May 2009

I heard Dan Ariely speak at the AMA Research Conference in Boston last September before I read this book (Predictably Irrational: The Hidden Forces That Shape Our Decisions, Harper, 2008).  He gave the audience a riveting account of his experience recovering from burns caused by the explosion of a magnesium flare.  You can read the account for yourself in the introductory chapter to this book (“How an Injury Led Me to Irrationality and to the Research Described Here”).  In a nutshell, Ariely questioned the conventional wisdom of the nursing staff that it was less painful overall to remove the bandages covering his burns quickly rather than gradually.  Ariely eventually put this to the test with experiments involving various sources of pain and concluded that the nurses, despite the best intentions, were wrong.

In the subsequent chapters, Ariely systematically dissects our decision processes, showing again and again that most of the time we are not the rational, utility maximizing creatures found in contemporary economic theory.  Ariely’s speech at the AMA conference (as I remember it) focused on asymmetric dominance.  Choosing between two different but attractive alternatives is very difficult for most people.  Ariely gave the example of choosing between a luxury vacation in Paris and a similar vacation in Rome.  But… if we throw another alternative into the mix–say a less luxurious package in Rome–the choice suddenly becomes much easier, and a majority of people will choose the more luxurious Rome vacation.  This is asymmetric dominance.  The luxurious Rome vacation becomes more attractive because we can more easily compare it to the lesser package for Rome.  We might not know whether the Paris package is better than the Rome package, but we definitely know that the more luxurious Rome package is better than the less luxurious package.  In effect, the introduction of the inferior Roman alternative has bumped Paris out of the choice process.  This topic is covered in the first chapter of the book, “The Truth About Relativity.”

This is important stuff for market researchers and marketers.  Asymmetric dominance can come into play in market research studies that rely on choice-based conjoint (CBC).  I’m pretty sure that I’ve designed a few CBC studies over the years where asymmetric dominance may have been at work (inadvertently, of course!).  

Other chapters are equally valuable.  In Chapter 5, “The Influence of Arousal,” we learn how decisions change as a function of the state of arousal.  Market researchers often ask consumer how likely they are to purchase some good or service in the future.  After reading this chapter, you’ll question whether the “cold” survey question can ever accurately capture what consumers will do in the “hot” purchase situation.  A practical implication–consumers are more likely to give “accurate” information about what they will do when they are immersed in the buying process.  There’s plenty more food for thought in chapters with titles like “Keeping Doors Open (Why Options Distract Us from Our Main Objective),” “The Effect of Expectations (Why the Mind Gets What It Expects)” and “The Power of Price (Why a 50-Cent Aspirin Can Do What a Penny Aspirin Can’t).”

Ariely writes in a personal, conversational style–you’ll not only learn something about irrationality, you’ll learn about Ariely, his family, his collaborators and students.  The subjects in his experiments also get the personal treatment.  Ariely describes his experiments in just enough detail to convey the systematic nature of his efforts, but not quite enough detail to convey the rigor required for sound psychological research.  Taking Ariely’s accounts at face value, it’s not clear that controls such as counterbalancing for order of presentation and similar procedures for assuring internal validity were employed.  Many of the experiments are conducted in natural settings.  We have to take Ariely’s word for the magnitude of the effects he observes, since we don’t get enough information to assess statistical conclusion validity.

The experiments described in this book are part of a long tradition of research in cognitive and social psychological processes that goes back at least as far as Fritz Heider (The Psychology of Interpersonal Relations, John Wiley & Sons, 1958).  I’m a little unsettled by the way in which this book seems to suggest that the investigation of irrationality is relatively recent and more or less exclusive to behavioral economics.  Ariely has every right to focus on his own research–there’s lots of fascinating stuff here–but psychologists have been studying these processes for a long time.  For some classic examples, see The Social Animal by Elliot Aronson and Mistakes Were Made (But Not by Me):  Why We Justify Foolish Beliefs, Bad Decisions and Hurtful Acts by Carol Tavris and Elliot Aronson.  The contribution of the behavioral economists is to extend those themes to areas involving monetary transactions.

Bottom line:  this is an enjoyable, thought-provoking read.  

Copyright 2009 by David G. Bakken


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Around the early ’90s, a new job title began to appear in many of the companies I consulted with–“Manager of Customer Insights” (and variations on this theme).  In many cases, this involved “rebadging” of many managers of market research.  The presumed goal of this renaming exercise was worthy–shifting the focus of market research from process to content–and from data collection and analysis to knowledge.  

These days, purchasers of market research services are likely to say that the one thing they most want from their research investment is insight.  Ask these buyers what they mean by insight, however, and they may be unable to answer.  A couple of years ago the CEO of a major market research company conducted a series of “client advisory” forums with the directors of market research (or “customer insights”) from several of the firm’s key clients.  Over the course of these sessions, the clients stressed again and again that they wanted their research partners to provide “insights.”  Finally, the research firm CEO asked them to define insight.  Of the dozen or so clients participating in the forums, only two could offer any type of definition.  One of these was a dictionary definition (see two representative dictionary definitions of “insight” at the end of this post).  

In my own experience, clients who complain that research offered no insight often say “I didn’t learn anything new” or “this doesn’t tell me what action I should take.”  This gives us a clue to the nature of insight and, perhaps, a method for achieving insight.  At one level, insight is seeing something that we have not seen before–a pattern or a connection between things.  As one example of pattern detection, check out a brief article in the New York Times (“Fast Arriving Fads Quick to Flame Out,” May 17, 2009) about a study conducted by Jonah Berger of the Wharton School and Gael Le Mens of Stanford University that looked at the prevalence of first names, as recorded by the U.S. Census.  Looking at data going back to 1880, they found that the faster a name becomes popular (based on number per one million children), the faster it declines to “pre-fad” levels.  Names with distinctive spikes (fast rise and decline) included “Dewey” (c. 1900), “Debra” (c. 1960), and “Amy” and “Jeremy” (1970’s-1980’s).  Some names (Patrick and Katherine, for example) are relatively stable over time.  And, if you’ve recently named a child “Ava” or “Aiden,” you’re part of an uptick in popularity for these names that might not last long.  I want to make two points about this study.  First, the “pattern” is only apparent when looking across a large number of names over a long time period (the  rise and fall might take a couple of decades or more) and second, visual examination of the data makes it easy to see the pattern (there’s a nifty graphic accompanying the Times article). 

In fact, proper visual display of quantitative information is sometimes crucial to drawing insight from data.  Edward Tufte makes this point powerfully in his description of the “failed” data analysis that preceded the disastrous launch of the space shuttle Challenger (see “Visual and Statistical Thinking:  Displays of Evidence for Decision Making” by Edward R. Tufte, 1997).

Patterns are not detectable when we look at data elements in isolation.  The typical survey-based research report is a linear summary of the answers to the survey questions, sometimes with responses reported by various subgroups (i.e., “banner points”). While it’s possible that such simple summary analysis is informative (“I didn’t know that so many of my customers are also buying from my competitors!”), patterns emerge only when we can see how the answers change across relevant dimensions (e.g., time, geography, attitudinal segments, and so forth).  

There’s another aspect to insight.  Sometimes we “see” something that completely changes our understanding.  The Tower of Hanoi puzzle provides an example.  You have a wooden base with three identical dowels.  On the first dowel are stacked several disks of increasing size (smallest on top, largest on the bottom).  The task is to move all of the disks to the third dowel, but…  you can move only one disk at a time AND you cannot place a larger disk on top of a smaller disk.  Solving this problem involves a specific insight–that you can move disks back and forth between all three dowels, as long as you move only one at a time and never put a larger disk on top of a smaller one.  The framing of the puzzle–in the physical design and the task instructions–appears to lead most people initially to attempt a solution in which disks are only removed from the first dowel.  After all, if the solution were immediately obvious, it would not be much of a puzzle.  

