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2 minutes reading time (365 words)

Predictive Analytics – The Predicament

Predictive Analytics (PA) is emerging as an important tool in the area of business decision. Predictive Analytics primarily deals with making a forecast based on several inputs. In this and the blogs that follow I will share my experiences with Predictive Modelling (PM), with a view to contributing to the current knowledge base that exists in the Predictive Analytics World.

In the world of business most predictive analytical tools are quantitative where numeric data is used for building an input-output model. The output is the prediction for specific inputs. For example: A 10% increase in advertising in January will result in an increase of 1% sale in May is a typical output from predictive analytics.

Common Mistake of Predictive Modelers: Assumption of linearity

Predictive Models are largely based on statistical techniques. Multiple Linear Regression (MLR) model is what most users will confront when they look at predictive models. This model works in the background whether one is using a multiple time series or multi-level modelling.

Multiple Linear Regression Models are developed based on a crucial assumption: the output is linearly dependent on the inputs. But all experience shows that in most business situations the assumption of linearity is not valid. Hence the statistical models have a poor fit and low predictive capability. In addition, the business world also suffers from Black Swan problems that no modelling can manage with any level of confidence.

The net effect of a linearity assumption, which is ubiquitous in almost all statistical modelling, and the resultant poor fit and low predictive capability has led to frustrated user community. Hence, a business executive looks at models with suspicion and trusts ‘gut’ to make decisions.

Predicament

The predicament of Predictive Modellers’ is: How do we get away from the linearity assumption on which almost all statistical tools are based, but it is known that this assumption is a poor, in fact a very poor, approximation of the real world behaviour?

The story of our approach to modelling starts from this predicament that we have been in, along with all others, and the path that we cut out to get out of it.

Dr. Cooram Ramacharlu Sridhar (Doc)
Managing Director and Advisor, Segmentation & Predictive Modeling

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