Dynamic Pricing Model for Expiring Product Notifications
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Solution Overview
Problem
Conventional digital inventory management systems for brick and mortar establishments are inefficient, rigid, and inaccurate, leading to significant waste and inefficiencies due to excessive resource usage and a 'one-size-fits-all' pricing approach that fails to accurately reflect the value of expiring products.
Innovation Solution
A dynamic price management system that utilizes a machine-learning model to generate customized discount prices based on product expiration data and customer history, providing tailored digital notifications to individual users, thereby optimizing pricing and reducing waste.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If conventional inventory systems apply a rigid one-size-fits-all discount approach to expiring products, then the system complexity is reduced, but the pricing accuracy and waste reduction effectiveness deteriorate
Solution Approach 1:
The patent implements dynamic pricing by continuously adjusting discount percentages based on real-time factors including days remaining until expiration, product category, customer purchase history, and seasonal variations. This replaces the static one-size-fits-all approach with a living pricing model that adapts to changing conditions, thereby improving pricing accuracy without requiring overly complex manual intervention.
Solution Approach 2:
The system changes multiple pricing parameters simultaneously including discount percentage, price tier, and promotional timing based on product expiration data and customer behavior patterns. By varying these parameters dynamically rather than using fixed rates, the system achieves higher pricing accuracy while managing complexity through automated parameter adjustment algorithms.
2Ease of operation
If conventional systems use a one-size-fits-all pricing approach, then the ease of operation is improved, but the adaptability to individual product values and customer preferences deteriorates
Solution Approach 1:
The pricing system performs self-service by automatically analyzing product expiration dates, customer purchase histories, and market conditions to generate customized pricing recommendations without requiring manual intervention. The system serves itself by collecting data, processing it through algorithms, and adjusting prices autonomously, thereby maintaining ease of operation while achieving high adaptability to individual product and customer characteristics.
Solution Approach 2:
The patent segments the market into different customer groups and product categories, applying distinct pricing strategies to each segment based on their specific characteristics. This segmentation allows the system to adapt pricing to individual values and preferences while maintaining operational simplicity through automated segment-based rule application rather than manual customization for each customer.
3Extent of automation
If conventional inventory systems expend significant computing resources for inventory management, then the automation level is improved, but the efficiency and waste reduction effectiveness deteriorate
Solution Approach 1:
The patent replaces complex mechanical inventory management processes with data-driven algorithms that analyze expiration patterns and customer behavior to optimize pricing decisions. This substitution reduces the need for extensive manual computing and analysis, allowing the system to maintain high automation while improving efficiency through smarter, data-based decision-making rather than brute-force processing.
Solution Approach 2:
Instead of continuously optimizing all inventory parameters at full computational intensity, the system applies partial action by focusing computing resources on critical factors such as products approaching expiration or those with high customer demand. This selective computational approach maintains necessary automation for key decisions while reducing overall computational waste and improving overall system efficiency.
4Ease of manufacture
If conventional systems apply generalized discounts to all expiring products, then the manufacturing simplicity is improved, but the loss of substance (waste) increases
Solution Approach 1:
The system performs preliminary analysis of product expiration dates, customer purchase patterns, and market conditions before setting prices. By preparing and analyzing this data in advance, the system can implement targeted pricing strategies that prevent waste through proactive sales of at-risk products, rather than reacting to expiration after the fact. This preliminary action maintains manufacturing simplicity through automated data processing while significantly reducing product waste.
Data Source
AI summary
Methods, systems, and non-transitory computer readable storage media are disclosed for dynamically generating discounted product digital notifications based on remaining product shelf life. For example, in one or more embodiments, the disclosed system determines an expiration date or a target product available for purchase from a merchant. Additionally, in one or more embodiments, the disclosed system utilizes a machine-learning model to dynamically generate discount prices for the target product over time based on the expiration date. In one or more embodiments, the disclosed system identifies a client device of a customer and provides a discount price for the target product for a given time window to the client device of the customer.