What do we want to know about our customers? What is a customer anyway?
The answer to the first question is likely the knee-jerk reaction: “Everything!”
The second is trickier; entire libraries have been written on how and what to capture, what a customer is might be of less immediate use, but as we shall see, more profound.
Being a philosophy buff, taking the metaphysical perspective comes natural to me. I hope you will enjoy it as well. Metaphysics for those unfamiliar with the term, was coined by Aristotle to designate his writing on stuff not physics. The current meaning is the study of reality itself, and what we through our biological lenses can learn of it.
In the beginning (not quite, but it suits our purpose) we thought of the world as being made up of singular entities: atoms, humans, companies, etc.
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Indeed this is what we can see around us, the world indeed seems to be made up of singular entities which sometimes interact with each other. From this sprung the still prevalent but erroneous notion of the atom with a nucleus in the middle and electrons as balls swirling around, much like our solar system. Likewise psychology started from a singular, self-contained view of the person. The birth of sociology with Emile Durkheim’s study of suicide rate differences between Protestants and Catholics suggested that persons are not self-contained but influenced by relationships. In physics Einstein’s wave-particle duality and theory of relativity came to the same conclusion, relationships and interactions are an integral part of reality.
The familiar term CRM, Customer Relationship Management, is built on this idea. Customers and companies are distinct entities which have a relationship. If we just manage this relationship properly customers will be loyal. Through marketing we try to nudge the customer to buy new products or attract suspects and prospects into buying from us. Through customer service we put a human face on our company making it easier to relate to.
Sales on the other hand assume Homo economicus, that we are rational beings looking to maximize our own benefit. This stands in stark contrast to the other two components which emphasize relationships and interaction. It has probably stuck as a model for microeconomics because of its ease of use and because in some circumstances it is an acceptable approximation.
There is a popular joke which come in many varieties and goes something like this:
A dairy farmer suffered from low milk production and wrote to the local university asking for advice. Unwittingly he sent it to the theoretical physics department. The physicists undaunted accepted the project and set to work. A few weeks later a physicist arrived at the farm with the result of their calculations: “Now, assuming spherical cows in a vacuum…
The point being that scientists in general and theoretical physicist in particular tend to make assumptions (spherical cows in a vacuum) which inhibit the results from being useful in the real world.
However, we have progressed.
Sociology came from the data analysis of seemingly unrelated events, but which signaled a pattern. Quantum field theory and later string theory came from the realization that the wave-particle duality was not restricted to the electron, and an attempt to describe quantum mechanics and general relativity in a consistent way. In business, the advent of Big Data indicates similarly that we cannot assume spherical cows, it is the whole, the universal wave function, the society which is important. Even if we know everything there is to know about a customer at a given moment, if we do not know the context, the environment, we still cannot predict the behavior.
Thus we have moved from a singular view, to a relational view, and finally to a holistic view. To make sense of customer behavior we need to see the customer in his or her context.
To go back to the original question, “What is a customer?”, it is not so much a person as a context, an eigenstate of the persons wave function, to make a physics analogy.
From this we infer that people are predictable, very much so, one study in 2010 was able to predict actions to 90% accuracy. Considering the data footprint of today, we can do better. But should we? That’s ethics and a different story.
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