Abstract: A computer-assisted method for providing re-quotations for insurance coverage may include receiving a list of insurance leads corresponding to individuals who received a previous quotation for insurance coverage but did not purchase the insurance coverage and identifying a difference between the previous quotation and a new quotation. This difference may include an increase in offered insurance coverage and/or a reduction in cost. A computing device may calculate a probability for each of the individuals on the list using a regression model based, at least in part, on the identified difference. In some cases, the regression model may be associated with individual states. In other cases, the regression model may correspond to a plurality of states. The regression model may output a probability that a resident of a particular state will purchase insurance in response to a re-quotation for insurance coverage, where individuals may then be ranked based on the probability.

Abstract: One or more devices in a data analysis computing system may be configured to receive and analyze movement data, and determine driving trips associated driving data based on the received movement data. Movement data may be collected by one or more mobile devices, such as smartphones, tablet computers, and the like. One or more driving trips may be identified based on the movement data collected by the mobile devices, including acceleration data, speed data, and rotation rate data. After identifying a driving trip, the previously collected movement data and/or additional driving data may be analyzed to determine additional driving trip data, such as the driver, vehicle, and/or additional driving factors.

Abstract: A computer-assisted method for providing re-quotations for insurance coverage may include receiving a list of insurance leads corresponding to individuals who received a previous quotation for insurance coverage but did not purchase the insurance coverage and identifying a difference between the previous quotation and a new quotation. This difference may include an increase in offered insurance coverage and/or a reduction in cost. A computing device may calculate a probability for each of the individuals on the list using a regression model based, at least in part, on the identified difference. In some cases, the regression model may be associated with individual states. In other cases, the regression model may correspond to a plurality of states. The regression model may output a probability that a resident of a particular state will purchase insurance in response to a re-quotation for insurance coverage, where individuals may then be ranked based on the probability.

Abstract: A system and method are disclosed to promote good driving among users. A user-customizable good driving (GD) wish list may be created, populated, modified, and/or used to promote good driving. The GD wish list may be used in conjunction with a vehicle telematics unit (VTU) to monitor, process, detect, and/or record good driving events, and communicate and/or generate positive feedback for the user. In addition to positive feedback, good driving (GD) points may be accumulated based on driving behavior. The accumulated GD points may be used in conjunction with a product and/or service offering, such as by an insurance company. Furthermore, in some examples, the system encourages users to promote good driving behavior by pledging themselves to a challenging GD wish list that will earn them more GD points and the associated benefits.

Abstract: Telematics and external data relating to the real-time driving of a driver of a taxi vehicle may be collected and used to calculate a driving score. A telematics device installed in the taxi vehicle may collect vehicle data, trip data, and passenger data from one or more sensors. A computing device in communication with the telematics device may calculate a driving score for the taxi vehicle during a trip based on a type of vehicle, a type of trip, a number of miles driven during the trip, a duration of the trip, a number of passengers in the taxi vehicle during the trip, and a type of the passengers in the taxi vehicle during the trip. As one example, the driving score may be provided to an employer of a driver of the taxi vehicle.

Abstract: A system and method are disclosed to promote good driving among users. A user-customizable good driving (GD) wish list may be created, populated, modified, and/or used to promote good driving. The GD wish list may be used in conjunction with a vehicle telematics unit (VTU) to monitor, process, detect, and/or record good driving events, and communicate and/or generate positive feedback for the user. In addition to positive feedback, good driving (GD) points may be accumulated based on driving behavior. The accumulated GD points may be used in conjunction with a product and/or service offering, such as by an insurance company. Furthermore, in some examples, the system encourages users to promote good driving behavior by pledging themselves to a challenging GD wish list that will earn them more GD points and the associated benefits.

Abstract: Systems and computer-implemented methods for estimating a cost to repair a damaged item are provided. In some example implementations, an indication identifying a portion of the damaged item is received. A series of images are selected that depict increasing degrees of damage to a reference item at the portion identified, and the series of images are provided for presentation. An indication is received that identifies one of the images in the series of images that has been selected as best representing the damage to the portion of the damaged item identified. An estimated cost to repair the damaged item is generated based, at least in part, on the image selected.

