1. Data Mining at British Airways
title subtitle
Data Mining at British Airways
Simon Cumming
Principal Operational Research Consultant
Royal Statistical Society. Reading, Feb2005

2. Data mining at British Airways
Introduction – British Airways & Operational Research
History and some examples of data mining at BA
Data mining and business complexity
Successful data mining

3. Introduction : British Airways
UK’s largest scheduled airline
159 destinations in 75 countries
114 from Heathrow
Flights are split into three areas;
Domestic
European
Longhaul
4 ‘cabins’ on long haul aircraft
First Class
Club World - Business Class
World Traveller Plus
World Traveller - Economy Class

4. The challenges BA has faced over the last 3 years
Middle East (war in Iraq etc.)
World Trade Centre aftermath / terror threats, security etc.
Low Cost carriers
SARS
Economic instability
Changing relations within the travel trade
The audience can probably think of others –
Future Sixe and Shape (FSAS)
Cash crisis
DVT, Cabin Air Quality,
Share Price, Profit Performance,
Debt status (but we’re better than all the American airlines!!)
Are we selling Waterside and all other assets to survive

5. Issues facing BA today Competing in ever tougher marketplace
- Customer service and innovation.
Improving punctuality and management of disruption.
Ensuring continued financial performance
- Return on investment for shareholders,
and ability to invest for future.
Making the most of new technologies, e.g. web, self-service.
Getting ready for Terminal 5 at Heathrow.
Reducing unnecessary complexity.
Right use of alliances, codeshares, franchises.

6. Operational Research at British Airways
OR at BA has been going for over 50 years.
The Airline industry has some complex and interesting OR problems, e.g.
Revenue management (yield management) – optimising number of seats available in different selling classes (prices).
“End-to-end” scheduling, I.e. scheduling, planning, rostering, etc.
Engineering inventory, vehicle fleets, etc.
“Commercial” – customer data, frequent flyer programme, transaction data, market research, consultancy
“Operational” – Check-in, queuing, seat allocation, punctuality, baggage etc.
The academic body for airline OR is AGIFORS, the Airline Group
of the International Federation of OR Societies (

7. Operational Research at BA
“Effective change through analytical excellence”
Problem Structuring
Clarification and understanding of a complex problem
Business Modelling
Implications of future options, decisions and scenarios
Quantitative and qualitative modelling of complex business areas or issues
Complex Data Analysis
Delivering insight into complicated issues and questions within the business, through uncovering trends, causes and relationships, to ensure decisions are made on basis of evidence that reflects the real world
There are also data mining people in the Sales and
Marketing departments.

8. Data mining – quick overview
Linear and logistic regression.
Decision trees (Classification & Regression Trees – Breiman et al, 1984) –recursive partitioning based on significance measure.
Cluster analysis. Ward , k-means, etc.
Self-organising map (Kohonen, 1982) – can think of as a structured set of clusters.
Neural network – works out an approximation to the function relating the inputs to the outputs.
Association rules – based on conditional probabilities p(y|x), e.g. If I buy bread, what is the probability I buy butter?

9. How a SOM works
Each dot represents a cluster centre, i.e. a vector of data with the same columns (dimensions) as your data set.
For each row of the data set, the algorithm finds the nearest cluster centre and moves it, and its neighbours, ‘towards’ the current data row by a small amount
This process iterates through the data set a number of times.

10. Example of cluster output from SOM
From the ‘statistics’ tab in the SOM node, do ‘save as’ and save the table as a
CSV file. Then the best thing to do is to open it in Excel, transpose it and ‘tidy it up’ to give something like the table shown above.
Note that in this example, clusters 7 and 8 are not used, in other words there are no records for which those are the nearest clusters.

11. Data mining commercial software example: SAS Enterprise Miner
Note that with some of the techniques, SAS have “reverse engineered” the idea and applied their existing products to solve the underlying problem, e.g. treated neural networks as a non-linear programming problem, SOMs as a clustering problem, etc.

12. Data mining methodology example: SAS Institute’s “SEMMA” concept
Sample - by creating one or more data sets
Explore - by searching for anticipated relationships, unanticipated trends, and anomalies in order to gain understanding and ideas
Modify - by creating, selecting, and transforming the variables to focus the model selection process
Model - by using the analytical tools
Assess - by evaluating the usefulness and reliability of the findings
You may not want to include all of these steps
It may be necessary to repeat one or more of the steps several times
Another examples of a data mining methodology is CRISP-DM (cross-industry platform for data mining)

13. Some examples of previous data mining work & research at BA
1989/90 - looking at neural nets for forecasting bookings and identifying special events.
Predicting “no-shows” (use of neural networks to predict, from the booking attributes, the number of people who have made a booking but do not check in for the flight)
1996/7 - Engine condition monitoring : feedforward neural network and self-organising maps used for ‘novelty detection’ to spot abnormal engine condition states and monitor trends (in addition to use of sophisticated conventional physical and data analysis techniques)
1996/7 - Neural network for estimation of work requirement for major engineering overhauls of aircraft.
Forecasting pilot training requirements
Patterns in takeup of electronic ticketing and check-in.
Effect of disruption and compensation on customer loyalty.

