", italic("Always"), " Expensive."))

arrow.x <- c(10, 10, 10, 10)
arrow.y <- c(35, 50, 65, 80)
arrow.x.end <- c(6, 6, 6, 6)
arrow.y.end <- arrow.y
d <- data.frame(arrow.x, arrow.y, arrow.x.end, arrow.y.end)

p + stat_function(fun=parabola) + xlim(-2,23) + ylim(-2,100) + 
     xlab("Time Spent and Information Gained (e.g. person-weeks)") + ylab("$$ COST $$") +
     annotate(geom="text", x=10, y=5, label="Some Effort, Lowest Cost!!", color="darkgreen") +
     geom_point(aes(x=10, y=10), colour="darkgreen") +
     annotate(geom="text", x=0, y=100, label="$$$$$", color="green") +
     annotate(geom="text", x=0, y=75, label="$$$$", color="green") +
     annotate(geom="text", x=0, y=50, label="$$$", color="green") +
     annotate(geom="text", x=0, y=25, label="$$", color="green") +
     annotate(geom="text", x=0, y=0, label="$ 0", color="green") +
     annotate(geom="text", x=2, y=0, label="minutes\nof effort", size=3) +
     annotate(geom="text", x=20, y=0, label="months\nof effort", size=3) +
     annotate(geom="text",x=3, y=85, label="Little (or no) Planning\nHIGH COST", color="red") +
     annotate(geom="text", x=18, y=85, label="Paralysis by Planning\nHIGH COST", color="red") +
     geom_vline(xintercept=0, linetype="dotted") + 
     geom_hline(yintercept=0, linetype="dotted") +
     geom_vline(xintercept=10) +
     geom_segment(data=d, mapping=aes(x=arrow.x, y=arrow.y, xend=arrow.x.end, yend=arrow.y.end),
     arrow=arrow(), color="blue", size=2) +
     annotate(geom="text", x=8, y=95, size=2.3, color="blue",
     label="we prefer to be\non this side of the\nloss function") +
     ggtitle(my.title) +
     theme(axis.text.x=element_blank(), axis.ticks.x=element_blank(),
     axis.text.y=element_blank(), axis.ticks.y=element_blank()) 

Now sometimes you need to make this investment! (Think nuclear power plants, or constructing aircraft carriers or submarines.) Don’t get caught up in getting your planning investment perfectly optimized — but do be aware of the trade-offs, and go into the decision deliberately, based on the risk level (and regulatory nature) of your industry, and your company’s risk appetite.

The Achilles Heel of Customer Journey Mapping

Journeying through western Wyoming in August 2011. Image Credit: me.

Achilles was that guy in Greek mythology whose mother, when he was born, wanted to protect him soooo much that she held him by the heel and dipped him in the power-giving waters of the River Styx — making him bullet proof (and much more; no bullets then), except at the heel, because for some reason she didn’t think about just dunking him a few inches deeper. Maybe she didn’t want to get her hand wet? Who knows. (In the research literature this is called perverse unintended consequences — it happens in business too. You try to make an improvement or protect against a particular hazard and oops, you made it worse.)

Customer Journey Maps (CJM)

I’ve been reading a lot about the Customer Journey Maps (CJM) technique used in marketing (see Folstad & Kvale (2018) for a fantastic and comprehensive review). It formalizes the very good suggestion that when you’re trying to figure out how to engage with prospects, you should put yourself in their shoes. Empathize with them. Figure out what they need, and when they need it. Then, identify how your company can not only meet them there — but connect with them in a compelling way.

CJM also goes beyond conceptual modeling. For example, Harbich et al (2017) uses Markov models to predict the most likely path and timing of a customer’s journey. Bernard & Andritsos (2018) mine actual customer journeys from sales force automation systems and use them in a Monte Carlo like way to uncover patterns. There’s even a patent on one method for mining journey data.

Benefits of Journey Maps

Annette Franz says that “done right, maps help companies in many ways, including to…

  • Understand experiences.
  • Design [new] experiences.
  • Implement and activate new experiences.
  • Communicate and share experiences.
  • Align the organization… get executive commitment for the customer experience (CX) strategy, get organizational adoption of the customer-centric focus, provide a line of sight to the customer for employees, and help employees understand how they impact the experience.”

But like Achilles, Customer Journey Mapping has a vulnerable spot that can wipe out all its potential benefits. (Fortunately, success lies in the way your organization wields the tool… so there’s a remedy.)

The Achilles Heel of CJM

Here’s the problem: creating a journey map does indeed ensure that you focus on the customer, but does not ensure that you’re focusing on that customer’s experience. Diagnosing Voice of the Customer (VoC) is hard [long explanation; shorter explanation], and there are tons of ways to do it! Through journey mapping, you may accidentally be focusing on your company’s experience of that customer throughout the stages of the journey. 