While we often can find patterns and connections using systematic, analytic approaches (like the analysis of census data on first names), the type of insight required to solve a puzzle like the Tower of Hanoi is qualitatively different–much more like an “aha” or “eureka” experience.  Once you’ve figured out the solution, you can solve similar puzzles quickly by recognizing the form of the problem.  

“Aha” insights sometimes happen in market research, but in my experience they are most likely to occur when we use qualitative methods, such as case histories, in-depth interviews, and immersion.

The second client complaint (“this doesn’t tell me which action to take”) usually reflects a failure to align the research with the business problem.  Most often, the link between the research activities and the actions available to the firm is missing.  I think this occurs because, by the time the customer insights department begins working with the market research partner, the process is two or three steps removed from the business problem.  It’s important to have a line of sight from the data to the actions that the firm can take.  Consider the auto industry as an example.  Once a model is introduced (that is, the vehicle is designed and engineered, the assembly line has been built, parts have been ordered, and so forth), the automaker has only two ways to impact the choices of consumers: advertising and price.  An attitudinal segmentation at this point might be nice, but unless it directly informs advertising or pricing decisions it’s not likely to help a manufacturer decide on a course of action.

So, it’s important to specify, up front, what we mean by “insight.”  If we’re looking for new knowledge, we need to know “what we know” as well as what we don’t know–and it’s important for clients to share this knowledge with their research partners.  We also need to recognize that insight often results from looking across multiple sources of information, enabling us to see patterns or connections that are not otherwise apparent.

Copyright 2009 by David G. Bakken

Here are the dictionary definitions of insight:

 From the Concise Oxford English Dictionary (Oxford University Press, 2004).  

insight n. 1 the capacity to gain an accurate and deep understanding of something; an understanding of this kind. 2 (Psychiatry) awareness by a mentally ill person that their mental experiences are not base in external reality.

From Merriam Webster’s Collegiate Dictionary 1oth Edition (Merriam-Webster, 1993).

insight n. 1: the power or act of seeing into a situation: PENETRATION  2: the act or result of apprehending the inner nature of things or of seeing intuitively  syn see DISCERNMENT.

There’s a scene in the biopic movie “Coal Miner’s Daughter” where a young Loretta Lynn (Sissy Spacek) is recording her first demo.  Halfway through the session, the engineer/producer tells Loretta’s husband, Mooney (Tommy Lee Jones) that he’s going to need to “get some more [guitar] pickers.”  “More pickers!  I can’t afford the ones you’ve got.”  The engineer calms Mooney, saying, “I mean more better pickers.”  In the recording business, the synergy created by a specific combination of musicians can be the difference between a hit and a song that doesn’t make the charts.  In the same way, more better customer knowledge can separate an industry leader from the also rans.

Having worked in the market research business for many years, I can say that in most industries–especially B2C industries–the key competitors tend to do the same kinds of market research and ask the same questions about their customers.  Many companies also purchase syndicated market research that gives all competitors access to exactly the same information.  The supplier industry is fairly close knit, and any innovations such as new analytical tools tend to be adopted quickly by most suppliers.  Ten years ago only a handful of consultants offered discrete choice modeling.  Now it’s one of the most popular methods of quantifying consumer preferences, and most market research suppliers offer choice modeling in some form.

So, given that most firms have access to similar or identical facts about their customers and prospects, how does customer knowledge become a competitive advantage?

When we talk about “knowledge” in connection with customers and prospects, we might be talking or thinking about any or all of the following:  facts (observations, answers to survey questions, numerical data, and so forth), summarization of the facts (via cross-tabulation or correlational analysis for example), and “insight” or understanding.  Each of these notions corresponds to a critical step in the creation of knowledge, but taken separately they don’t constitute knowledge.  Knowledge emerges from the intersection of the patterns in the data and the previous experiences of the individual who is looking at those patterns.  An important activity in this process is conversation, which lets us find out what others think about these patterns.

