This page is a sub-page of our page on User Modeling.


The EE(S+O+C)(M+O+P) model also models exceptions at the Customer level (of company X). For example, the fact that the customers are no longer buying a certain product raises an exception that is transferred to the Strategic level and hopefully results in some changes in this product (or the introduction of a replacement) that improves this situation.

It is important to realize that – to a much larger degree than what companies are in general aware of – their customers are embedded in different communities within society. This is reflected in the model displayed in Figure 38.

To a large extent, a company’s relations to society are defined by its customers’ relations to society (and its different communities) and therefore it becomes important for the company to keep track of these relationships (presumably within the legal restrictions involved in such forms of “customer surveillance”).

The relationship of a \, C_{ompany} \, to \, S^{ociety} \, can be modeled as shaped by the company’s \, P_{roducts} \, and their \, C_{ustomers} \, as well as by the customers’ identification with different \, C^{ommunities} \, within \, S^{ociety} . In business algebra these relationships can be represented by the matrix product:

\, C_{ompany} P_{roducts} P^{roducts} C_{ustomers} C^{ustomers} C_{ommunities} C^{ommunities} S^{ociety} .

Since we are considering only one company and onesociety, we can make the identifications

\, C_{ompany} P_{roducts} \equiv P_{roducts} \, and \, C^{ommunities} S^{ociety} \equiv C^{ommunities} .

However, for the sake of clarity we will often keep the longer terms.

Because the matrix product is associative, it is “parenthesis agnostic”, so we are free to introduce parentheses anywhere we like in the \, C_{ompany} S^{ociety} \, relationship chain described above. If we write, for example,

\, (C_{ompany} P_{roducts} P^{roducts}) (C_{ustomers} C^{ustomers}) (C_{ommunities} C^{ommunities} S^{ociety})

then the \, C_{ompany} S^{ociety} \, relationship is described as the product of three quadratic matrices (remembering the identifications that we have just made above).

We can also write the full-fledged \, C_{ompany} S^{ociety} \, relationship chain as a product of the two factors

\, (C_{ompany} P_{roducts} P^{roducts} C_{ustomers}) (C^{ustomers} C_{ommunities} C^{ommunities} S^{ociety})

where the first factor is under the control of the \, C_{ompany} \, , and the second one is under the control of the \, C_{ustomers} \, . It is important to notice that in the first factor, the \, C_{ustomers} \, are grouped according to \, P^{roducts} \, , while, in the second factor, the \, C^{ustomers} \, are grouped according to \, C_{ommunities} \, .

If, for the sake of simplicity, we restrict the customer models to dealing only with \, W_{hy} \, and \, H_{ow} \, a \, C_{ustomer} \, is buying a certain \, P_{roduct} \, , we get the situation depicted in Figure 38, where the company’s anticipated and observed customer models are in agreement

Figure 38: Customer analytics: Both ‘How’ and ‘Why’ are coherent:
Customer analytics is coherent

The green colour on the \, H_{ow} \, and \, W_{hy} \, buttons indicate that the company’s model of the anticipated customer behaviour is in agreement with the observed customer behaviour – both with respect to how the customers are buying the product and why they are doing so. In short, the customers are behaving “as expected”, which is indicated by the two # signs in the figure. Both the why-triangle and the how-triangle are commutative. This situation is analogous to the experience of being in the flow, described in ch. 8.6 and depicted in the left part of Figure 11. The modification with respect to the present situation is that the company now plays the part of the user.

Unexpected behaviour from the side of the customers – either with respect to how they are buying the product (Figure 39) or why they are buying it (Figure 40) – breaks the flow of expectations. This generates exceptions that indicate the need to update the corresponding parts of the customer model.

Figure 39: Customer analytics: ‘How’ is incoherent, which indicates a lack of understanding of how the customers are interacting with a certain product, e.g., that they seem to buy the product online and not in a traditional store:
Customer analytics - 'How' is incoherent

Figure 40: Customer analytics: ‘Why’ is incoherent, which indicates a lack of understanding of why the customers are interacting with a certain product, e.g., why they are not buying it any longer (as they did before):
Customer analytics - 'Why' is incoherent

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