How to Optimize Revenues Using Dynamic Pricing?

Introduction

Dynamic pricing is a common strategy used by companies like Uber/Ola, IRCTC, and Amazon to adjust prices based on various factors such as demand, competition, and supply. This article aims to provide beginners with a theoretical understanding of dynamic pricing and resources to build a basic algorithm.

Learning Objectives

• Understand the basics of pricing and different methods of pricing
• Explore dynamic pricing, including its advantages, disadvantages, methods, and use cases
• Learn about revenue management basics
• Implement a Simple Dynamic Pricing Algorithm using Python to maximize revenue

This article was published as a part of the Data Science Blogathon.

What is ‘Price’?

Price is a crucial factor influenced by market dynamics, demand, and supply. Setting the right price at the right time is essential for business growth. Pricing managers rely on data and analytics to determine the optimal market price.

Factors Influencing Pricing

• Organizational factors: Product availability, budget constraints
• Marketing mix: Product life cycle stage, Product, Price, Place, Promotion
• Product cost: Production and raw material costs
• Demand: Customer demand for the product or service
• Competition: Competitor pricing influences internal pricing decisions

What is Dynamic Pricing?

Dynamic pricing involves adjusting prices based on real-time data such as customer behavior, demand, and competition. It allows businesses to sell goods at different price points to maximize revenue and customer satisfaction.

Dynamic pricing is effective when demand is elastic, allowing businesses to adjust prices based on customer sensitivity to price changes. The ultimate goal of dynamic pricing is to maximize revenue, profits, and customer satisfaction.

What are the Goals of Dynamic Pricing?

• Increased profits, revenue, flexibility, market share, and customer satisfaction
• Optimized inventory utilization and supply-demand balance

Success in dynamic pricing is measured by the YOY increase in revenue and units sold. Various metrics like Average Order Value (AOV), Conversion Rate (CR), Revenue per Visitor (RPV), and Gross Margin Percentage (GMP) are used to evaluate dynamic pricing performance.

Factors Influencing Dynamic Pricing

• Supply and Demand: Prices vary based on supply and demand fluctuations
• Inventory levels: Clearance sales and old inventory impact pricing
• Customer preferences: Different pricing tiers based on customer segments
• Seasonality and festivals: Prices may surge during peak seasons
• Location and time: Pricing may vary based on location and time of day
• Competitor pricing: Competitor prices influence pricing decisions

Types of Dynamic Pricing

• Segmented Pricing: Offering discounts based on customer segments
• Time-based Pricing: Adjusting prices based on time of booking
• Peak Pricing: Surge pricing during high-demand periods
• Price Elasticity: Pricing based on product sensitivity to price changes

Revenue/Yield Management

Revenue management involves optimizing pricing, inventory, and distribution to maximize revenue. It focuses on selling the right product to the right customer at the right time and price.

• Segmentation, forecasting, optimization, and pricing are key components of revenue management
• Revenue management works best for price elastic products/services

Is Dynamic Pricing Legal in India?

Dynamic pricing raises legal and ethical concerns, especially in markets like India. Regulations like the Competition Act 2002 prohibit price fixing and collusion among enterprises to control market prices. Implementing dynamic pricing requires careful consideration of legal and ethical implications.

Problem Statement

FlyAirportByAir, a taxi-chopper service in Bangalore, aims to introduce dynamic pricing to optimize revenue. A pricing function needs to be developed to maximize revenue based on factors like prebooking days, total seats per day, and daily demand variations.

• Prebooking starts 100 days before
• Total seats per day is 100
• Demand varies between 100 to 200 per day
• Pricing formula: Price = Demand – Tickets sold

The challenge is to find the optimal price for each day based on days left to book, total seats available, and daily demand.

## Global Variables

DAYS = 100

SEATS = 100



DEMAND_MIN = 100

DEMAND_MAX = 200

Forecasting demand and determining the right pricing strategy are crucial steps in developing a dynamic pricing algorithm.

demand_hist = [np.random.randint(DEMAND_MIN, DEMAND_MAX) for i in range(10000)]

plt.hist(demand_hist, bins = 100)

print("mean", np.mean(demand_hist) )

print("STD", np.std(demand_hist) 

The average demand is 150 seats per day with a standard deviation of 28.9.

Example

Consider a scenario where demand for a journey is higher than the available seats. A linear pricing function can be used to calculate the price based on demand and tickets sold.

def linear_demand(days_left, ticket_left, demand_level):



  tickets_sold_per_day = int(ticket_left/days_left)  

  price = demand_level - tickets_sold_per_day  

  return max(0,price)

A recursive revenue function can be used to calculate cumulative revenue for different scenarios, optimizing pricing based on demand and available seats.

Stimulations Using Pricing Functions

Stress testing different pricing functions like linear_demand, linear_adj, and linear_opti_variable through simulations can help identify the most effective pricing strategy for revenue maximization.

Evaluation of Pricing Functions

Based on revenue maximization, the linear_adj pricing function is identified as the most effective strategy. Continuous evaluation and optimization of pricing functions can lead to improved revenue performance over time.

Conclusion

Dynamic pricing is a powerful tool for businesses to optimize revenue and customer satisfaction. By implementing the right pricing strategies and continuously refining them based on data and analytics, companies can achieve significant growth and competitive advantage.

Key Takeaways:

• Dynamic pricing aims to maximize revenue, profits, and customer satisfaction
• Methods and strategies for dynamic pricing vary across industries and markets
• Continuous evaluation and optimization are key to improving dynamic pricing performance
• Dynamic pricing is most effective when demand elasticity exists

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Frequently Asked Questions

References

• Kaggle Mini Courses: Airline Price Optimization Microchallenge
• Coursera: Fundamentals of revenue management
• HBR Review: 7 Lessons on Dynamic Pricing (Courtesy of Bruce Springsteen)
• Dynamic Pricing Model: Online bus ticketing platform pricing
• Dynamic Pricing Strategies: Multiproduct revenue management problems
• Price Optimization: Exploration to productionizing

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