Dynamic Pricing Platform for Restaurant Table Utilization
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Solution Overview
Problem
The restaurant industry faces inefficiencies due to expiring inventory and the inability of small local restaurants to compete with chain restaurants, as they lack the technical and financial resources to create an online presence and effectively manage market share, leading to empty tables and significant economic losses.
Innovation Solution
A system and method that enables dynamically-priced deals contingent on consumer spending, arrival time, and location, allowing restaurants to offer targeted promotions and consumers to find attractive offers without requiring pre-payment or financial information, while ensuring restaurants only fulfill commitments if consumers honor their obligations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If small local restaurants create an online presence to compete with chain restaurants, then their advertising and marketing impact increases, but their technical ability and financial resources are insufficient
Solution Approach 1:
The patent introduces a third-party online platform as an intermediary that provides ready-made digital marketing infrastructure, analytics tools, and customer management systems. Small restaurants can access these sophisticated tools without needing to build them themselves, leveling the playing field against chain restaurants while avoiding the technical and financial burden of developing proprietary systems.
Solution Approach 2:
The online platform provides universal tools that serve multiple functions: customer acquisition, order management, inventory tracking, and analytics. This multi-functionality allows small restaurants to achieve comprehensive digital presence and operational efficiency through a single system, rather than needing separate solutions for each function.
2Productivity
If restaurants offer promotions to fill empty tables, then revenue increases during slow periods, but the profitability decreases due to reduced prices
Solution Approach 1:
The system implements dynamic pricing that automatically adjusts promotion levels based on real-time demand signals, reservation data, and historical patterns. During very slow periods, more aggressive promotions are offered to stimulate demand, while during moderately slow periods, milder promotions maintain profitability. This dynamic approach optimizes the balance between filling tables and preserving profit margins.
Solution Approach 2:
The patent changes multiple parameters simultaneously: pricing levels, promotion duration, target customer segments, and dish recommendations. By adjusting these parameters based on demand forecasts, the system can offer targeted promotions that fill tables during slow periods while minimizing the impact on overall profitability through intelligent parameter optimization.
3Productivity
If restaurants use traditional marketing methods, then they maintain simple operations, but they cannot effectively compete for market share against chain restaurants
Solution Approach 1:
The online platform serves as an intermediary that bridges traditional small restaurant operations with modern digital marketing capabilities. It provides access to sophisticated marketing tools, customer data analytics, and targeted advertising systems without requiring the restaurant to develop these capabilities in-house, thereby increasing market share potential while maintaining operational simplicity.
4Reliability
If restaurants accept all customer requests, then customer satisfaction increases, but operational efficiency decreases due to empty tables and poor resource utilization
Solution Approach 1:
The system implements continuous feedback loops that monitor customer preferences, ordering patterns, and satisfaction metrics. This feedback is used to dynamically adjust menu recommendations, promotion offerings, and inventory management. By responding to actual customer behavior data rather than making assumptions, the system maintains high customer satisfaction while optimizing operational efficiency through data-driven decision-making.
Data Source
AI summary
A computer-implemented system and method are disclosed for facilitating dynamically-priced offer data between a restaurant and a consumer, the data reflecting dynamically-priced offers which are contingent upon the consumer first honoring in full their contractual promises to the restaurant before the restaurant is bound to honor its ‘side’ of the deal. In an embodiment, the method and system enables a consumer to specify, in advance, a minimum amount of money the consumer contractually binds themselves to spend at a restaurant as well as the specific hour of the day of a specific day of the week of a specific week of the year the consumer promises to arrive at the restaurant or expects delivery of an order from a restaurant, in return for more of the goods or services the restaurant contractually, but contingently binds themselves to provide the consumer. Further disclosed is a system and method for dealing with instances where a consumer fails to meet their contractual obligation to the restaurant, upon which assurances the restaurant's bid was contingent. In an embodiment, a controller is provided for an authorized restaurant representative to use to access a data storage device in which is stored restaurant ‘deal’ offers which may be triggered by consumer's Request For Bid (RFB). The same controller receives those RFB's from consumer and accesses the same data storage device as it attempts to present responsive bids from local area restaurants.