Xeligence Architecture

Xeligence Architecture

Predictive modelling is a process that results in the discovery of patterns and relationships in data that may be used to make valid predictions.

As an example, a model that is built to understand when and why a customer is likely to leave the business (churn), can be used to predict the likelihood of a customer to leave based on changes in their current behaviour.

Building predictive models is part of a larger process that includes everything from defining the basic problem the model will solve, to deploying it into a working environment.

The Xeligence solution automates the process of developing and maintaining end-to-end data mining solutions. This incorporates all ETL from source systems, variable selection and classification, modelling and scoring phases, and deployment and ongoing tuning and maintenance. This enables your company to save thousands of statistician work hours and decrease time-to-market using predictive models for business decisions. Xeligence also incorporates self learning predictive analytics—the predictive modelsels automatically learn from each new data variable or data update by autonomously updating and deploying responses in real time.

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