PropheZy is a suite of statistical software that classifies, models and predicts events. Z/Yen uses PropheZy to help organisations prosper by making better choices. Clients use PropheZy to identify favourable or adverse patterns or anomalies in order to get a “detailed grip on the big picture”. Z/Yen has built a number of predictive systems, e.g. health forecasting, a financial markets risk estimator, a marketing event attendance predictor, a weather risk calculator and an advertising revenue forecaster.
PropheZy makes predictive applications using readily available data. Based on ASP, XML architecture, it integrates with existing IT architectures. The predictive system is known as Dynamic, Anomaly & Pattern Response (DAPR):
DAPR in Finance:
Successful tests have been conducted in the following areas:
PropheZy consists of the following components:
PropheZy is based upon sophisticated statistical techniques that classify and model multivariate data to make predictions on new data. PropheZy works best on continuous numerical data but can handle discrete, categorical data and time series data. PropheZy improves on traditional approaches such as neural networks and provides information about the classification confidence levels, so that appropriate human choices can be made.
PropheZy can be rolled-out to thousands through a browser interface by running an ASP service. Applications can be updated frequently to take account of changing conditions. PropheZy is inherently parallel, thus ensuring that successful applications are scalable for complex, real-time applications. Potential applications include increasing success rates among targets; reducing customer churn; identifying best sales techniques, bid approaches or cross-sales; setting targets for client revenue, profitability, client satisfaction.
PropheZy has been extensively tested using standardised datasets - its performance is amongst the best available.
PropheZy is often used in conjunction with VizZy – a visualisation package which allows data to be more easily analysed and understood.