From the time of the first product ever sold, innovators, entrepreneurs, marketers and business people have been trying to understand customer behavior – how customers adopt (or don’t adopt) the products, services and experiences that they do. What has confounded these ‘sellers’ from the very early times is the nature of the human condition as a complex combination of the rational and the irrational, the fickle and the purposeful, the emotional and the reasoned and, in all cases, the non-linear – to the detriment of most marketers and to the benefit of the few who can ‘intuit’ the mind of the customer.

Over the years, much has been written about customer and market research. There are literally hundreds of methods, tools and techniques that today’s marketer has available to anticipate future customer actions.

In order to make sense out of this vast array of consumer research science and methodology, it is useful to consider Figure 1 below. This chart depicts the nature of consumer research efforts along two dimensions – the size and scope of the samples on the vertical axis, and the ‘cause-effect’ spectrum of consumer action on the horizontal.


This ‘cause-effect’ dimension has its roots in human psychology.

Behaviorist Psychology – pioneered by John Watson & B.F. Skinner, and
Cognitive Psychology – pioneered by Donald Broadbent & Daniel Kahneman

The left side represents the behaviorist camp and the right side the cognitive camp. Customer and market research today is squarely in the behaviorists camp with ethnography, demographics, and the research methods they employ.

More recent efforts have emerged to move beyond the behaviorist approaches that underlie most of the market and customer research today. These new fields employ discoveries in cognitive psychology to get at the underlying reasons of why people behave the way they do.

Customer Research Tools & Methods

Mirroring the divide between the behaviorist and the cognitive view of the customer is the struggle between quantitative, statistical methods (surveys, scales, conjoint analysis etc.) and qualitative methods (focus groups, ethnographic studies etc.) employed by consumer researchers. This struggle boils down to the desire for rigorous, numerical assessments, on the one hand, and the acknowledgement, on the other hand, that the human condition is more subtle and complex than can be captured in a numerical coding scheme.

Product design and ethnography have recently emerged as areas of intensive research and development and are being used as ways to understand and connect with customers. These areas are two sides of the same coin of customer’s wants (i.e., needs and desires) and what will they respond to. They serve to illustrate the key issue facing every innovator today – how do I base my decisions and actions on the ‘soft and fuzzy’ statements and behaviors of my future customers? How do I interpret their innermost needs and desires based on what they tell me?

The problem is how to make the ‘soft and fuzzy’ realm of customers inner, subconscious needs and desires ‘hard and clear’ enough to make decisions and take action. How do you go from the ‘cotton ball’ of customer experiences to the ‘crystal ball’ of analytic insight?

The answer to this dilemma is to move from behaviorist to cognitive research. Much has been learned about the cognitive processes of people and models that employ this knowledge can be used effectively to answer questions about customers.

Principles of Cognitive Modeling

A relatively recent development in the domain of customer research and understanding is the creation and use of the ‘persona’ as a means of capturing what’s behind the behavior of the customer. Inovo has taken the persona-based approach and extended it to create software models that provide the ability to analyze the mind of the customer. This new field of customer research is based on cognitive models that have as their basis the following principles:

• There are common human behaviors that are driven by the way our brains work.
• The way our brains work can be modeled and these models can be useful for gaining insight into customer behavior.
• Any valid cognitive model must accommodate (and explain) the wide range of observed behaviors and individual differences.
• Every person (i.e., potential customer) is different, but it is possible to categorize people into classes based on the way they think and choose in a specific context.
• Any valid cognitive model must be able to produce predictions about plausible future customer behaviors.

Note that for any model, cognitive or otherwise, there are always issues of accuracy and precision, complexity and abstraction that need to be addressed. What we are looking for in this first instance is a model of sufficient robustness and usefulness yet one that is not overly complex or that attempts to be too precise. We do not desire to try to model the entire range of human emotion, thought and behavior in all aspects of human life. We do desire a model that is accurate and that sufficiently represents the important attributes of customers as they adopt new products and services. It is in this context that we develop the ‘persona’ models that will give us insight into the mind of our customers.

From the Cotton Ball to the Crystal Ball

The cognitive model of the customer is a precise construction. It can be embodied in many forms ranging from a prose description of dynamics, to a computational model in a spreadsheet, to a full-fledged dynamic model (ideally). Each model has specific elements, inputs, outputs, controls and parameters. In this respect, the cognitive model is as hard and clear as you can get. It is the ‘crystal ball’ through which you view your understanding of the customer’s mind.

At the other end of the spectrum is the individual: the future customer or community member who has, through an engagement, told about their needs and desires using stories, metaphors, and analogies. Their answers use all of unstructured, ambiguous and incomplete articulations that language allows. This is the ‘cotton ball’ of subconscious, unmet needs and desires from which you need to discern ‘truth’.

Personas are the bridge from the cotton ball to the crystal ball. They are what allow you to translate articulated but unstructured needs and desires into actionable models of customer thinking and, consequently, behavior. Personas are categories that group people by how they think and choose – rather than by their surface characteristics or organizational roles. The persona represents the underlying causes of demand that drive adoption.

Unlike the descriptions used in user-centeric design, personas are not intended to be a fully articulated and realistic description of a ‘typical’ user or even a real person. Personas merely capture the important characteristics and suppress the ones that aren’t. They are intended to increase the ‘signal-to-noise’ ratio. Persona descriptions usually look like caricatures, focusing on specific aspects of a group of people and totally ignoring others. A persona is not a complete person. Even more importantly, Personas can be expressed in a structured manner so they can be used in ways impossible with purely narrative descriptions.

Conclusion

The new way to study future customer behavior bridges the world of ‘soft and fuzzy’ descriptive models and ‘hard and clear’ analytic models. The former provides the richest and most complete picture of people’s needs and desires while the latter provides the quantitative analyses that are the staple of modern business methods. The unifying component is the persona model, which provides a natural method for segmenting customers by how they think and choose. The persona can be represented at varying levels of precision through narrative, structured description or computational models. These, in turn, provide the innovator, entrepreneur and businessperson with the tools to gain insight into plausible future scenarios of adoption for new products and services. This approach works no matter what the industry, technology or nature of the innovation – whether incremental or disruptive. The outcome is that innovation becomes more science and less art.