HyperCube® is a mathematical algorithm and rule generation technology. It identifies the sets of simultaneous conditions in data that yield a higher frequency of specific occurrences. Simply put, it shows patterns of probable outcomes from variable inputs.
Some examples of applications:
- Insurance. A client wanted to find out the factors that affected the attrition of life assurance policyholders. The key factors that HyperCube® uncovered were: customer age, number of children, number of bank accounts, marketing segment and gender.
- Retail. The client wanted to discover the main factors affecting the sales performance of their retail stores. HyperCube® found the crucial factors to be: the store location (which all retailers know to be important) and - more surprisingly - the length of shelving holding products aimed at children.
- Healthcare. HyperCube® is often used in clinical trials and diagnostics. It can provide industry-specific business rules that can be developed for clients and applied to their production models.
The technology is non-statistical, meaning it does not take a sample and use algorithms in order to validate a hypothesis. Instead, it takes input from a large volume of data and outputs the results from the data alone. This means that all the available data is taken into account.
The lack of a hypothesis is another advantage of HyperCube® over statistics. HyperCube® exposes the rules and dependencies that are indicated by the data, and is not tied to any previously held view. Statistics, on the other hand, test data to see whether it proves a specified scenario.