14. More recent data mining on Marketing data
1999 – Decision trees used in customer value prediction (PCV).
1999 – Self-organising maps used in “Travel Service” CRM.
2000/1 – attrition models & segmentation for Executive Club (frequent flyer) data.
2001 – September 11th L
2002/3 – Analysis of on-board customer survey data (global performance monitor)
In-flight retail. Analysis of who buys what, on-board.
2004 – Executive Club travel pattern segmentation

15. British Airways Executive Club
“Frequent flyer” scheme (but also includes “partner” organisations e.g. car hire, hotels, credit cards, foreign exchange etc. )
BA Miles – can redeem these for free flights (and other things)
Tier points – count towards promotion from Blue to Silver and Gold Tiers.
Silver and Gold members are eligible for “benefits” such as lounge access, preferential check-in etc.
Data kept on flights booked and travelled and miles earnt with partner companies.

16. BA Data Mining Examples (1) : some Executive Club models
UK&US attrition models (who is reducing their flying ?)
“Behavioural” segmentation (patterns of travel, e.g. occasional longhaul premium, regular shorthaul commuter, etc. )
“Commercial partners” usage segmentation (car hire, hotels, financial cards, etc. )
“Segment management” (specific business propositions for top segment “frequent premium stars”)
“New joiners” model (predict value from customer attributes and patterns)
Techniques used … .
Cluster analysis
Self-organising maps
Logistic regression
Classification & Regression Trees
Software used : SAS, Enterprise Miner

17. BA Examples (2): “Travel Service”
Leisure travel scheme whereby customer gave details of favourite destinations, activities, plus time of year and budget, and BA sent details of tailored offers.
(now discontinued)
Self-organising maps (SOMs) used to cluster database and select groups for matching. (1998/9)
The diagram shows 16 customer segments (the green squares within each box) viewed on 20 different variables, to show booking, tavel and destination patterns. The area of the small squares shows magnitude.
Note: this chart was not generated using Enterprise Miner,
though SAS was used in some of the analysis

18. “Travel Service” – some customer clusters
Cluster as % of total
% of cluster who have made a booking
Sun seekers who want all components included (13.5,2.8)
Blue tier exec club members with city breaks (1.2,4.3)
Busy people who get away when can & are not price sensitive (2.3,8.2)
Adventure Trail Finders (2.6,3.2)
Longhaul package type person (0.4,2.0)
Type of person who just ticks “all offers” box (2.3,4.8)
Retired Southerners looking for Australia? (9.7,2.3)
Diners & shoppers (or who like to think they do) (3.2,1.3)
The bookers who have not provided us with all info (8.5,20.5)

19. BA Example (3) : In-flight retail
This example
shows a cluster with
preferences for jewellery / watches and “experience”
packages.

20. BA Example (3) : In-flight retail
A (small) cluster of shopaholics!
Variables listed in order of
Difference of this cluster from overall mean
Blue squares show average
across all clusters
Purple squares show normalised mean
For this specific cluster
This example shows the use of a SOM in Enterprise Miner to identify a small cluster of customers with
very high value purchase patterns

21. Data mining and Complexity

22. Commercial complexity and the airline business
An airline is a very complex business
In this presentation, we are just considering commercial complexity, that is in the selling process.
Operational complexity is very important to us too, but is another subject!
Some of this complexity is there for good reasons,
e.g. good commercial sense, supply and demand economics,
or for the convenience of the customer
However, some is ‘historic’ or dictated by third parties,
or is not serving its purpose.
One area in which British Airways is interested at the moment is,
How should we measure commercial complexity?
and how effective are the many different ‘ways’ of selling tickets ?
and does the complexity matter?

23. Using data mining methods to measure complexity
How can we use data mining methods to try to measure complexity ?
Data mining techniques are good at adjusting their parameters to represent the level of complexity in the data (number of dimensions, or interactions, or ‘different things going on’).
Machine learning theory makes use of measures such as entropy (information), minimum description length, VC-dimension, etc.
Take a decision tree, for example.
It will continue to partition the data set recursively until it can no longer find significant splits.
So, in the right circumstances, a decision tree can show which parts of the business are ‘simple’ and which are complex. If we set the target variable to be a measure of revenue or profitability, we can also see how the complexity relates to yield, in a crude sort of way. (Note I have taken no account of ‘cost’ here for the moment)

24. Decision tree : “tree-ring” diagram representation in Enterprise Miner
The outside of the diagram
represents the lowest levels
of subdivision
The colours are used
to represent the mean
value of the target variable
within a group (darker colour
= higher value)
“tree ring” diagram
The centre of the diagram represents the ‘root’ of the tree, i.e. the whole data set
An alternative way
of viewing different
levels of structure
in different parts of the
tree

25. Using a decision tree to measure commercial complexity
In this example, a decision tree is used to show aspects of commercial complexity.
The input data was for a London-Edinburgh flight on a single day.
The input variables represent
different ticket classes,
‘channels’ (agents, call centres, website and so on),
corporate deals,
special fares,
different currencies, etc.
Highly fragmented areas such as here represent many different rates and specific circumstances.
“tree ring” diagram
Large simple areas such as this one for undiscounted club tickets represent low complexity in this sense. There may be other kinds of complexity e.g. due to ticket or booking changes.

