Project: Data Mining on East-West airlines Case Solution
Descriptive Statistics
Descriptive Statistics was also calculated to identify the description and other basic statistical values from the data however, the analyst didn’t rely on the descriptive statistics for the final predictions and recommendations. Therefore, the analyst moved towards two step Cluster Analysis. However, the values of descriptive statistics are mentioned in the excel spreadsheet.
Cluster Analysis
Two Step cluster analysis was used to mine the data and to find those dimensions, which could enable the company to make proper predictions and which would also be able to identify that who are the potential customers or dimensions that could help the company to maximize its revenue and margins. However, Hierarchical cluster analysis was not applicable here as, there is not a hierarchy mentioned in the scenario.
Cluster analysis (Appendix 2) shows that there are two basic clusters which can be derived from the data however, it can be seen that the quality of the clusters is below the standard due to the large size and limited exposure of the data. On the other hand, the cluster summary shows that (appendix 3) there are two clusters where the first cluster occupies 53% of the data while the second contains the 47% of the data.
In addition, (appendix 4) shows the characteristics and classifications of both the clusters which have been made in the data. From this final characteristics, it can be seen that the company has no significant gains while investing in telecon as, the characteristics of both of the clusters are almost same and the buying and spending habits of customers which are present in both clusters are almost similar. Therefore, it can be concluded that if the company invests in telecon or wants to analyze that how many customers were attracted through phone sales and direct emails, then the answer is that there are a very few customers that are attracted by these services. If there was a significant number of customers available that were affected by these services, then the cluster would show different statistics. However, in our case the clusters are almost same, as a result showing that there is not a significant advantage to the company by investing in email and telephone services.
Conclusion and Recommendations
Findings suggested an insignificant relation among the key variables i.e. phone sales and email. However, the other variables are showing significant positive and negative relation both. On this basis, it can be said that the key variables here in this case are flight miles 12mo and Flight Trans 12. Therefore, it is recommended for the company to focus on these variables without being distracted by other irrelevant and insignificant variables.
Appendices
Appendix 1
| Â | Balance | Qualmiles | cc1miles | cc2miles | cc3miles | Bonusmiles | Bonustrans | Flightmiles12mo | Flighttrans12 | Online12 | Clubmember | Anyccmiles12mo | Phonesale | |
| Balance | 1 | |||||||||||||
| Qualmiles | 0.028472 | 1 | ||||||||||||
| cc1miles | 0.075789 | 0.019829 | 1 | |||||||||||
| cc2miles | 0.05802 | 0.045477 | 0.072491 | 1 | ||||||||||
| cc3miles | 0.058759 | 0.043139 | 0.119185 | 0.368451 | 1 | |||||||||
| Bonusmiles | 0.085947 | 0.044864 | 0.770402 | 0.107418 | 0.201669 | 1 | ||||||||
| Bonustrans | 0.029607 | 0.03517 | 0.481625 | -0.05814 | -0.02619 | 0.511989 | 1 | |||||||
| Flightmiles12mo | 0.015042 | 0.130198 | 0.040784 | 0.034866 | 0.027154 | 0.188977 | 0.332805 | 1 | ||||||
| Flighttrans12 | 0.018057 | 0.123942 | 0.008661 | -0.10347 | -0.10707 | 0.144637 | 0.401139 | 0.829515 | 1 | |||||
| Online12 | -0.01851 | 0.01072 | -0.01368 | -0.02991 | -0.00453 | -0.02181 | -0.02845 | 0.008882 | -0.00014 | 1 | ||||
| 0.001028 | -0.01411 | 0.002934 | -0.01126 | -0.00442 | -0.00918 | -0.02539 | 0.00678 | 0.015567 | -0.0253 | 1 | ||||
| Clubmember | -0.00137 | -0.01664 | 0.00529 | -0.00672 | -0.00349 | 0.026947 | 0.017368 | 0.014333 | 0.015527 | 0.000326 | 0.008716 | 1 | ||
| Anyccmiles12mo | -0.03276 | 0.005938 | 0.024217 | 0.006717 | 0.007016 | 0.005799 | -0.00212 | -0.01307 | -0.02114 | 0.018921 | -0.0098 | 0.019443 | 1 | |
| Phonesale | 0.009723 | -0.01404 | -0.0057 | 0.010865 | 0.015468 | -0.0262 | -0.00985 | -0.03058 | -0.03592 | -0.01412 | -0.00956 | -0.022 | -0.02139
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