Abstract: A system and method for a process performed on a computer for constructing recommendation-based predictive models is disclosed. The system and methods access a collection of data records comprising a composite numerical representation of a primary performance indicator or PPI. The PPI comprises an ordinal data point having a calculated ordinal data level. A set of key drivers are determined having an influence on the PPI. Each of the key drivers comprises an ordinal data point having a calculated ordinal data level. An ordinal logistical regression is utilized to calculate the probability of increasing the PPI if each of the members of the set of key drivers are independently increased by a single ordinal data level. A recommended action from a user customizable candidate set of recommendations corresponding to the key driver having the highest probability of increasing the PPI is provided.

Abstract: A system and method for a process performed on a computer for constructing recommendation-based predictive models is disclosed. The system and methods access a collection of data records comprising a composite numerical representation of a primary performance indicator or PPI. The PPI comprises an ordinal data point having a calculated ordinal data level. A set of key drivers are determined having an influence on the PPI. Each of the key drivers comprises an ordinal data point having a calculated ordinal data level. An ordinal logistical regression is utilized to calculate the probability of increasing the PPI if each of the members of the set of key drivers are independently increased by a single ordinal data level. A recommended action from a user customizable candidate set of recommendations corresponding to the key driver having the highest probability of increasing the PPI is provided.