Abstract: A method, software tool, and system for generating realistic synthetic ride-requests associated with a mobility or transportation service, including: utilizing a generative adversarial network, learning the spatial-temporal distribution of a plurality of real ride-requests; and, utilizing the generative adversarial network and based on the learning step, generating one or more synthetic source and destination ride-request geolocations that retain a statistical distribution of the plurality of real ride-requests. The generative adversarial network is a Wasserstein generative adversarial network.

Abstract: The present disclosure provides systems, methods, and metrics that filter out online visitor behavioral data that represents a potential lead with a high likelihood to convert to a vehicle sale from online visitor behavioral data that does not represent a potential lead with a low likelihood to convert to a vehicle sale, based on a mapping of sales back to observed website and vehicle configurator data. This enables more effective lead generation and the more efficient targeting of online incentive offers and sales “nudges,” for example. Further, the present disclosure enables web analytics data to be combined with sales data for sales forecasting in general.

Abstract: The present disclosure provides systems, methods, and metrics that filter out online visitor behavioral data that represents a potential lead with a high likelihood to convert to a vehicle sale from online visitor behavioral data that does not represent a potential lead with a low likelihood to convert to a vehicle sale, based on a mapping of sales back to observed website and vehicle configurator data. This enables more effective lead generation and the more efficient targeting of online incentive offers and sales “nudges,” for example. Further, the present disclosure enables web analytics data to be combined with sales data for sales forecasting in general.

Abstract: A method, software tool, and system for generating realistic synthetic ride-requests associated with a mobility or transportation service, including: utilizing a generative adversarial network, learning the spatial-temporal distribution of a plurality of real ride-requests; and, utilizing the generative adversarial network and based on the learning step, generating one or more synthetic source and destination ride-request geolocations that retain a statistical distribution of the plurality of real ride-requests. The generative adversarial network is a Wasserstein generative adversarial network.