Not only do we have considerable knowledge of IBM SPSS Statistics, but we also know how to leverage the insights to feed them back into your business planning and reporting to provide meaningful insights that improve performance. The potential scope for the application of predictive analytics is enormous, the list below gives some common use cases that we have built:

• Predicting likely sales demand – Analysis of multiple years of customer orders by product to predict likely orders and allow reasonable lead times for production planning
• Forecasting Inventory requirements and likelihoods of demand at low, medium, high levels and balancing risk of stock out v over stock
• Price Elasticity analysis – predicting demand change in a product at different sell prices, to allow profit maximisation
• Analysis of correlating factors which tend to lead to high bacteria count results
• Analysis of the effect on seasonal events which may not recur at the same time of year, e.g. Easter holidays or Warm Weather or both together
• Customer Lifetime Value models can predict future revenues from customers, allowing decisions to be made to increase retention
• Financial Services Predictive Modelling – predicting customer behaviour and internal bank behaviour in statutory and regulatory risk environments including liquidity and credit risk