Abstract: Systems and methods for processing insurance claims are provided. A notice of loss associated with a damaged item and damage information describing the damage may be received. Repair cost data may be retrieved based on the damage information received. A repair cost model may be configured based on the damage information and the repair cost data and used to generate a repair cost for repairing the damaged item. A non-negotiable payment amount may be selected based on the repair cost generated and included in a work order along with the damage information. The work order may be transmitted to a repair service provider that accepted the terms of the work order. Feedback may be received from the repair service provider that indicates an actual cost to repair the damaged item, and the repair cost data may be updated based on the feedback received.

Abstract: Methods, computer-readable media, systems and apparatuses for determining and implementing dynamic usage-based insurance policies are presented. A cost per day and a cost per mile associated with the dynamic usage-based insurance policy may be determined. The cost per mile may be computed independently for each of a plurality of road segments comprising a trip based on a time of day that the vehicle traveled each road segment, a road type associated with each road segment. The cost per mile of each road segment of the driving trip may further include whether or not hard braking or hard cornering events were encountered during a specified cumulative driving distance that may include, at least a portion, of a plurality of driving trips. The cost per mile for each of the plurality of driving trips may be adjusted once a threshold distance has been traveled by the vehicle based on the number of hard braking events and hard cornering events occurred during that distance.

Abstract: Telematics and external data relating to the real-time driving of a driver of a taxi vehicle may be collected and used to calculate a driving score. A telematics device installed in the taxi vehicle may collect vehicle data, trip data, and passenger data from one or more sensors. A computing device in communication with the telematics device may calculate a driving score for the taxi vehicle during a trip based on a type of vehicle, a type of trip, a number of miles driven during the trip, a duration of the trip, a number of passengers in the taxi vehicle during the trip, and a type of the passengers in the taxi vehicle during the trip. As one example, the driving score may be provided to an employer of a driver of the taxi vehicle.

Abstract: A computer-assisted method for providing re-quotations for insurance coverage may include receiving a list of insurance leads corresponding to individuals who received a previous quotation for insurance coverage but did not purchase the insurance coverage and identifying a difference between the previous quotation and a new quotation. This difference may include an increase in offered insurance coverage and/or a reduction in cost. A computing device may calculate a probability for each of the individuals on the list using a regression model based, at least in part, on the identified difference. In some cases, the regression model may be associated with individual states. In other cases, the regression model may correspond to a plurality of states. The regression model may output a probability that a resident of a particular state will purchase insurance in response to a re-quotation for insurance coverage, where individuals may then be ranked based on the probability.

Abstract: One or more devices in a data analysis computing system may be configured to receive and analyze movement data, and determine driving trips associated driving data based on the received movement data. Movement data may be collected by one or more mobile devices, such as smartphones, tablet computers, and the like. One or more driving trips may be identified based on the movement data collected by the mobile devices, including acceleration data, speed data, and rotation rate data. After identifying a driving trip, the previously collected movement data and/or additional driving data may be analyzed to determine additional driving trip data, such as the driver, vehicle, and/or additional driving factors.

Abstract: A computer-assisted method for providing re-quotations for insurance coverage may include receiving a list of insurance leads corresponding to individuals who received a previous quotation for insurance coverage but did not purchase the insurance coverage and identifying a difference between the previous quotation and a new quotation. This difference may include an increase in offered insurance coverage and/or a reduction in cost. A computing device may calculate a probability for each of the individuals on the list using a regression model based, at least in part, on the identified difference. In some cases, the regression model may be associated with individual states. In other cases, the regression model may correspond to a plurality of states. The regression model may output a probability that a resident of a particular state will purchase insurance in response to a re-quotation for insurance coverage, where individuals may then be ranked based on the probability.

