Revenue and Pricing Analytics with Excel & Python.

PRICING ANALYTICS · REVENUE MANAGEMENT · PRICE ELASTICITY · EMSR · YIELD MANAGEMENT · PYTHON · INVENTORIZE · SEGMENTATION · WILLINGNESS TO PAY · MARKDOWN OPTIMISATION

★ Included in Udemy for Business — Chosen by Companies for Pricing and Revenue Training

This course is part of the Udemy for Business catalogue — selected by companies for training their pricing, revenue management, commercial, and supply chain analytics teams. With 18,200+ enrolled students, it is the course that organisations trust when they need their revenue managers, brand managers, and pricing analysts to move beyond instinct and build quantitative pricing models that actually work.

★ Grounded in Robert R. Phillips’ “Pricing and Revenue Optimization” — The Standard Academic Text in the Field

This course draws heavily from Robert R. Phillips’ landmark textbook “Pricing and Revenue Optimization” — the definitive academic reference used in MBA programmes and revenue management training at airlines, hotels, and retailers worldwide. Price response functions, Littlewood’s rule, EMSR-a, nesting, capacity allocation, network management, and customised pricing are all taught through the rigorous analytical framework that Phillips formalised. Most pricing courses on Udemy are strategy overviews. This course teaches the science.

★ The Elasticity and Revenue Maximisation Models in This Course Were Deployed for Real Clients

Haytham deployed a revenue maximisation algorithm based on elasticity techniques for Sharaf Group Adventure HQ — a multi-location adventure and retail group in the UAE — and the algorithm has been in production use since 2019. The pricing models, elasticity frameworks, and Python optimisation tools you learn in this course are not textbook examples. They are the same analytical constructs that have been built for, tested by, and measured in live commercial operations.

★ Revenue Management Taught at Airline Industry Depth — Littlewood, EMSR-a, Network LP, and Overbooking

Section 5 of this course covers revenue management at a depth you will not find in any other Udemy course outside an airline or hospitality MBA. Littlewood’s rule for two-class capacity allocation, the EMSR-a (Expected Marginal Seat Revenue) algorithm for multi-class fare optimisation, network management with linear programming across multiple flight legs, and the mathematics of overbooking — all with worked examples and graded assignments. These are the techniques that American Airlines used to defeat People Express, that Marriott uses for room pricing, and that every modern revenue management system is built on.

★ Python Pricing Automation with Inventorize — Scale to Thousands of Products Simultaneously

Excel is powerful for one product. Python is necessary for thousands. Sections 7–10 apply Python and the Inventorize library to multi-product pricing: simulate demand, calculate linear and logit elasticity across your entire SKU range, model competing product relationships with multinomial choice models, run logit optimisation for-loops across products, and build customised pricing models with bid price response functions, cross-validation, and interaction term modelling. A complete Python crash course is included from Section 6. No prior coding experience is required.

COURSE DESCRIPTION

In the late 1970s, airline ticket prices in the United States were regulated and almost fixed. Then deregulation brought People Express — a carrier with fares so cheap that customers abandoned American Airlines en masse. What happened next changed how the world thinks about pricing forever. American Airlines introduced segmentation and yield management techniques, attracted People Express customers back, and increased profit by 47% in a single year. People Express went out of business. The techniques American Airlines used — Littlewood’s rule, EMSR, nesting, capacity allocation, and network management — are now standard in every industry that manages perishable inventory: hotels, car rentals, advertising, retail, and supply chain.

This course teaches those techniques from first principles — grounded in Robert R. Phillips’ “Pricing and Revenue Optimization”, the standard academic reference in the field — and applies them in both Excel and Python. You will build price response functions, calculate elasticity for linear and logit models, simulate willingness to pay, optimise profit by segmentation, apply Littlewood’s rule and EMSR-a for capacity allocation, model competing products with multinomial choice models, and automate everything across thousands of products using Python and the Inventorize library.

This course is included in Udemy for Business and taught by a Ph.D. consultant whose elasticity-based revenue maximisation model has been deployed in live operations at Sharaf Group since 2019. No Python experience is needed. No prior pricing or economics background is required. The course genuinely starts from the question: what is pricing, and why does getting it right change everything?

