Dynamic Pricing Algorithm Adjusting Prices by User Traffic
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Solution Overview
Problem
Conventional pricing mechanisms for online products and services are inexact and fail to reflect real-time demand, limiting optimal pricing for both buyers and sellers in e-commerce and auction platforms.
Innovation Solution
The Peeractive Pricing system dynamically adjusts prices based on real-time user traffic, using an algorithm to increase or decrease prices in correlation with the number of users viewing a product, allowing prices to change continuously without user action, and providing a real-time, user-friendly pricing experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional pricing mechanisms are used, then pricing simplicity is maintained, but pricing accuracy and real-time demand reflection deteriorate
Solution Approach 1:
The patent replaces conventional static pricing mechanisms with a dynamic algorithmic pricing system that automatically adjusts prices based on real-time user traffic data. The system substitutes manual or fixed pricing with an automated computational approach that continuously monitors user interactions and recalculates prices using demand functions, thereby achieving precise real-time pricing without manual intervention.
Solution Approach 2:
The pricing system operates autonomously by automatically collecting user traffic data, calculating demand metrics, and adjusting prices without requiring manual input from sellers or administrators. The algorithm self-regulates pricing based on real-time market conditions, performing the pricing function independently while reflecting actual demand dynamics.
2Productivity
If dynamic real-time pricing is implemented, then pricing accuracy improves, but computational complexity and processing requirements worsen
Solution Approach 1:
The system implements pricing updates at strategic intervals rather than continuously, performing full recalculations only when necessary based on threshold triggers or time-based schedules. This partial action approach maintains pricing accuracy while reducing unnecessary computational overhead and energy consumption during periods when demand conditions remain relatively stable.
Solution Approach 2:
The system pre-calculates demand functions and pricing algorithms during off-peak periods or when user traffic is low, preparing pricing models in advance for rapid deployment when needed. This preliminary computation reduces real-time processing requirements by having pricing logic ready ahead of demand surges or critical pricing decisions.
3Measurement precision
If prices change continuously without user action, then real-time demand reflection improves, but user control and predictability worsen
Solution Approach 1:
The system provides continuous feedback to users about pricing changes by displaying the current price, the factors influencing it (such as user traffic levels), and sometimes the trajectory of price changes. This transparency helps users understand the dynamic pricing mechanism and make informed decisions, partially compensating for the reduced direct control over pricing.
Data Source
AI summary
The number of users viewing a given variable directly affects the rate of change and/or outcome of said variable. In the case of eCommerce, pricing of products and/or services is based upon a user accessing a website on which products and/or services are for sale. An initial price indicia associator associates initial price indicia with the products and/or services files. The initial pricing can be based upon historical indicia or the engine itself. Thereafter, a price indicia adjuster adjusts the pricing responsive to user access of the website or related website. A user interface meter shown on the website indicates to potential buyers how much interest there is in the product and/or service being sold so that peer activity is exhibited to potential buyers to encourage sales and impulsive buying behavior. Pricing is dynamic and adjusts in real-time at a rate determined by the amount of users accessing the website.