Method, apparatus, and computer program product for forecasting demand
First Claim
1. A method for virtual offer demand forecasting comprising:
- generating, by one or more processors via a virtual offer generation module, a virtual offer for each combination of a plurality of attributes, wherein the attributes comprise only price data, one product category or one service category, and location data,wherein the generation of the virtual offer for each combination of the plurality of attributes comprises;
accessing both a category taxonomy model and a location taxonomy model to identify a plurality of sub-category and hyper-location pairs, the category taxonomy model defining a hierarchical structure of service categories and sub-categories that is found in one or more local or hyper-local regions and the location taxonomy model defining a hierarchical structure that is based on locations, sub-locations, and hyper-local regions, andutilizing a set of guidelines to match at least one of one or more price ranges to the sub-category and hyper-location pair resulting in a set of virtual offers comprised of each combination of a plurality of attributes;
accessing, via a consumer data database, consumer data comprising one or more users and user data related to each of the one or more users;
calculating, by one or more processors via a probability generation module, a probability value that a particular user from the one or more users would buy a particular offer from the set of virtual offers comprised of each combination of a plurality of attributes in a particular time frame for at least a portion of the one or more users and for each of the plurality of the virtual offers;
generating a pivot table using the list of consumers and their associated probabilities for each of the plurality of virtual offers;
converting the pivot table into a listing of the plurality of virtual offers and related probabilities for each consumer;
determining an estimated number of units to be sold for at least a portion of the plurality of virtual offers as a function of at least the probability associated with each of the plurality of virtual offers;
correlating the at least one of the plurality virtual offers to a real promotion by matching the price data, product category or service category, and the location data of the at least one of the plurality of virtual offers to price data, a product or service category and the location data of the real promotion;
accessing information indicative of the available inventory of the real promotion;
calculating a residual demand as a function of the forecasted demand and the available inventory, anddynamically adjusting the residual demand, subsequent to a process in which merchants are contacted based on their availability to service the residual demand, based on a change in the available inventory of the real promotion due to a closed contract and diversity constraint information requiring merchant diversity and diversity among categories, sub-categories, locations, and price ranges.
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Accused Products
Abstract
Provided herein are systems, methods and computer readable media for forecasting demand. An example method comprises generating a virtual offer for one or more combinations of a category or sub-category, location, and price range, accessing consumer data comprising one or more users and user data related to each of the one or more users, calculating a probability that a particular user would buy a particular offer in a particular time frame for at least a portion of the plurality of users and for each of the virtual offers, and determining an estimated number of units of to be sold for at least a portion of the one or more virtual offers as a function of at least the probability associated with each of the one or more virtual offers.
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Citations
24 Claims
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1. A method for virtual offer demand forecasting comprising:
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generating, by one or more processors via a virtual offer generation module, a virtual offer for each combination of a plurality of attributes, wherein the attributes comprise only price data, one product category or one service category, and location data, wherein the generation of the virtual offer for each combination of the plurality of attributes comprises; accessing both a category taxonomy model and a location taxonomy model to identify a plurality of sub-category and hyper-location pairs, the category taxonomy model defining a hierarchical structure of service categories and sub-categories that is found in one or more local or hyper-local regions and the location taxonomy model defining a hierarchical structure that is based on locations, sub-locations, and hyper-local regions, and utilizing a set of guidelines to match at least one of one or more price ranges to the sub-category and hyper-location pair resulting in a set of virtual offers comprised of each combination of a plurality of attributes; accessing, via a consumer data database, consumer data comprising one or more users and user data related to each of the one or more users; calculating, by one or more processors via a probability generation module, a probability value that a particular user from the one or more users would buy a particular offer from the set of virtual offers comprised of each combination of a plurality of attributes in a particular time frame for at least a portion of the one or more users and for each of the plurality of the virtual offers; generating a pivot table using the list of consumers and their associated probabilities for each of the plurality of virtual offers; converting the pivot table into a listing of the plurality of virtual offers and related probabilities for each consumer; determining an estimated number of units to be sold for at least a portion of the plurality of virtual offers as a function of at least the probability associated with each of the plurality of virtual offers; correlating the at least one of the plurality virtual offers to a real promotion by matching the price data, product category or service category, and the location data of the at least one of the plurality of virtual offers to price data, a product or service category and the location data of the real promotion; accessing information indicative of the available inventory of the real promotion; calculating a residual demand as a function of the forecasted demand and the available inventory, and dynamically adjusting the residual demand, subsequent to a process in which merchants are contacted based on their availability to service the residual demand, based on a change in the available inventory of the real promotion due to a closed contract and diversity constraint information requiring merchant diversity and diversity among categories, sub-categories, locations, and price ranges. - View Dependent Claims (2, 3, 4, 5, 6, 7, 22, 23, 24)
