Join the BSI team as they help AirLondon realize the value of real-time, integrated data and analytics to enable smarter decision-making from operations to gate agents. In the case of The Misconnecting Passengers, the BSI Team builds a dashboard and underlying rules engine using life-time value, profitability, passenger preferences, and CCR data to enhance rebooking processes and improve customer satisfaction. For more information, please visit http://on.fb.me/BSI_Teradata

1. How We Did The Investigations The Case of the Misconnecting Passengers

3. BSI Teradata Presents … The Case of the Misconnecting Passengers Case in progress You can help!

5. The Problem At Frankfurt We didn’t have to do any work here – you can do a Google search to find the Airport Trackers for most hubs, including this one for Frankfurt. The planes report their locations every 15 seconds so at all points in time AirLondon can load this information into their Teradata database and Operations can see status.

6. BSI Assignment – Do Better Rebooking Jodice runs a tight ship and expects fast work on all our client cases. Because we all fly, it didn’t take long to understand the problem (always the right starting point!). And all the BSI staff fly a lot so we “know” how frustrating it is – for passengers and for the airlines, when things go wrong.

7. AirLondon’s System Archtiecture CRM Corp LAN Teradata Production 2 Nodes 5600H Dev – 1 Node 5450H Test – 1 Node 4550H Reporting 5TB Business Analytics Teradata Relationship Manager Teradata IWI and Value Analyzer

8. 4 Impacted Passengers Missing LHR Flight AirLondon uses security cameras at check-ins, so we just loaded the camera shots and grabbed a frame to store with the Passenger record. It’s just a “blob” of bits to Teradata – but sometimes it’s nice to see who you are helping.

9. IMPACTED PASSENGERS – Basic Info Matt mocked up this table, reformatting info from the Customer FF and Bookings tables

10. Revenues, Profitability and Frequency Scoring We calculated the information in this table using some predictive modeling capabilities from SAS, along with Teradata Value Analyzer to compute profit once we knew historical revenue Actuals as well as costs. We also pulled info from the future Bookings table to compute Freq Score

11. Channel Use Chi and Matt loaded Channel information into Teradata, including records for each interaction and each booking. We then summarized that into a rating (but we could have put a 0-100 score). Notes come from the bailout analysis part of the IWI web tool as well loading some Siebel call notes.

13. Screen for Passenger Agents - LANA History and Real-Time Factors

15. Steffi’s Not Having A Good Day We put this Call information on two screens – here you can see it on the Channel view screen, and that negative comment could be run through Attensity to get a sentiment score that we could add to the mix of rebooking factors. We also put it on Steffi’s individual screen for agents.

16. Creative Idea – Interact With Passengers This is a mockup that Matt came up with. The idea is to connect Teradata directly, again with Portlets, to the Web rendering engine with SOA callouts. We created the ordered list of passengers that we need to rebook and go through them one at a time, giving them each an option. If they time out by not responding within 3 minutes, then we cancel this screen and move to the next until we have exhausted the possibilities. It’s a simple litle program, but a powerful idea!

17. Creative Idea The application paints the possibilities for each customer, and since AirLondon has direct connections with the Flughafen Hotel (and others), we can automate the lookup to see if they have availability. We also go ahead and print a letter with the updated gameplan, including the dinner voucher and updated flight itinerary for the next day’s flight, and hand that to him when he arrives. Teradata also can send it to him via SMS or e-mail messages if he prefers.

19. The Better Rebooking Rules Engine There is really no limit to the number of factors that can be added to the Rebooking Engine. All the factors are modeled within the Teradata Travel Logical Data Model Over a period of time, as next steps, it would be important to compare the various treatments of customers with their subsequent booking activities and customer feedback surveys to see what’s working and not.