26. Data mining and complexity: Output of process
Low complexity, high revenue
- e.g. undealt Club class tickets
High complexity, high revenue
e.g. corporate deals
Low complexity, low revenue
- e.g web bookings
High complexity, low revenue
e.g. groups
Profitability
Complexity

27. Data mining and complexity: Caveats
Data representation. Need to allow enough detail not to average out the effect we are trying to measure, but need to limit it so we get a workable model.
Choosing a target variable. There may be elements of complexity which we are interested in, but which do not cause a change in the ‘target’ variable, and vice versa.
Problem with decision tree if the output is a straightforward linear function of the input (it will try to model it as step-functions).
This analysis does not tell us necessarily whether the complexity we are looking at is good or bad, but gives us places to start looking.
Much of the time, of course, we are not bothered about the number of combinations, because the different variables are decoupled.
There may of course be good reasons for retaining the complexity !

28. Using a self-organising map to look at patterns in ticket sale data
revenue
E-tickets
Web bookings
BA ticketed
Each of the 8
diagrams shows
the value of
a specific variable
for each of the 100
(10x10) clusters.
Frequency (number of passengers in each cluster ) is not shown but should be examined alongside these charts.
Currency : GB £
Corporate dealt
Multi-leg flights
Fully flexible tickets
The input data were for a London-Edinburgh flight on a single day.
The input variables represent different ticket classes, ‘channels’ (agents, call centres, website and so on), corporate deals,
special fares, different currencies, etc. A subset of 8 variables is shown here.
Key: red = high value or proportion, yellow = low

29. Using a self-organising map to measure complexity
Here, there is no target variable
We are using the SOM to find structure in the data
We could find the size of SOM needed to model the ‘envelope’ which covers the data, and use that size as a direct measure of complexity, in the same way as we could use the size of a decision tree to measure this ‘dimension’.
We need to be careful how we represent the data, that we are not just measuring artefacts of the representation.
In the SOM, we can also visually ‘overlay’ the patterns of different variables as a way of visualising correlations and fine structure.
In the example shown, some findings are immediately evident, e.g...
Most non-e-tickets on these flights were multi-leg flights (i.e. transfers) ticketed by other airlines, in foreign currencies.
Web bookings, though accounting for a relatively large number of transactions, show up as low complexity.

30. “So what?” – how is this measuring complexity?
We gave the SOM the space to form
100 clusters. It actually populated 90 of them.
Part of the objective is to find out how much of
the business falls into ‘simple’ and ‘complex’
categories.
18% of the passengers fell into one cluster,
That is,
web bookings sold by BA in the UK,
blue executive club tier,
non-flexible ticket classes.
However over 25 of the clusters had less than
5 passengers in.

31. Successful data mining

32. Some possible difficulties with Data Mining
Expectations either too high or too low.
Myths of data mining.
Loose use of the term ‘data mining’
Asking the wrong questions.
Wrong positioning in the company.
Does not fit ‘standard’ approach.
Data driven and iterative, so cannot necessarily plan in advance.
Can get swamped by results / options / model versions.
Danger of stating the obvious or not being believed.
Data quality, data definition and business understanding issues.

33. Successful Data Mining: Spreading understanding
It is often difficult initially to communicate the place, nature and benefits of data mining, even to experienced statisticians, operational researchers, or artificial intelligence people, but once people “get it” they are enthusiastic.
Engineers, Revenue Management and Marketing analysts are often the closest to the ideas.
Often difficult to convey complex results in meaningful business terms.
There is sometimes a need to convince ‘upstream’ processes of the value of collecting, cleaning and maintaining data for data mining.

34. Successful Data Mining : asking the right questions
Much of the skill in data mining is in helping the client to articulate the question that they really want to answer and decide if it is really a data mining question.
E.g.
How many executive club members travelled to New York in business class last year ? n
What should our marketing strategy be for the Far East region?
What factors influence a customer’s propensity to recommend BA? y
To which customers should we send our next campaign ?
Are there any patterns in these data? ?

35. Successful Data Mining : the right mix of knowledge
With today’s computing tools, it is easy to get ‘results’ from a data mining exercise.
The difficult part is interpreting these, sense-checking them, and articulating a simple message from what is often a complex picture.
Mix of technical and business knowledge essential.
Close involvement of clients and business domain experts.

36. Successful Data Mining: the right tools and infrastructure
Algorithms:
Robustness and clarity often most important
‘Build vs buy’ decisions
What BA is looking for in a data mining tool …
Set of algorithms with good coverage of problem types.
Scalability
Ease of implementation of models / generated code
Integration with data sources: ‘openness’
Compatibility with other software and company policy
Justifiable value

37. Any questions ? Simon Cumming British Airways PLC Waterside (HDA3)
PO box 365, Harmondsworth
Middlesex UB7 0GB
Tel / fax