# Lecture 12: Revenue Management - Dynamic Pricing and Capacity Optimization **Date:** August 8 **Duration:** 1.5 hours **Instructor:** Prof. Vivek Farias --- ## manual zara don't like doing markdown, but the first season = clearance absolute value similar, elasciticy jump velocity, price = 0 max revenue (constraint: inventory you have; discounts you're giving, ) inventory is nothing to get rid of infinite inventory, hurts inventory scare inventory, start competing -> when we start competing what are decision variables? how long i keep the price at 100, 90, 80... constraints are limited inventory (<=2000) and time (total time <=16) given i set the price, limited p(sell rate) * x_H, p(sell rate) * x_L max p(sell rate) * x_H * $/unit, p(sell rate) * x_L * $/unit 🚿rinse and repeat ---- ## Learning Objectives - Understand revenue management principles and applications - Analyze dynamic pricing strategies and their effectiveness - Apply capacity allocation and overbooking models - Evaluate revenue optimization vs. customer satisfaction trade-offs ## Key Concepts ### Revenue Management Fundamentals - **Definition:** The application of disciplined analytics to predict customer behavior and optimize product availability and price to maximize revenue growth - **Core Principle:** Sell the right product to the right customer at the right time for the right price - **Prerequisites:** Perishable inventory, heterogeneous customers, advance booking capability ### Industry Applications #### Airlines - **Perishable Inventory:** Seats on specific flights - **Price Discrimination:** Business vs. leisure travelers - **Booking Patterns:** Advance purchase vs. last-minute - **Capacity Constraints:** Fixed number of seats per flight #### Hotels - **Perishable Inventory:** Room-nights - **Demand Patterns:** Business vs. leisure, seasonal variations - **Length of Stay:** Different value segments - **Ancillary Revenue:** Food, beverage, services #### Other Industries - **Rental Cars:** Fleet utilization optimization - **Cruise Lines:** Cabin category management - **Theaters/Sports:** Event ticket pricing - **Restaurants:** Peak time pricing - **Ride-sharing:** Surge pricing (Uber/Lyft) ### Customer Segmentation #### Willingness to Pay - **Business Travelers:** High willingness to pay, low price sensitivity - **Leisure Travelers:** Lower willingness to pay, high price sensitivity - **Price-Sensitive:** Budget-conscious customers - **Convenience-Focused:** Value time over money #### Booking Behavior - **Early Bookers:** Plan ahead, price-sensitive - **Late Bookers:** Less flexible, higher willingness to pay - **Group Bookings:** Different pricing dynamics - **Frequent Customers:** Loyalty program considerations ### Demand Patterns #### Time-Based Variation - **Advance Booking Curve:** How demand evolves over time - **Seasonal Patterns:** Predictable demand cycles - **Day-of-Week Effects:** Business vs. leisure patterns - **Time-of-Day Variations:** Peak vs. off-peak periods #### Price Sensitivity - **Elasticity:** Demand response to price changes - **Reference Pricing:** Customer expectations based on past prices - **Competitor Pricing:** Market positioning considerations - **Value Perception:** Quality-price relationship ## Revenue Management Models ### Single-Resource Models #### Static Pricing - **Newsvendor Extension:** Optimal capacity given fixed price - **Price Optimization:** Find price that maximizes expected revenue - **Demand Function:** Relationship between price and demand - **Mathematical Formulation:** max p·E[min(D(p), C)] #### Dynamic Pricing - **Price Evolution:** How prices change over booking period - **Information Updates:** Adjust prices based on observed demand - **Optimal Control:** Dynamic programming approaches - **Real-Time Optimization:** Continuous price adjustment ### Capacity Allocation Models #### Two-Class Problem - **High-Fare Class:** Limited inventory, higher revenue - **Low-Fare Class:** Remaining capacity, lower revenue - **Booking Limit:** Maximum low-fare reservations to accept - **Protection Level:** Capacity reserved for high-fare class #### Multi-Class Extensions - **Fare Class Hierarchy:** Multiple price points - **Nested Booking Limits:** Cumulative availability controls - **Bid Price Models:** Opportunity cost pricing - **Network Revenue Management:** Multi-leg optimization ### Overbooking Models #### No-Show Problem - **Customer Behavior:** Some confirmed customers don't arrive - **Revenue Loss:** Empty capacity due to no-shows - **Service Risk:** Denied boarding if everyone shows up - **Optimization:** Balance revenue gain vs. service cost #### Overbooking Decision - **Costs:** Denied boarding compensation and customer dissatisfaction - **Benefits:** Revenue from additional bookings - **Probability Model:** No-show and demand distributions - **Optimal Level:** Minimize expected total cost ## Implementation Strategies ### Demand Forecasting - **Historical Data:** Past booking and consumption patterns - **Market Intelligence:** Competitor pricing and capacity - **External Factors:** Events, weather, economic conditions - **Machine Learning:** Advanced predictive models ### Price Optimization - **A/B Testing:** Experiment with different pricing strategies - **Competitive Monitoring:** Track competitor prices and availability - **Demand Sensing:** Real-time demand signal detection - **Price Elasticity:** Measure customer response to price changes ### Technology Systems - **Revenue Management