Project: Data Mining on East-West airlines Harvard Case Solution & Analysis

Project: Data Mining on East-West airlines Case Solution

Data Description

The data which we used in our analysis comprises of 16 variables including continuous and categorical variables while there were almost 5,000  observers or respondents from whom the data has been collected and then the final analysis was performed.

Techniques Used

Several techniques were used to identify that who are the potential customers that are interested on the company’s products/services to increase their revenue and ultimately profits. However, this analysis is very important as, the company wants to analyze that on which customers it has to promotional. Therefore, the data which we took had the following variables.

Field Name Data Type Max Data Length Raw Data or Telkom Created Field? Description
ID# NUMBER Telkom Unique ID
Topflight CHAR 1 Raw Indicates whether flyer has attained elite "Topflight" status, 1 = yes, 0 = no
Balance NUMBER 8 Raw Number of miles eligible for award travel
Qual_miles NUMBER 8 Raw Number of miles counted as qualifying for Topflight status
cc1_miles? CHAR 1 Raw Has member earned miles with airline freq. flyer credit card in the past 12 months (1=Yes/0=No)?
cc2_miles? CHAR 1 Raw Has member earned miles with Rewards credit card in the past 12 months (1=Yes/0=No)?
cc3_miles? CHAR 1 Raw Has member earned miles with Small Business credit card in the past 12 months (1=Yes/0=No)?
Bonus_miles NUMBER Raw Number of miles earned from non-flight bonus transactions in the past 12 months
Bonus_trans NUMBER Raw Number of non-flight bonus transactions in the past 12 months
Flight_miles_12mo NUMBER Raw Number of flight miles in the past 12 months
Flight_trans_12 NUMBER Raw Number of flight transactions in the past 12 months
Online_12 NUMBER Raw Number of online purchases within the past 12 months
Email CHAR 1 Raw E-mail address on file. 1= yes, 0 =no?
Club_member NUMBER Telcom Member of the airline's club (paid membership), 1=yes, 0=no
Any_cc_miles_12mo NUMBER Telcom Dummy variable indicating whether member added miles on any credit card type within the past 12 months (1='Y', 0='N')
Phone sale NUMBER Telcom Dummy variable indicating whether member purchased Telcom service as a result of the direct mail campaign (1=sale, 0=no sale)

In order to keep the analysis simple, we first used dimension reduction techniques such as factor analysis,however the results were not acceptable. As a result, the analyst used the following techniques to perform the final analysis.

  1. Correlation
  2. Two Step Cluster Analysis
  3. Descriptive Statistics

However, we used 4985 observations which were under consideration to avoid outliers and other data related issues. On the other hand, the analyst used some other technique to ensure that the data is reliable and contains no or minimum outliers.

Normality Check

In order to identify that the data is consistent and there are no or minimum outliers in the data, we used parallel lines and Q-Q plots from the Xlminer and found that there were no outliers and that the data is normal.

Correlation

The correlation was also calculated to identify that what is the relation between variables and either it exists or not, and if it does exist than what are its strength and its significance. The results of the regression analysis are shown below:

From appendix 1, it can be seen that the relation is either negative or positive among the variables however, the most significant positive relation has been found among Flight Miles 12mo and Flight Trans 12 as, both the effects are positively affecting each other and would affect one another by almost 83%. This indicates that if one mile is increased in flight miles 12mo, then flight Trans will be increased by 83%. However, these significant results have been made based on these variables and these are the most special and important variables among the overall regression model...........

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