Revenue prediction for a sales pipeline using optimized weights

A computer program product for performing revenue prediction for a sales pipeline using optimized weights, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the program instructions executable by a processor to cause the processor to perform a method comprising:

• receiving historical sales data, utilizing the processor;
• transforming a nonlinear, non-convex optimization model for weighting optimization into a linear optimization model, utilizing the processor;
• determining, utilizing the processor, a number of historical periods of the historical sales data and weights to be applied to a historical conversion rate and a historical growth rate for the number of historical periods of the historical sales data, utilizing the nonlinear, non-convex optimization model or the linear optimization model;
• predicting, utilizing the processor, a future optimal conversion rate and a future optimal growth rate by applying the weights to the historical conversion rate and the historical growth rate for the number of historical periods of the historical sales data;
• and
• applying, utilizing the processor, the future optimal growth rate and the future optimal conversion rate to current sales pipeline data to determine future sales data.

A system, comprising:

• a processor and logic integrated with and/or executable by the processor, the logic being configured to:
• receive historical sales data;
• transform a nonlinear, non-convex optimization model for weighting optimization into a linear optimization model;
• determine a number of historical periods of the historical sales data and weights to be applied to a historical conversion rate and a historical growth rate for the number of historical periods of the historical sales data, utilizing the nonlinear, non-convex optimization model or the linear optimization model;
• predict a future optimal conversion rate and a future optimal growth rate by applying the weights to the historical conversion rate and the historical growth rate for the number of historical periods of the historical sales data;
• and
• apply the future optimal growth rate and the future optimal conversion rate to current sales pipeline data to determine future sales data.