WHAT MAKES THIS COURSE DIFFERENT

[ SCI ]

Pricing science, not pricing strategy

Price response functions, elasticity, Littlewood, EMSR-a, network LP, segmentation simulation, multinomial choice models — the quantitative toolkit that revenue managers at airlines, hotels, and retailers actually use.

[ EMSR ]

Revenue management at airline industry depth

Littlewood’s rule, EMSR-a, multi-class fare optimisation, network management with LP, and overbooking — the techniques that power every modern revenue management system. Rare content on any online platform.

[ SCALE ]

Excel for one product, Python for thousands

Build pricing intuition in Excel. Then apply Inventorize in Python to run elasticity, logit models, and multi-product optimisation across your entire SKU range simultaneously — automatically.

TOOLS AND LIBRARIES COVERED

Microsoft Excel | Excel Solver | Python | Inventorize | Jupyter / Anaconda

WHAT YOU WILL LEARN

✓ Understand the history and economics of pricing: market dynamics, service industry characteristics, ERP pricing systems, and the evolution of e-commerce pricing

✓ Build linear and logistic price response functions, estimate the logit price function, and simulate price scenarios in Excel

✓ Calculate price elasticity for linear and logit models, apply polynomial response function variants, and identify the point of maximum profit

✓ Optimise prices using Excel Solver for single and multi-product scenarios with logit and linear demand functions

✓ Simulate and quantify the profit gain from customer segmentation vs uniform pricing — and design optimal segmentation structures

✓ Apply group pricing, channel segmentation, coupons, volume discounts, and supply-constrained profit optimisation

✓ Apply variable and non-variable pricing optimisation to maximise revenue under different demand and capacity structures

✓ Apply Littlewood’s rule for two-class capacity allocation and EMSR-a for multi-class fare optimisation with worked examples

✓ Build and solve network management models with linear programming for multi-leg, multi-class revenue optimisation

✓ Model overbooking decisions analytically to balance the cost of denied boarding against the cost of flying empty seats

✓ Use Python and Inventorize for multi-product elasticity, logit and linear demand simulation, and price optimisation at scale

✓ Model competing product relationships with multivariate regression and multinomial choice models in Python

✓ Build customised pricing models with bid price response functions, cross-validation, and interaction term modelling

✓ Optimise multi-period markdowns with Excel Solver: problem formulation, salvage value, forecasting integration, and sensitivity analysis

COURSE CONTENT — 12 SECTIONS · 160 LECTURES · 13 HOURS · 42 DOWNLOADABLE RESOURCES

PART 1 — PRICING FUNDAMENTALS

SECTION 1: The economics and history of pricing

Why does pricing matter so much — and why have the rules changed so dramatically in the past 50 years? Trace the history of pricing from regulated fixed prices through deregulation, the rise of early adopters, the distinction between products, services, and resources, the characteristics of the service industry and its perishable inventory problem, the role of ERP systems in pricing, the evolution of e-commerce pricing, and the different pricing strategies available across market structures. Includes a graded quiz.

Concepts

PART 2 — PRICE RESPONSE, ELASTICITY & OPTIMISATION

SECTION 2: Price response functions, elasticity, and profit optimisation

The quantitative core of pricing. Build linear and logistic price response functions from first principles. Estimate the logit price function. Simulate price scenarios. Calculate price elasticity for both linear and logit models. Explore polynomial response function variants. Measure willingness to pay. Identify the point of maximum profit. Optimise logit and linear models simultaneously with Excel Solver. Graded assignment and quiz.

Excel Solver

SECTION 3: Customer segmentation for pricing

The same price for all customers leaves money on the table. Understand how grouping customers by willingness to pay increases realised profit. Simulate and compare profit with and without segmentation on real data. Run two segmentation simulations to quantify exactly how much value a well-designed pricing structure unlocks. Graded quiz.

Excel

SECTION 4: Pricing tactics: group pricing, discounts, and variable pricing

Apply segmentation to real pricing decisions. Build group pricing models. Design channel segmentation strategies with coupons. Optimise volume discount structures. Maximise profit under supply constraints. Apply variable and non-variable pricing optimisation to capacity-constrained revenue problems. Graded assignment.