Diagnosing the Symptoms

How can you tell? Here’s a non-exhaustive list of ways to diagnose the symptoms, based on recent research and observing companies who do this since about 2009 (please add in the comments if you’ve observed any other ones):

  • Do you ever hear “How can we move the customer from [this stage] to [the next stage]?”
  • … or “How do we get more customers to join us [at this stage of the journey]?”
  • … or maybe “How can we get customers to [take this action] [at this stage of the journey]?”
  • Does your customer journey address differences in customer personas, or do you have a one-size-fits-all map? Rosenbaum et al (2016) says “We contend that most customer journey maps are critically flawed. They assume all customers of a particular organization experience the same organizational touchpoints and view these touchpoints as equally important.”
  • Do you systematically gather, analyze, and interpret data about what your current customers are experiencing, or do you just kind of guess or rely on your “experience”? (Hint: subconscious biases are always in play, and you’ll never know they’re there because they are subconscious).
  • Do you systematically gather, analyze, and interpret data about what your prospects would benefit from experiencing with/through you, or do you just kind of guess or rely on your “experience”?
  • Do you focus on ease of use over utility? (Just like perfect is the enemy of perfectly OK, easy can be the enemy of possible if you’re not careful. This often shows up in the journey mapping process.)

Like I mentioned earlier, this is definitely not a comprehensive list.

The Solution

What’s the solution? ASK. Ask your customer what they need. Find out about their pain points. Ask them what would make it easier for them to do their job. Finally, ask them if you’re getting it right! And even though I said “customer” — I do mean you should ask more than one of them, because needs and interests vary from person to person and industry to industry. Just interacting with one customer isn’t going to cut it.

Ask early, ask often! (As people learn and evolve, their needs change.)

Improving the Method

How can we improve the quality of customer journey mapping? Share your insights and lessons learned! CJM is a promising technique for helping organizations align around empathetic value propositions, but just like agile methods, it’s got to be applied strategically and deliberately… and then checked on a continuous basis to make sure the map is in tune with reality.

Yes, You Do Need to Write Down Procedures. Except…

近代工芸の名品― [特集展示] 

A 棗 from http://www.momat.go.jp/cg/exhibition/masterpiece2018/ — I saw this one in person!!

Several weeks ago we went to an art exhibit about “tea caddies” at the Tokyo Museum of Modern Art. Although it might seem silly, these kitchen containers are a fixture of Japanese culture. In Japan, drinking green tea is a cornerstone of daily life.

It was about 2 in the afternoon, and we had checked out of our hotel at 11. Wandering through the center of the city, we stumbled upon the museum. Since we didn’t have to meet our friends for several more hours, we decided to check it out.

Confession: I’m not a huge fan of art museums. Caveat: I usually enjoy them to some degree or another when I end up in them. But I didn’t think tea caddies could possibly be useful to me. I was wrong!

When to Write SOPs

One of the features of the exhibit was a Book of Standard Operating Procedures. It described how to create a new lacquered tea caddy from paper. (Unfortunately, photography was prohibited for this piece in particular.) The book was open, laying flat, showing a grid of characters on the right hand side. The grid described a particular process step in great detail. On the left page, a picture of a craftsman performing that step was attached. The card describing the book of SOPs explained that each of the 18 process steps was described using exactly the same format. This decision was made to ensure that the book would help accomplish certain things:

  • Improve Production Quality. Even masters sometimes need to follow instructions, or to be reminded about an old lesson learned, especially if the process is one you only do occasionally. SOPs promote consistency over time, and from person to person. 
  • Train New Artists. Even though learning the craft is done under the supervision of a skilled worker, it’s impossible to remember every detail (unless you have an eidetic memory, which most of us don’t have). The SOP serves as a guide during the learning process.
  • Enable Continuous Improvement. The SOP is the base from which adjustments and performance improvements are grown. It provides “version control” so you can monitor progress and examine the evolution of work over time.
  • Make Space for Creativity. It might be surprising, but having guidance for a particular task or process in the form of an SOP reduces cognitive load, making it easier for a person to recognize opportunities for improvement. In addition, deviations aren’t always prohibited (although in high-reliability organizations, or industries that are highly regulated, you might want to check before being too creative). The art is contributed by the person, not the process.

When Not to Write SOPs

Over the past couple decades, when I’ve asked people to write up SOPs for a given process, I’ve often run into pushback. The most common reasons are “But I know how to do this!” and “It’s too complicated to describe!” The first reason suggests that the person is threatened by the prospect of someone else doing (and possibly taking over) that process, and the second is just an excuse. Maybe.

Because sometimes, the pushback can be legitimate. Not all processes need SOPs. For example, I wouldn’t write up an SOP for the creative process of writing a blog post, or for a new research project (that no one has ever done before) culminating in the publication of a new research article. In general, processes that vary significantly each time they’re run, or processes that require doing something that no one has ever done before — don’t lend themselves well to SOPs.

