MarketingMuses

How to Create a New Technology Category with Gartner

A former colleague recently asked if I had experience creating a new category with technology analyst firms, specifically Gartner. I said I had, and I knew, firsthand, that inclusion in a Gartner report was the "Holy Grail" for many technology startups. However, if you don't fit one of the preexisting Magic Quadrants, you have to convince Gartner to create a new technology category—and this is not easy or simple.

In 2005, I worked with a security software company that had a unique technological approach to passively monitor Web application traffic, compare user activity from the most recent session with historical activity, and alert security personnel to anomalies in user behavior that indicated fraudulent access. But it did not fit any of the established network security categories, such as firewall, anti-virus protection, intrusion detection and prevention. In nine months, we were able to create new data security categories of Transaction Anomaly Detection with Gartner and Online Fraud Risk Monitoring with Forrester.

Here’s how we did it, and what I learned in the process.

 

USE SPECIALTY ANALYST FIRMS AS ADVISORS

Before briefing Gartner and Forrester, the major technology analyst firms, we met with TowerGroup, a leading global financial services research and advisory firm, and Javelin Strategy & Research, a quantitative and qualitative research firm focused on global financial services. We described our technological approach and our belief that it complied with security measures recommended in a recent update to Federal Financial Institutions Examination Council (FFEIC) guidelines on online customer authentication by financial institutions.

Both firms agreed, and provided more insight into the probable impact on regulatory compliance for online banking and financial services, which led us to establish online fraud detection, identity theft protection, and regulatory compliance as our value proposition. Furthermore, they provided information on the number of financial institutions affected by the guidelines (market size) and other companies with similar security approaches. This helped us develop two items that were critical to creating a new information security technology category. 

 

DEVELOP A MARKET MODEL AND COMPETITIVE LANDSCAPE

Gartner, Forrester, Frost & Sullivan, and other analyst firms make money by selling research and advisory services. Many of their customers are the very companies their analysts cover. Therefore, one of the factors that influence the creation of a new category is whether it is big enough and valuable enough to create new customers for the analyst firm. We developed a market model that showed U.S. financial institutions could spend over $1B to comply with the new guidelines, and that at least 4 new and 4 established companies would compete in the new space.

 

LAND A MARQUEE ACCOUNT

Despite a unique technological approach that did not fit an existing category, a compelling value proposition, a relatively large total available market (TAM), and a considerable competitive landscape, one last element was missing. An analyst told me: I would love to write a research note on you and this new space, but you need to land a marquee account—either a top 10 bank or brokerage. As I pressed the analyst on this factor, he said: I don’t care if you give the product to someone, as long as they install and use it. That will legitimize the category and position you as a credible competitor. So over the next six months, we worked to land a top 10 bank. But rather than give away a product, we expanded our basic offering (a point solution) to meet the enterprise needs of a bank.  

In the end, we landed a marquee, enterprise account at a price 10 times higher than the average sales price (ASP) of our point solution, and positioned ourselves as a noteworthy competitor in new categories created by the major technology analyst firms. And, ultimately, this helped the company achieve a successful exit.

 

Risky Business (Part 3)

Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets

“This book is about luck—or, more precisely, about how we perceive and deal with luck in life and business.  Set against the backdrop of the most conspicuous form in which luck is mistaken for skill—the world of [financial] trading.”

 

Nassim Nicholas Taleb is extremely smart, extremely well-read, and has studied deeply and broadly how we humans fool ourselves into believing that being in the right place at the right time is, in fact, proof that we, or others, possess above-average knowledge and skill.  He has been described as an “erudite raconteur,” and this characteristic serves him well in his writing, where he interweaves interesting, entertaining stories of how people fool themselves (or are fooled by others) with rigorous explanations of why this occurs—over and over again.

A primary reason activities and events that involve human behavior cannot be modeled on physical science (see Mandelbrot’s The Variation of Certain Speculative Prices in an earlier post) is that we humans have a built-in bias to attribute our successes to skill, and our failures to bad luck.  Paul E. Meehl demonstrated the “attribution bias” in his 1954 study of experts comparing their perceived abilities to their statistical ones.  “It shows a substantial discrepancy between the objective record of people’s success in prediction tasks and the sincere beliefs of these people about the quality of their performance.”

Taleb is characteristically blunt in his assessment of risk managers in financial institutions.

“Corporations and financial institutions have recently created the strange position of risk manager, someone who is supposed to monitor the institution and verify that it is not to deeply involved in the business of playing Russian roulette.

