Demand Generation System with Attribution Feedback Loop
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Solution Overview
Problem
Businesses in the tourism and hospitality industry face challenges in predicting future demand and evaluating the effectiveness of efforts to increase demand due to external fluctuations, complex consumer preferences, competitive factors, and seasonal variations, compounded by the dispersion and inaccuracy of relevant data.
Innovation Solution
A demand generation system that combines data from multiple sources to generate predictive demand scores, output actionable recommendations to increase demand, and refine these recommendations through an attribution model that tracks the impact of actions on demand.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data from multiple sources is aggregated to improve prediction accuracy, then demand forecast reliability improves, but data processing complexity increases
Solution Approach 1:
The system segments data processing by creating separate modules for different data sources (internal business data, external economic indicators, competitor data, event data) and processes each segment independently before aggregation. This modular approach improves reliability through comprehensive data coverage while managing complexity through structured organization.
Solution Approach 2:
The patent introduces an intermediary data processing layer that standardizes and normalizes data from multiple sources before feeding it to prediction models. This intermediary layer handles data cleaning, validation, and formatting, which improves forecast reliability while containing processing complexity within a dedicated intermediate stage.
2Measurement precision
If historical data is extensively analyzed to improve prediction accuracy, then demand forecast precision improves, but data availability decreases due to dispersion and inaccessibility
Solution Approach 1:
The system creates a universal data aggregation platform that can access and process multiple types of data sources (internal reservations, external economic indicators, competitor pricing, event calendars) through a single integrated interface. This multi-functional approach improves forecast precision by combining diverse data while maintaining data availability through centralized access.
Solution Approach 2:
The patent implements preliminary data collection and storage actions by maintaining comprehensive historical databases of various data types before they are needed for forecasting. Data is pre-aggregated, pre-processed, and pre-stored in accessible formats, ensuring both high forecast precision and data availability when predictions are generated.
3Measurement precision
If complex prediction models are used to improve demand accuracy, then forecast precision improves, but system complexity increases
Solution Approach 1:
The system employs dynamic prediction models that adapt their complexity based on the specific forecasting needs and data availability. Different algorithms (time series analysis, regression models, machine learning) are selectively applied to different data segments and time periods, improving overall forecast precision while managing system complexity through conditional model selection.
Solution Approach 2:
The patent implements self-service prediction capabilities where the system automatically selects and applies appropriate prediction algorithms based on the characteristics of the input data and forecasting requirements. The system autonomously determines model complexity levels, reducing the need for manual configuration and simplifying system operation despite using sophisticated prediction techniques.
4Productivity
If actionable recommendations are generated to increase demand, then business productivity improves, but measurement difficulty increases for evaluating effectiveness
Solution Approach 1:
The system implements feedback mechanisms that track the outcomes of generated recommendations by comparing actual demand changes against predicted changes. This feedback loop measures the effectiveness of each recommendation type (pricing adjustments, marketing campaigns, inventory changes) and feeds results back into the prediction model, improving productivity through proven strategies while enabling systematic measurement of effectiveness.
Data Source
AI summary
A business demand generation system may generate a demand score based on typical demand and predicted future demand from a business segment mix for a future time period. The demand score is processed through a action recommendation model to generate an actionable recommendation that is tied to an attribution model that evaluates the impact of the recommended action on actual segmental demand of a business. The evaluated impact is fed back through the recommendation model to create a continuous learning loop that refines the model to improve future recommendations to drive better and more accurate results.