Booking Availability Engine for Promotion Scheduling
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Solution Overview
Problem
Existing methods for promotion management struggle to accurately predict demand for merchant resources, leading to inefficiencies in scheduling promotions, which can result in idling resources and reputational damage due to poor redemption experiences for consumers and merchants.
Innovation Solution
A system that calculates hidden demand for time slots and determines optimal booking limits using reinforcement learning techniques, allowing for more accurate forecasting and intelligent promotion scheduling, thereby improving the availability of time slots for consumers and increasing customer volume for merchants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional demand prediction methods are used for promotion scheduling, then system complexity is low, but demand forecasting accuracy deteriorates leading to resource idling and poor consumer experiences
Solution Approach 1:
The system implements feedback mechanisms by continuously monitoring actual redemption patterns, consumer bookings, and merchant capacity utilization. This feedback loops into the reinforcement learning model to continuously refine demand predictions, improving accuracy while managing complexity through iterative optimization rather than static complex models
Solution Approach 2:
The reinforcement learning system performs self-service by automatically adjusting booking limits and scheduling promotions without manual intervention. The model learns optimal scheduling strategies autonomously from historical data, eliminating the need for complex manual forecasting processes while improving prediction accuracy
2Ease of operation
If booking limits are set too high to maximize consumer availability, then consumer access to promotions improves, but merchant capacity is exceeded leading to service quality deterioration
Solution Approach 1:
The system dynamically adjusts booking limits based on real-time merchant capacity data, historical redemption patterns, and predicted demand. Rather than using static high limits, the reinforcement learning model continuously optimizes booking limits to balance consumer access with merchant service quality, adapting to changing conditions
Solution Approach 2:
The system changes key parameters including booking limits, time slot durations, and promotion scheduling based on learned patterns from historical data. These parameter adjustments optimize the balance between consumer availability and merchant capacity utilization, preventing both underbooking and overbooking scenarios
3Productivity
If promotions are scheduled without considering hidden demand, then scheduling simplicity is maintained, but resource utilization deteriorates due to idling merchant capacity
Solution Approach 1:
The system replaces traditional mechanical scheduling methods with reinforcement learning-based intelligent scheduling. The model processes historical redemption data, consumer behavior patterns, and merchant capacity information to automatically determine optimal promotion schedules, substituting complex analytical processing for simple but ineffective rule-based scheduling
Solution Approach 2:
The system performs preliminary actions by pre-calculating optimal booking limits and promotion schedules based on historical data and predicted demand patterns. This advance planning allows the system to maximize resource utilization while maintaining scheduling simplicity during execution, as the complex optimization work is done beforehand
Data Source
AI summary
A method, apparatus, and computer program product are disclosed. The method includes calculating hidden demand for one or more time slots, the one or more time slots defining a time duration during which a performance of a service offered by a promotion and marketing system can be performed. The method further includes determining a booking limit for each time slot of the one or more time slots based on the calculated hidden demand for the time slot and a determined capacity for the time slot, determining that one or more of the time slots comprise available time slots based on a comparison of the booking limits for the one or more time slots to a number of bookings for each of the time slots, and displaying the one or more available time slots in conjunction with the particular promotion A corresponding apparatus and computer program product are also provided.