Walmart Apollo, LLC (20240338721). SYSTEM AND METHOD FOR DYNAMICALLY AND AUTOMATICALLY UPDATING ITEM PRICES ON E-COMMERCE PLATFORM simplified abstract
Contents
- 1 SYSTEM AND METHOD FOR DYNAMICALLY AND AUTOMATICALLY UPDATING ITEM PRICES ON E-COMMERCE PLATFORM
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 SYSTEM AND METHOD FOR DYNAMICALLY AND AUTOMATICALLY UPDATING ITEM PRICES ON E-COMMERCE PLATFORM - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Dynamic Pricing Technology
- 1.13 Original Abstract Submitted
SYSTEM AND METHOD FOR DYNAMICALLY AND AUTOMATICALLY UPDATING ITEM PRICES ON E-COMMERCE PLATFORM
Organization Name
Inventor(s)
Huidong Zhang of Cupertino CA (US)
Akshit Sarpal of Santa Clara CA (US)
Elliot Grenn Silva of New York NY (US)
Fangjing Fu of Bentonville AR (US)
Fangping Huang of Sunnyvale CA (US)
Yang Song of San Bruno CA (US)
Weihang Ren of Foster City CA (US)
Lijie Wan of Mountain View CA (US)
SYSTEM AND METHOD FOR DYNAMICALLY AND AUTOMATICALLY UPDATING ITEM PRICES ON E-COMMERCE PLATFORM - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240338721 titled 'SYSTEM AND METHOD FOR DYNAMICALLY AND AUTOMATICALLY UPDATING ITEM PRICES ON E-COMMERCE PLATFORM
Simplified Explanation
The patent application describes systems and methods for automatically updating item prices on e-commerce platforms. It involves generating markdown prices based on price elasticity and predicted demand data, then applying upper and lower bounds to determine the final price.
- Price elasticity and predicted demand data used to generate markdown prices
- Upper and lower bounds applied to finalize the price
- Automatic updating of item prices on e-commerce platforms
Key Features and Innovation
- Utilizes price elasticity and predicted demand data for pricing decisions
- Offers dynamic pricing based on availability of data
- Ensures automatic updating of item prices on e-commerce platforms
Potential Applications
The technology can be applied in various industries such as retail, e-commerce, and online marketplaces to optimize pricing strategies and maximize profits.
Problems Solved
- Efficiently updates item prices based on data-driven insights
- Helps in setting competitive prices in real-time
- Streamlines pricing processes on e-commerce platforms
Benefits
- Maximizes revenue by adjusting prices according to demand
- Enhances customer satisfaction with competitive pricing
- Improves operational efficiency by automating price updates
Commercial Applications
Dynamic Pricing Technology for E-Commerce Platforms: Enhancing pricing strategies and revenue optimization in online retail environments.
Prior Art
Readers can explore prior art related to dynamic pricing algorithms, machine learning in pricing strategies, and e-commerce pricing optimization techniques.
Frequently Updated Research
Stay updated on the latest advancements in dynamic pricing algorithms, machine learning applications in pricing, and e-commerce pricing strategies.
Questions about Dynamic Pricing Technology
How does dynamic pricing benefit e-commerce businesses?
Dynamic pricing helps e-commerce businesses stay competitive by adjusting prices in real-time based on demand and market conditions. This strategy can lead to increased sales and revenue.
What are the key factors considered in dynamic pricing algorithms?
Dynamic pricing algorithms consider factors such as price elasticity, predicted demand, current price, and availability of data to generate optimal pricing strategies.
Original Abstract Submitted
systems and methods for dynamically and automatically updating item prices on e-commerce platform are disclosed. in some embodiments, an item is offered for purchase with a current price on a website. when price elasticity data and predicted demand data for the item are both available, a first markdown price is generated for the item using a first model based on: the price elasticity data, the predicted demand data, and the current price. when the price elasticity data and the predicted demand data are not both available, a second markdown price is generated for the item using a second model based on: a decay rate, the current price, and availability of the predicted demand data. a bounded price is generated by applying an upper bound and a lower bound to either the first markdown price or the second markdown price; and transmitted to a computing device for updating the current price of the item on the website.
- Walmart Apollo, LLC
- Huidong Zhang of Cupertino CA (US)
- Akshit Sarpal of Santa Clara CA (US)
- Elliot Grenn Silva of New York NY (US)
- Fangjing Fu of Bentonville AR (US)
- Fangping Huang of Sunnyvale CA (US)
- Yang Song of San Bruno CA (US)
- Weihang Ren of Foster City CA (US)
- Lijie Wan of Mountain View CA (US)
- G06Q30/0201
- CPC G06Q30/0206