Mathematical Modelling

Data has always been at the heart of our business. Collating it, presenting it, interpreting it and using it to drive better decision making are the core skills in which we excel.

Using statistical analysis to extract the messages from the noise and generate actionable conclusions is one of the key tools in our portfolio. These same statistical methods can also be used to build models to help businesses to predict the results of their actions. Examples of such applications are:

• Building sales forecasts
• Predicting customer lifetime value
• Modelling financial risk and risk-adjusted returns
• Understanding consumer decision making using techniques such as factor analysis
• Perceptual mapping using multi-dimensional scaling
• Cost/process optimisation

These models can either be integrated into a company’s forecasting/planning systems or run stand-alone to let users carry out ‘what-if’ scenario testing. Either way, they form an invaluable part of the toolkit available to sophisticated businesses to help them improve their decision-making.