Groupon, Inc.

Bytedance Inc.

Brian Mullins

Matt DeLand

John Stokvis

Nolan Finn

Stephen Lang

Shafiq Shariff

Zahra Ferdowsi

Rajesh Girish Parekh

Shawn Ryan Jeffery

Derek Nordquist

Bob Beaty

Kris Urie

Zahra Ferdowski

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.

Method, apparatus, and computer program product for determining closing metrics

Provided herein are systems, methods and computer readable media for providing sales pipeline automation. An example method comprises calculating a forecasted demand, the forecasted demand comprising at least one or more virtual offers and a demanded quantity of at least a portion of the one or more virtual offers, identifying one or more merchants, wherein the one or more merchants are identified as able to supply at least a portion of the residual demand, assigning the one or more merchants to one or more sales resources in accordance with at least a calculated sales value of the one or more merchants.

Method, apparatus, and computer program product for sales pipeline automation

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.

Method, apparatus, and computer program product for determining a provider return rate

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Chicago, IL, United States of America

Brian Mullins