Software:** Automated optimization engines - **Distribution Channels:** Online, travel agents, direct sales - **Real-Time Updates:** Dynamic pricing and availability - **Performance Monitoring:** KPI tracking and reporting ## Strategic Considerations ### Customer Experience - **Price Fairness:** Customer perception of pricing practices - **Transparency:** Clear pricing rules and policies - **Loyalty Programs:** Preferential treatment for frequent customers - **Service Recovery:** Handling overbooking situations ### Competitive Dynamics - **Price Wars:** Destructive pricing competition - **Capacity Discipline:** Industry-wide capacity management - **Differentiation:** Non-price value propositions - **Market Leadership:** Setting vs. following pricing strategies ### Regulatory Environment - **Consumer Protection:** Rules about pricing disclosure - **Anti-Trust:** Coordination with competitors - **Denied Boarding:** Compensation requirements - **Privacy:** Customer data usage regulations ## Performance Metrics ### Revenue Metrics - **Revenue per Available Unit (RevPAR):** Total revenue / Available capacity - **Average Daily Rate (ADR):** Total revenue / Units sold - **Load Factor:** Capacity utilization percentage - **Yield:** Revenue per unit of capacity ### Customer Metrics - **Customer Satisfaction:** Service quality scores - **Retention Rate:** Repeat customer percentage - **Net Promoter Score:** Customer recommendation likelihood - **Complaint Rate:** Service failure frequency ### Operational Metrics - **Forecast Accuracy:** Prediction vs. actual demand - **Overbooking Rate:** Frequency of capacity overselling - **Denied Service Rate:** Customers turned away - **Price Realization:** Actual vs. target pricing ## Case Examples ### Airline Revenue Management - **American Airlines:** Pioneer in revenue management - **Multiple Fare Classes:** First, business, economy with restrictions - **Booking Curve Management:** Price increases as departure approaches - **Network Optimization:** Connecting flight coordination ### Hotel Revenue Management - **Marriott:** Dynamic pricing based on demand patterns - **Length of Stay Controls:** Minimum stay requirements - **Group vs. Transient:** Different pricing strategies - **Channel Management:** Direct vs. third-party bookings ### Technology Companies - **Amazon:** Dynamic pricing for products - **Netflix:** Subscription tier optimization - **Software as a Service:** Usage-based pricing models - **Cloud Computing:** Spot pricing for excess capacity ## Discussion Questions 1. **Industry Applicability:** In what industries is revenue management most effective? What characteristics make it suitable? 2. **Ethical Considerations:** Is dynamic pricing fair to customers? How should companies balance profit maximization with customer satisfaction? 3. **Technology Impact:** How have mobile apps and real-time data changed revenue management practices? 4. **Competitive Strategy:** When should companies use revenue management as a competitive weapon vs. when should they avoid price competition? ## Modern Developments ### Advanced Analytics - **Machine Learning:** AI-powered demand forecasting - **Real-Time Optimization:** Continuous price adjustment - **Personalization:** Individual customer pricing - **Behavioral Economics:** Psychology-based pricing strategies ### Digital Transformation - **Mobile Apps:** Direct customer engagement - **Social Media:** Brand perception monitoring - **IoT Sensors:** Real-time demand sensing - **Blockchain:** Transparent pricing mechanisms ### COVID-19 Impact - **Demand Volatility:** Unprecedented uncertainty levels - **Health Protocols:** Capacity constraints for safety - **Cancellation Policies:** Flexible booking terms - **Recovery Strategies:** Rebuilding demand confidence ## Key Takeaways - **Revenue Optimization:** Sophisticated pricing can significantly increase revenue without adding capacity - **Customer Segmentation:** Understanding different customer types enables better pricing strategies - **Dynamic Adaptation:** Prices should respond to changing demand and market conditions - **Technology Enablement:** Modern revenue management requires advanced analytics and systems - **Balance Required:** Must balance revenue optimization with customer satisfaction and competitive positioning ## Mathematical Framework ### Basic Revenue Function - **Revenue = Price × Quantity Sold** - **R(p) = p × min(D(p), C)** - Where D(p) is demand function and C is capacity ### Optimal Pricing - **First-Order Condition:** MR = MC (Marginal Revenue = Marginal Cost) - **With Capacity Constraint:** Consider opportunity cost of capacity - **Dynamic Setting:** Future revenue implications of current decisions ### Capacity Allocation - **Expected Marginal Revenue:** EMR of last unit in each class - **Optimal Allocation:** Equalize EMR across classes - **Protection Levels:** Reserve capacity for higher-value segments ## Preparation for Next Class - Review customer choice modeling concepts - Read [[Lec13_Which_Products_Should_You_Stock_]] and [[Lec13_Blue_Apron__Turning_Around_the_Struggling_Meal_Kit_Market_Leader]] - Consider how customer preferences affect operational decisions ## Teaching Notes - Use real-world examples from airlines and hotels - Emphasize the mathematical foundations while keeping intuitive explanations - Connect to earlier inventory management concepts - Discuss ethical implications of dynamic pricing - Prepare students for choice modeling and customer centricity topics