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Objectives
The main focus of this course is to develop analytical quantitative skills helpful in (B2B) pricing. The course provides the students with the understanding of probability, theory of estimation, and hypothesis testing.
Prerequisities
Mathematics, statistics, microeconomics, macroeconomics, marketing.
Textbooks
Mendenhall, W., Scheaffer, R. L., Wackerly, D. D. (1981), Mathematical Statistics with Applications, 2nd. edition, Duxbury Press, Boston
Scheaffer, R. L., McClave, J. T. (2007), Probability and Statistics for Engineers , 2nd. edition, PWS-Kent Publishing Company, Boston.
Bickel, P. J., Doksum, K. A. (1977), Mathematical Statistics. Basic Ideas and Selected Topics, Holden-Day, Inc., San Fransisco.
Kabacoff, R. I. (2011), R in Action. Data Analysis and Graphics with R, Manning Publications Co., Shelter Island.
Bodea, T., Ferguson, M. (2012), Pricing Segmentation and Analytics, Business Expert Press, New York.
Dolan, R. J., H. Simon, H. (1996), Power Pricing: How Managing Price Transforms the Bottom Line, NY: Free Press.
Software
R:
download R
RStudio:
download RStudio
download RStudio
R Hints
Please download all R-scripts and csv data files to the same catalogue.
Presentation: introduction to R
download file
Course Outline
Block 1: Introduction to Pricing
The Aims: To present selected problems and techniques employed in B2B pricing
The Case: Special emphasis on B2B pricing
Key Words: B2B pricing, price elasticity, price rigidities, pricing experiments, price sensitivity meter, value map, price waterfall, discount matrix,
inflation, deflation, disinflation
Time: 60 minutes
Block 2: Probability
The Aims: [1] To estimate and interpret the descriptive statistics via probability distribution [2] To employ contingency and frequency
tables to estimate conditional probabilities [3] To present graphically the probability distribution [4] To fit the probability distribution and to
present inference based on the estimated moments
Key Words: descriptive statistics, probability, probability distribution, histogram, conditional probability
Time: 90 minutes
Block 3: Linear Regression
The Aim: To estimate the coefficients of a linear price function and to perform simulations and forecasts based thereon
The Case: The curious case of internet auction pricing: Audi Q5
Key Words:Functional relationship, correlation, regression, causality, OLS, goodness-of-fit, diagnostics, residuals, forecasting and control
Time: 60 minutes
Block 4: Modelling conditional probability: binary choice
The Aims: To model the binary-choice variable (e.g. granting a special discount, winning a project opportunity)
The Case: The curious case of granting special discounts, or how expected sales volume affects the probability of granting special discount
Key Words: binary choice, logit, probit, marginal effects
Time:
Block 5: Modelling conditional probability: ordinal models
The Aims: [1] To model the ordinal choice data [2] To simmulate probability response for various scenarios
The Case: Price change probability: historical pricing data from commodity markets
Key Words: ordinal choice, logit, marginal effects
Time:
Block 6: Modelling the impact of pricing: count data models
The Aims: To model integert-valued variables (e.g. quantities sold, numbers of sales rep visits)
The Case: final (net-net) price, sales-volume-potential, and the quantities sold
Key Words: count data, Poisson and negative binomial probability distributions, marginal effects, price elasticity
Time:
Block 6: Outlier Analysis
The Aims: To use the descriptive statistics to detect the abnormalities in distribution
The Case:
Key Words: outliers, abnormal observations, median absolute deviation
Time: 45 min
The Case: Special emphasis on B2B pricing
Key Words: B2B pricing, price elasticity, price rigidities, pricing experiments, price sensitivity meter, value map, price waterfall, discount matrix,
inflation, deflation, disinflation
Time: 60 minutes
Block 2: Probability
The Aims: [1] To estimate and interpret the descriptive statistics via probability distribution [2] To employ contingency and frequency
tables to estimate conditional probabilities [3] To present graphically the probability distribution [4] To fit the probability distribution and to
present inference based on the estimated moments
Key Words: descriptive statistics, probability, probability distribution, histogram, conditional probability
Time: 90 minutes
Block 3: Linear Regression
The Aim: To estimate the coefficients of a linear price function and to perform simulations and forecasts based thereon
The Case: The curious case of internet auction pricing: Audi Q5
Key Words:Functional relationship, correlation, regression, causality, OLS, goodness-of-fit, diagnostics, residuals, forecasting and control
Time: 60 minutes
Block 4: Modelling conditional probability: binary choice
The Aims: To model the binary-choice variable (e.g. granting a special discount, winning a project opportunity)
The Case: The curious case of granting special discounts, or how expected sales volume affects the probability of granting special discount
