The Client
Contribution analysis is the process of determining how much, exactly, each program or action contributes to a final outcome. By predicting how much each program or action might contribute to a future outcome, a business or group can determine which program or action to use. The process is typically performed by linear models due to their simplicity and ease in which they can be understood, but their simplicity is also their greatest weakness as they are too simple to achieve any high degree of accuracy.
The Solution
OneClick.ai’s platform introduces a new way to perform contribution analysis by using non-linear models. Non-linear models offer two advantages over linear models. First, they are significantly more accurate than linear models. Second they allow us to determine the contributions of feature combinations, determining how much groups of features contribute when used together, rather than alone. This novel approach of using non-linear modeling creates a significantly more accurate analysis as the foundation used to make the contribution analysis, the non-linear model, is more accurate.
The Benefits
Knowing the individual and combined contribution of each feature is paramount to smart decision making and program selection. OneClick.ai perfects this task by rejecting the traditional methods and choosing instead to innovate new ones. Our non-linear models provide unsurpassed accuracy and combination feature analysis, something no one else can do. Any business or group can now easily identify poorly performing programs or actions to be cut, actions to be promoted, or how a combination of choices can produce the best result.
The real world is determined by factors acting together. OneClick.ai offers the real world solution of analyzing multiple competing or cooperating programs, and then providing an accurate contribution analysis that can tell any school exactly how well each program contributes. In that way a school can easily identify poorly performing programs to be cut, and high performing programs to be promoted.
