Abstract
Businesses use pre-sales questionnaires to quantify the value of incoming leads. Results from such questionnaires can improve allocations of sales resources and optimize advertising campaigns. A given questionnaire may ask for dozens of pieces of information from a prospective client. However, it is well understood that the questions are not always predictive of the likelihood that a client completes a purchase.
This disclosure provides machine-learning techniques that discover the predictive ability of questions on a questionnaire. The techniques enable a business to select or design questions or experiments that correlate with the closure of a sale. The business benefits by improving lead quality and optimizing sales and advertising resources.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Hoyne, Neil, "Machine learning to optimize controlled experiments", Technical Disclosure Commons, (December 19, 2018)
https://www.tdcommons.org/dpubs_series/1793