Dynamic Pricing System Real-Time Event Adjustment
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Solution Overview
Problem
Current dynamic pricing systems rely on manual, time-consuming periodic adjustments and primarily respond to competitor price changes, failing to account for real-time factors such as customer behavior and events unrelated to specific customers.
Innovation Solution
A dynamic pricing system that automatically adjusts item prices in real-time based on various events, including customer proximity, browsing history, and previous user behavior, using a cloud-based architecture with event orchestrators, trend analyzers, and price optimizers to determine price changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual periodic price adjustments are used, then pricing can be controlled and implemented, but the process is time-consuming and laborious
Solution Approach 1:
The system enables self-service pricing by automatically monitoring events, analyzing trends, and adjusting prices without human intervention. The pricing system serves itself by incorporating event data, competitor pricing, and demand signals to autonomously determine optimal price points, eliminating the need for manual price adjustment processes
Solution Approach 2:
The patent replaces the mechanical manual pricing process with an automated computational system. Instead of human operators manually adjusting prices, the system uses algorithms that process event data, competitor pricing information, and demand trends to automatically calculate and implement price adjustments, substituting human labor with automated computing processes
2Adaptability or versatility
If pricing is adjusted based only on competitor price changes, then the process is simple to manage, but real-time factors such as customer behavior and events are not accounted for
Solution Approach 1:
The pricing system performs multiple functions within a single integrated framework: it monitors competitor pricing, tracks customer behavior, detects relevant events, analyzes demand trends, and adjusts prices accordingly. This multi-functional approach allows the system to respond to diverse real-time factors without requiring separate systems for each function
Solution Approach 2:
The system introduces an intermediary event processing layer that translates various real-time factors (customer behavior, events, competitor pricing) into standardized pricing signals. This intermediary layer processes diverse inputs through trend analysis and event evaluation mechanisms, converting them into actionable pricing decisions without requiring direct complex interactions between all input factors
3Productivity
If automated real-time pricing adjustments are implemented, then pricing efficiency and responsiveness improve, but system complexity increases
Solution Approach 1:
The pricing system is segmented into distinct functional modules: event monitoring components, competitor pricing trackers, customer behavior analyzers, trend analysis engines, and price optimization algorithms. Each module handles specific aspects of pricing intelligence independently, allowing the system to process multiple data streams in parallel while maintaining manageable complexity through modular architecture
Data Source
AI summary
Example dynamic pricing systems and methods are described. A system can comprise one or more programs comprising instructions to receive a request from a graphical user interface of a mobile device of a user for a price of one or more items; receiving an indication of an occurrence of a first event unrelated to a particular customer, wherein the first event is related to the one or more items; receive an indication of an occurrence of a second event unrelated to a particular customer, wherein the second event is related to the one or more items; access data regarding the first event and the second event; determine whether to adjust the price associated with the one or more items based on the data regarding the first event and the second event; responsive to determining to adjust the price associated with the one or more items, determine a new price for the one or more items based on the first event, the second event, and previous user behavior comprising at least one of user loyalty, user returns, or user interests of the user; generate a search listing comprising the one or more items responsive to the request of the user based on the price, as adjusted, of the one or more items in the search listing; when it is determined that a price adjustment is not required for the one or more items, displaying the price without adjustment of the one or more items in response to the request of the user; and facilitating displaying, to the graphical user interface of the mobile device, the final search listing in response to the request of the user. Other embodiments are disclosed herein.