Relevance-to-Revenue Engine (Pricing & Explainability)
An end-to-end decision intelligence system that combines demand modeling, revenue optimization, and learning-to-rank with GenAI explanations, translating complex pricing and relevance decisions into clear, human-understandable insights.
Tags & Technologies
Key Impact & KPIs
- Estimated price elasticity quantified for listings, enabling informed adjustments
- Expected revenue uplift trade-offs surfaced in narratives
- Booking probability insights contextualized for business decisions
- Ranking coherence between demand, quality and revenue signals
- Stakeholder explainability via clear plain-English narratives
Project Overview
1. End-to-End Decision Intelligence Pipeline
Designed an end-to-end decision intelligence pipeline that links marketplace demand signals, pricing optimization, and relevance ranking into a single, coherent system—enabling data-backed pricing and prioritization decisions rather than isolated model outputs.
2. Demand Sensitivity Quantification
Quantified demand sensitivity through price elasticity modeling, allowing the system to surface how booking likelihood and expected revenue change under different pricing scenarios, supporting informed trade-offs between growth, revenue, and risk.
3. Transparent Ranking Framework
Built a transparent ranking framework that balances demand strength, revenue potential, and listing quality, ensuring that top-ranked results are not only relevant but also economically rational.
4. GenAI Communication Layer
Introduced a GenAI communication layer (Mistral-7B) that translates numerical model outputs into concise, plain-English explanations—bridging the gap between technical models and non-technical stakeholders without allowing the LLM to influence core decisions.
5. Production-Ready System
Delivered a reproducible, demo-ready system with modular pipelines, validation notebooks, and an interactive Streamlit interface—demonstrating how advanced analytics and GenAI can be operationalized responsibly in revenue- and risk-sensitive environments.
Model Selection Rationale
- Models/LLMs used: Regression & ranking models; Flan-T5-Small (templated explanations) + Mistral-7B (rich narratives).
- Cost & speed: Flan-T5-Small handled high-volume, low-cost explanation tasks; Mistral-7B used selectively to control inference cost.
- Fit-for-purpose: Regression gave interpretable elasticity estimates for pricing decisions; LLMs were a communication layer.