“The risk managers’ job feels strange: As we said, the generator of reality is not observable.  They are limited in their power to stop profitable traders from taking risks…On the other hand, the occurrence of a blowup [severe trading losses] would cause them to be responsible for it.  What to do in such circumstances?

“Their focus becomes to play politics, cover themselves by issuing vaguely phrased internal memoranda that warn against risk-taking activities yet stop short of completely condemning it, lest they lose their job. (Personal note: this description could also be applied to the public pronouncements of a certain former fed chairman.)

“From the standpoint of an institution, the existence of a risk manager has less to do with actual risk reduction than it has to do with the impression of risk reduction.”

Anyone who has read a mutual fund prospectus is likely familiar with the phrase, “Past performance is no guarantee of future performance.”  However, financial trading institutions routinely make decisions on the basis of past performance.  One routinely used risk management tool is value at risk (VaR), defined as, “a technique used to estimate the probability of portfolio losses based on the statistical analysis of historical price trends and volatilities.”  Taleb is adamant in his opposition to the use of VaR for managing risk.  “An interesting problem is the ‘value at risk’ issue where people imagine that they have a way to understand the risk using ‘complicated mathematics’ and running predictions on rare events—thinking that they were able from past data to observe the probability distributions.  The most interesting behavioral aspect is that those who advocate it do not seem to have tested their past predicting record, another Meehl type of problem.”

So, if the deck is so stacked against 1) accurately assessing the probability of a rare event, 2) including the consequence of a rare event in trading decisions, and 3) avoiding the madness of crowds, how did a small group of traders foresee the financial market collapse and make millions placing contrarian bets?  That’s the subject of the next book. 

 

Paul Solman interviews Nassim Taleb and Benoit Mandelbrot

 

Nassim Taleb urges the U.S. Congress to stop the use of Value at Risk (VaR)

Guest Commentaries in Bio-IT World and Drug Discovery News

I’ve recently contributed guest commentaries to two industry publications: Bio-IT World and Drug Discovery News.  Following are brief summaries of the commentaries, with links to the full text.

 

Software Standards in Next-Gen Genomics

Standards are emerging in next generation sequencing (NGS), thanks mainly to the critical investments labs have made in instrumentation. Sequencing proceeds faster and produces higher quality results when labs are able to consistently and rapidly prepare samples for sequencing, monitor sequencing runs in real time, and deliver appropriately formatted data for analysis. Each of these capabilities can be supported with preconfigured workflows in a Laboratory Information Management System (LIMS) built to support the best practices associated with running a lab’s preferred instrumentation.

 

Data management that speeds—rather than impedes—advanced DNA sequencing applications

The data volumes produced by modern sequencing applications require new approaches to data management that center on the workflows prescribed by sequencers and the specific needs of two different types of users: lab technicians and lab directors. From my perspective, how labs choose to manage their data may very well determine which have the most success in applying DNA sequencing to the development of advanced molecular diagnostics and therapeutics.

 

Reading Steve Jobs

“I wanted my kids to know me.  I wasn’t always there for them, and I wanted them to know why and to understand what I did.  Also, when I got sick, I realized other people would write about me if I died, and they wouldn’t know anything.  They’d get it all wrong.  So I wanted to make sure someone heard what I had to say.” – Steve Jobs reason for having Walter Isaacson write his biography

 

I did not intend to read this book.  

I did not know Steve Jobs, but I know several people who did, including a few who worked with him.  Most described the experience of working with him as 1) priceless, and 2) one that no amount of money could get them to repeat.  I have heard many stories of his brilliance and brutality.  I have read several books about Apple (the early years and after Jobs returned), and watched an excellent documentary on Pixar.  And I don’t usually read biographies.

The fact that I did read the book is testimony to the genius of Steve Jobs (and Jeff Bezos) and the talent of Walter Isaacson.  I was in a hotel room one evening having trouble falling asleep, and downloaded the first few chapters of the Kindle edition (for free) to my iPad.  No one has approached Jobs’ genius for creating an immediate, exhilarating user experience in the digital age, and it’s hard to imagine he could have chosen a better biographer than Isaacson.  Within fifteen minutes, I was hooked.

Isaacson clearly did his research, and the fact that Jobs gave him complete access to his life, and encouraged friends and foes (in Jobs life, many people are both) to speak honestly about their relationships and experiences with Jobs gave him the detail he needed.  But his talent allows him to tell a richly textured, deeply affecting story from cover-to-cover (undoubtedly, an antiquated expression for a digital book). 

