Revenue and Pricing Analytics with Excel & Python.

Explore how price affects demand by modeling a linear price response function from willingness-to-pay data, and learn to fit this demand curve with linear regression in Excel and Python.

Learn how to compute the linear regression formula using ordinary least squares to relate price to demand, identifying the intercept and slope in the linear response function.

Explore the linear price response function that links demand to price, highlighting segment and channel variations, competitive market effects, and time-dependent, continuous, downward-sloping, non-negative demand.

Compare linear regression and logit models to optimize price and revenue using elasticity; fit both, select the lower error model, and estimate demand as probability times population.

Explore the logistic price response function versus linear models, including how to fit, compare, and optimize price for revenue versus profit, using Excel techniques.

Explore linear price function estimation to optimize product pricing by fitting linear and logit price responses, visualizing demand versus price, and comparing models with trend lines and r-squared.

Corrects a miscalculated coefficient in the linear price response function, updates the intercept and elasticity figures, and notes revised maximum revenue figures.

Learn to fit the logit function with dummy parameters, then optimize them to minimize the error of the fit against population and demand data.

Experiment with Excel-driven variable fitting, compare linear and logit demand models, simulate prices from 12 to 46, and identify revenue-maximizing price around 26 using the better logit fit.

Explore elasticity as the sensitivity of demand to price, distinguish elastic, inelastic, and unit-elastic cases, and learn revenue optimization via the derivative-based elasticity condition and price response function.

Explore elasticity and how percentage price changes affect demand via the price response function, using its derivative to assess revenue. Identify elastic, inelastic, and elasticity equals one for revenue optimization.

Analyze elasticity for logit and linear models to locate revenue-maximizing prices using Excel and Python, distinguishing inelastic, elastic, and unit elastic ranges.

Solve this assignment by fitting the product demand with linear and other models, identify the optimum price for each model using maximum likelihood estimation, and perform budget optimization.

Apply linear fitting to estimate demand with slope -22.108 and intercept 1084, and assess fit with squared errors. Optimize best price near 29–30 using budgeting and server optimization.

Explore polynomial regression variants to model demand as a function of price, including quadratic and cubic terms, and compare fitting with regression analysis, logit, and sum of squared errors.

Explore willingness to pay, reservation price, and the price response function, illustrated by a 20000-person market with a uniform willingness-to-pay distribution from 0 to 10 dollars.

Compute the point of maximum profit and maximum revenue using the price response function, derivatives, and linear models in Excel, then apply Python for optimization and pricing analytics.

Compare linear and logistic models to maximize revenue and profit, solve for the optimal price P*, and apply the A over two B formula for quick estimates.

Explore maximizing revenue and profit using linear and logit models, compare simulations with solver optimization, and identify market price and price-sensitivity points.

Describe linear and reduced price response functions and their uses, define elasticity as the percentage change in demand over price, and illustrate linear and logistic models with examples and segmentation.

Explore how segmentation and product versioning tailor prices to customer willingness to pay. Learn channel, regional, demographic, time-based, and volume pricing strategies to boost revenue.

Learn how price differentiation groups customers into segments to boost profit. See examples including hotel pricing and Coca-Cola Brazil backlash to illustrate the risks of misjudging willingness to pay properly.

This lecture demonstrates pricing optimization with a linear demand function, yields an optimal price of 14 USD and profit of 96,800, and introduces willingness-to-pay segmentation for kids and others.

Segment market into kids and adults over 25, use two price response functions—lower 12000 minus 800 price and upper 20000 minus 800 price between 15 and 25—to analyze pricing scenarios.

Compare profit with segmentation versus no segmentation, showing how upper and lower segment pricing shapes demand and profitability, and highlight optimizing prices via Excel simulations.

Explain segmentation simulation using a linear demand function, where demand equals a minus B p, to find the revenue-maximizing price. Analyze willingness to pay and segment sizes to determine pricing.

Simulate segmentation to optimize prices and maximize contribution by analyzing lower and upper segment demand, willingness to pay, and the impact of price cutoffs.

Model a linear demand for 16,000 daily visitors, set prices with a $5 beverage cost, and assess segmentation by willingness to pay to maximize profit.

The lecture derives the profit-optimizing price using a linear price response and computes a $12.50 price yielding $45,000 profit without segmentation, then examines segmentation with a uniform willingness-to-pay distribution.

Analyze segmentation and price optimization using willingness to pay and a price response function across two segments, revealing an optimal price of 12.5 usd and associated profits.