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8. An apparatus comprising at least one processor and at least one memory including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the apparatus at least to:
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generate, via a virtual offer generation module, a virtual offer for each combination of a plurality of attributes, wherein the attributes comprise only price data, one product category or one service category, and location data, wherein the generation of the virtual offer for each combination of the plurality of attributes comprises; accessing both a category taxonomy model and a location taxonomy model to identify a plurality of sub-category and hyper-location pairs, the category taxonomy model defining a hierarchical structure of service categories and sub-categories that is found in one or more local or hyper-local regions and the location taxonomy model defining a hierarchical structure that is based on locations, sub-locations, and hyper-local regions resulting in a set of virtual offers comprised of each combination of a plurality of attributes, and utilize a set of guidelines to match at least one of one or more price ranges to the sub-category and hyper-location pair; access, via a consumer data database, consumer data comprising one or more users and user data related to each of the one or more users; calculate, via a probability generation module, a probability value that a particular user from the one or more users would buy a particular offer from the set of virtual offers comprised of each combination of a plurality of attributes in a particular time frame for at least a portion of the one or more users and for each of the plurality of the virtual offers; generate a pivot table using the list of consumers and their associated probabilities for each of the plurality of virtual offers; convert the pivot table into a listing of the plurality of virtual offers and related probabilities for each consumer; determining an estimated number of units to be sold for at least a portion of the plurality of the virtual offers as a function of at least the probability associated with each of the plurality of virtual offers; correlate the at least one of the plurality virtual offers to a real promotion by matching the price data, product category or service category, and the location data of the at least one of the plurality of virtual offers to price data, a product or service category and the location data of the real promotion; access information indicative of the available inventory of the real promotion; calculate a residual demand as a function of the forecasted demand and the available inventory; and dynamically adjust the residual demand, subsequent to a process in which merchants are contacted based on their availability to service the residual demand, based on a change in the available inventory of the real promotion due to a closed contract and diversity constraint information requiring merchant diversity and diversity among categories, sub-categories, locations, and price ranges. - View Dependent Claims (9, 10, 11, 12, 13, 14)
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15. A computer program product comprising at least one non-transitory computer-readable storage medium having computer-executable program code instructions stored therein, the computer-executable program code instructions comprising program code instructions for:
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generating, via a virtual offer generation module, a virtual offer for each combination of a plurality of attributes, wherein the attributes comprise only, price data, one product category or one service category, and location data, wherein the generation of the virtual offer or each combination of the plurality of attributes comprises; accessing both a category taxonomy model and a location taxonomy model to identify a plurality of sub-category and hyper-location pairs, a sub-category and hyper-location pair, the category taxonomy model defining a hierarchical structure of service categories and sub-categories that is found in one or more local or hyper-local regions and the location taxonomy model defining a hierarchical structure that is based on locations, sub-locations, and hyper-local regions, and utilizing a set of guidelines to match at least one of one or more price ranges to the sub-category and hyper-location pair resulting in a set of virtual offers comprised of each combination of a plurality of attributes; accessing, via a consumer data database, consumer data comprising one or more users and user data related to each of the one or more users; calculating, via a probability generation module, a probability value that a particular user from the one or more users would buy a particular offer from the set of virtual offers comprised of each combination of a plurality of attributes in a particular time frame for at least a portion of the one or more users and for each of the virtual offers; generating a pivot table using the list of consumers and their associated probabilities for each of the plurality of virtual offers; converting the pivot table into a listing of the plurality of virtual offers and related probabilities for each consumer; determining an estimated number of units to be sold for at least a portion of the one or more virtual offers as a function of at least the probability associated with each of the one or more virtual offers; correlating the at least one of the plurality virtual offers to a real promotion by matching the price data, product category or service category, and the location data of the at least one of the plurality of virtual offers to price data, a product or service category and the location data of the real promotion; accessing information indicative of the available inventory of the real promotion; calculating a residual demand as a function of the forecasted demand and the available inventory and dynamically adjusting the residual demand, subsequent to a process in which merchants are contacted based on their availability to service the residual demand, based on a change in the available inventory of the real promotion due to a closed contract and diversity constraint information requiring merchant diversity and diversity among categories, sub-categories, locations, and price ranges. - View Dependent Claims (16, 17, 18, 19, 20, 21)
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Specification