Get on the Same Page

The biggest reason to document SOPs is to literally get everyone on the same page. You’d be surprised how often people think they’re following the same process, but they’re not! An easy test for this is to have each person who participates in a process draw a flow chart showing the process steps and decisions are made on their own, and then compare all the sketches. If they’re different, work together until you’re all in agreement over what’s on one flow chart — and you’ll notice a sharp and immediate improvement in performance and communication.

What Protests and Revolutions Reveal About Innovation

The following book review will appear in an issue of the Quality Management Journal later this year:

The End of Protest: A New Playbook for Revolution.   2016.  Micah White.  Toronto, Ontario, Canada. Alfred A. Knopf Publishing.  317 pages.

You may wonder why I’m reviewing a book written by the creator of the Occupy movement for an audience of academics and practitioners who care about quality and continuous improvement in organizations, many of which are trying to not only sustain themselves but also (in many cases) to make a profit. The answer is simple: by understanding how modern social movements are catalyzed by decentralized (and often autonomous) interactive media, we will be better able to achieve some goals we are very familiar with. These include 1) capturing the rapidly changing “Voice of the Customer” and, in particular, gaining access to its silent or hidden aspects, 2) promoting deep engagement, not just in work but in the human spirit, and 3) gaining insights into how innovation can be catalyzed and sustained in a truly democratic organization.

This book is packed with meticulously researched cases, and deeply reflective analysis. As a result, is not an easy read, but experiencing its modern insights in terms of the historical context it presents is highly rewarding. Organized into three sections, it starts by describing the events leading up to the Occupy movement, the experience of being a part of it, and why the author feels Occupy fell short of its objectives. The second section covers several examples of protests, from ancient history to modern times, and extracts the most important strategic insight from each event. Next, a unified theory of revolution is presented that reconciles the unexpected, the emotional, and the systematic aspects of large-scale change.

The third section speaks directly to innovation. Some of the book’s most powerful messages, the principles of revolution, are presented in Chapter 14. “Understanding the principles behind revolution,” this chapter begins, “allows for unending tactical innovation that shifts the paradigms of activism, creates new forms of protest, and gives the people a sudden power over their rulers.” If we consider that we are often “ruled” by the status quo, then these principles provide insight into how we can break free: short sprints, breaking patterns, emphasizing spirit, presenting constraints, breaking scripts, transposing known tactics to new environmental contexts, and proposing ideas from the edge. The end result is a masterful work that describes how to hear, and mobilize, the collective will.

 

Reviewed by

Dr. Nicole M. Radziwill

 

Free Speech in the Internet of Things (IoT)

Image Credit: from "Reclaim Democracy" at http://reclaimdemocracy.org/who-are-citizens-united/

IF YOUR TOASTER COULD TALK, IT WOULD HAVE THE RIGHT TO FREE SPEECH. Image Credit: from “Reclaim Democracy” at http://reclaimdemocracy.org/who-are-citizens-united/

By the end of 2016, Gartner estimates that over 6.4 BILLION “things” will be connected to one another in the nascent Internet of Things (IoT). As innovation yields new products, services, and capabilities that leverage this ecosystem, we will need new conceptual models to ensure quality and support continuous improvement in this environment.

I wasn’t thinking about quality or IoT this morning… but instead, was trying to understand why so many people on Twitter and Facebook are linking Justice Scalia’s recent death to Citizens United. (I’d heard of Citizens United, but quite frankly, thought it was a soccer team. Embarrassing, I know.) I was surprised to find out that instead, Citizens United is a conservative U.S. political organization best known for its role in the 2010 Supreme Court Case Citizens United v. FEC.

That case removed many restrictions on political spending. With the “super-rich donating more than ever before to individual campaigns plus the ‘enormous’ chasm in wealth has given the super-rich the power to steer the economic and political direction of the United States and undermine its democracy.” Interesting, sure… but what’s more interesting to me is that the Citizens United case, according to this source

  • Strengthened First Amendment protection for corporations, 
  • Affirmed that Money = Speech, and
  • Affirmed that Non-Persons have the right to free speech.

The article goes on to state that “if your underpants could talk, they would be protected by free speech.”

Not too long ago, a statement like this would just be silly. But today, with immersive IoT looming, this isn’t too far-fetched. 

  • What will the world look (and feel) like when everything you interact with has a “voice”?
  • How will the “Voice of the Customer” be heard when all of that customer’s stuff ALSO has a voice?
  • What IS the “Voice of the Customer” in a world like this?

