Method, apparatus, and computer program product for forecasting demand using real time demand
First Claim
1. A method for forecasting demand comprising:
- generating a virtual promotion, wherein the virtual promotion comprises a combination of a category or sub-category, a location, and a price range;
calculating a probability that a particular consumer would buy the virtual offer in a predetermined time period, wherein the probability is generated at least based on historical data related to the particular consumer and one or more related consumers;
determining an estimated number of units to be sold for the virtual offer as a function of at least the probability, the estimated number of units representing a predicted demand;
calculating a real time demand, wherein the real time demand is generated based on a plurality of generated identification pairs for the predetermined time period, wherein calculation of the real-time demand comprises;
accessing, in real-time, user search data, the user search data generated by a user interacting with a promotion and marketing service to identify a requested promotion, the user search data comprising at least location specific data;
generating an identification pair for the search data, the identification pair comprising a first classification and a second classification, the first classification identifying a promotion tuple, comprising at least a category of promotion, and the second classification identifying a location identified by the locations specific data,the first classification and the second classification generated by;
normalizing the user search data, supplying the normalized user search data to a classifying model as attribute data and training the classifying model to recognize one or more patterns of attribute data,wherein the first classifier and the second classifier is a trainable classifier adapted using a supervised learning method, the first classifier and the second classifier adapted based on a training data set;
determining, using a processor, total demand by summing the predicted demand and the real time demand; and
subsequent to the determination of the total demand, distributing the total demand to multiple hyper-locations and multiple sub-categories due to the determination of the total demand for a portion of the virtual offers being identified by or including high level locations or categories, wherein the distribution of the total demand among the multiple hyper-locations and among the sub-categories comprising the distribution of the real time demand among the multiple hyper-locations and among the sub-categories.
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Abstract
Provided herein are systems, methods and computer readable media for managing a sales pipeline, and in some embodiments, generating demand based on real time demand and predicted demand. An example method comprises generating a virtual promotion, wherein the virtual promotion comprises a combination of a category or sub-category, a location, and a price range, calculating a probability that a particular consumer would buy the virtual offer in a predetermined time period, wherein the probability is generated at least based on historical data related to the particular consumer and one or more related consumers, determining an estimated number of units to be sold for the virtual offer as a function of at least the probability, the estimated number of units representing a predicted demand, calculating a real time demand, wherein the real time demand is generated based on a plurality of generated identification pairs for the predetermined time period, and determining, using a processor, total demand by summing the predicted demand and the real time demand.
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Citations
45 Claims
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1. A method for forecasting demand comprising:
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generating a virtual promotion, wherein the virtual promotion comprises a combination of a category or sub-category, a location, and a price range; calculating a probability that a particular consumer would buy the virtual offer in a predetermined time period, wherein the probability is generated at least based on historical data related to the particular consumer and one or more related consumers; determining an estimated number of units to be sold for the virtual offer as a function of at least the probability, the estimated number of units representing a predicted demand; calculating a real time demand, wherein the real time demand is generated based on a plurality of generated identification pairs for the predetermined time period, wherein calculation of the real-time demand comprises; accessing, in real-time, user search data, the user search data generated by a user interacting with a promotion and marketing service to identify a requested promotion, the user search data comprising at least location specific data; generating an identification pair for the search data, the identification pair comprising a first classification and a second classification, the first classification identifying a promotion tuple, comprising at least a category of promotion, and the second classification identifying a location identified by the locations specific data, the first classification and the second classification generated by; normalizing the user search data, supplying the normalized user search data to a classifying model as attribute data and training the classifying model to recognize one or more patterns of attribute data, wherein the first classifier and the second classifier is a trainable classifier adapted using a supervised learning method, the first classifier and the second classifier adapted based on a training data set; determining, using a processor, total demand by summing the predicted demand and the real time demand; and subsequent to the determination of the total demand, distributing the total demand to multiple hyper-locations and multiple sub-categories due to the determination of the total demand for a portion of the virtual offers being identified by or including high level locations or categories, wherein the distribution of the total demand among the multiple hyper-locations and among the sub-categories comprising the distribution of the real time demand among the multiple hyper-locations and among the sub-categories. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15)
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16. 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:
- generating a virtual promotion, wherein the virtual promotion comprises a combination of a category or sub-category, a location, and a price range;
calculating a probability that a particular consumer would buy the virtual offer in a predetermined time period, wherein the probability is generated at least based on historical data related to the particular consumer and one or more related consumers;
determining an estimated number of units to be sold for the virtual offer as a function of at least the probability, the estimated number of units representing a predicted demand;
calculating a real time demand, wherein the real time demand is generated based on a plurality of generated identification pairs for the predetermined tins period, wherein calculation of the real-time demand comprises;
accessing, in real-time, user search data, the user search data generated by a user interacting with a promotion and marketing service to identify a requested promotion, the user search data comprising at least location specific data;
generating an identification pair for the search data, the identification pair comprising a first classification and a second classification, the first classification identifying a promotion tuple, comprising at least a category of promotion, and the second classification identifying a location identified by the locations specific data, the first classification and the second classification generated by;
normalizing the user search data, supplying the normalized user search data to a classifying model as attribute data and training the classifying model to recognize one or more patterns of attribute data, wherein the first classifier and the second classifier is a trainable classifier adapted using a supervised learning method, the first classifier and the second classifier adapted based on a training data set;
determining, using a processor, total demand by summing the predicted demand and the real time demand; and
subsequent to the determination of the total demand, distributing the total demand to multiple hyper-locations and multiple sub-categories due to the determination of the total demand for a portion of the virtual offers being identified by or including high level locations or categories, wherein the distribution of the total demand among the multiple hyper-locations and among the sub-categories comprising the distribution of the realtime demanded among the multiple hyper-locations and among the sub-categories. - View Dependent Claims (17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30)
- generating a virtual promotion, wherein the virtual promotion comprises a combination of a category or sub-category, a location, and a price range;
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31. 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 processor, cause the apparatus to at least:
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generate a virtual promotion, wherein the virtual promotion comprises a combination of a category or sub-category, a location, and a price range; calculate a probability that a particular consumer would buy the virtual offer in a predetermined time period, wherein the probability is generated at least based on historical data related to the particular consumer and one or more related consumers; determine an estimated number of units to be sold for the virtual offer as a function of at least the probability, the estimated number of units representing a predicted demand; calculate a real time demand, wherein the real time demand is generated based on a plurality of generated identification pairs for the predetermined time period, wherein calculation of the real-time demand comprises; accessing, in real-time, user search data, the user search data generated by a user interacting with a promotion and marketing service to identify a requested promotion, the user search data comprising at least location specific data; generating an identification pair for the search data, the identification pair comprising a first classification and a second classification, the first classification identifying a promotion tuple, comprising at least a category of promotion, and the second classification identifying a location identified by the locations specific data, the first classification and the second classification generated by; normalizing the user search data, supplying the normalized user search data to a classifying model as attribute data and training the classifying model to recognize one or more patterns of attribute data, wherein the first classifier and the second classifier is a trainable classifier adapted using a supervised learning method, the first classifier and the second classifier adapted based on a training data set; determine, using a processor, total demand by summing the predicted demand and the real time demand; and subsequent to the determination of the total demand, distributing the total demand to multiple hyper-locations and multiple sub-categories due to the determination of the total demand for a portion of the virtual offers being identified by or including high level locations or categories, wherein the distribution of the total demand among the multiple hyper-locations and among the sub-categories comprising the distribution of the real time demand among the multiple hyper-locations and among the sub-categories. - View Dependent Claims (32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45)
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Specification