Sources of Information

The Customer Alignment Lifecycle™ Infographic is based on two primary sources of information:

The first source is published and unpublished articles, correspondence and papers written by: Roger Blackwell PhD., Tom Peters, Regis McKenna, Everett Rogers, Lee James and Charles D. McGrew (a professor at Ohio State University who worked with Everett Rogers1 ). Some of these unpublished works are included below as PDF files.

The second source is 30 years of “qualitative” research conducted by Warren Schirtzinger while working with startups and emerging high-tech companies. Qualitative research was used to gain an understanding of underlying reasons, opinions, and motivations. The only way to legitimately understand human behavior, such as feelings and motivations, is through qualitative research.

The Elements of Customer Alignment

Timing and luck always play a role in determining the success of a new product or innovation. But the essence of building a long-term, distinctive and sustainable business is the ability to understand and act on the continuing need for transformation — particularly in the critical areas of customer alignment.

To be successful throughout the entire lifecycle of a product or innovation, a company must be in alignment with the target customer and his/her:

1. desired value to be received

2. product expectations and requirements

3. sales/distribution preferences

4. communications content and needs

5. acceptance of risk

6. cost-benefit (or pain-gain) ratio requirements.

Systems thinking is required. All of these components play a critical role, and any one cannot dominate the others.

New “post-COVID-19” innovations will only succeed when all six elements are aligned with the evolving needs and characteristics of the customer.

The Customer Alignment Lifecycle concept is reinforced by the following models, concepts, frameworks and research:

Infographic Sources of Information

Diffusion of Innovations — Rogers, E. M., First edition. New York: The Free Press. (1962)
Diffusion of Innovations is the most popular model for understanding how new products and innovations gain momentum and diffuse (or spread) through a specific market or population. It is the de facto standard for encouraging the adoption of a new idea, behavior, or product. The core insight embedded in Rogers Diffusion Curve is that the adoption of new ideas occurs in a specific order through a social system comprised of five distinct segments or “categories” — innovators, early adopters, early majority, late majority, and laggards.

customer alignment training

Marketing Transformation: The Process of Continuing Change That Drives Market Leadership — James, Lee R. (1989)

An unpublished [internal] training document developed as part of the Regis McKenna, Inc. Professional Development Series3.

Lee James developed a powerful adaptation of Rogers’ Diffusion Theory. This framework characterizes the elements of customer behavior, including the dynamic nature of how products are purchased. James’ adaptation included the description of buyer personas to correspond with each adopter type in the Innovation-Diffusion model. These personas form the psychographic foundation of The Customer Alignment Lifecycle.

The concept of a value proposition, or the desired value to be delivered, is a belief from the customer about how value will be experienced and acquired.

customer alignment for products

The Eye of the Beholder — Peters, Thomas J. (1986)
In an unpublished newsletter sent to his subscribers in 1986, Tom Peters proposed an extension to Theodore Levitt’s total product concept2.

The total product concept developed by Ted Levitt describes the discrepancy between insider and customer perceptions. The winners in any industry come closer to seeing the world as their customers do. In technology-based companies, technical factors favored by insiders, such as technical reliability and special features, are contrasted with factors that customers consider important such as product support, standards and company reputation.

As products move through the adoption process, intangibles assume more importance. Often, pioneering new products lose their initial prominence because a new entrant is more successful in product positioning based on a more effective mix of intangibles. This can be the case even if the second product is not technically superior. [see unpublished letter from Regis McKenna, written in 2007]

Infographic Sources of InformationWilson Counselor Selling — Wilson, Larry  (1982)
The Wilson Counselor Selling framework speaks to the personal motivations and behavioral characteristics of people (promoter, supporter, controller, analyzer). It emphasizes creating messaging for each personality type, and also describes how to overcome the four broad categories of objections and reasons people don’t move forward: No Trust, No Need, No Help, No Hurry. Wilson built five successful companies, and Wilson Learning Corporation became the second largest training organization in the United States4.

Information sources from Regis McKenna

Relationship Marketing: Successful Strategies for the Age of the Customer  — McKenna, Regis (2006)
McKenna developed the concept of “dynamic positioning” that requires messaging to change as a product or innovation moves through the adoption process. McKenna’s infrastructure concept recognizes the need for multiple messages to multiple segments, and for changing but consistent messages.

New product introductions are especially effective when the entrepreneur understands the infrastructure concept and plans based on the requirement to constantly reposition a product.

Blackwell model of market transformationThe Engel-Blackwell-Kollat or EBK model Blackwell, Roger D. (1968)
Roger Blackwell is arguably the most prolific marketing academic next to Phil Kotler and Jag Sheth5. In 1968, Blackwell joined researchers James F. Engel, and David T. Kollat in developing a five-step model of the consumer buying decision process. A key feature of the EKB model is a description of the differences between high and low involvement as part of the buying process. High involvement is present in a high risk purchase. Low involvement is present in a low risk purchase.

