Method, apparatus, and computer program product for facilitating dynamic pricing

1. A computer-implemented method for programmatically facilitating dynamic pricing of a promotion, in real-time, based on one or more factors by adjusting a first promotion price of a price adjustable promotion to a second promotion price, the method comprising:

electronically providing, via a communication interface, a plurality of promotions, configured for display on a user interface of a consumer device, the plurality of promotion including the price adjustable promotion at a first promotion price, the price adjustable promotion comprising a plurality of redemption parameters;

executing an iterative learning model configured to receive, as input, market reaction data, and output an updated promotion price,

wherein the execution of the iterative learning model comprises:

collecting market reaction data, indicating a volume and a velocity at which the price adjustable promotion sells;

identifying the price adjustable promotion from the plurality of promotions by determining eligibility for a price adjustment, the price adjustable promotion having (i) the first promotion price, (ii) a forecasted demand that provides an indication of a promotion inventory exhaustion period and a plurality of redemption parameters that comprise at least a promotional period, (iii) a promotional value, and (iv) a redemption expiration;

wherein the determination of the eligibility for the price adjustment comprises:

determining a probability that the price adjustable promotion will sell out based on the market reaction data, utilizing the iterative learning model;

comparing a confidence value indicative of the strength of the determined probability that the price adjustable promotion will sell out;

outputting the forecasted demand for the price adjustable promotion in an instance in which the confidence value satisfies the predetermined revenue threshold; and

determining that the promotion inventory exhaustion period overlaps a next price change time demonstrating that the first promotional price is adjustable prior to an expiration of the promotion inventory exhaustion period, wherein the next price change time defines a next time that the first promotional price can be adjusted and is calculated based on consumer behavior that is tracked in real time;

utilizing the market reaction data to generate, using a processor of a dynamic price calculator module executing on a pricing apparatus, the updated promotion price for the price adjustable promotion by adjusting a previous promotion price based on (i) the forecasted demand, (ii) a remaining inventory of the promotion, and (iii) an estimated revenue;

electronically providing, via the communication interface, configured for display at the user interface of the consumer device, the price adjustable promotion at the updated promotion price while at least one of the plurality of redemption parameters for the price adjustable promotion remain constant,

wherein, during a first iteration, the previous promotion price is the first promotion price;

determining if the price adjustable promotion is sold out; and

in an instance in which the price adjustable promotion is not sold, continuing with the execution of the iterative learning model.

2. The method according to claim 1 , further comprising:

accessing promotion data for the price adjustable promotion, wherein the promotion data includes a quantity of units that make up the price adjustable promotion inventory and at least one of a category of the price adjustable promotion, a category of a merchant offering the price adjustable promotion, or a merchant quality score of the merchant offering the price adjustable promotion.

3. The method according to claim 1 , wherein determining a price adjustable promotion from a plurality of promotions further comprises:

determining that the price adjustable promotion is eligible for a price adjustment based on the forecasted demand and promotion data for the promotion being received within a predefined time window.

4. The method according to claim 1 , further comprising:

determining a value of f that will solve equation:

P ( s∈S|f *)>τ*

which defines unknown factor as ϕ=f*.

5. The method according to claim 4 , wherein the probability that the price adjustable promotion will sell out is based on a pre-feature of the promotion and a calculated intensity of the demand for the promotion during the pre-feature.

6. The method according to claim 4 , wherein the probability that the price adjustable promotion will sell out is based on historical data.

7. The method according to claim 1 , wherein the next pricing change time is a function of a next communication to one or more consumers that indicates a promotional price for the price adjustable promotion.

8. The method according to claim 1 , wherein the next pricing change time is a function of a market reaction data, collected periodically.

9. The method according to claim 1 , wherein the next pricing change time is a time at which the price adjustable promotion is first offered via the promotion service.

10. The method according to claim 1 , wherein next pricing change time is generated by:

estimating a velocity of sales of the price adjustable promotion based on at least one of the forecasted demand, historical sales data or current sales data for the price adjustable promotion; and

determining the next pricing change time as a function of a remaining quantity of units that make up the price adjustable promotion inventory and a revenue value.

11. The method according to claim 1 , wherein generating the second promotional price for the price adjustable promotion further comprises:

comparing a promotion quantity factor to a first pricing threshold value, wherein in an instance in which the promotion quantity factor satisfies the first pricing threshold value generating a second promotion price for the promotion as a function of a first pricing factor and a margin value generated by the first promotion price; and

comparing the promotion quantity factor to a second pricing threshold value in an instance in which the promotion quantity factor does not satisfy the first dynamic pricing threshold value, wherein in an instance in which the promotion quantity factor satisfies the second pricing threshold value, generating a second promotion price as a function of a second pricing factor and a margin value generated by the first promotion price.

