Abstract: A system, method, and computer-readable medium for implementing a hardware resource class. An electronic processor may be configured to determine a rate of a hardware resource. The hardware resource may be configured to allow a hardware-based solution provider to provide a service to an end user. The rate may be configured to be remunerated by a source different than the end user. The electronic processor may be further configured to determine an inflow amount for the hardware-based solution provider to remunerate to the source based at least partially on the rate of the hardware resource. The end user may utilize the service from the hardware-based solution provider. The service from the hardware-based solution provider may be at least partially based on data provided by the hardware resource.

Abstract: A system, method, and computer-readable medium for implementing a hardware resource class. An electronic processor may be configured to determine a rate of a hardware resource. The hardware resource may be configured to allow a hardware-based solution provider to provide a service to an end user. The rate may be configured to be remunerated by a source different than the end user. The electronic processor may be further configured to determine an inflow amount for the hardware-based solution provider to remunerate to the source based at least partially on the rate of the hardware resource. The end user may utilize the service from the hardware-based solution provider. The service from the hardware-based solution provider may be at least partially based on data provided by the hardware resource.

Abstract: System and method for analyzing driver behavior based on telematics data are disclosed. In an example, a probability of a user driving a vehicle is computed and a risk score is generated to develop at least one driver profile based on the probability. Further, routes taken by said user driving said vehicle are clustered to generate enhanced driver profile and using the clustered output to develop dynamic intelligent contexts for each said route and adding contextual intelligence messages to customize said risk score. Furthermore, the routes taken by the said user in real time are predicted. In addition, a missing route is identified through imputation of missed routes to compute annualized mileage, and a missing distance is imputed in an analysis of at least one trip of the driver in the vehicle. Also, independent trips are stitched based on at least one recommendation from an analytics engine.

Abstract: The disclosure relates generally to estimating lifetime values, and more particularly to estimating Prospect Lifetime Values (PLTVs) for prospective customers for an organization. In one example, for estimating PLTV for a prospective customer, a distance of the prospective customer from each of existing customers of the organization is computed using existing customer data and prospective customer data. The existing customer data includes at least one of existing customer demographic variables and existing customer profile variables. Further, the prospective customer data includes at least one of prospective customer demographic variables and prospective customer profile variables. Subsequently, for the prospective customer, a pre-defined number of existing customers are determined from amongst the plurality of existing customers based on the distance. Thereafter, PLTV for the prospective customer is estimated using an average of Customer Lifetime Values (CLTVs) for the determined existing customers.

Abstract: System and method for analyzing driver behavior based on telematics data are disclosed. In an example, a probability of a user driving a vehicle is computed and a risk score is generated to develop at least one driver profile based on the probability. Further, routes taken by said user driving said vehicle are clustered to generate enhanced driver profile and using the clustered output to develop dynamic intelligent contexts for each said route and adding contextual intelligence messages to customize said risk score. Furthermore, the routes taken by the said user in real time are predicted. In addition, a missing route is identified through imputation of missed routes to compute annualized mileage, and a missing distance is imputed in an analysis of at least one trip of the driver in the vehicle. Also, independent trips are stitched based on at least one recommendation from an analytics engine.

Abstract: The disclosure relates generally to estimating lifetime values, and more particularly to estimating Prospect Lifetime Values (PLTVs) for prospective customers for an organization. In one example, for estimating PLTV for a prospective customer, a distance of the prospective customer from each of existing customers of the organization is computed using existing customer data and prospective customer data. The existing customer data includes at least one of existing customer demographic variables and existing customer profile variables. Further, the prospective customer data includes at least one of prospective customer demographic variables and prospective customer profile variables. Subsequently, for the prospective customer, a pre-defined number of existing customers are determined from amongst the plurality of existing customers based on the distance. Thereafter, PLTV for the prospective customer is estimated using an average of Customer Lifetime Values (CLTVs) for the determined existing customers.

Abstract: This disclosure relates generally to computation of credit scores, and more particularly to methods and devices for computing optimized credit scores. A computer implemented method for computing optimized credit scores comprises transmitting a data retrieval request to a data source for retrieving profile data associated with a plurality of individuals. Availability of social media data for each individual is ascertained based on corresponding profile data. Each of the plurality of individuals is classified into a first set and second set based on the availability of the social media data associated with the individual. A social media score for each individual present in the first set is determined. A social media score for each individual present in the second set is determined based on social media scores of the first set. A credit score for each individual is determined based on corresponding social media score of the individual.