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Learn more about WinTree

WinTree is a system that allows people to specify complex selection and
segmentation rules that are to be applied to alarge database. The rules are developed using a graphical editor. Once developed, the rules are passed to a
mining engine that applies the rules to the database. The process can produce statistical data, data for analysis and data for other purposes such as mailing and personalization.

Multiple sets of rules can be run at the same time with only one pass of the database. In addition, WinTree has tightly integrated advanced data analysis tools. The tools can be used to improve the accuracy of theselection rules.

WinTree has four main features that address the problems of complex selection:

  1. A graphical and syntactic language to express the rules that define sets of names and the relationships between those sets. The language also allows the user to specify how the names in a set are to be treated by letting the user indicate what actions are to be taken on the names.

  2. A dictionary system that allows rules to be represented by user chosen names. These names constitute a set of corporate business definitions that can be shared by many people. They help to provide a uniform and consistent set of selection rules. Named rules may be combined in various ways to create more complex rules. For example, in a marketing context,one could have a rule called SportsAffinity and another rule called RecentPromotion. One could make a new rule by combining these two rules in the following way:

    SportsAffinity AND NOT RecentPromotion

    You may also give this new rule a name so that it may easily be reused and shared. In this way complex rules may be built up from basic components. The logic behind the two rules may be quite complex but, as you can see, they are easy to use.

  3. A view of data that can readily represent almost any collection of data with little or no manipulation. We call this view a Micro-database. It provides a view of a customer's data as a collection of tables. The data may come from various sources. For example, customer personal information may come from one file, order/payment history from another and promotion history from yet another file. In addition, demographic information could be added. The information is merged together in memory at selection time to form a composite view of a customer.

  4. Built in features that are designed to support the use of analytical analysis to enhance the selection process. Examples of such features are the RECODE function and the SAMPLE action. The RECODE function allows variables to be categorized and also provides a means to define cross tab variables. The SAMPLE action provides a means of collecting data for analysis in such a way that the results of analysis can be easily fed back into the selection system.
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