12. An apparatus for programmatically facilitating dynamic pricing of a promotion, in real-time, based on one or more factors by adjusting a first promotion price of a price adjustable promotion to a second promotion price, the apparatus comprising at least a processor, and a memory associated with the processor having computer coded instructions therein, with the computer instructions configured to, when executed by the processor, cause the apparatus to:

electronically provide, via a communication interface, a plurality of promotions, configured for display on a user interface of a consumer device, the plurality of promotion including the price adjustable promotion at a first promotion price, the price adjustable promotion comprising a plurality of redemption parameters;

execute an iterative learning model configured to receive, as input, market reaction data, and output an updated promotion price,

wherein the execution of the iterative learning model comprises:

collect market reaction data, indicating a volume and a velocity at which the price adjustable promotion sells;

identify the price adjustable promotion from the plurality of promotions by determining eligibility for a price adjustment, the price adjustable promotion having (i) the first promotion price, (ii) a forecasted demand that provides an indication of a promotion inventory exhaustion period and a plurality of redemption parameters that comprise at least a promotional period, (iii) a promotional value, and (iv) a redemption expiration;

wherein the determination of the eligibility for the price adjustment comprises:

determining a probability that the price adjustable promotion will sell out based on the market reaction data, utilizing the iterative learning model;

comparing a confidence value indicative of the strength of the determined probability that the price adjustable promotion will sell out;

outputting the forecasted demand for the price adjustable promotion in an instance in which the confidence value satisfies the predetermined revenue threshold; and

determining that the promotion inventory exhaustion period overlaps a next price change time demonstrating that the first promotional price is adjustable prior to an expiration of the promotion inventory exhaustion period, wherein the next price change time defines a next time that the first promotional price can be adjusted and is calculated based on consumer behavior that is tracked in real time;

utilize the market reaction data to generate, using a processor of a dynamic price calculator module executing on a pricing apparatus, the updated promotion price for the price adjustable promotion by adjusting a previous promotion price based on (i) the forecasted demand, (ii) a remaining inventory of the promotion, and (iii) an estimated revenue;

electronically provide, via the communication interface, configured for display at the user interface of the consumer device, the price adjustable promotion at the updated promotion price while at least one of the plurality of redemption parameters for the price adjustable promotion remain constant,

wherein, during a first iteration, the previous promotion price is the first promotion price;

determine if the price adjustable promotion is sold out; and

in an instance in which the price adjustable promotion is not sold, continue with the execution of the iterative learning model.

13. The apparatus according claim 12 , wherein the at least one memory and the computer program code are further configured to, with the processor, cause the apparatus to:

access promotion data for the price adjustable promotion, wherein the promotion data includes a quantity of units that make up the price adjustable promotion inventory and at least one of a category of the price adjustable promotion, a category of a merchant offering the price adjustable promotion, or a merchant quality score of the merchant offering the price adjustable promotion.

14. The apparatus according to claim 12 , wherein determining a price adjustable promotion from a plurality of promotions further comprises:

determining that the price adjustable promotion is eligible for a price adjustment based on the forecasted demand and promotion data for the promotion being received within a predefined time window.

15. The apparatus according claim 12 , wherein the at least one memory and the computer program code are further configured to, with the processor, cause the apparatus to:

determine a value of f that will solve equation:

P ( s∈S|f {circumflex over ( )}*)>τ{circumflex over ( )}*

which defines unknown factor as ϕ=f{circumflex over ( )}*.

16. The apparatus according to claim 15 , wherein the probability that the price adjustable promotion will sell out is based on a pre-feature of the promotion and a calculated intensity of the demand for the promotion during the pre-feature.

17. The apparatus according to claim 15 , wherein the probability that the price adjustable promotion will sell out is based on historical data.

18. The apparatus according to claim 12 , wherein the next pricing change time is a function of a next communication to one or more consumers that indicates a promotional price for the price adjustable promotion.

19. The apparatus according to claim 12 , wherein the next pricing change time is a function of a market reaction data, collected periodically.

20. The apparatus according to claim 12 , wherein the next pricing change time is a time at which the price adjustable promotion is first offered via the promotion service.

21. The apparatus according to claim 12 , wherein next pricing change time is generated by:

estimating a velocity of sales of the price adjustable promotion based on at least one of the forecasted demand, historical sales data or current sales data for the price adjustable promotion; and

determining the next pricing change time as a function of a remaining quantity of units that make up the price adjustable promotion inventory and a revenue value.

22. The apparatus according to claim 12 , wherein generating the second promotional price for the price adjustable promotion further comprises:

comparing a promotion quantity factor to a first pricing threshold value, wherein in an instance in which the promotion quantity factor satisfies the first pricing threshold value generating a second promotion price for the promotion as a function of a first pricing factor and a margin value generated by the first promotion price; and

comparing the promotion quantity factor to a second pricing threshold value in an instance in which the promotion quantity factor does not satisfy the first dynamic pricing threshold value, wherein in an instance in which the promotion quantity factor satisfies the second pricing threshold value, generating a second promotion price as a function of a second pricing factor and a margin value generated by the first promotion price.