Therein lies the competitive advantage in customer knowledge–it’s difficult to duplicate.  Even when competitors have access to the same facts, they may not draw the same (or “correct”) inferences from those facts.  Moreover, knowledge is cumulative–different patterns emerge when we look across facts gathered at different points in time, different geographies and different samples of customers and prospects.  Each company’s history will be somewhat different in this regard.  Add to that the unique perspective that each individual brings to the interpretation of the facts, and knowledge quickly becomes a powerful point of competitive advantage.  There’s a downside to this, of course.  As people come and go, the collective customer knowledge changes.  More importantly, the knowledge that gave the organization its competitive advantage can be lost over time.  In companies where advancement means moving into different business units or functions with any one assignment lasting only a couple of years or so, there can be a high rate of “knowledge turnover.”

To get an idea of how serious knowledge loss can be, consider NASA.  In his book Lost Knowledge: Confronting the Threat of an Aging Workforce (Oxford University Press, 2004), David W. Delong describes the way in which the space agency lost the know-how required to send humans to the moon.  Engineers who designed and built the massive Saturn 5 booster rocket were encouraged to take early retirement in the 1990’s, and along the way the detailed blueprints for the rocket were lost.  And consider the bottom line–$24 billion was spent over 10 years to send Americans to the moon.  It will cost at least twice as much to recreate that achievement.

As I noted in my first post, companies and other organizations spend several billion dollars each year gathering data about customers and markets.  Positive return on that investment requires active and effective customer knowledge management.

Copyright 2009 by David G. Bakken.  All rights reserved.

Why Popcorn Costs So Much at the Movies and Other Pricing Puzzles, Richard B. McKenzie, Copernicus Books/Springer, 2008.

One of the rituals of high school is the senior picture.  Nowadays, that often means a trip to a professional studio for a shoot involving multiple outfits, poses and backgrounds.  The student is not obligated to buy any photos–the yearbook photo is provided for free–but the studios hope to sell a package of prints to the student and his or her family.  When one of my children went through this a few years ago, we were presented with a confusing array of packages that varied in the number of poses, the sizes of the prints, and the number of prints of each size that were included.  Say you want to have a more formal pose for the yearbook (that counts as one pose, even though that picture is “free”) and an informal pose for wallet-size prints–you’ll have to buy a package that includes two poses.  That’s not all–in order to get those two poses and a certain number of wallet-size prints, you may be forced to take so many 5x7s and a couple of 10x12s or some other mix of sizes.  And don’t think you’ll be able to figure out the individual prices for wallet-size, 5×7, and 10×12 prints.  There likely is no a la carte offering.

When I went through this with one of my children a few years ago, I wondered how the studio arrived at this particular bundling strategy.  This is precisely the kind of pricing “puzzle” that Richard McKenzie, the Walter B. Gerken Professor of Enterprise and Society in the Paul Merage School of Business at the University of California, Irvine, dissects in this intriguing but (at least for me) occasionally frustrating book.

Pricing makes the economy go ’round.  Pricing is also complex, and economic theory can leave us wanting when it comes to understanding the way that prices work in the “real” world.  Professor McKenzie does a good job of tackling this complexity head on, and anyone whose job is remotely connected to pricing will benefit from reading this book.  Consumers who are curious about the prices they pay (or don’t, for “free” goods) and how they got that way are likely to enjoy this book as well.

McKenzie has given a lot of thought to a wide range of pricing anomalies.  In addition to the title’s question about the high price of popcorn at the movies, here’s a sampling of the puzzles McKenzie ponders:  why there are reduced price sales, why there are so many discount coupons, why some goods are free, why printers are cheap and ink cartridges are expensive, why ticket prices are the same for all movies, why so many prices end with “9,” and why manufacturers offer rebates.

The underlying economic principle in many of these chapters is price discrimination.  In other words, these pricing anomalies arise as a way to distinguish customers who are more price sensitive from those less price sensitive with respect to a given category of goods or services.  A classic example of price discrimination can be found in discount airline fares that require a Saturday night stay in the destination city (thus selecting out the less price sensitive business travelers).  However, this book is nothing if not comprehensive, and McKenzie covers other pricing effects, including distortions introduced by market interventions.

