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
Related Blogs
DeeperThanBlue launches Analytics Practice to drive clients’ business performance and growth
17th July 2018, Sheffield DeeperThanBlue today announced the launch of its Analytics practice that turns data into insights which help drive business performance and growth. Whilst many companies may have invested in Business Intelligence technologies over the years, very few
Read MoreHow Analytics helps businesses ‘See, Plan, Predict’ success
Analytics – what is it and how can it help you? Watch this fun animation on how Analytics can help your company read and digest mass Data. So you can See, Plan and Predict your companies success! What questions
Read MoreUsing Analytics to Predict
So, you may have seen us predicting the World Cup matches? Let me tell you why… Here at DeeperThanBlue Analytics, our aim is to help our customers use their own data better, to improve business performance. We break out
Read More