1. Continental Airlines - Worst to First?
Any of you flown on Continental? Experience?
Status of Airlines Today? Why?
Data Warehouses / Collection of all data
Do other companies use data warehouses?

2. Why a Data Warehouse? Has the investment paid off? ROI?
No more “flying by the seat of their pants”
Right decisions / Spot opportunities
Performance Management – Like Baseball?
“You can’t manage what you can’t measure.” Why? P-O-S-D-C needs data for control.

3. Go Forward Plan
Fly to Win – What customers want? Willing to pay for? M
Fund the Future – Costs and Cash Flow for operation? F
Make Reliability a Reality – Destination safely, on-time (with luggage)? O
Work Together – Team Culture who want to come to work? HR

4. Major Components Revenue Management from O to D
Tracking and Forecasting
Fare Design and Analysis
Matching aircraft with daily demand
Customer Relationship Marketing
High Value Customers
Bottom Line Impact

5. Sources of Data? Internal Data (25 Operational Systems)
Schedules, Inventory, Reservations, Tickets, Freq Flyer, Payroll, Crews, etc.
External Data (2 Sources)
Some Warehouse Data goes back to Data Sources

6. New Business Strategies
Where costs can be cut? (B.O.)
Where revenue opportunities exist? (B.O.)
“People needed to learn new habits, as more powerful information-driven tools become available.” (B.T.)
“Building a customer-centric culture…”
“Build employee trust, because becoming customer-centric begins with each and every employee.”

7. Data Mining @ Continental
Finding unexpected patterns in mountains of data
Predictive vs. Descriptive
Descriptive: what patterns exist in the data and what do these patterns mean?
Affinity grouping (Amazon), clustering/segmentation (Continental)
Predictive: what is the likelihood of a particular outcome? what is the most likely outcome?
Classification, decision trees (Ch. 7)

8. Segmentation/Clustering
3 clusters/segments of customers
Elite, high value, others
HIGH VALUE
N=1000?
High fares
Personal service
ELITE
N=100,000s
Frequent fliers
Elite access
Mix of low and high
fares
OTHERS
N=millions
Low fares
Infrequent
Smooth experience
“Clean, safe, reliable”

9. Segmentation/Clustering
Top 1% represent $1B of $8.5B total (the 20/80 rule)
High value customers / track and map them
Personalize
Build loyalty / written communication led to increased revenue
Flight cancelled/postponed -> Reaction?

10. Other Applications
Preventing fraud / use the system to catch those who try to beat the system
Alliances / Join others to work together / System boundary expansion
Disaster Response (Know who is where)

11. Relate to: Bus/System Objectives/Tactics Information Cross
Warehouse to Data Marts to Analytic Tools
Who did this? “All of the support people originally worked in the user areas they now support…”