As a father of an adopted son, I was especially moved by the description of an incident when Jobs, at six or seven years old, told a neighborhood girl he was adopted, to which she responded, “So does that mean your real parents didn’t want you?”

“I remember running into the house, crying.  And my parents said, ‘No, you have to understand.’  They were very serious and looked me straight in the eye.  They said, ‘We specifically picked you out.’  Both of my parents said that and repeated it slowly for me.  And they put an emphasis on every word in that sentence.”

Throughout the book, Isaacson reports similar details that give the reader keen insights into the events that shaped Jobs, without injecting amateur psychological suppositions.  The result is great reporting delivered in a wonderful narrative.  As I read the book, I realized that my image of Jobs, while accurate, was a caricature.  By the end of the book, I had a fully formed picture of a brilliant, passionate, and deeply flawed human being.

Steve Jobs never read any part of the biography.  He gave Walter Isaacson complete control of the story, although, after seeing the initial design for the book cover, he threatened to kill the project, until he received complete creative control of the cover design.  Initially, I was amazed that someone who had guarded his privacy so closely would relinquish control of his life story.  But then I realized that he simply followed the process he had used in business. 

Jobs did not personally create technology, films, commercials, etc.  But he carefully selected incredibly talented people, provided them the resources to create “insanely great” products, and exerted absolute control over product packaging.  Steve Jobs, the book, is not insanely great, but is an informative, interesting, and worthy result of his and Walter Isaacson’s creative process.

If you prefer to watch, rather than read, I strongly recommend the 60 Minutes video story and interview with Isaacson.

 

Risky Business (Part 2)

The Quants: How a New Breed of Math Whizzes Conquered Wall Street and Nearly Destroyed It

The book jacket states that, “The Quants is at once a timeless masterpiece of explanatory journalism, a gripping tale of ambition and hubris…and an ominous warning about Wall Street’s future.”  Unfortunately, the book doesn’t quite live up to this characterization, but 50 pages are essential to an understanding of financial risk management. 

 

Scott Patterson, the author and a staff reporter at The Wall Street Journal, opens the book in March 8, 2006 at the Wall Street Poker Night Tournament (now the Math for America Poker Night Tournament).  Gambling is a common interest with many of the quants, and has obvious parallels to the men who developed the science of probability hundreds of years earlier.  The main plot centers on four quants: Peter Muller, head of Morgan Stanley’s PDT hedge fund; Ken Griffin, Manager of Chicago hedge fund Citadel Investment Group; Cliff Asness, founder of AQR Capital Management; and Boaz Weinstein, derivatives trader at Deutsche Bank and head of its Saba internal hedge fund. 

The non-linear narrative shifts between events central to the financial collapse in 2007 and 2008, the back-stories of how the main characters rose from math whizzes to high-powered hedge fund managers, and the role complex mathematics (differential calculus, quantum physics, and advanced geometry), high-speed computing, and the use of immoderate leverage (trading on margin with over 97% borrowed money) played in enabling people who knew little about finance to control billions of dollars in global trading positions.  The story reaches its climax as it describes events in August of 2007, when the billions the hedge funds had gained over a decade of using quantitative techniques were wiped out over the course of a few days.  While this approach makes for a lively story, it is too superficial in its explanation of the role quantitative analysts played in developing the complex structured asset-backed securities (ABS), such as collateralized debt obligations (CDO), that spread risk and uncertainty about the value of the underlying assets more widely, rather than reducing risk through diversification, as promoted. 

50 Pages Well Worth Reading

The fifty pages that comprise chapters two, three, and four are a concise explanation of how quantitative analysis used models from the physical sciences to create a statistical trading advantage, and how strict adherence to these models eventually lead to disaster.  The author uses Ed Thorp, the “Godfather” of the quants, to make what could be a dry, academic subject interesting and engaging. 

