# Contact

# 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

# 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

# Learning Materials

### Block 1: Introduction to Pricing Analytics

Please download all R-scripts and csv data files to the same catalogue.