Abstract: Telematics and external data relating to the real-time driving of a driver of a taxi vehicle may be collected and used to calculate a driving score. A telematics device installed in the taxi vehicle may collect vehicle data, trip data, and passenger data from one or more sensors. A computing device in communication with the telematics device may calculate a driving score for the taxi vehicle during a trip based on a type of vehicle, a type of trip, a number of miles driven during the trip, a duration of the trip, a number of passengers in the taxi vehicle during the trip, and a type of the passengers in the taxi vehicle during the trip. As one example, the driving score may be provided to an employer of a driver of the taxi vehicle.

Abstract: Methods, computer-readable media, systems and apparatuses for determining and implementing dynamic usage-based insurance policies are presented. A cost per day and a cost per mile associated with the dynamic usage-based insurance policy may be determined. The cost per mile may be computed independently for each of a plurality of road segments comprising a trip based on a time of day that the vehicle traveled each road segment, a road type associated with each road segment. The cost per mile of each road segment of the driving trip may further include whether or not hard braking or hard cornering events were encountered during a specified cumulative driving distance that may include, at least a portion, of a plurality of driving trips. The cost per mile for each of the plurality of driving trips may be adjusted once a threshold distance has been traveled by the vehicle based on the number of hard braking events and hard cornering events occurred during that distance.

Abstract: Telematics and external data relating to the real-time driving of a driver of a taxi vehicle may be collected and used to calculate a driving score. A telematics device installed in the taxi vehicle may collect vehicle data, trip data, and passenger data from one or more sensors. A computing device in communication with the telematics device may calculate a driving score for the taxi vehicle during a trip based on a type of vehicle, a type of trip, a number of miles driven during the trip, a duration of the trip, a number of passengers in the taxi vehicle during the trip, and a type of the passengers in the taxi vehicle during the trip. As one example, the driving score may be provided to an employer of a driver of the taxi vehicle.

Abstract: A system and method for evaluating, scoring, and encouraging group performance towards a common goal is disclosed. In an embodiment, individuals of a group have an incentive to encourage other group members to drive better and achieve an improved overall driving score for the group. A group driving score is determined based on the driving performances of members of the group. The group may establish a driving score goal and work toward achieving that goal using real-time group reinforcement communications.

Abstract: A driving analysis server may be configured to receive vehicle location data and/or operation data from one or more vehicle systems, identify driving trips and/or driving patterns based on the vehicle data, determine risk assessment values corresponding to the driving trips and driving patterns, and calculate driver scores based on the analyzed driving trip and driving pattern data. Destination locations may be identified for a vehicle's driving trips, and information relating to the destination locations may be retrieved and analyzed to determine risk factors and risk assessment values associated with driving to and from the destination, as well as parking at the destination. Specific driving trip types or purposes may be identified, and driving scores may be calculated based on the vehicle location and time data, including the risk factors, risk assessment values, and the determined trip types or purposes.

Abstract: A computer-assisted method for providing re-quotations for insurance coverage may include receiving a list of insurance leads corresponding to individuals who received a previous quotation for insurance coverage but did not purchase the insurance coverage and identifying a difference between the previous quotation and a new quotation. This difference may include an increase in offered insurance coverage and/or a reduction in cost. A computing device may calculate a probability for each of the individuals on the list using a regression model based, at least in part, on the identified difference. In some cases, the regression model may be associated with individual states. In other cases, the regression model may correspond to a plurality of states. The regression model may output a probability that a resident of a particular state will purchase insurance in response to a re-quotation for insurance coverage, where individuals may then be ranked based on the probability.

Abstract: One or more devices in a data analysis computing system may be configured to receive and analyze movement data, and determine driving trips associated driving data based on the received movement data. Movement data may be collected by one or more mobile devices, such as smartphones, tablet computers, and the like. One or more driving trips may be identified based on the movement data collected by the mobile devices, including acceleration data, speed data, and rotation rate data. After identifying a driving trip, the previously collected movement data and/or additional driving data may be analyzed to determine additional driving trip data, such as the driver, vehicle, and/or additional driving factors.