McKenzie’s writing is engaging and readable.  The typical chapter begins with an exploration of the common sense or “obvious” explanation for a pricing anomaly (popcorn costs more at the movies because the consumers are captive, allowing the theater owners to exploit their desire for popcorn) and works his way through competing plausible explanations.  As in, maybe popcorn costs more at the movies in order to keep ticket prices lower.

Maybe it’s just McKenzie’s style, but all too often the explanations for these pricing puzzles come across as speculative hypotheses awaiting empirical verification.  When evidence is offered, as with data on the prices of printers and ink cartridges, it is usually consistent with McKenzie’s explanation, but not necessarily conclusive.  McKenzie gives much more weight to the consumer (demand) side of the pricing problem than he does to the producer (supply) side.  In my experience, manufacturers struggle with a host of cost considerations in setting prices that McKenzie barely touches on.

The chapter titled “Why movie ticket prices are all the same” exemplifies the things I really like about this book, as well as some of the things I found a bit frustrating.  McKenzie provides a fairly rich picture of the relevant economics of the motion picture industry as he explains the apparent anomaly that prices for the most popular movies are the same as those for the least popular. According to McKenzie, this pricing makes sense given that, prior to release there is a lot of uncertainty surrounding the potential popularity of a particular film.  Moreover, there are other pricing “mechanisms” that provide greater returns to the most popular films.  For example, less popular films disappear from first-run theaters fairly quickly, perhaps moving to second and third run screens with lower ticket prices, resulting in higher average ticket prices for the most popular films.

In the case of movie ticket prices, as with some of the other pricing anomalies covered in this book, McKenzie omits or is perhaps unaware of information that might favor alternative explanations.  For example, theater owners do exert some price differentiation for films that are expected to be more popular.  When I went to see “Star Trek” last week, there was a notice at the box office that discount coupons offered by the theater would not be accepted for that movie (at least for the opening weekend). Another nit–there’s no mention of the impact of the multiplex theater configuration on uniform ticket prices.  In the multiplex setting, charging different prices for different films likely would require additional staff to police admittance to the individual movies, thus raising the theater owner’s costs. For a another take on the economics of movie ticket pricing, see The Economic Naturalist:  In Search of Explanations for Everyday Enigmas by Robert H. Frank of Cornell University (Basic Books, 2007).  Frank covers some of the same territory as McKenzie, and I recommend his book as well.

Final verdict–this is a must read book for anyone who deals with pricing.

Welcome to my new blog, “The Customer Knowledge Advantage.”  TCKA is dedicated to discussion, observations, and musings about creating and using customer knowledge to achieve sustainable competitive advantage.  

Market intelligence–the myriad activities that provide firms with facts and information about their customers and prospects–is big business.  In 2007, total revenues for the top 25 global market research firms totaled $15.5 billion (US) according to the Honomichl Report on the market research industry.  And that’s only a fraction of the total that firms spend on all market intelligence when you take into account spending on things like CRM systems, secondary research, and the staff required to support all of these activities.  

There’s also been an explosion in casual and spontaneously generated customer data on the web, from click-throughs to text postings to the linkages on social networking sites.  This opens up entirely new pathways to observing and understanding consumer behavior.  At the same time, these data streams present new challenges for analysis and interpretation.

TCKA is based on my belief that customer knowledge emerges over time from the intersection of facts–things we can observe, count, categorize, and so forth–and the unique experiences of the observers.  In other words, knowledge is a characteristic of people, and two people with different experience looking at the same facts may arrive at different understanding.

So what can you expect from future posts on TCKA?  For starters, comment and observation on both new and old practices for creating customer knowledge, with examples ripped from the business headlines.  Reviews of books (new and old) that I think will be of interest to marketers or market researchers (first up, Why Popcorn Costs So Much at the Movies by Richard B. McKenzie).  And, in the future, contributions from folks I’ve collaborated with over the years.  

Thanks for joining me.