Sabre Senior Design ProjectHUNTER ROSS, RAMON TRESPALACIOS, MARY LIZ TUTTLEMay 1st, 2014

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

The Problem

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.

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.

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

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.

Methodology»Used k-means clustering in R

»Found optimal number of clusters

»Interpreted the output

»Used subjective analysis to determine any conclusions

Number of Clusters vs R2

R2

Number of Clusters

▪ 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

C1- Last Minute Single Business Traveler

C2- Extended Stay Leisure Traveler

C3- Cheap Business Traveler

C4- Planned Vacation Traveler

C5- Recurrent Business Traveler

C6- Quick-Trip Business Traveler

▪ Same rules as for Post-booking but with the removal of the following variables:oFareoOutbound Travel TimeoInbound Travel Time

Pre-booking Classification Rules

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

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

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

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

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

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

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.

World Changers Shaped Here