Mastering Dynamic Pricing & Revenue Management: Excel to Python Analytics

What you will learn:

Determine optimal discount strategies to maximize product revenue.
Implement advanced price optimization techniques using both Excel and Python.
Develop customized pricing models for individual customer segments or products using Python.
Perform comprehensive customer analytics to understand buying behavior and preferences.
Apply various strategic pricing models tailored to different product types and market conditions.
Accurately assess customer willingness to pay through analytical methods.
Build and fit demand curves using appropriate response functions (linear, logit, polynomial).
Strategically differentiate products and customize pricing for various market segments.
Grasp the concept of nesting in revenue management and apply it to enhance profitability.
Optimize retail pricing strategies for diverse inventory.
Maximize business profit through data-driven pricing decisions.
Utilize data science methodologies for advanced pricing analytics.
Apply Python programming for scalable and complex pricing and revenue management tasks.

Description

Course Image by @agent_illustrateur-Christine Roy from unsplash.

Includes a dedicated Python Fundamentals section!

Rewind to the late seventies, a time when air travel in the U.S. was far less complex, with regulated fares and a singular service class. At the forefront stood American Airlines. The landscape dramatically shifted with deregulation, ushering in agile newcomers like People Express, who offered significantly cheaper tickets. This disruption caused a mass exodus of customers from American Airlines to its budget-friendly competitor.

The events that followed didn't just alter American Airlines' trajectory; they fundamentally redefined our understanding of pricing – transforming it from a mere cost recovery mechanism into a potent strategic tool for enhancing profitability and ensuring product availability. American Airlines ingeniously implemented segmentation and sophisticated revenue management practices, famously known as "yield management," to reclaim its market share. People Express, unable to adapt, eventually ceased operations, while American Airlines reported an astounding 47% increase in profit that very year. This pivotal moment set a new industry standard.

This pioneering approach swiftly spread across diverse sectors, from Ford's car rentals and Marriott's hotel bookings to NBC and ABC's ad placements, and now permeates virtually every business model globally.

This comprehensive course invites you on an enlightening journey to decipher consumer behavior and master the science of pricing. Discover how to strategically price your offerings to maximize revenue and ensure optimal product availability. Whether you're an entrepreneur, a product line manager, or preparing to launch a new product or service, this program provides the essential toolkit for a successful market entry and sustained growth.

Beyond foundational concepts, modern businesses grapple with thousands of products, making manual Excel-based optimization impractical. This course uniquely addresses this by integrating pricing and revenue management with Python. No prior Python experience is necessary; we guide you through every step, ensuring you gain proficiency from the ground up.

Packed with insightful lectures, core concepts, practical coding sessions, engaging exercises, and downloadable spreadsheets, this course is designed for active learning. We don't just present code; we build it collaboratively, line by line. By the end of this transformative experience, you will possess the ability to:

Leveraging Excel for:

Understanding inventory perishability and its implications.
Exploring diverse pricing strategies tailored to various market conditions.
Determining customer willingness to pay through analytical methods.
Accurately fitting demand curves using appropriate response functions.
Calculating product elasticity and applying it for strategic price setting.
Implementing product differentiation and segmented pricing.
Applying the concept of nesting in revenue management.
Utilizing Littlewood’s Rule and EMSR for setting optimal booking limits across service offerings.
Simultaneously optimizing prices for multiple products.
Executing effective markdown strategies.

Utilizing Python for:

Þ Grasping Python fundamentals: functions, loops, and data structures.

Þ Fitting demand models using linear and logit functions.

Þ Performing multi-product optimization at scale.

Þ Developing customized pricing models.

Course Methodology

This course adopts an experiential learning module structure. The initial modules focus on building a strong understanding of pricing principles, followed by hands-on application of optimization techniques. Rest assured, if Python is new to you, a dedicated fundamental section will equip you with the necessary skills to confidently engage with the programming modules.

We eagerly anticipate your participation and trust you'll find immense value in this class.

Happy Data Mining!

Haytham

Rescale Analytics

Curriculum

Introduction

This foundational module introduces the strategic importance of pricing, tracing its historical evolution from regulated markets to today's dynamic e-commerce landscape. You'll explore fundamental concepts like market dynamics, internal price dimensions, and various pricing strategies, understanding how products, services, and resources differ in their pricing approaches. The section culminates with practical examples and an initial quiz to solidify your grasp of core principles, setting the stage for advanced analytics. Key topics include the "Game Changer" moments in pricing history, the role of ERP systems, and the evolution of e-commerce pricing.