As with others who contributed to the science of probability, Ed Thorp initially applied his research to gambling, specifically blackjack.  After reading a ten-page article on blackjack strategy in the Journal of the American Statistical Association by U.S. Army mathematician Roger Baldwin and three colleagues, Thorp contacted Baldwin and obtained the data from the study in 1959.  Thorp was in the process of moving from UCLA, where he had completed his Ph.D. in physics, to an instructor position at MIT.  Once there, he used a university computer (IBM 704) to evolve the blackjack strategy into what would become the basis for his success in casinos and his 1962 bestselling book, Beat the Dealer.  What separated Thorp from those who only focus on probability was his incorporation of a wager strategy that would mitigate his risk of a prolonged losing streak (a statistically wide deviation from the norm) busting him.  He derived his betting strategy from a 1956 paper, A New Interpretation of Information Rate, by John Kelly Jr., a physics researcher at Bell Laboratories.  Using Kelly criterion, he could determine how much to wager, depending on the situation—larger bets when the odds were in his favor and smaller bets when they were not—that maximized return during advantageous periods of play while minimizing the risk of losing everything during periods of disadvantage. 

In 1964, Thorp shifted his sights from blackjack to Wall Street—“the biggest casino of all”—and devised a precise, quantitative method for pricing stock warrants (basically long-term contracts, much like a call option, that investors can convert into common stock).  He combined his pricing formula with a strategy of shorting warrants he judged to be overpriced while going long (buying) an appropriate amount of stock to hedge his bet.  Theoretically, the inverse correlation of the warrant and stock would offset each other, providing the necessary hedge if the warrant price did not fall within the contract period.  In the best-case scenario, the price of the (overpriced) warrant would decline and the stock would rise, closing the inefficiency gap and providing a gain on each side of the trade. 

This strategy, commonly called convertible bond arbitrage (a specific application of delta hedging) became one of the most successful and lucrative trading strategies ever devised.  The only risk was the unlikely event that the price of the warrant would increase while the price of the underlying stock continued to decline.  Thorp understood this risk, and continued to apply Kelly criterion to determine how much he should wager on each trade in order to mitigate the risk of such an unlikely event.  Unlike many hedge funds that focused only on outcome probability, Thorp understood and accounted for the other critical variable in risk management—the potentially devastating consequence of a low probability outcome.

Another person who figures prominently in financial risk management is Benoit Mandelbrot, who showed that unlikely events in financial markets are not that unlikely.  He starts with the dissertation of Louis Bachelier at the University of Paris in 1900 called, “The Theory of Speculation,” that applied Brownian motion from physical sciences to market prices.  Bachelier’s formula showed that the future course of the market is essentially a coin flip.  This discovery came to be called the random walk, and is visually described by a bell curve.  But Mandelbrot observed that the behavior of prices in financial markets (using data from cotton, wheat, railroad stocks, and interest rates that he ran on the supercomputers at IBM, where he worked at the time) frequently demonstrated huge leaps to the outer edges of the bell curve.  In a paper detailing his findings. “The Variation of Certain Speculative Prices,” he asserted that the standard bell curve was not applicable to model financial markets because, “Large price changes are much more frequent than predicted.”  Mandelbrot borrowed a mathematical technique devised by the French mathematician Paul Levy, whom he had studied under in Paris, to measure the erratic behavior of prices.  When plotted on a chart, the curve produced bubbled out on both ends, and became known as “fat tails.” 

Paul Cootner, an MIT finance professor viciously attacked Mandelbrot’s paper in his 1963 book, The Random Character of Stock Market Prices.  He, and other critics, claimed that while Mandelbrot’s model might be accurate for brief time periods, over longer time periods, prices appear to move in a more orderly Brownian fashion.  However, this misses Mandelbrot’s main point that “prices can gyrate wildly over short periods of time—wildly enough to cause massive, potentially crippling losses to investors who’ve made large leveraged bets.”  Mandelbrot’s theories were shelved by the financial engineers who preferred the orderliness of the bell curve and a random walk to the messy, chaotic world of a fat-tail distribution.  But from time to time events such as Black Monday (October 19, 1987), the Long Term Capital Management (LTCM) bailout in September 1998, and the Emergency Economic Stabilization Act of 2008, the mother of all bailouts (at least until several countries in the European Union default on sovereign debt), remind us of the consequences of ignoring unlikely events.

Nassim Nicholas Taleb, a critic of quant models, argues that investors who believe the market moves according to a random walk are “fooled by randomness,” and that models based on historical trends and expectations of a random walk, are bound to lead their users to destruction.  That’s the subject of the next book.

 

Risky Business (Part 1)

In 2005 and 2006, I worked with a security software startup.  The company had a superb technological approach that passively monitored Web application traffic, compared user activity from the most recent session with historical activity, and alerted security personnel to anomalies in user behavior that indicated fraudulent access.  My initial goal was to focus the company’s marketing, which had been highly diffused, on the most promising market. 