23. A computer program product for programmatically facilitating dynamic pricing of a promotion, in real-time, based on one or more factors by adjusting a first promotion price of a price adjustable promotion to a second promotion price, the computer program product comprising a non-transitory computer readable medium having computer program instructions stored therein, said instructions when executed by a processor:

electronically providing, via a communication interface, a plurality of promotions, configured for display on a user interface of a consumer device, the plurality of promotion including the price adjustable promotion at a first promotion price, the price adjustable promotion comprising a plurality of redemption parameters;

executing an iterative learning model configured to receive, as input, market reaction data, and output an updated promotion price,

wherein the execution of the iterative learning model comprises:

collecting market reaction data, indicating a volume and a velocity at which the price adjustable promotion sells;

identifying the price adjustable promotion from the plurality of promotions by determining eligibility for a price adjustment, the price adjustable promotion having (i) the first promotion price, (ii) a forecasted demand that provides an indication of a promotion inventory exhaustion period and a plurality of redemption parameters that comprise at least a promotional period, (iii) a promotional value, and (iv) a redemption expiration;

wherein the determination of the eligibility for the price adjustment comprises:

determining a probability that the price adjustable promotion will sell out based on the market reaction data, utilizing the iterative learning model;

comparing a confidence value indicative of the strength of the determined probability that the price adjustable promotion will sell out;

outputting the forecasted demand for the price adjustable promotion in an instance in which the confidence value satisfies the predetermined revenue threshold; and

determining that the promotion inventory exhaustion period overlaps a next price change time demonstrating that the first promotional price is adjustable prior to an expiration of the promotion inventory exhaustion period, wherein the next price change time defines a next time that the first promotional price can be adjusted and is calculated based on consumer behavior that is tracked in real time;

utilizing the market reaction data to generate, using a processor of a dynamic price calculator module executing on a pricing apparatus, the updated promotion price for the price adjustable promotion by adjusting a previous promotion price based on (i) the forecasted demand, (ii) a remaining inventory of the promotion, and (iii) an estimated revenue;

electronically providing, via the communication interface, configured for display at the user interface of the consumer device, the price adjustable promotion at the updated promotion price while at least one of the plurality of redemption parameters for the price adjustable promotion remain constant,

wherein, during a first iteration, the previous promotion price is the first promotion price;

determining if the price adjustable promotion is sold out; and

in an instance in which the price adjustable promotion is not sold, continuing with the execution of the iterative learning model.

24. The computer program product according to claim 23 , wherein the computer-executable program code portions further comprise program code instructions for:

accessing promotion data for the price adjustable promotion, wherein the promotion data includes a quantity of units that make up the price adjustable promotion inventory and at least one of a category of the price adjustable promotion, a category of a merchant offering the price adjustable promotion, or a merchant quality score of the merchant offering the price adjustable promotion.

25. The method according to claim 23 , wherein determining a price adjustable promotion from a plurality of promotions further comprises:

determining that the price adjustable promotion is eligible for a price adjustment based on the forecasted demand and promotion data for the promotion being received within a predefined time window.

26. The computer program product according to claim 23 ,

wherein the computer-executable program code portions further comprise program code instructions for:

determining a value of f that will solve equation:

P ( s∈S|f {circumflex over ( )}*)>τ{circumflex over ( )}*

which defines unknown factor as ϕ=f{circumflex over ( )}*.

27. The computer program product according to claim 26 , wherein the probability that the price adjustable promotion will sell out is based on a pre-feature of the promotion and a calculated intensity of the demand for the promotion during the pre-feature.

28. The computer program product according to claim 26 , wherein the probability that the price adjustable promotion will sell out is based on historical data.

29. The computer program product according to claim 23 , wherein the next pricing change time is a function of a next communication to one or more consumers that indicates a promotional price for the price adjustable promotion.

30. The computer program product according to claim 23 , wherein the next pricing change time is a function of a market reaction data, collected periodically.

31. The computer program product according to claim 23 , wherein the next pricing change time is a time at which the price adjustable promotion is first offered via the promotion service.

32. The computer program product according to claim 23 , wherein next pricing change time is generated by:

estimating a velocity of sales of the price adjustable promotion based on at least one of the forecasted demand, historical sales data or current sales data for the price adjustable promotion; and

determining the next pricing change time as a function of a remaining quantity of units that make up the price adjustable promotion inventory and a revenue value.

33. The computer program product according to claim 23 , wherein generating the second promotional price for the price adjustable promotion further comprises:

comparing a promotion quantity factor to a first pricing threshold value, wherein in an instance in which the promotion quantity factor satisfies the first pricing threshold value generating a second promotion price for the promotion as a function of a first pricing factor and a margin value generated by the first promotion price; and

comparing the promotion quantity factor to a second pricing threshold value in an instance in which the promotion quantity factor does not satisfy the first dynamic pricing threshold value, wherein in an instance in which the promotion quantity factor satisfies the second pricing threshold value, generating a second promotion price as a function of a second pricing factor and a margin value generated by the first promotion price.