Key Words: binary choice, logit, probit, marginal effects
Time:
Block 5: Modelling conditional probability: ordinal models
The Aims: [1] To model the ordinal choice data [2] To simmulate probability response for various scenarios
The Case: Price change probability: historical pricing data from commodity markets
Key Words: ordinal choice, logit, marginal effects
Time:
Block 6: Modelling the impact of pricing: count data models
The Aims: To model integert-valued variables (e.g. quantities sold, numbers of sales rep visits)
The Case: final (net-net) price, sales-volume-potential, and the quantities sold
Key Words: count data, Poisson and negative binomial probability distributions, marginal effects, price elasticity
Time:
Block 6: Outlier Analysis
The Aims: To use the descriptive statistics to detect the abnormalities in distribution
The Case:
Key Words: outliers, abnormal observations, median absolute deviation
Time: 45 min
Time: 60 minutes
Block 2: Probability
The Aims: [1] To estimate and interpret the descriptive statistics via probability distribution [2] To employ contingency and frequency
tables to estimate conditional probabilities [3] To present graphically the probability distribution [4] To fit the probability distribution and to
present inference based on the estimated moments
Key Words: descriptive statistics, probability, probability distribution, histogram, conditional probability
Time: 90 minutes
Block 3: Linear Regression
The Aim: To estimate the coefficients of a linear price function and to perform simulations and forecasts based thereon
The Case: The curious case of internet auction pricing: Audi Q5
Key Words:Functional relationship, correlation, regression, causality, OLS, goodness-of-fit, diagnostics, residuals, forecasting and control
Time: 60 minutes
Block 4: Modelling conditional probability: binary choice
The Aims: To model the binary-choice variable (e.g. granting a special discount, winning a project opportunity)
The Case: The curious case of granting special discounts, or how expected sales volume affects the probability of granting special discount
Key Words: binary choice, logit, probit, marginal effects
Time:
Block 5: Modelling conditional probability: ordinal models
The Aims: [1] To model the ordinal choice data [2] To simmulate probability response for various scenarios
The Case: Price change probability: historical pricing data from commodity markets
Key Words: ordinal choice, logit, marginal effects
Time:
Block 6: Modelling the impact of pricing: count data models
The Aims: To model integert-valued variables (e.g. quantities sold, numbers of sales rep visits)
The Case: final (net-net) price, sales-volume-potential, and the quantities sold
Key Words: count data, Poisson and negative binomial probability distributions, marginal effects, price elasticity
Time:
Block 6: Outlier Analysis
The Aims: To use the descriptive statistics to detect the abnormalities in distribution
The Case:
Key Words: outliers, abnormal observations, median absolute deviation
Time: 45 min
The Aim: To estimate the coefficients of a linear price function and to perform simulations and forecasts based thereon
The Case: The curious case of internet auction pricing: Audi Q5
Key Words:Functional relationship, correlation, regression, causality, OLS, goodness-of-fit, diagnostics, residuals, forecasting and control
Time: 60 minutes
Block 4: Modelling conditional probability: binary choice
The Aims: To model the binary-choice variable (e.g. granting a special discount, winning a project opportunity)
The Case: The curious case of granting special discounts, or how expected sales volume affects the probability of granting special discount
Key Words: binary choice, logit, probit, marginal effects
Time:
Block 5: Modelling conditional probability: ordinal models
The Aims: [1] To model the ordinal choice data [2] To simmulate probability response for various scenarios
The Case: Price change probability: historical pricing data from commodity markets
Key Words: ordinal choice, logit, marginal effects
Time:
Block 6: Modelling the impact of pricing: count data models
The Aims: To model integert-valued variables (e.g. quantities sold, numbers of sales rep visits)
The Case: final (net-net) price, sales-volume-potential, and the quantities sold
Key Words: count data, Poisson and negative binomial probability distributions, marginal effects, price elasticity
Time:
Block 6: Outlier Analysis
The Aims: To use the descriptive statistics to detect the abnormalities in distribution
The Case:
Key Words: outliers, abnormal observations, median absolute deviation
Time: 45 min
The Case: The curious case of granting special discounts, or how expected sales volume affects the probability of granting special discount
Key Words: binary choice, logit, probit, marginal effects
Time:
Block 5: Modelling conditional probability: ordinal models
The Aims: [1] To model the ordinal choice data [2] To simmulate probability response for various scenarios
The Case: Price change probability: historical pricing data from commodity markets
