Abstract: The subject disclosure relates to ways to improve route duration calculations e.g., estimated time of arrival (ETA) approximations, by taking consideration of reroute probabilities along a given vehicle path. In some aspects, the disclosed technology encompasses a process including steps for identifying a route between a first destination to a second destination, determining a reroute likelihood associated with at least one AV maneuver along the route, and calculating an estimated time of arrival (ETA) based on the reroute likelihood associated with at least one AV maneuver. Systems and machine-readable media are also provided.

Abstract: A method, apparatus and computer program product are provided for calculating closing metrics regarding a contract between a promotion service and provider. A promotional system may calculate a probability of closing, and an estimated time to close. The promotion service may offer a promotion to consumers for a discounted product or service, to be honored by the provider. A category, lead source, historical data, stage in sales, and/or size of the provider may be used in calculating a probability of close and/or time to close. An example method may comprise supplying a classifying model with a dataset, wherein the dataset comprises an identification of a provider and attributes corresponding to the provider and identifying a class of the provider in accordance with the plurality of corresponding attributes, wherein the identification is determined based on one or more patterns determinative of a return rate by the classifying model.

Abstract: The subject disclosure relates to ways to improve route duration calculations e.g., estimated time of arrival (ETA) approximations, by taking consideration of reroute probabilities along a given vehicle path. In some aspects, the disclosed technology encompasses a process including steps for identifying a route between a first destination to a second destination, determining a reroute likelihood associated with at least one AV maneuver along the route, and calculating an estimated time of arrival (ETA) based on the reroute likelihood associated with at least one AV maneuver. Systems and machine-readable media are also provided.

Abstract: The subject disclosure relates to ways to improve route duration calculations e.g., estimated time of arrival (ETA) approximations, by taking consideration of reroute probabilities along a given vehicle path. In some aspects, the disclosed technology encompasses a process including steps for identifying a route between a first destination to a second destination, determining a reroute likelihood associated with at least one AV maneuver along the route, and calculating an estimated time of arrival (ETA) based on the reroute likelihood associated with at least one AV maneuver. Systems and machine-readable media are also provided.

Abstract: The subject disclosure relates to ways to improve route duration calculations e.g., estimated time of arrival (ETA) approximations, by taking consideration of reroute probabilities along a given vehicle path. In some aspects, the disclosed technology encompasses a process including steps for identifying a route between a first destination to a second destination, determining a reroute likelihood associated with at least one AV maneuver along the route, and calculating an estimated time of arrival (ETA) based on the reroute likelihood associated with at least one AV maneuver. Systems and machine-readable media are also provided.

Abstract: A method, apparatus and computer program product are provided for calculating closing metrics regarding a contract between a promotion service and provider. A promotional system may calculate a probability of closing, and an estimated time to close. The promotion service may offer a promotion to consumers for a discounted product or service, to be honored by the provider. A category, lead source, historical data, stage in sales, and/or size of the provider may be used in calculating a probability of close and/or time to close. An example method may comprise supplying a classifying model with a dataset, wherein the dataset comprises an identification of a provider and attributes corresponding to the provider and identifying a class of the provider in accordance with the plurality of corresponding attributes, wherein the identification is determined based on one or more patterns determinative of a return rate by the classifying model.

Abstract: A method, apparatus and computer program product are provided for calculating closing metrics regarding a contract between a promotion service and provider. A promotional system may calculate a probability of closing, and an estimated time to close. The promotion service may offer a promotion to consumers for a discounted product or service, to be honored by the provider. A category, lead source, historical data, stage in sales, and/or size of the provider may be used in calculating a probability of close and/or time to close. An example method may comprise supplying a classifying model with a dataset, wherein the dataset comprises an identification of a provider and attributes corresponding to the provider and identifying a class of the provider in accordance with the plurality of corresponding attributes, wherein the identification is determined based on one or more patterns determinative of a return rate by the classifying model.

Abstract: Provided herein are systems, methods and computer readable media for classifying a provider of products, services or experiences as a provider that should be engaged based on a predicted return rate for any products, services or experiences that may be offered and purchased by a consumer. An example method may comprise supplying a classifying model with a dataset, wherein the dataset comprises an identification of a provider and a plurality of attributes corresponding to the provider and identifying a class of the provider in accordance with the plurality of corresponding attributes, wherein the identification is determined based on one or more patterns determinative of a return rate by the classifying model.

Abstract: Provided herein are systems, methods and computer readable media for classifying a provider of products, services or experiences as a provider that should be engaged based on a predicted return rate for any products, services or experiences that may be offered and purchased by a consumer. An example method may comprise supplying a classifying model with a dataset, wherein the dataset comprises an identification of a provider and a plurality of attributes corresponding to the provider and identifying a class of the provider in accordance with the plurality of corresponding attributes, wherein the identification is determined based on one or more patterns determinative of a return rate by the classifying model.

Abstract: A method, apparatus and computer program product are provided for calculating closing metrics regarding a contract between a promotion service and provider. A promotional system may calculate a probability of closing, and an estimated time to close. The promotion service may offer a promotion to consumers for a discounted product or service, to be honored by the provider. A category, lead source, historical data, stage in sales, and/or size of the provider may be used in calculating a probability of close and/or time to close. An example method may comprise supplying a classifying model with a dataset, wherein the dataset comprises an identification of a provider and attributes corresponding to the provider and identifying a class of the provider in accordance with the plurality of corresponding attributes, wherein the identification is determined based on one or more patterns determinative of a return rate by the classifying model.

Abstract: Provided herein are systems, methods and computer readable media for classifying a provider of products, services or experiences as a provider that should be engaged based on a predicted return rate for any products, services or experiences that may be offered and purchased by a consumer. An example method may comprise supplying a classifying model with a dataset, wherein the dataset comprises an identification of a provider and a plurality of attributes corresponding to the provider and identifying a class of the provider in accordance with the plurality of corresponding attributes, wherein the identification is determined based on one or more patterns determinative of a return rate by the classifying model.

Abstract: Provided herein are systems, methods and computer readable media for classifying a provider of products, services or experiences as a provider that should be engaged based on a predicted return rate for any products, services or experiences that may be offered and purchased by a consumer. An example method may comprise supplying a classifying model with a dataset, wherein the dataset comprises an identification of a provider and a plurality of attributes corresponding to the provider and identifying a class of the provider in accordance with the plurality of corresponding attributes, wherein the identification is determined based on one or more patterns determinative of a return rate by the classifying model.