My timing was fortunate, in that several high profile security breaches had led the Federal Financial Institutions Examination Council (FFEIC) to recently issue an update to its guidelines on online customer authentication by financial institutions.  This was the external driver that we used to focus our technical solution on a value proposition (fraud detection and regulatory compliance) for a specific application (online banking) in a highly regulated market (financial services). 

Researching regulatory requirements for financial institutions was my first real exposure to risk assessment and management.  It led me to the Basel Accords, specifically Basel II, which created an international banking standard that would guard against various types of financial and operational risks and protect the international financial system from the types of problems that might arise should a major bank or a series of banks collapse.  Little did I realize, at the time, that the types of risk management practiced by global financial institutions would be tested very soon, and would be found sorely inadequate.

Since 2006, I’ve read several books about risk, in general, and, specifically, how the theoretical basis of financial risk management was circumvented, adulterated, and simply ignored by major financial institutions leading up to the global financial crisis that started to show its effects in 2007 and continues through today.  This is the first in a series of posts that will review the books, and discuss the lessons I learned from each.

Against the Gods: The Remarkable Story of Risk

Any serious study of risk should start with this book.  As the video interview with the author, Peter L. Bernstein, notes, he was an “economist, historian, and strategist.”  All of these attributes are on display in this most interesting and entertaining historical survey of risk management, beginning with the invention of numbers and the writing of Liber Abaci, or Book of the Abacus, in 1202 AD by 27-year-old Leonardo Pisano, better known as Fibonacci.

The foundation of risk management is probability, or how likely or unlikely it is that a certain outcome will occur.  From 1200 to 1700, many of the advancements in the science of probability were driven by men who shared two traits: a deep understanding of mathematics and a strong affinity for games of chance.  In several cases, the latter trait manifested itself, quite clearly, as a gambling addiction.

But probability is only one of two indispensible (but, unfortunately, not inseparable) factors in risk management.  The other is the consequence of the various outcomes, especially highly unlikely outcomes.  For example, placing a $1 bet on a game of Russian Roulette, where your own head is the target, with a single bullet in a 100-chamber revolver for $100,000 has a 99% chance of success and only a 1% chance of failure (100000:1 return with a 99:1 probability of success).  However, the consequence of failure—most likely death or severe brain damage—is sufficient to dissuade most people from accepting such compelling odds.

The book proceeds from 1700 to 1950 by tracing intersecting advances that contributed to a better understanding of risk from numerous disciplines including philosophy, logic, heredity, economics, statistics, chaos theory, game theory, and linear programming.  Collectively, the various studies contributed to the following general understanding of risk management.

The essence of risk management lies in maximizing the areas where we have some control over the outcome while minimizing the areas where we have absolutely no control over the outcome and the linkage between effect and cause is hidden from us.

Surprisingly, at least to me, the field of investment risk management did not really start until June 1952 with the publication of a 14-page article in the Journal of Finance titled “Portfolio Selection.”  Harry Markowitz, an unknown 25-year-old graduate student at the University of Chicago, authored the paper.  His objective was to factor risk into the construction of a portfolio for investors who “consider expected return a desirable thing and variance of return an undesirable thing.”  This led to Markowitz’s key insight of the strategic role of diversification in investment portfolio risk management.

The subject of the penultimate chapter titled “The Fantastic System of Side Bets,” is derivatives—the most sophisticated of financial instruments, the most intricate, the most arcane, and the most risky.  To quote from the book, “This fantastic system of side bets is not based on old-fashioned human hunches but on calculations designed and monitored by computer wizards using abstruse mathematical formulas…developed by so-called quants, short for quantitative analysts.”

And this is the subject of the next book.

 

 

All Things Must Pass Away

On Wednesday, October 5, I was away from home on business.  Around 4:30 pm, I learned that Steve Jobs had passed away.  That evening and the next morning, I read and watched reports of his life and the tributes to him, quite fittingly, on my iPad.  I frequently had to wipe tears from my eyes, and felt a profound sense of loss for someone I had never personally met.  It was a déjà vu experience, and I couldn’t exactly place when or where I had felt similarly, until I returned home on Friday.

That evening, while scanning TV channels, I saw the listing for the Martin Scorsese documentary, George Harrison: Living in the Material World.  Suddenly, I realized that the feeling of loss I had a couple of days before was exactly the same one I had experienced nearly a decade earlier, driving home on a gray November evening and listening to a remembrance of George Harrison on NPR that ended with his song, All Things Must Pass.

