Dynamic timing and pricing for online retail platform

1 . A method for dynamically adjusting one or more elements associated with an electronic transaction at a server hosting an online retail platform, comprising:

initiating a time period for receiving live offers, via an online interface, for an item associated with a listing on the online retail platform, the listing being associated with the time period and an offer increment;

receiving, from a remote buyer, an offer on the item;

filtering a respective internet protocol (IP) address of each remote device connected to the server to determine a quantity of unique connections to the server, each IP address being filtered by monitoring network traffic received via one or more wired or wireless connections to the server, each remote device having a distinct IP address that is different from a user name of a remote user associated with the remote device;

adjusting, via a machine learning model trained to entice bidding on the item, the time period and the offer increment based on receiving the offer and the quantity of unique connections, as well as one or more of a time between successive offers, or a rating of the remote buyer;

generating, via the machine learning model, one or more simulated offers on the item, each simulated offer of the one or more simulated offers being autonomously placed by the machine learning model to increase a current offer on the item, and each simulated offer simulating a real offer from a human buyer;

repeating the adjusting of the time period and the offer increment in accordance with the quantity of unique connections until the time period expires; and

ending the listing based on an expiration of the time period.

2 . The method of claim 1 , wherein:

the listing of the item is one listing of a group of listings; and

the group of listings is associated with one or more listing categories.

3 . The method of claim 2 , further comprising receiving, from the remote buyer, a group of offers, each offer of the group of offers associated with a respective listing of the group of listings, wherein the group of offers are simultaneously placed on the respective listings of the group of listings.

4 . The method of claim 1 , wherein the time period and the offer increment are further adjusted based on one or more of a number of offers on the item associated with the online listing, an amount of time remaining in the time period, a current highest offer, a rating of the remote buyer, a reserve price, or a number of pre-offers.

5 . The method of claim 1 , wherein:

each of the one or more simulated offers is an incremental offer based on a total value of the offer received from the remote buyer; and

the one or more simulated offers are placed until a highest offer on the item is equal to or greater than a threshold value.

6 . The method of claim 1 , wherein the online listing is associated with a reserve price.

7 . The method of claim 6 , wherein each of the one or more simulated offers is less than the reserve price.

8 . The method of claim 6 , further comprising:

receiving, from a seller associated with the item, a message requesting a decrease in the reserve price; and

decreasing the reserve price based on receiving the message, wherein the seller is located remotely.

9 . A server hosting an online retail platform, comprising:

at least one processor; and

at least one memory coupled with the at least one processor and storing instructions operable, when executed by the at least one processor, to cause the server to:

initiate a time period for receiving live offers, via an online interface, for an item associated with a listing on the online retail platform, the listing being associated with the time period and an offer increment;

receive, from a remote buyer, an offer on the item;

filter a respective internet protocol (IP) address of each remote device connected to the server to determine a quantity of unique connections to the server, each IP address being filtered by monitoring network traffic received via one or more wired or wireless connections to the server, each remote device having a distinct IP address that is different from a user name of a remote user associated with the remote device;

adjust, via a machine learning model trained to entice bidding on the item, the time period and the offer increment based on receiving the offer and the quantity of unique connections, as well as one or more of a time between successive offers, or a rating of the remote buyer;

generate, via the machine learning model, one or more simulated offers on the item, each simulated offer of the one or more simulated offers being autonomously placed by the machine learning model to increase a current offer on the item, and each simulated offer simulating a real offer from a human buyer;

repeat the adjusting of the time period and the offer increment in accordance with the quantity of unique connections until the time period expires; and

end the listing based on an expiration of the time period.

10 . The server of claim 9 , wherein:

the listing of the item is one listing of a group of listings; and

the group of listings is associated with one or more listing categories.

11 . The server of claim 10 , wherein execution of the instructions further cause the apparatus to receive, from the remote buyer, a group of offers, each offer of the group of offers associated with a respective listing of the group of listings, wherein the group of offers are simultaneously placed on the respective listings of the group of listings.

12 . The server of claim 9 , wherein the time period and the offer increment are further adjusted based on one or more of a number of offers on the item associated with the online listing, an amount of time remaining in the time period, a current highest offer, a rating of the remote buyer, a reserve price, or a number of pre-offers.

13 . The server of claim 9 , wherein: each of the one or more simulated offers is an incremental offer based on a total value of the offer received from the remote buyer; and

the one or more simulated offers are placed until a highest offer on the item is equal to or greater than a threshold value.

14 . The server of claim 9 , wherein each of the one or more simulated offers is less than the reserve price.

15 . The server of claim 14 , wherein execution of the instructions further cause the apparatus to:

receive, from a seller associated with the item, a message requesting a decrease in the reserve price; and

decrease the reserve price based on receiving the message, wherein the seller is located remotely.

16 . A non-transitory computer-readable medium having program code recorded thereon for dynamically adjusting one or more elements associated with an electronic transaction, the program code executed by a processor and comprising:

program code to initiate a time period for receiving live offers, via an online interface, for an item associated with a listing on the online retail platform, the listing being associated with the time period and an offer increment;

program code to receive, from a remote buyer, an offer on the item;

program code to filter a respective internet protocol (IP) address of each remote device connected to the server to determine a quantity of unique connections to the server, each IP address being filtered by monitoring network traffic received via one or more wired or wireless connections to the server, each remote device having a distinct IP address that is different from a user name of a remote user associated with the remote device;

program code to adjust, via a machine learning model trained to entice bidding on the item, the time period and the offer increment based on receiving the offer and the quantity of unique connections, as well as one or more of a time between successive offers, or a rating of the remote buyer;

program code to generate, via the machine learning model, one or more simulated offers on the item, each simulated offer of the one or more simulated offers being autonomously placed by the machine learning model to increase a current offer on the item, and each simulated offer simulating a real offer from a human buyer;

program code to repeat the adjusting of the time period and the offer increment accordance with the quantity of unique connections until the time period expires; and

program code to end the listing based on an expiration of the time period.

17 . The non-transitory computer-readable medium of claim 16 , wherein the program code further comprises program code to receive, from the remote buyer, a group of offers, each offer of the group of offers associated with a respective listing of the group of listings, wherein the group of offers are simultaneously placed on the respective listings of the group of listings.

18 . The non-transitory computer-readable medium of claim 16 , wherein the time period and the offer increment are further adjusted based on one or more of a number of offers on the item associated with the online listing, an amount of time remaining in the time period, a current highest offer, a rating of the remote buyer, a reserve price, or a number of pre-offers.

19 . The non-transitory computer-readable medium of claim 16 , wherein each of the one or more simulated offers is an incremental offer based on a total value of the offer received from the remote buyer; and the one or more simulated offers are placed until a highest offer on the item is equal to or greater than a threshold value.

20 . The non-transitory computer-readable medium of claim 16 , wherein each of the one or more simulated offers is less than the reserve price.