1. Sabre Senior Design Project HUNTER ROSS, RAMON TRESPALACIOS, MARY LIZ TUTTLE May 1 st, 2014

2. ▪Sabre Airline Solutions would like to provide traveler segmentation services for their customer reservation system to support various marketing programs. The Problem

3. Clustering A cluster is a small group or bunch of something. We chose k-means clustering, which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean.

4. Use Cases ▪If an airline creates a new ticket fare product, but our segmentation shows a wide variety in client demographics (30% leisure, 40% business, and 30% other) then perhaps the product is not well-defined or well-targeted. ▪A good segmentation can provide insights into the design of good fare products upfront. ▪Conduct break down analysis of various fare products by post- booking and pre-booking segmentation.

5. Information Given ▪Sabre provided ticketing data in an excel spreadsheet, with rows representing each ticket: - 14 days of ticketing - 2,535,955 tickets - Booking date, departure date, return date (and day-of-week) - Fare or fare ratio (ratio to lowest fare) - Travel time or ratio (ratio to non-stop time) OR number of stops (outbound and inbound), Market, Travel agency type (big global, small, online agency) ▪Sample R code referencing the data metrics above

6. Our Goal ▪Create a set of rules that creates a unique category for any given ticket, along with a report showing validation efforts. Our report will also describe the techniques used, pros and cons, and rationale for the final recommended set of rules.

7. Methodology »Used k-means clustering in R »Found optimal number of clusters »Interpreted the output »Used subjective analysis to determine any conclusions

8. Number of Clusters vs R2 R2 Number of Clusters

9. ▪Set of Rules to classify clusters: 1. Determine if the value displayed in the table is above or below the mean for the following variables: Advanced Purchase, Length of Stay, Fare, Inbound Travel Time and Outbound Travel Time 2. Determine the most popular of the following variables: Departure Day of Week, Return Day of Week, Agency Type, and Number of Passengers 3. Check percentage of tickets in the cluster that stays on Saturdays (we need to determine if its high or low) Post-booking Classification Rules

10. C1- Last Minute Single Business Traveler

11. C2- Extended Stay Leisure Traveler

12. C3- Cheap Business Traveler

13. C4- Planned Vacation Traveler

14. C5- Recurrent Business Traveler

15. C6- Quick-Trip Business Traveler

16. ▪Same rules as for Post-booking but with the removal of the following variables: o Fare o Outbound Travel Time o Inbound Travel Time Pre-booking Classification Rules

17. Cluster 1- Leisure Weekend Cluster 1 – leisure weekend Increased Advanced Purchase and LOS Departs Thursday/Friday Return Sunday/Monday Gonline 96% Stay Saturday 70% Single

18. Cluster 2- Leisure Traveler Cluster 2 – leisure travelers Increased Advanced Purchase and LOS Departs Any Day Return Any Day Gonline 85% Stay Saturday 80% Single

19. Cluster 3- Holiday/Vacation Travelers Cluster 3 – quick-trip business travelers Short L.O.S. Departs Monday/Tuesday Gcorp 93% Cluster 3 – holiday/vacation travelers Increased Advanced Purchase and LOS Departs Any Day Return Sunday Gonline & Gcorp 50% Stay Saturday 67% Single, 22% Couple, 11% Family

20. Cluster 4- Week-long Business Traveler Cluster 4 – quick-trip business travelers Short L.O.S. Departs Monday/Tuesday Gcorp 93% Cluster 4 – week-long business traveler Avg. Advanced Purchase and increased LOS Departs Sunday/Monday Return Thursday/Friday Gcorp 0.03% Stay Saturday 95% Single

21. Cluster 5- Quick-Trip Business Travelers Cluster 5 – quick-trip business travelers Short L.O.S. Departs Monday/Tuesday Gcorp 93% Cluster 5 – quick-trip business travelers Avg. Advanced Purchase and decreased LOS Departs Monday/Tuesday/Wednesday Return Thursday/Friday FSC and Unclass No Stay Saturday 93% Single

22. Cluster 6- Gcorp Business Travelers Cluster 6 – quick-trip business travelers Short L.O.S. Departs Monday/Tuesday Gcorp 93% Cluster 6 – gcorp business travelers Avg. Advanced Purchase and decreased LOS Departs Monday/Tuesday/Wednesday Return Thursday/Friday 100% Gcorp No Stay Saturday 99% Single

23. Proposals Use post-booking clusters to offer discounts or premiums in post- booking services such as hotels and other reservations. o i.e. Cluster 5: Recurrent Business Travelers could receive a discount in airport lounges or partnered hotels. Use pre-booking clusters to choose a more appropriate fare targeted to each cluster. o i.e. Cluster 3: Vacation travelers would prefer to pay a cheaper fare because they book in advance.

24. World Changers Shaped Here