To maximize top-line revenue, occupancy rates, and overall route yield across all direct and third-party booking channels. This role takes direct ownership of real-time dynamic pricing, competitor trend tracking, demand-supply analysis, and strategic bus scheduling to ensure optimal fleet monetization in a fast-changing market environment.
Key Responsibilities (KRAs)
1. Dynamic Pricing & Yield Management
- Cross-Channel Pricing: Execute and manage real-time seat pricing across all distribution channels (company website/app, OTA partners, offline branch counters, and agents).
- Yield Optimization: Adjust fares dynamically based on booking lead times, day of the week, seasonality, local festival spikes, and real-time seat inventory consumption.
- Competitor & Market Intelligence: Continuously track competitor fare structures, frequency shifts, and route demand to capitalize on market opportunities immediately.
2. Route Planning & Fleet Scheduling
- Schedule Optimization: Collaborate with fleet operations to analyze route profitability and optimize bus departure times, stops, and frequencies for maximum passenger capture.
- Capacity Allocation: Recommend route shifts, extra bus deployments during peak rush, or temporary schedule consolidations during low-demand periods.
3. Data Analysis & Financial Modeling
- Advanced Excel Analysis: Build and maintain detailed spreadsheet models to monitor route-wise Average Fare Realization (AFR), Load Factors (LF), Revenue Per Available Seat Kilometer (RASK), and total route earnings.
- Post-Trip Performance Audits: Conduct daily/weekly post-departure revenue reviews to evaluate pricing strategy success and refine future fare algorithms.
4. Stakeholder & Channel Coordination
- Channel Management: Coordinate with internal tech/OTA account teams to ensure smooth price updates, promotional campaign implementation, and seat inventory visibility.
- Branch Alignment: Work closely with branch managers and cargo teams to align ticket pricing and cargo revenue goals on key corridors.
Key Performance Indicators (KPIs)
- Revenue Growth: Achievement of route-wise and total fleet gross revenue targets.
- Occupancy & Yield Balance: Optimization of Average Load Factor (%) alongside Average Realized Fare (AFR).
- RASK Optimization: Maximizing Revenue Per Available Seat Kilometer (RASK) across assigned sectors.
- Market Agility: Speed of reaction to competitor price drops or surge demand trends.
- Data & Pricing Accuracy: Zero pricing upload errors or revenue leakages across booking platforms.
Desired Candidate Profile & Skillset
- Experience: Minimum 2+ years in revenue management, dynamic pricing, route yield planning, or commercial analytics within passenger transport, online travel aggregators (OTAs), airlines, or logistics.
- Advanced MS Excel Skills: Highly proficient in MS Excel (pivot tables, complex formulas, data modeling, trend charts) to analyze large data sets rapidly.
- Analytical & Market Mindset: Exceptional quantitative ability with a natural knack for spotting market trends, reading supply-demand curves, and acting on micro-shifts.
- Agility & Decisiveness: Fast, confident decision-maker capable of executing price adjustments under time pressure in a fast-paced market.
- Education: Graduate in Business, Economics, Statistics, Engineering, Finance, or related analytical fields
Qualifications & Experience
- Education: Bachelors degree in Business Administration, Economics, Statistics, Mathematics, Finance, or Engineering (MBA/PGDM in Operations or Supply Chain is a plus).
- Experience: Minimum 2+ years of hands-on experience in dynamic pricing, revenue management, yield optimization, or commercial strategy in passenger transport, OTAs, airlines, or logistics.
- Technical Skills: Advanced MS Excel proficiency (nested formulas, pivot tables, VLOOKUP/XLOOKUP, Index-Match, data modeling, trend charts) and hands-on exposure to Central Reservation Systems (CRS) or OTA extranets.
- Key Attributes: Fast, confident decision-maker with a sharp quantitative mindset, capable of reading market supply-demand curves quickly under tight time constraints.