The risk of failure is always high in the case of high involvement decisions. Such decisions are also more complex than the low involvement decisions. So, routine response behavior cannot be expected in the case of high involvement decisions. This element of minimizing RISK is probably the most important element in the innovation-diffusion process.

the marketing chasm

The Marketing Chasm (an unpublished letter to a client) (1988)
Lee James and his colleagues at Regis McKenna Northwest developed a marketing framework called The Marketing Chasm in the 1980s. The chasm concept is widely discussed and frequently misunderstood in Silicon Valley.

The marketing chasm exists because the buying criteria and performance expectations of early adopters are so dramatically different than the mainstream early majority. The Marketing Chasm is a gap between early adopters and the early majority, requiring very different marketing. Several years after writing this letter to a client, a book was written about the marketing chasm that created even more confusion.


1. Before becoming a professor at Ohio State University in 1958, my maternal grandfather worked as an agricultural extension agent. It was his job to help farmers adopt new methods of farming and increase agricultural production. (See the biographical profile for my maternal grandfather “C. Dan McGrew” on The Ohio State University, College of Food, Agricultural and Environmental Sciences, Dairy Science Hall of Service) Upon joining the faculty at Ohio State, my grandfather met a new colleague named Everett Rogers who had been studying the way innovations spread by observing the patterns of adoption among farmers. This exposure to Everett Rogers has allowed me to begin studying, testing, refining and re-examining the innovation-adoption process in 1967.

2. In the late 1980s and early 1990s I worked as a consultant at Regis McKenna, Inc. (RMI) where I had access to unpublished documents and newsletters created by Tom Peters.

3. In 1989, Lee James articulated the need for “marketing transformation” which calls for the reinvention of products and innovations to match the innovation-adoption lifecycle. Lee James was my mentor at Regis McKenna, Inc. (RMI), and later became my business partner when we formed Alliance Consulting Group. Mr. James was also the first person to recognize and describe the “marketing chasm” in Rogers’ diffusion-of-innovation curve.

4. In the early 1980s I worked for Measurex Corporation (now a division of Honeywell) where I was given sales training by representative of Wilson Learning Corporation. The Counselor Selling framework provides a method for changing your selling process for different types of audiences and buyers.

5. In 1978, I entered graduate school at Ohio State University and worked with Roger Blackwell PhD., who was my academic advisor and mentor. Our research project was focused on repositioning components of a product — from tangible to intangible — to encourage consumer acceptance and purchase.

Frequently Asked Questions

Business people love numbers because numbers make them feel secure. But in emerging markets, numbers are rarely reliable. And managers that rely on numbers are unlikely to succeed.

In many cases, quantitative analyses use the past to predict the future. But we live in an era when the future almost never resembles the past. It is extremely difficult to take the pace of technology into account. Extrapolating today’s trends into the future almost never works.

Bare statistics tend to miss the nuances of the market. A survey might show that 60 percent of all customers use a company’s product. But a qualitative approach might reveal that the customers are unhappy with the company’s service, and many are considering switching to a competitor.

Yet, as companies grow, they tend to rely more and more on quantitative techniques. They become locked up in numbers and big data. They end up with products that do not match the needs of the market nearly as well as the products of entrepreneurs. Creativity is squeezed out of the system.

When you are creating new markets, no one really knows where you are headed. You have to be more creative. A well-known CEO at Apple once said he is wary of numbers-oriented analysis: The only quantitative data I use are what people have done, not what they are going to do. No great marketing decisions have ever been made on quantitative data.

Other than confusing the difference between “disruptive” and “discontinuous,” the most common misunderstanding among people who read Crossing the Chasm is they tend to believe that all markets and populations have a chasm. But the chasm model only applies to “discontinuous innovations” and does NOT offer useful guidance for continuous innovations.

This distinction is not presented with enough detail in the chasm book, and the key to differentiating the two types of innovation requires more than looking for a change in behavior. The degree of discontinuity is also influenced by the ability of an industry or population to learn new things.

This is one of the fundamental misrepresentations in Crossing the Chasm. An innovation can be easy for a specific market segment to learn about and adopt, even though it requires a change in behavior.

However not all industries or social systems learn at the same rate. So an innovation that is continuous for one group or market, might be a discontinuous innovation in a different geographical area or culture or industry, because learning new things is more difficult for some populations.

The chasm model does not acknowledge the potential duality inherent in some innovations.

The field of statistics is based on fact that it is often impossible to collect the data of an entire population. Instead of trying to measure an entire population, we can gather a subset of that data and use statistics to draw conclusions about the population. This subset of data is called a “random sample.”

The Central Limit Theorem states that the distribution of your statistical [random] samples approaches a normal distribution as the sample size gets larger — no matter what the shape of the population distribution. This fact holds especially true for sample sizes over 30. As you take more samples, especially large ones, your graph of those sample averages will look more like a bell curve, a.k.a. a normal distribution.