Our team can analyse sales, marketing, and operations data to identify the cause and effect relationships driving your results. We start with careful data preparation by our analysts to link data sets; ensure completeness and quality; match, link, and pin consumer identities where appropriate; and selectively integrate third party data sets. We will review and prepare your data, handle missing values, smooth rough data, extract features and normalise variables where necessary prior to analysis. By understanding your data and problem space we will narrow the search and reduce the computation required to find the most accurate formulae to describe the relationships in your data.
We then apply machine learning to identify relationships within data that describe your operations. We can unlock process improvements by finding dependencies and key relationships. This is achieved without prior assumptions. Instead, the models are inferred from the data.
The output of our analysis are easily explained formulae that predict the outcome. More simply, we form hypotheses, test, and continually refine and combine to produce a maximally accurate formula while minimising complexity. Accompanying each formula is an analysis of its accuracy, error metrics, effectiveness and computational cost.
Importantly, the techniques apply equally to smaller data sets that describe the majority of actual business applications as much as to massively scaled data. Much valuable business data comes in “small data”, which we can readily optimize.
Our goal is substantial and lasting improvements to business results. Examples in action include: