An insurance company, for example, might want to know the contracts that its customers have signed. Similarly a bank might want to know the accounts of their customers and the amount of transactions per account.
These are only a few examples of the kind of information that data tables can contain. The data tables can also contain data other than customer data. A manufacturing company might collect information about the production of its products, or a retail chain might collect information about its stores.
If you have data tables that contain this kind of information, you might want to know whether this data set has an inherent structure, or if it contains groups of objects that are very similar.
Knowing about such groups enhances your business operations immensely because you can treat your customers according to the group that they belong to. For example, you can define specific product offerings or marketing campaigns targeted for each important group instead of treating all customers equally.
The Easy Mining procedure ClusterTable might find a group of customers that prefers healthy food of high quality. This is the appropriate customer group for a promotion of ecologically produced French cheese. It does not make sense to send this group an advertisement about frozen pizza.