Explore the limits of segmentation in pricing, including imperfect segmentation, cannibalization, arbitrage risks, and various group pricing strategies such as student, senior, family discounts and loyalty programs.

Explore channel segmentation and coupon strategies to optimize revenue by aligning willingness to pay across online, retail, and time-sensitive pricing, using self-selection and location-based distinctions.

Explore how volume discounts drive price segmentation, leveraging economies of scale and contribution margins to boost revenue, while considering customer utility and price sensitivity.

Explore volume discounts to maximize revenue, setting a maximizing price of 125 with 250 buyers, then attract the remaining 250 through discounts, tested in Excel.

Explore how a linear demand function drives the optimal price and profit for a theme park, and how capacity constraints shift pricing from 14 to 16.25 to balance demand.

Apply variable pricing across time to balance fixed capacity and perishable inventory, charging higher prices at peak times and lower prices at off-peak times, with electricity and barbershop examples.

Explore revenue and pricing analytics with Excel and Python by comparing non variable pricing and variable pricing against a demand function and 1000-seat capacity, maximizing revenue through price optimization.

Explore variable pricing optimization to maximize revenue under a 1000-unit capacity, adjusting daily prices to balance demand and yield a 29 percent revenue increase.

Analyze pricing scenarios using a linear demand model with intercept and slope: a single price capped at 2000 capacity and weekly variable pricing, via the attached Excel sheet.

Explore variable pricing and revenue optimization under capacity limits, using Excel and Python, by setting price to maximize revenue as the minimum of demand and capacity.

Learn how revenue management optimizes capacity to maximize revenue by balancing spoilage and dilution, using booking limits, overbooking, and network planning for airlines and hotels.

Revenue management optimizes perishable service inventory by managing ahead-of-time seat bookings and class-based pricing, as illustrated by airlines’ deregulation and the introduction of barebone tickets.

Explain how supersaver pricing, booking limits, and fare restrictions shaped revenue management by segmenting leisure and business customers to maximize profit.

Explore how strategic, tactical, and operational decisions drive segmentation, product versioning, channel pricing, allotment, nesting, and the investing strategy shift to maximize revenue.

Learn how nesting in revenue analytics uses booking limits and protection levels to reserve seats for higher classes, maximizing revenue across first, second, and third class.

Explore revenue management components and techniques, including resources, products, and booking limits, and master capacity allocation, network management, and overbooking to optimize protection levels and price decisions.

Balance capacity allocation between protecting higher class tickets and avoiding seat spoilage using the Littlewood rule in a two-class airline pricing scenario.

Using the Littlewood rule, the lecture shows how to reserve about 92 seats for higher fare first class, allocate remaining seats to second class, and prevent revenue dilution.

Calculate protection levels and booking limits for fare classes using averages, standard deviation, and the normal distribution, yielding 94–95 rooms for first class and 105 for discount class.

Extend EMSR to multiclass scenarios, calculate booking limits and protection levels for each class, and apply the expected marginal seat framework to a three-class concert example using Excel.

Apply EMSR-a to set class booking limits and protection levels, computing limits from prices, probabilities, and standard deviations to optimize capacity and avoid overbooking across multiple classes.

Calculate the booking limit and protection level for each class using expected margin and marginal seat revenue, given class prices and seat totals, and discuss results in the next lecture.

Compute protection levels between classes using Littlewoods rule and the community distribution function, then derive booking and walking limits under demand and capacity 40000 at price 200.

Explore network management to maximize revenue by using multiple resources to offer multiple products, via linear programming. A hotel example shows two room types across seven days creating product combinations.

Apply linear programming to maximize airline revenue by allocating seats across two routes and two classes, considering capacity constraints and demand, using Excel and Python.

Set up a linear programming model to maximize revenue from two flights, with six products, capacity constraints of 200 and 300 seats, and demand caps.

Maximize revenue by choosing the number of seats for each product under theater capacity and integer constraints, using product demand limits to determine the optimal mix.

learn a practical overbooking approach to maximize revenue by accounting for cancellations and no-shows, using capacity divided by show rate; illustrated with a 250-room hotel example and refund considerations.

Explore optimizing seat bookings across business, economy, and first class on two flight legs, considering dedicated first-class capacity and price differences, to maximize revenue under capacity constraints.

Maximize booked capacity across flight routes and classes by aggregating aircraft capacity, enforcing integer decision variables and demand constraints in a linear programming model.

Explore how Python complements Excel for pricing analytics and revenue management. Learn Python basics, set up Anaconda and Spyder, and practice importing data, filtering, loops, and functions for pricing applications.

