MMA 831 Marketing Analytics

Dr. C. Kolsarici

DOS Assignment #2 July 3, 2015

Mark Liu & Simon Campbell

Order of files:

Filename Pages Comments and/or Instructions

MMA831 DOS #2 Mark Liu & Simon Campbell.doc

5 Page count include this cover page

Additional Comments:

Data Anomalies Some key data anomalies were observed, including:

• The In-flight entertainment (IFE) field contained all 9’s (or coded as “unknown answer”) for all 500

observations thus it was not considered in our analysis because of its lack of usefulness in a correlation or

regression analysis

• The lounge field also contained mostly 9’s and similarly it was not considered in our analysis; although not all

500 observations were coded as 9, we felt that the lack of usefulness of the data collected would skew our

analysis

In fact, all other variables that were not useful in a regression analysis was removed to create a subset table with:

Overall Satisfaction, Check In Process, Departure Process, Cabin Environment and Food Drink Options. Otherwise

the survey data looked fairly clean, in general, for regression analysis; we didn’t want to remove all other

observations with ratings greater than 8 in one variable to avoid removing too many data points

Correlation

We started our data analysis with a correlation matrix of the subset table described above and produced the

following correlation plot produced by R:

Some key observations about the correlation matrixi:

• Strong correlation between overall customer satisfaction (CSAT) and the departure process; and overall CSAT

and the aircraft cabin environment

• To a lesser extent, CSAT was also affected by food and beverage options offered onboard

• Customers’ overall satisfaction is least impacted by the check-in procedure

Linear Regression Model

A linear regression model was built using both R and SAS, using Overall Satisfaction (dependent variable) as a

function of the following independent variables: Check-In Procedure, Departure Process, Cabin Environment and

Food & Drink options onboard.

Analysis of Variance

Source DF Sum of

Squares Mean

Square F Value Pr > F

Model 4 713.17181 178.29295 131.71 <.0001

Error 495 670.07619 1.35369

Corrected Total 499 1383.24800

Root MSE 1.16348 R-Square 0.5156

Dependent Mean 3.24800 Adj R-Sq 0.5117

Coeff Var 35.82148

Parameter Estimates

Variable DF Parameter

Estimate Standard

Error t Value Pr > |t|

Intercept 1 -0.13435 0.16747 -0.80 0.4228

CheckIn 1 0.03108 0.02637 1.18 0.2391

Depart 1 0.47511 0.04390 10.82 <.0001

CabinEnv 1 0.39564 0.05041 7.85 <.0001

FoodDrink 1 0.11975 0.03195 3.75 0.0002

• Over 50% of the data is explained by the model as per the adjusted R-square value

• Low F-sig value suggest that the probability of a zero-slope (no relationship) between all variables can be

discounted

• Examining the regression model statistic, note that the residual values suggest a relatively normal

distribution (see SAS residual distribution below); very low p-values for departure process and cabin

environment coefficients suggest strong association to overall sat

The Coefficients

Looking at the coefficient plot below produced in R, indicates that:

• Adjusting for customer’s satisfaction with the other three factors, for every point increase in with the

Departure Process, yield more than a half-point increase in overall satisfaction

• Similarly, for every satisfaction point increase with the Cabin Environment, yield close to half-point point

increase in overall satisfaction

• At the other end, an incremental point increase with Check-in Procedure may not impact overall

satisfaction at all

Summary of Analysis

It’s clear from the regression analysis that customer’s overall satisfaction is driven by timely departure and the cabin

environment. Based on this observation, the airline should take the following steps to improve overall CSAT:

• Commence the departure process at the posted boarding time

• Properly enforce boarding sequence to deter queue jumpers

• Stricter enforcement of carry-on luggage dimensions to expedite boarding

• Timely take-off and reduced tarmac stand-by delays

• Consider increasing leg room for economy class seats

• Make sure the lavatories are clean and adequately stocked with toiletries

• Better selection of onboard food and beverage items

Appendix

i The correlation matrix produced by R: