METHOD, APPARATUS, AND COMPUTER PROGRAM PRODUCT FOR FORECASTING DEMAND
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.
1 .- 30 . (canceled)
31 . A method for supply identification of one or more merchants, based on programmatically adjusting a demand forecast, comprising:
receiving, by at least one processor via a communication interface, a demand forecast;
applying, via a demand adjustment module, an adjustment factor set to create an adjusted demand forecast, wherein the adjustment factor set comprises: diversity constraints, hyper-local constraints, and seasonality constraints;
accessing, by the at least one processor, a current offer inventory comprising available offers;
determining, by the at least one processor, a residual adjusted demand based on the adjusted demand forecast and the current offer inventory;
accessing, by the at least one processor, a merchant database;
identifying, by the at least one processor, one or more new merchants to fill the residual adjusted demand;
determining, by the at least one processor, a sales value for each of the one or more new merchants;
prioritizing, by the at least one processor, the one or more new merchants based on the determined sales value for each of the one or more new merchants; and
generating, by the at least one processor, a list of prioritized merchants.
32 . The method of claim 31 , wherein determining the residual adjusted demand comprises subtracting the current offer inventory from the adjusted demand forecast.
33 . The method of claim 32 , wherein the current offer inventory may be at least one of: an inventory related to offers closed within a predetermined time period, an inventory related to offers closed within a second predetermined time period, an existing offer inventory adjusted for quality, and a recurring inventory.
34 . The method of claim 33 , further comprising:
sorting, by the at least one processor, the current offer inventory into a category, a sub-category, a location, or a price range; and
determining, by the at least one processor, one or more allowable substitutions for the current offer inventory based on at least a similar category or a similar sub-category and a similar location or a similar price range between the available offers.
35 . The method of claim 31 , wherein the sales value for each of the one or more new merchants comprises determining a merchant value, a merchant quality score, a risk potential, a probability to close, or a lead time to close for each of the one or more new merchants.
36 . The method of claim 31 , further comprising:
determining, by the at least one processor, a dynamic deal optimization (DDO) score based on data related to a limited performance of a particular offer, wherein the limited performance comprises performance data related to a limited sub-set of consumers distributed based on age, gender, location, or hyper-local region.
37 . The method of claim 31 , wherein the list of prioritized merchants is configured for transmission via the communication interface and for rendering via a graphical user interface on a display device.
38 . 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:
receive, by the at least one processor via a communication interface, a demand forecast;
apply, via a demand adjustment module, an adjustment factor set to create an adjusted demand forecast, wherein the adjustment factor set comprises: diversity constraints, hyper-local constraints, and seasonality constraints;
access, by the at least one processor, a current offer inventory comprising available offers;
determine, by the at least one processor, a residual adjusted demand based on the adjusted demand forecast and the current offer inventory;
access, by the at least one processor, a merchant database;
identify, by the at least one processor, one or more new merchants to fill the residual adjusted demand;
determine, by the at least one processor, a sales value for each of the one or more new merchants;
prioritize, by the at least one processor, the one or more new merchants based on the determined sales value for each of the one or more new merchants; and
generate, by the at least one processor, a list of prioritized merchants.
39 . The apparatus of claim 38 , wherein the at least one memory and computer program code are configured to, with the at least one processor, cause the apparatus to:
determine the residual adjusted demand by subtracting the current offer inventory from the adjusted demand forecast.
40 . The apparatus of claim 39 , wherein the current offer inventory may be at least one of: an inventory related to offers closed within a predetermined time period, an inventory related to offers closed within a second predetermined time period, an existing offer inventory adjusted for quality, and a recurring inventory.
41 . The apparatus of claim 40 , wherein the at least one memory and computer program code are configured to, with the at least one processor, cause the apparatus to:
sort, by the at least one processor, the current offer inventory into a category, a sub-category, a location, or a price range; and
determine, by the at least one processor, one or more allowable substitutions for the current offer inventory based on at least a similar category or a similar sub-category and a similar location or a similar price range between the available offers.
42 . The apparatus of claim 38 , wherein the sales value for each of the one or more new merchants comprises determining a merchant value, a merchant quality score, a risk potential, a probability to close, or a lead time to close for each of the one or more new merchants.
43 . The apparatus of claim 38 , wherein the at least one memory and computer program code are configured to, with the at least one processor, cause the apparatus to:
determine, by the at least one processor, a dynamic deal optimization (DDO) score based on data related to a limited performance of a particular offer, wherein the limited performance comprises performance data related to a limited sub-set of consumers distributed based on age, gender, location, or hyper-local region.
44 . The apparatus of claim 38 , wherein the list of prioritized merchants is configured for transmission via the communication interface and for rendering via a graphical user interface on a display device.
45 . A computer program product comprising at least one computer readable non-transitory memory medium having program code instructions stored thereon, the program code instructions which when executed by an apparatus, cause the apparatus at least to:
receive, by the at least one processor via a communication interface, a demand forecast;
apply, via a demand adjustment module, an adjustment factor set to create an adjusted demand forecast, wherein the adjustment factor set comprises: diversity constraints, hyper-local constraints, and seasonality constraints;
access, by the at least one processor, a current offer inventory comprising available offers;
determine, by the at least one processor, a residual adjusted demand based on the adjusted demand forecast and the current offer inventory;
access, by the at least one processor, a merchant database;
identify, by the at least one processor, one or more new merchants to fill the residual adjusted demand;
determine, by the at least one processor, a sales value for each of the one or more new merchants;
prioritize, by the at least one processor, the one or more new merchants based on the determined sales value for each of the one or more new merchants; and
generate, by the at least one processor, a list of prioritized merchants.
46 . The computer program product of claim 45 , further comprising program code instructions, the program code instructions which when executed by the apparatus further cause the apparatus at least to:
determine the residual adjusted demand by subtracting the current offer inventory from the adjusted demand forecast.
47 . The computer program product of claim 46 , wherein the current offer inventory may be at least one of: an inventory related to offers closed within a predetermined time period, an inventory related to offers closed within a second predetermined time period, an existing offer inventory adjusted for quality, and a recurring inventory.
48 . The computer program product of claim 47 , further comprising program code instructions, the program code instructions which when executed by the apparatus further cause the apparatus at least to:
sort, by the at least one processor, the current offer inventory into a category, a sub-category, a location, or a price range; and
determine, by the at least one processor, one or more allowable substitutions for the existing inventory based on at least a similar category or a similar sub-category and a similar location or a similar price range between the available offers.
49 . The computer program product of claim 45 , wherein the sales value for each of the one or more new merchants comprises determining a merchant value, a merchant quality score, a risk potential, a probability to close, or a lead time to close for each of the one or more new merchants.
50 . The computer program product of claim 45 , further comprising program code instructions, the program code instructions which when executed by the apparatus further cause the apparatus at least to:
determine, by the at least one processor, a dynamic deal optimization (DDO) score based on data related to a limited performance of a particular offer, wherein the limited performance comprises performance data related to a limited sub-set of consumers distributed based on age, gender, location, or hyper-local region.