Key Words: ordinal choice, logit, marginal effects
Time:
Block 6: Modelling the impact of pricing: count data models
The Aims: To model integert-valued variables (e.g. quantities sold, numbers of sales rep visits)
The Case: final (net-net) price, sales-volume-potential, and the quantities sold
Key Words: count data, Poisson and negative binomial probability distributions, marginal effects, price elasticity
Time:
Block 6: Outlier Analysis
The Aims: To use the descriptive statistics to detect the abnormalities in distribution
The Case:
Key Words: outliers, abnormal observations, median absolute deviation
Time: 45 min
Time:
Block 5: Modelling conditional probability: ordinal models
The Aims: [1] To model the ordinal choice data [2] To simmulate probability response for various scenarios
The Case: Price change probability: historical pricing data from commodity markets
Key Words: ordinal choice, logit, marginal effects
Time:
Block 6: Modelling the impact of pricing: count data models
The Aims: To model integert-valued variables (e.g. quantities sold, numbers of sales rep visits)
The Case: final (net-net) price, sales-volume-potential, and the quantities sold
Key Words: count data, Poisson and negative binomial probability distributions, marginal effects, price elasticity
Time:
Block 6: Outlier Analysis
The Aims: To use the descriptive statistics to detect the abnormalities in distribution
The Case:
Key Words: outliers, abnormal observations, median absolute deviation
Time: 45 min
The Case: Price change probability: historical pricing data from commodity markets
Key Words: ordinal choice, logit, marginal effects
Time:
Block 6: Modelling the impact of pricing: count data models
The Aims: To model integert-valued variables (e.g. quantities sold, numbers of sales rep visits)
The Case: final (net-net) price, sales-volume-potential, and the quantities sold
Key Words: count data, Poisson and negative binomial probability distributions, marginal effects, price elasticity
Time:
Block 6: Outlier Analysis
The Aims: To use the descriptive statistics to detect the abnormalities in distribution
The Case:
Key Words: outliers, abnormal observations, median absolute deviation
Time: 45 min
Time:
Block 6: Modelling the impact of pricing: count data models
The Aims: To model integert-valued variables (e.g. quantities sold, numbers of sales rep visits)
The Case: final (net-net) price, sales-volume-potential, and the quantities sold
Key Words: count data, Poisson and negative binomial probability distributions, marginal effects, price elasticity
Time:
Block 6: Outlier Analysis
The Aims: To use the descriptive statistics to detect the abnormalities in distribution
The Case:
Key Words: outliers, abnormal observations, median absolute deviation
Time: 45 min
The Case: final (net-net) price, sales-volume-potential, and the quantities sold
Key Words: count data, Poisson and negative binomial probability distributions, marginal effects, price elasticity
Time:
Block 6: Outlier Analysis
The Aims: To use the descriptive statistics to detect the abnormalities in distribution
The Case:
Key Words: outliers, abnormal observations, median absolute deviation
Time: 45 min
Time:
Block 6: Outlier Analysis
The Aims: To use the descriptive statistics to detect the abnormalities in distribution
The Case:
Key Words: outliers, abnormal observations, median absolute deviation
Time: 45 min
The Case:
Key Words: outliers, abnormal observations, median absolute deviation
Time: 45 min
Time: 45 min
Learning Materials
Block 1: Introduction to Pricing Analytics
Please download all R-scripts and csv data files to the same catalogue.
Presentation: Introduction to (B2B) Pricing download file;
Presentation: Introduction to R download file;
Data: Audi Q5 pricing data download file;
Summary: Brief summary or a dummy's guide to quantitative analyticsdownload file;
Yet another summary: slightly more complicated onedownload file;
Block 2: Probability
R Script: descriptive statistics, histogram, and kernel density
download file
R Script: probability distributions: normal, gamma, and beta
download file
Data: NFL meets Pricing download file;
R Script: hypothesis testing (NFL meets Pricing)
download file
Presentation: contingency table, frequency table, conditional probability
download file
Presentation: contingency table, frequency table, conditional probability in R
download file
R Script: conditional probability: contingency and frequency tables
download file
Block 3: Linear Regression
Presentation: OLS in R download file;
R Script: OLS basics download file
Block 4: Binary Choice Models
Presentation: Probit and Logit Models download file;
Data: granting a special discount download file;
R Script: binary choice in R download file
Block 5: Ordinal Choice Models
Presentation: Estimating the probability of price change download file;
Data: price change probability download file;
R Script: ordinal logit in R download file
Block 6: Count Data Regression
Presentation: Count data models download file;
R Script: Count data download file
Data: Data in .csv-format download file
download file
download file