Machine learning (ml) model based low-ball offer determination

A method, comprising:

• in a system:
• receiving a first data set including a set of historical list prices for a first product, and a set of historical offer prices corresponding to the set of historical list prices;
• training a machine learning (ML) model based on the received first data set;
• receiving a first list price, associated with a first seller, for the first product;
• receiving a first offer price, associated with a first buyer and associated with the first list price, for the first product;
• applying the trained ML model on the received first offer price based on the received first list price to determine a threshold price for the first product, wherein
• the determined threshold price for the first product corresponds to an average offer price associated with the first buyer,
• the average offer price is an average of the set of historical offer prices made by the first buyer to a plurality of sellers for a plurality of products similar to that of the first product,
• the plurality of sellers is different from the first seller, and
• the determined threshold price is less than the first list price;
• determining whether the received first offer price corresponds to a low-ball offer based on the application of the trained ML model, wherein
• the low-ball offer corresponds to an offer price that is a predefined value lesser than the determined threshold price for the first product;
• transmitting, to a buyer device related to the first buyer, a first notification indicative of the first offer price from the first buyer, based on the determination that the received first offer price corresponds to the low-ball offer;
• and
• transmitting, to a seller device related to the first seller, the first notification corresponding to the first offer price from the first buyer, based on the determination that the received first offer price does not correspond to the low-ball offer.

A non-transitory computer-readable medium having stored thereon, computer-executable instructions that when executed by a system, causes the system to execute operations, the operations comprising:

• receiving a first data set including a set of historical list prices for a first product, and a set of historical offer prices corresponding to the set of historical list prices;
• training a machine learning (ML) model based on the received first data set;
• receiving a first list price, associated with a first seller, for the first product;
• receiving a first offer price, associated with a first buyer and associated with the first list price, for the first product;
• applying the trained ML model on the received first offer price based on the received first list price to determine a threshold price for the first product, wherein
• the determined threshold price for the first product corresponds to an average offer price associated with the first buyer,
• the average offer price is an average of the set of historical offer prices made by the first buyer to a plurality of sellers for a plurality of products similar to that of the first product,
• the plurality of sellers is different from the first seller, and
• the determined threshold price is less than the first list price;
• determining whether the received first offer price corresponds to a low-ball offer based on the application of the trained ML model, wherein
• the low-ball offer corresponds to an offer price that is a predefined value lesser than the determined threshold price for the first product;
• transmitting, to a buyer device related to the first buyer, a first notification indicative of the first offer price from the first buyer, based on the determination that the received first offer price corresponds to the low-ball offer;
• and
• transmitting, to a seller device related to the first seller, the first notification corresponding to the first offer price from the first buyer, based on the determination that the received first offer price does not correspond to the low-ball offer.