Filtering Customer Data

Measuring the Customer Experience
June 23, 2017
Should It All Stop after Sales?
June 15, 2017

Filtering Customer Data

Adapted from Dr. Claes Fornell's book | June 20, 2017
The Satisfied Customer: Winners and Losers in the Battle for Buyer Preference

The next phase in the information revolution will be purification, sorting, and filtering. The companies that excel here will have a much better chance at success than those that continue to use data in lieu of information.

It strikes me as odd that IT departments in general seem not to have a great deal of measurement competence. After all, they deal with information. But they seem to be more concerned with transmission and compilation of data, without enough attention to what the data mean, how they are measured, and what purpose they serve. Many of the best IT companies have the worst performance measurement systems—especially when it comes to customer satisfaction.

And yet, I think it was Bill Gates who proclaimed that whether you win or lose is determined by how you gather, manage, and use information. Gates is probably correct and his argument more compelling if posed in the negative. Bad measurement leads to bad information. Bad information leads to bad decisions. Bad decisions lead to competitive vulnerability. Competitive vulnerability leads to shrinking earnings and loss of capital. Show me a loser and I will show you a company with poor customer measurement systems.

The next phase in the information revolution will be purification, sorting and filtering. We have technologies for transmitting enormous amounts of data, but we don't yet have good systems for separating the bad from the good or the trivial from the relevant. The companies that excel here will have a much better chance at success than those that continue to use data in lieu of information.

Data are the raw material from which information is made. Data must go through a cleansing process, a refinery, a filtering mechanism, and some form of analysis in order to be useful. Same with crude oil and gasoline. Cars don't run on crude oil. Companies should not run on raw data.

Once data has been converted to information, we judge its quality by: (1) accuracy, (2) relevance, and (3) actionability. Accuracy refers to precision, the ability to separate signal from noise and how the information is (how to generalize from the particular to the general). Relevance has to do with the impact on things that matter. Actionability calls for prescriptive information. That is, it should direct action.

There are measurement systems about customer assets that satisfy these criteria. They are based on scientific principles, some of which involve fairly difficult mathematics and statistics. But conceptually, the principles are not difficult to grasp. I will try to explain them in prose rather than in equations.

Let's start with the very notion of satisfaction itself. What is it and what do we know about it? Economists have long expressed reservations about whether an individual's satisfaction or utility can be measured, compared, or aggregated.

Classical economics, starting with Jeremy Bentham in the late eighteenth century, viewed consumer satisfaction and utility as equivalent. That is, utility was referred to as that property in any object, whereby it tends to produce benefit, advantage, pleasure, good, or happiness or to prevent the happening of mischief, pain, evil or unhappiness.1

In neoclassical economics, the perspective is narrower. Utility is derived from observing how consumers choose. Like economists would argue that stock markets can be efficient even though their participants can be irrational, George Katona thought that the summation of ignorance can produce knowledge because of self-canceling of random factors. The idea that the summation of ignorance can produce knowledge is pretty appealing. It's like finding nuggets of value in refuse. It is also consistent with the idea of filtering out noise in order to find a real signal, weak though it may be.

This brings us to the quantification of the unobservable. It may sound impossible that we can measure things we can't see and, perhaps even more astounding, that we can incorporate these unobservables into systems of equations that delineate causes and effects.

This is of great importance. Virtually all business decisions assume cause and effect. We lower prices to increase demand. We improve quality to increase customer satisfaction. It doesn't really matter whether we're on the road, trying to figure out how to get from point "a" to point "b," or whether we're trying to achieve some business objective. Same thing. We need to know where we are and where to go.

It would also be helpful to know what happens once we get there. Business managers need to know the current status of customer relationships, how satisfied or dissatisfied customers are, what the value of the customer asset is, how to improve that value, and what the net effect is likely to be.

1. Jeremy Bentham, The Principals of Moral and Legislation (London 1789).

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