“None of life’s strings can last, so I must be on my way.  All things must pass.  All things must pass away.”

Parallels between the two men are numerous.  Both men were private, by nature, but both were public performers of the highest order.  Both were passionate and positive creators, but each said and did things that deeply hurt others.  And both died far too young: Jobs at 56 and Harrison at 58.  But the most important thing they shared was the realization of Abraham Lincoln’s assertion: “And in the end, it’s not the years in your life that count.  It’s the life in your years.” 

So while their bodies are gone, their spirits live on.  I can still experience the joy of a George Harrison song or the inspiration of Steve Job’s Stanford address on my iPad anytime.  And so my initial sense of loss has turned to a tremendous appreciation for the gift of being alive at the same time as these two extraordinary men.

 

Good to Great (to Indispensible)

“You must never confuse faith that you will prevail in the end—which you can never afford to lose—with the discipline to confront the most brutal facts of your current reality, whatever they might be.” – Admiral James Stockdale

I recently led a 2-day offsite meeting, on the heels of a major restructuring, to align executives and middle managers across the company behind a new strategic direction and operational discipline.  In preparing to lead the meeting, I reread two books that I have revisited repeatedly since my initial readings: Crossing the Chasm and Good to Great.

Crossing the Chasm is my handbook for guiding an emerging-technology company from early to mainstream markets.  I have covered important aspects of the book in prior posts; including Crossing the Chasm is Counterintuitive and Developing a Whole Product Roadmap for Applications Software.

Good to Great is my handbook for guiding organizational change in turnaround situations.  The Stockdale Paradox, succinctly stated at the beginning of this post, was an apt starting point for the meeting.  In fact, I used three of the four basic practices in the chapter, “Confront the Brutal Facts,” as ground rules for group discussions:

1. Lead with questions, not answers.
2. Engage in dialogue and debate, not coercion.
3. Conduct autopsies, without blame.

The principles of focus and discipline pervade Good to Great.  The “Hedgehog Concept” and “Culture of Discipline” have become fundamental to my leadership practices.  And getting the right people on the bus, while essential to an ongoing enterprise, is critical to a successful turnaround. 

As the book states, “First, if you begin with who, rather than what, you can more easily adapt to a changing world.  Second, if you have the right people on the bus, the problem of how to motivate and manage people largely goes away.  Third, if you have the wrong people, it doesn’t matter whether you discover the right direction.”

Good to Great has taken some knocks over the past few years as some of the example companies have fallen from grace, including Circuit City and, most notably, Fannie Mae.  However, of the many business books I have read, this is one of a handful that I return to again and again for direction and inspiration.  To me, it is indispensible. 

 

Integrating LIMS in Three Steps

An article by two of my colleagues, Michael Kuzyk and Mike Sanders, and me recently appeared in Laboratory Equipment. The information is non-commercial and specific to integrating a LIMS to instrumentation in a genomics lab, but has broader applications. Here is a summary and a link to the full article.

 

A successful integration of instrumentation and LIMS encompasses three phases, each of which should be addressed out-of-the-box by the LIMS: integrating the LIMS with a lab’s preferred instrumentation; configuring and customizing the LIMS to track the quality of data coming off the instruments; and ensuring rapid, reliable access to instrument results and sample information. 

READ THE FULL ARTICLE

Developing a Whole Product Roadmap for Applications Software

“The point of greatest peril in the development of an emerging technology-based market lies in making the transition from an early market dominated by a few visionary customers to a mainstream market dominated by a large block of customers who are predominantly pragmatists.”

“Pragmatists evaluate and buy whole products.”

“Any company that executes a whole product strategy competently has a high probability of mainstream market success.”

     – Geoffrey Moore, Crossing the Chasm

 

Developing a Whole Product Roadmap for Applications Software

View more presentations from Bruce Pharr

The whole product concept, initially described by Theodore Levitt, plays a central role in Geoffrey Moore’s classic book on technology marketing, Crossing the Chasm: Marketing and Selling Disruptive Products to Mainstream Customers.  The whole product includes everything necessary for the pragmatic mainstream customer to derive immediate value without having to build or buy additional products or services. 

Application software poses special whole product issues, given its abstract nature. 

• Where does the whole application software product begin and end? 
• What are the complete dimensions of a whole application software product?
• What whole product capabilities should be built into the application software?
• What whole product capabilities should partners deliver?

While the whole application software product strategy includes documentation, training and services, it begins with a whole product roadmap.