A Robust Approach to Determining Voice of the Customer (VOC)

Image Credit: Doug Buckley of http://hyperactive.to

Image Credit: Doug Buckley of http://hyperactive.to

I got really excited when I discovered Morris Holbrook’s 1996 piece on customer value, and wanted to share it with all of you. From the perspective of philosophy, he puts together a vision of what we should mean by customer value… and a framework for specifying it. The general approach is straightforward:

“Customer Value provides the foundation for all marketing activity…
One can understand a given type of value only by considering its relationship to other types of value.
Thus, we can understand Quality only by comparison with Beauty, Convenience, and Reputation; we can understand Beauty only by comparison with Quality, Fun, and Ecstasy.”

There are MANY dimensions that should be addressed when attempting to characterize the Voice of the Customer (VOC). When interacting with your customers or potential customers, be sure to use surveys or interview techniques that aim to acquire information in all of these areas for a complete assessment of VOC.

The author defines customer value as an “interactive relativistic preference experience”:

  • Interactive – you construct your notion of value through interaction with the object
  • Relativistic – you instinctively do pairwise comparisons (e.g. “I like Company A’s customer service better than Company B’s”)
  • Preference – you make judgments about the value of an object
  • Experience – value is realized at the consumption stage, rather than the purchase stage

Hist typology of customer value is particularly interesting to me:

typology-customer-value

Most of the time, we do a good job at coming up with quality attributes that reflect efficiency and excellence. Some of the time, we consider aesthetics and play. But how often – while designing a product, process, or service – have you really thought about status, esteem, ethics, and spirituality as dimensions of quality?

This requires taking an “other-oriented” approach, as recommended by Holbrook. We’re not used to doing that – but as organizations transform to adjust the age of empathy, it will be necessary.

Holbrook, M. B. (1996) . “Special Session Summary Customer Value C a Framework For Analysis and Research”, in NA – Advances in Consumer Research Volume 23, eds. Kim P. Corfman and John G. Lynch Jr., Provo, UT : Association for Consumer Research, Pages: 138-142. Retrieved from http://www.acrwebsite.org/search/view-conference-proceedings.aspx?Id=7929

Value Proposition Design: A Fun and Engaging (New!) Guidebook

Alex Osterwalder's "Value Proposition Design" toolkit is now available

Alex Osterwalder’s “Value Proposition Design” toolkit is now available on Amazon

I just finished reviewing Alex Osterwalder‘s new book, Value Proposition Design, for ASQ’s Quality Management Journal. Although my review won’t be published until January 2015, this is such a refreshing and exciting book that I wanted to make sure all of you know about it now: because it will be available on Amazon tomorrow (Monday, October 20th)!

I met Alex this past September at BIF10 in Providence, Rhode Island, which (if you haven’t heard of it yet… or attended) is an inspiring and intimate two-day gathering of dynamic storytellers and equally dynamic participants. Everyone at BIF is engaged in some kind of social, civic, or business innovation — and many of the projects and ideas you hear about challenge outmoded assumptions in refreshing ways.

Alex is a little different… he’s a catalyst for other innovators. His company aims to provide individuals and teams with the tools they need to create new ventures, or improve existing projects and organizations, by critically examining the entire process of value creation and delivery. And this new release doesn’t disappoint — in large part, because the tools, techniques, and approaches that he promotes are consistent and aligned with various quality bodies of knowledge.

“The authors have created a fun and engaging text, full of cartoon-like pictures and exercises, that will be easily accessible to any member of a business development or quality improvement team. There are practical examples and stories provided throughout, which illuminate the concepts effectively and can help teams expand, refine, enhance, and articulate their visions by applying best practices through successful templates. The only weakness of this book is that it does not tie any of its assertions or practices to the academic literature. However, the Value Proposition Design canvas that this book describes in detail has demonstrated clear value already for many practitioners, and may provide researchers with ideas for making additional connections between established quality tools, principles, and practices.” — Me, in my January 2015 review of this book for the Quality Management Journal

Wherever you flip open the book, it’s organized so you’re presented with a complete idea that spans the left and right pages. This makes it very browsable and engaging, and an effective form for interlacing new ideas with repackaged perspectives on older techniques. For example, the “Find your Earlyvangelist” page reminds me of a new, more agile take on the 3M Lead User process, which many organizations have used over the past two decades to fine-tune their product characteristics and service delivery before wider release. I also like how several of the left page-right page idea blocks are aligned with broader concepts. The picture below shows one such example, where “learning” is the unifying concepts, and the pages that follow describe each of the techniques on the right in details:

vpd2

Overall, this was a really fun book to read and review. Are you looking for a way to get teams with diverse backgrounds on the same page for value creation? If so, this would be an excellent guidebook to help make it happen.

“[Alex’s new book] is a strong new contribution to the practitioner literature in quality management, and outlines many new approaches for value creation.” — Me, in my January 2015 review of this book for the Quality Management Journal

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