Method, apparatus, and computer program product for offering and processing promotions
Embodiments of the present invention provide methods, systems, apparatuses, and computer program products for classifying merchants. In one embodiment a method is provided comprising providing, a first promotion specifying a redemption and a charge monetary values, the redemption monetary value being specified by redemption parameters of the first promotion and a value indicative of a total amount of funds purchased in response to accepting an instance of the first promotion, wherein the funds purchased can be redeemed for offerings of one or more merchants specified by the promotion, the charge monetary value being a monetary value charged for accepting the first promotion, and providing a first classification classifying the first promotion as a promotion having a redemption monetary value that may be used to purchase offerings of the one or more merchants over the course of multiple transactions.
1. An apparatus comprising at least one processor and at least one non-transitory computer readable storage medium storing instructions that, with the at least one processor, cause the apparatus to:
in response to determining, based at least in part on a predictive model, that reclassifying a promotion from a first classification to a second classification is likely to result in a programmatically generated predicted number of likely transactions to occur in association with the promotion exceeding a predefined threshold, reclassify the promotion to the second classification; and
transmit, to a first user device, the promotion.
2. The apparatus of claim 1 , wherein the first classification classifies the promotion as a promotion that may not be redeemed over multiple transactions.
3. The apparatus of claim 1 , wherein the second classification classifies the promotion as a promotion that may be redeemed over multiple transactions.
4. The apparatus of claim 1 , wherein the predictive model comprises a machine learning model.
5. The apparatus of claim 1 , wherein the predictive model comprises performance data.
6. The apparatus of claim 5 , wherein the performance data is based at least in part on a promotion category associated with the promotion.
7. The apparatus of claim 1 , wherein the at least one non-transitory computer readable storage medium storing instructions that, with the at least one processor, further cause the apparatus to:
receive, from the first user device, a request to use the promotion to pay, a first portion of a purchase price value of one or more offerings of one or more merchants; and
process the request.
8. 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 to:
in response to determining, based at least in part on a predictive model, that reclassifying a promotion from a first classification to a second classification is likely to result in a programmatically generated predicted number of likely transactions to occur in association with the promotion exceeding a predefined threshold, reclassify the promotion to the second classification; and
transmit, to a first user device, the promotion.
9. The computer program product of claim 8 , wherein the first classification classifies the promotion as a promotion that may not be redeemed over multiple transactions.
10. The computer program product of claim 8 , wherein the second classification classifies the promotion as a promotion that may be redeemed over multiple transactions.
11. The computer program product of claim 8 , wherein the predictive model comprises a machine learning model.
12. The computer program product of claim 8 , wherein the predictive model comprises performance data.
13. The computer program product of claim 12 , wherein the performance data is based at least in part on a promotion category associated with the promotion.
14. The computer program product of claim 8 , wherein the computer-executable program code instructions further comprise program code instructions to:
receive, from the first user device, a request to use the promotion to pay, a first portion of a purchase price value of one or more offerings of one or more merchants; and
process the request.
15. A computer implemented method comprising:
in response to determining, based at least in part on a predictive model, that reclassifying a promotion from a first classification to a second classification is likely to result in a programmatically generated predicted number of likely transactions to occur in association with the promotion exceeding a predefined threshold, reclassifying the promotion to the second classification; and
transmitting, to a first user device, the promotion.
16. The computer implemented method of claim 15 , wherein the first classification classifies the promotion as a promotion that may not be redeemed over multiple transactions.
17. The computer implemented method of claim 15 , wherein the second classification classifies the promotion as a promotion that may be redeemed over multiple transactions.
18. The computer implemented method of claim 15 , wherein the predictive model comprises a machine learning model.
19. The computer implemented method of claim 15 , wherein the predictive model comprises performance data.
20. The computer implemented method of claim 19 , wherein the performance data is based at least in part on a promotion category associated with the promotion.