Pricing Analytics and Revenue Management

Pricing Analytics and
Revenue Management

Contents
INTRODUCTION...........................................................................................................................1
TASK 1............................................................................................................................................1
1. Linear regression model..........................................................................................................1
2. `Log volume' and `log price'....................................................................................................2
3 (a)..............................................................................................................................................3
3 (b)..............................................................................................................................................3
CONCLUSION................................................................................................................................4

INTRODUCTION
Price analytics is a process of analysing the prices of the goods and services which are sold
by the organisation. This concept of price analytics helps in improving profitability and market
share. The main aim of this piece of work is to analyse the sales data given in ssanner.csv. For
this purpose, in this report regression model is used to analyse the relationship between various
variables. The software application of SPSS and Microsoft Excel are used in this report to
develop new log variables and then analyse them with using linear regression model.
TASK 1
1. Linear regression model
Linear regression models are used to demonstrate the relation among two quantities or
parameters or to forecast that. The factors that are used to determine the dependent variable's
value are named the independence factors. According to this model volume take as dependent
variable and other variables are taken as independent.
Model Summary
Model R R Square
Adjusted R
Square
Std. Error of the
Estimate
1 .721a .519 .503 91.53172
a. Predictors: (Constant), both_promotions, price, coupon_only, dis-
play_only
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 1076970.870 4 269242.718 32.137 .000b
Residual 996988.699 119 8378.056
Total 2073959.569 123
a. Dependent Variable: sales
b. Predictors: (Constant), both_promotions, price, coupon_only, display_only
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) 62.508 97.076 .644 .521
1