Sales Prediction Modelling – Artificial Neural Network Approach

By Sarah Stowe | 29 Oct 2015 View comments

Introduction


Sales prediction modelling involves deriving a mathematical model by studying existing outlets and then using this information to predict the performance of prospective outlets. It is safe to say that most businesses employ the traditional technique of least square regression to make these sales projections, which has been effective to date. The last few years have seen major advances in computer technology, and with that, several modelling techniques have emerged. One such modelling technique is artificial neural networks. Artificial neural networks are analytic techniques derived from the learning processes in the human cognitive system and the neurological functions of the brain.


What has attracted the most interest in neural networks is the concept of learning by example, which in practice means the ability of the algorithm to learn from historical data by training itself iteratively.


Neural networks have been known to outperform more traditional prediction techniques in numerous situations. This can be attributed to the ability of neural networks to detect complex non-linear relationships between drivers and the dependent variable.


Spectrum has built a system that enables one to employ the power of neural networks to model complex real-world data with high accuracy. This includes artificial neural network software featuring 15 state-of-the-art neural models and various scientifically proven algorithms that have been developed in-house.



Benefits

  • Understand the contribution various factors make to sales performance 
  • Estimate the performance of proposed outlets at any location
  • Prioritize ÔNew BuildÕ areas
  • Get an indication of the effect competitors have on sales performance
  • Compare actual vs. potential performance of existing outlets


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