Predictive model development

Abstract

Models are generated using a variety of tools and features of a model generation platform. For example, in connection with a project in which a user generates a predictive model based on historical data about a system being modeled, the user is provided through a graphical user interface a structured sequence of model generation activities to be followed, the sequence including dimension reduction, model generation, model process validation, and model re-generation.In connection with a project in which a user generates a predictive model based on historical data about a system being modeled, and in which the project includes a series of user choice points and actions or parameter settings that govern the generation of the model based on rules, which direct the user to select and apply an optimal model.

Description

BACKGROUND[0001]This description relates to predictive modeling.[0002]Predictive modeling, for example applied to targeted marketing, refers to modeling (a) which of a company's customers would likely buy a new or additional product (that is, would be susceptible to a cross-sell or up-sell effort); or (b) which prospects from a population of potential customers would be likely to accept an offer for a product or service (called acquisition by response or look-alike); or (c) which existing customers are most likely to cancel a current service (called retention or chum reduction); or (d) trigger points for behavior outside the normal range; or (e) to estimate the expected value or magnitude of a predicted outcome. Modeling is typically done by an analyst who is either an employee of a company or of an external consulting or service bureau. The analyst uses his experience and skill to create a custom model using available model building software applied to currently available data. The...

AI Technical Summary

Benefits of technology

[0014]Among the advantages of these features and aspects are one or more of the following. Project management of the workflow of model development, including scored list generation, report production, and model maintenance is achieved. Complete documentation and project replication or project refinements are easily performed at a later date, even by analysts without prior involvement. Control is maintained in a high volume production environment without requiring analyst initiative. The efficiency of orchestrating the workflow is improved so as to minimize development time. The system documents both successful and unsuccessful approaches as the analyst applies serial testing of alternative data treatments and alternative techniques, enabling easy replication. The unifying graphical user interface assures that the analyst uses the model generation platform's integrated techniques correctly. The interface controls the staging of the successive techniques and reduces inefficiencies. The analyst is discouraged from making inappropriate decisions at choice points by enabling only choices that fit the data types. The datasets of interest for such models involve numerous records with ...

Problems solved by technology

The cost and accuracy of the model depend on the ability of the analyst, the time permitted to develop the model, the quality of the data used, and the performance of the model development software tools.

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