Trace Python’s origins from a late-80s programming language to a leading data science tool, and explore installing and using Anaconda with Jupyter notebook and Spyder.

Learn how to download and install Anaconda for Windows, Mac, or Linux by visiting the download page and selecting the appropriate installer.

Follow along as we install Anaconda together, completing the setup in about two to three minutes and exploring what applications will run on its user.

Learn the Spyder IDE overview, switching between notebook and Spyder, creating a supply chain data science project, running simple Python scripts, and customizing shortcuts and file imports.

Compare Spyder and Jupyter labs as web-based interactive shells inside Anaconda. Two options to work with, delivering identical Python code and instant cell outputs.

Explore Python data science libraries such as pandas, matplotlib, and numpy, and learn to import them in an Anaconda environment using Jupyter Notebook or Spyder to manipulate and visualize data.

Install the inventories package in Python using pip, the most updated version, and stay current with future updates by running pip install inventories.

Explore Python data types and structures, including floats, integers, strings, booleans, and dates, and learn how to import and clean data with lists, dictionaries, tuples, and arrays.

Explore dataframes in python and excel workflows, learning how to create and manipulate tables with parentheses for functions and assignment, and understanding observations and attributes in tabular data.

Practice basic Python arithmetic in a Jupiter notebook, creating and saving variables like addition and multiplication, and building a prime numbers list. Use zero-based indexing to access list items.

Master Python lists by learning zero-based indexing and end-exclusive slicing to subset lists and data frames, including building lists of quoted strings.

Learn the basics of dictionaries, mapping keys to values with braces, and how to access keys and values using dot notation and chained commands.

Explore arrays as an efficient, multi-dimensional data structure, compare them to lists and dictionaries, create them from a library, and apply element-wise arithmetic like addition and division across all elements.

Import and explore a UK retailer online sales dataset in Python using pandas, loading into a data frame in Jupyter and inspecting with head, tail, shape, and describe.

Learn to subset dataframes using index locations and column names, select the first five rows and columns, and add a new euro price column by converting the USD price.

Master conditions and logical operators to filter a data frame, test multiple criteria, and implement if statements in loops after subsetting data and converting usd.

Write a Python function using def to determine a person's status (child, teenager, adult) from age, illustrating indentation, conditional logic, and printing results.

Use the Python map function to apply a function to every element in a list, creating a map object and converting it to a list to reveal each status.

Master for loops as essential iteration to apply an operation to every element in a list, such as printing each age in a class list, and compare with map.

Loop a function in python, replace print with return, and apply a status function to ages with for loops and map, storing results in a list for data frame workflows.

Create a function to flag United Kingdom customers in a retail dataframe, apply it to the country column, and add a true/false uk column via map.

Learn to loop over a dataframe using a for loop and apply on a first-10 rows subset. Compare map and for loop outputs, and create a first column across rows.

Master python fundamentals with dictionaries and keys, import and subset data using pandas, and learn functions, mapping categories, and looping over frames for analytics.

Explore the first assignment by loading a 400-car dataset in Python, compute key stats, rename a column, create price categories, and classify budget, midrange, and expensive cars.

Analyze a car dataset to support revenue and pricing analytics by computing summary statistics, identifying extremes, and exploring features such as horsepower, price, and cylinders using Python pandas.

Learn to rename the car name column, create a car pricing subset, and build a pricing_category function with thresholds to classify prices as budget, suitable, or expensive; apply results.

Learn to apply Python for pricing and revenue management using inventories for linear, logit, and multi-product optimization, with the Winterreise package.

Experiment with the price response function using weekly price and demand data, import a data frame, and perform linear regression to analyze price-demand relationships.

Simulate demand using a linear price response model, estimate the intercept and price coefficient, generate simulated price data, compute expected demand, revenue, and profit, and identify the optimal price.

Analyze simulated demand, revenue, and total cost to identify the price that maximizes profit, with peak around 27 as demand declines with price; elasticity will be explored in upcoming lectures.

In this assignment, fit a linear regression using population size and price, simulate prices from 0 to 3 dollars, identify revenue-maximum point, and compute cost to derive the optimal price.

Explains solving an assignment with linear regression to optimize pineapple juice price and demand. Simulate data with intercept 200 and price coefficient -50, then find max revenue and profit points.

Analyze elasticity to identify optimal prices for profit and revenue, noting elasticity of one marks maximum revenue. The example shows price 23, cost 2.5, with profit 27.4 and revenue 26.16.

Parse dates to extract weekly components and analyze revenue and pricing with Excel and Python. Build a linear response function to measure elasticity and identify the optimum price and profit.