Price Response function, Willingness to pay and Elasticity.

Dive deep into the core analytical tools for pricing in this module. You'll learn to model consumer behavior using linear and logistic regression to define robust Price Response Functions. We'll cover estimating these functions, simulating price impacts, and mastering the concept of price elasticity—a crucial metric for strategic pricing decisions. Discover how to accurately determine customer willingness to pay and identify the point of maximum profit. The section includes practical assignments, detailed answers, and a comprehensive quiz to reinforce your understanding of optimization techniques for both linear and logit models.

Price Differentiation

This module unpacks the power of price differentiation and customer segmentation. You'll learn effective strategies for grouping customers, understanding how segmentation impacts realized profit, and simulating various scenarios to optimize your pricing. Explore advanced techniques like group pricing, channel segmentation with coupons, and volume discounts. Crucially, this section also addresses optimizing profit under supply constraints and delves into variable versus non-variable pricing optimization, providing practical examples and assignments to hone your skills in adapting prices to different market segments.

Revenue management

Embark on an insightful journey into the world of revenue management, beginning with its historical roots and the pivotal moments that shaped its adoption. This module covers essential components and techniques, including allotment and nesting strategies. You'll gain practical experience in capacity allocation, applying Littlewood's Rule and EMSR-a for multi-class fare management to set optimal booking limits. The section also introduces network management, linear programming for complex scenarios, and the critical concept of overbooking, providing detailed examples and assignments to build your expertise in maximizing revenue from perishable inventory.

Installing Anaconda

Prepare your development environment with this practical module. You'll trace Python's history, then receive step-by-step guidance on downloading and installing Anaconda – an essential platform for data science. Get a comprehensive overview of Spyder and Jupyter Notebooks, two popular IDEs for Python development. The section also covers managing Python packages, including an introduction to the 'Inventorize' package, ensuring you have all the necessary tools for the Python-based analytics ahead.

Python Crash section

This dedicated crash course is designed to get you up and running with Python, even if you're a complete beginner. You'll learn fundamental concepts such as working with dataframes, performing arithmetic calculations, and mastering core data structures like lists, dictionaries, and arrays. The module covers essential operations like importing data, subsetting dataframes, applying conditional logic, and writing your own functions. Crucially, you'll learn to leverage `map` and `for` loops for efficient data manipulation and function application on dataframes, concluding with practical assignments to solidify your Python skills.

Linear response function with Python

Transition your pricing analytics skills to Python in this module. Starting with a motivation for price functions in Python, you'll learn to simulate demand and identify the point of maximum profit using Python code. The section provides hands-on assignments and explanations for linear elasticity calculations with the 'Inventorize' package. You'll also explore practical data handling techniques like parsing dates and extracting unique SKUs, culminating in applying linear elasticity across multiple products, including error handling, and a summary of single optimization, complete with an assignment.

Logit Price response function

Building on linear models, this module introduces logistic modeling using the 'Inventorize' package in Python. You'll gain a deep understanding of logit price response functions and learn to compare their effectiveness against linear models. The section emphasizes applying logit models through for-looping for scalable analysis, providing a practical assignment and its detailed answer to ensure mastery of this advanced demand modeling technique for more nuanced consumer behavior.

Multi-product optimization

Elevate your pricing strategies to encompass complex product ecosystems in this module. You'll explore the dynamics of competing products and understand the interrelations among them. The module introduces multivariate regression in Python to model these relationships effectively. Delve into multinomial choice models across multiple parts to understand consumer preferences when faced with several options. Finally, learn how to implement multi-competing product optimization in Python, culminating in a concise summary to grasp the complexities of pricing interdependent products.

Markdowns

This module focuses on the strategic implementation of markdowns. You'll understand the underlying reasons for markdowns and how different customer segments respond to them. The section covers problem formulation for markdown optimization, particularly for multiple periods, and setting up a solver to achieve optimal outcomes. Explore concepts like salvage value and integrating forecasting into markdown strategies. The module concludes with sensitivity analysis and a practical look at markdowns for a single period, alongside a concluding assignment to apply your knowledge.

Deal Source: real.discount