What is likely to happen

Predict – Predictive Analytics

Helping you predict future outcomes and maximise opportunities

Predictive Analytics is the name given to a branch of advanced analytics that helps to predict likely results from analysis of historic data and trends, and external data sources often called Big data Statistical algorithms are used to identify probabilities of unknown future events. Machine learning and regression techniques can be used to assess relationships between multiple variables and data sets Once you can understand how the different factors of your business behave, and at which point is a result an anomaly to act upon, you can use these insights to plan and monitor, and decide when to take corrective action At DeeperThanBlue we have the understanding of how to use these techniques to get insights that add value. We avoid the analysis paralysis by focussing on what drives your business performance, and evaluating which factors contribute to positive and negative results. Our experienced blend of commercial experience, mathematical knowledge and systems understanding provide us with the ability to cut through the distractions to help you drive performance

Predictive Analytics uses and Demand Prediction example

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

Sales demand prediction using SPSS, the top chart illustrates how the recurring pattern of sales can be smoothed and repeated taking into account seasonality, cyclic patterns and underlying seasonally adjusted trends

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