Identify unique skus by year and week in excel and python, aggregate total demand and price per sku, explore price relationships, and implement per-sku loops to apply functions in steps.

Explore linear elasticity across multiple products by building and filtering an elasticity data frame, calculating elasticity from price and sales, and assembling a final dataset.

Convert elasticity values into floats using dict comprehension to build a one-dimensional data frame. Apply try-except to manage empty data and potential missing samples.

Compute elasticity, determine the optimum price for profit and revenue, and simulate price effects using data frames and dictionary comprehension, while accounting for seasonality and trend.

Analyze a linear price response function that links price and demand, using parameters a and b to derive revenue and profit optima and elasticity to locate the revenue-maximizing price.

Analyze the linear price response function using elasticity to verify optimum profit and revenue, then determine current price elasticity to decide whether to raise or lower price for revenue optimization.

Apply elasticity by computing elasticity as the derivative of the price response function times the current price, divided by the price response function, to determine unit elastic revenue.

Explore logistic modeling to link price and demand and elasticity, compare logit and linear regression, and identify the optimum revenue price using single product optimization in inventory.

Compare logit and linear models by analyzing predictions and plots, then identify how better fit leads to higher revenue and optimal prices.

Explore logit for looping in pricing analytics by building a dictionary-based loop to run single product optimization, compare logit and linear revenue price predictions, and interpret weekly sales results.

Test Leanyer and price response functions to determine which model best fits demand, using single product optimization to compare errors and maximize revenue with the logit price response function.

Explore single-product price optimization using logit versus linear models, fit to historical data, and determine the optimum price and revenue, with predictions and model comparison.

Analyze how correlated products affect pricing using multivariate regression and multinomial logit models to optimize profits while understanding portfolio effects and model constraints.

Learn how competing products shape pricing decisions in revenue analytics by accounting for interdependent demand, customer value, and intangible factors like brand image to optimize assortment pricing and profitability.

Explore multivariate multiple regression to model multiple dependent variables with several predictors, revealing how competing products influence each other's demand, price, and revenue.

Explore multivariate regression in Python using ordinary least squares to assess how four product prices affect weekly demand, examining p-values, coefficient significance, intercepts, and inter-product competition across four models.

Apply multinomial choice method to model individual product choices and optimize prices using event-level data and logistic regression-based insights for maximum profitability.

Apply multinomial choice models with logistic regression to estimate product attractiveness and maximize profit across four products using multi-competitive optimization; analyze per-customer transactions and evolving prices to capture attractiveness efficiently.

This lecture demonstrates multi competing optimization to set optimized prices across four products by estimating each product's attractiveness, utility, and the probability of purchase to maximize profit.

Apply multivariate regression for product relationships amid noise, refine with ANOVA, analyze correlations with price and sales, and use multinomial logit model to estimate probability of choosing product over others.

Explore how markdowns drive traffic and revenue through time-based and descending promotions. Learn how to optimize markdown strategies, bundle promotions, and salvage inventory using Excel tools to maximize profit.

Examine how markdowns reduce prices to boost demand, including time-based sales and bundles. Assess end-of-life discounts, exclusivity, and capacity considerations that influence pricing strategies.

Explain how markdowns target budget, value, and luxury customers to boost sales, clear end-of-season inventory, and drive footfall during a crisis.

Segment customers into budget, value, and high-roller groups and tailor markdowns with targeted offers, clearance alerts, and market basket analysis to boost revenue and pricing strategy.

Formulate a three-segment pricing problem using discounting and salvage value to maximize revenue, considering inventory constraints, price response, and bid-based discrimination across periods, with deterministic demand or alpha-based demand.

Explore markdowns for multiple periods by formulating demand using a price response function, tracking inventory and salvage, and computing revenue across periods.

Set up the solver to maximize the objective by changing three variable cells, add the constraints, and verify the formulation yields prices and salvage value across periods.

Set salvage value to zero to show unsold items are written off, and a linear price response indicates 300 is the optimal quantity versus 400, with salvage and non-salvage divisions.

Explore how uncertain demand is modeled with linear regression to capture seasonality and trend, and embed forecasted demand into pricing optimization.

Analyze demand-driven pricing with Excel and Python, using linear regression and optimization to show how price adjustments, seasonality, salvage values, and markdowns influence revenue, inventory, and write-offs.

apply a simple markdown for one period, target myopic customers first and strategic customers later to maximize revenue, guided by linear regression and elasticity in p1.

Practice profit optimization using an Excel assignment in two cases: deterministic demand with known price response, and non deterministic demand with seasonality and trend.