Dynamic Product Option System for Revenue Optimization
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Solution Overview
Problem
Companies face challenges in matching customer preferences with product offerings due to incomplete understanding of customer needs and dynamic pricing utilities, leading to inefficiencies in product allocation and revenue optimization, particularly in industries like airlines where overbooking and oversale situations result in customer dissatisfaction and lost revenue.
Innovation Solution
A system and method that dynamically integrates customer preferences with internal company economics to optimize value for both customers and companies by unbundling product features, allowing for real-time customization and flexible pricing based on customer utilities and company operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If companies use traditional demand forecasting to build product quantities, then production planning is simplified, but forecast imperfections lead to shortage or excess supply and opportunity loss
Solution Approach 1:
The system performs preliminary actions by collecting customer preference data and company economic data before the sales transaction occurs. This advance data collection and integration allows the system to pre-calculate optimized product offerings and pricing, eliminating the need for complex post-hoc adjustments and reducing reliance on imperfect forecasts.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring actual customer preferences and company economic performance, then using this information to refine future product recommendations and pricing strategies. This closed-loop feedback system improves forecast accuracy over time without increasing production planning complexity.
2Device complexity
If companies bundle all product features together, then product offering is simplified, but customers either pay for undesired features or lose customers due to high price
Solution Approach 1:
The system segments the bundled product into distinct features or components, allowing it to selectively include only those features that match the customer's expressed preferences. This segmentation enables customized product configurations without requiring complex manual negotiation, as the system automatically assembles appropriate feature combinations based on integrated data analysis.
3Productivity
If companies overbook products to prevent opportunity loss, then revenue potential is maximized, but customer dissatisfaction and lost revenue occur when oversale situations arise
Solution Approach 1:
The system dynamically adjusts product availability and pricing based on real-time integration of customer preferences and company economic conditions. This dynamic approach replaces static overbooking strategies with adaptive resource allocation that responds to actual demand patterns, maximizing revenue while maintaining service reliability through continuous optimization.
4Measurement precision
If companies collect and integrate dynamic customer and company data to close the gap, then customer preference matching is improved, but the technical complexity and computational requirements increase
Solution Approach 1:
The system enables self-service functionality where customers can directly express their preferences and receive customized product recommendations without extensive manual intervention. The automated integration of customer and company data, combined with algorithmic optimization, reduces the need for complex human-mediated analysis while maintaining high measurement precision of customer preferences.
Data Source
AI summary
A computer-implemented system and method to provide options on products to enhance customers' experience. A computer-implemented system is operated that delivers to a customer an option to utilize up to n of m selected products, where m and n are whole numbers and n is less than or equal to m. Information is recorded in a data store, pertaining to said option. In addition, a system is operated to define each of the n chosen products, whereby after each of the n chosen products is defined, the customer can utilize said chosen product. The information pertaining to said defined products is recorded in a data store.