Excel Solver

PART 3 — REVENUE MANAGEMENT

SECTION 5: Revenue management: Littlewood, EMSR-a, network management, and overbooking

The most technically rigorous section of the course — and the one that covers the methods that changed the airline industry. Understand revenue management fundamentals, allotment, and nesting. Apply Littlewood’s two-class rule for capacity allocation. Calculate EMSR-a (Expected Marginal Seat Revenue) for multi-class fare optimisation with full worked examples. Extend to network management: solve multi-leg, multi-class revenue optimisation with linear programming. Model overbooking decisions. Multiple graded assignments throughout.

Excel Solver

PART 4 — PYTHON PRICING TOOLS

SECTION 6: Python crash course for pricing professionals

No Python experience? No problem. Install Anaconda, explore Jupyter Notebook and Spyder, and build Python fundamentals from scratch with a pricing mindset: dataframes, arithmetic, lists, dictionaries, arrays, data import, subsetting, conditions, functions, mapping, and for loops. The Inventorize package is introduced and installed. Two-part graded assignment.

Python Anaconda Inventorize

SECTION 7: Pricing optimisation with Python and Inventorize

Scale what you built in Excel to unlimited products. Apply the Inventorize library to motivate price function fitting in Python: simulate demand, identify the point of maximum profit. Apply linear elasticity with Inventorize across multiple SKUs with date parsing and error handling. Apply logistic modelling with Inventorize and compare logit vs linear performance. Single-product and multi-product optimisation assignments.

Python Inventorize

SECTION 8: Competing products and multinomial choice models

Price one product and you affect all the others. Model competing product relationships using multivariate regression in Python. Build and apply multinomial choice models (Parts 1 and 2) to optimise pricing across a set of competing products simultaneously. Multi-competing products implementation in Python.

Python

PART 5 — MARKDOWNS & CUSTOMISED PRICING

SECTION 9: Markdown optimisation with Excel Solver

Markdowns are one of the most consequential pricing decisions in retail and supply chain. Understand why markdowns happen and how different customer segments respond. Formulate single-period and multi-period markdown problems. Set up and solve with Excel Solver. Account for salvage value. Integrate forecasting into markdown decisions. Run sensitivity analysis on markdown solutions.

Excel Solver

SECTION 10: Customised pricing with bid price response functions

The most advanced pricing section: loan and bid pricing where every customer is quoted an individualised price based on their attributes. Understand the difference between customised and list prices. Calculate expected contribution. Build bid price response functions with customer attributes. Explore and prepare data. Fit an interest rate model. Run simulations. Calculate probability P and expected margin. Scale attribute weights. Build and validate a logistic model with cross-validation. Model interaction terms for improved accuracy.

Python Excel

THIS COURSE IS NOT FOR YOU IF...

✗ You are looking for a general marketing strategy course — this course teaches quantitative pricing models and revenue optimisation, not brand strategy or campaign planning

✗ You want a Python data science course without a pricing focus — every technique in this course is applied directly to pricing and revenue problems; generic ML applications are covered separately

✗ You need an accounting or financial modelling course — this course focuses on demand-side revenue optimisation, not cost accounting or P&L modelling

✗ You are looking only for pricing strategy frameworks — this course builds quantitative models that output optimal prices; if you want only frameworks without the maths, a shorter overview course may be more appropriate

WHAT STUDENTS AND CLIENTS SAY

“It was exactly what I was looking for at this time. The depth of the revenue management content and the way the course bridges theory to Python implementation is genuinely rare.”

Christiaan Daniel Dirk — Verified Udemy student

“Already gaining a lot of knowledge about maximising pricing and revenue. The real business cases and examples make the concepts immediately applicable to decisions I am making right now.”

Nana — Verified Udemy student

“I participated in the Supply Chain Forecasting & Management training conducted by Haytham. It helped me enormously in my daily work. Haytham has the pedagogy to explain very difficult calculations and formulas in a simple way. I highly recommend this training.”

Djamel Bouremiz — Purchasing Manager, Mineral Circles Bearings W.L.L.

