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?

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.

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

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

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

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.”

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)

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”

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?

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)

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…”