“Très bon cours. The combination of rigorous pricing theory with practical Excel and Python implementation is exactly what revenue professionals need. The revenue management section alone is worth the price of the course.”

Mathieu — Verified Udemy student

WHO THIS COURSE IS FOR

Revenue managers and pricing analysts

You set prices for products, services, or capacity and want to move from rules-of-thumb to validated quantitative models — price response functions, elasticity, segmentation simulation, and multi-class fare optimisation.

Marketing and brand managers

You manage product lines, promotions, and discount decisions and want the economic framework — willingness to pay, elasticity, volume discount optimisation — to justify every pricing call with data.

Commercial and sales professionals

You negotiate prices, manage channel partners, and set discount structures and want to understand the analytical models behind optimal pricing — group pricing, channel segmentation, coupons, and variable pricing.

Airline, hospitality, and service industry professionals

You work in an industry where revenue management is standard practice and want the full quantitative toolkit — Littlewood, EMSR-a, nesting, capacity allocation, overbooking, and network LP — built and solved from first principles.

Supply chain and operations professionals

You manage product availability, promotions, and supply-constrained pricing decisions and want the revenue management models — including markdown optimisation and customised pricing with bid price functions — to maximise margin.

Entrepreneurs and product managers

You are pricing a new product or service and want to understand how customers respond to price changes, how to segment your market to extract more value, and how to automate pricing decisions in Python as your product range grows.

REQUIREMENTS

● Basic knowledge of Microsoft Excel — formulas, simple models. No advanced Excel or Solver experience required; both are covered step by step in the course.

● Motivation to increase revenue and profit — the only other requirement listed on the course page, and intentionally so. No pricing, economics, or statistics background is assumed.

● No Python experience needed — Section 6 is a complete Python crash course covering all the fundamentals before any pricing code is written.

● A computer with Excel and Anaconda (free) — installation is guided step by step inside the course. Inventorize and all Python libraries are free and open-source.

WHAT IS INCLUDED

● 12 sections, 160 lectures, and 13 hours of on-demand content covering the complete pricing and revenue optimisation workflow: from price response functions to Python multi-product automation

● 42 downloadable resources: Excel workbooks, Solver models, Python project files, and datasets for every section

● Revenue management section (Section 5) with full worked examples: Littlewood’s rule, EMSR-a multi-class fare optimisation, network LP, and overbooking — multiple graded assignments

● Inventorize Python library for multi-product elasticity and pricing optimisation — Sections 7 and 8, taught in full depth by its creator

● Customised pricing with bid price response functions (Section 10) — the most advanced pricing technique in the course, applied to loan and commercial bid pricing

● Graded assignments in every section — applied to real pricing scenarios, not synthetic data

● Lifetime access to all content and any future updates to the curriculum

● 30-day money-back guarantee — no questions asked

● Certificate of completion upon finishing the course

YOUR INSTRUCTOR

Haytham Omar, Ph.D.

Supply Chain & Business Intelligence Consultant · Developer · Trainer — UAE & France · Co-Founder, Keip

Haytham holds a Ph.D. in Supply Chain and Forecasting from the University of Bordeaux and a Master of Science in Global Supply Chain Management from Bordeaux École de Management. He deployed a revenue maximisation algorithm based on price elasticity techniques for Sharaf Group Adventure HQ — a multi-location adventure and retail group in the UAE — and the algorithm has been in active production use since 2019.

He is co-founder of Keip — a SaaS platform for retail management and analytics — and an active consultant who works with retailers and supply chain organisations including Sephora France and Sharaf Group Dubai. He has trained over 70,000 professionals across 70+ workshops in the UAE. Additional clients include Aster Group, DNO, PWC Training Academy Dubai, Qarar, and the Higher College of Technology.

He is also the creator of the Inventorize package for Python and R — used by over 90,000 supply chain and retail professionals worldwide. The Inventorize library is used throughout the Python pricing sections of this course for multi-product elasticity modelling and optimisation.

Stop setting prices by instinct. Start optimising them by model.

12 sections · 13 hours · Excel + Python · Elasticity · EMSR-a · Inventorize · Udemy for Business · Ph.D. instructor