pnr-based no-show forecast agifors res/yield management study group annual meeting new york, 21-24...

PNR-Based No-Show Forecast

AGIFORS Res/Yield Management Study Group Annual MeetingNew York, 21-24 March 2000

Kai-Uwe Kalka Project Manager Revenue Management

Klaus Weber Senior Scientific AnalystRevenue Management

AGIFORS Res/Yield Management Study Group Annual Meeting New York, 21-24 March 2000

Chart 2


Knowledge Discovery in Databases (KDD)

Rule Generation Through Induction Trees

Results

Conclusions, Perspective

Motivation

Integration in Revenue Management Process


Chart 3

Motivation I

Overbooking research has the longest history in revenue management. (McGill 99)

No-shows and cancellations forecast= base for overbooking

Two main approaches

Time series models Causal models

Hybrid models0

10

20

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Apr 9

7

Jun

97

Aug 9

7

Oct 9

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Dec 9

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Feb 9

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Apr 9

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Jun

98

Aug 9

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Oct 9

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Dec 9

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Feb 9

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Apr 9

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showed upbooked


Chart 4

Motivation II

Common assumption

Historical no-show / cancellation behaviourwill be repeated

Difference

Time series model analyzes forecast variable Causal model analyzes explanatory variable

Better estimation for explanatory variable


Chart 5

Motivation III

Rich source of explanatory variables

Passenger Name Records (PNR)

PNRPRO digs up this treasure

booking class # record updates seat reservation

segment miles # passengers # booking changes

# name changes booking office booking time

electronic ticket time to other flight protection booking

status stretcher day of week

free travel special meal ...


Chart 6




Results


Motivation



Chart 7

Knowledge Discovery in Databases (KDD) I

Common Definition (Fayyad et al 96)

Knowledge Discovery in Databases is the

nontrivial process of identifying

valid,

novel,

potentially useful, and

ultimately understandable patterns in data.


Chart 8

Principle of Parsimony / “Ockham‘s razor“

Frusta fit per plura quod potest fieri per pauciora.

Quando propositio verificatur pro rebus, si duae res sufficiunt ad eius veritatem, superfluum est ponere tertiam.

William of Ockham, ca. 1286-1347 (Hoffmann 97)

Knowledge Discovery in Databases (KDD) II

It is futile to do with more what can be done with fewer.

When a proposition comes out true for things, if two things suffice for its truth, it is superfluous to assume a third.


Chart 9

Knowledge Discovery in Databases (KDD) III

KDD Goals (Anand 98a)

classification

regression

discovery of associations

discovery of sequential patterns

temporal modelling

deviation detection

dependency modelling

clustering

characteristic rule discovery

Will this customer cancel his booking, will he be a no-show, or will he ultimately show up?


Chart 10

Knowledge Discovery in Databases (KDD) IV

What is what?

Data mining

Statistics

Machine learning

Data Warehousing

OLAP

(Wrobel 98, OLAP Council 95)


Chart 11

Knowledge Discovery in Databases (KDD) IVa

What is what?

Data mining

Statistics

Machine learning

Data Warehousing

OLAP

synonymously used in commercial area

in the narrow sense:

part of KDD process search and evaluation of

hypotheses


Chart 12

Knowledge Discovery in Databases (KDD) IVb

What is what?

Data mining

Statistics

Machine learning

Data Warehousing

OLAP

Statistics (traditional):certainty of set hypothesis on the basis of given data

KDD:automatic computer-aided search for hypotheses


Chart 13

Knowledge Discovery in Databases (KDD) IVc

What is what?

Data mining

Statistics

Machine learning

Data Warehousing

OLAP

size of data base

KDD emphasizes scalability up to very large data bases


Chart 14

Knowledge Discovery in Databases (KDD) IVd

What is what?

Data mining

Statistics

Machine learning

Data Warehousing

OLAP

process which

extracts data from different data base systems

merges these data stores it appropriately for

further analysis


Chart 15

Knowledge Discovery in Databases (KDD) IVe

What is what?

Data mining

Statistics

Machine learning

Data Warehousing

OLAPon-line analytical processing

OLAP functionality characterization:

dynamic multi-dimensional analysis

of consolidated enterprise data

supporting end user analytical and navigational activities


Chart 16

Knowledge Discovery in Databases (KDD) V

What is what?

Data mining

Statistics

Machine learning

Data Warehousing

OLAP

PNR-basedno-show forecastwith induction trees


Chart 17




Results


Motivation



Chart 18

Rule Generation through Induction Trees I

Induction inductive learning

Discover novel patterns in data(KDD definition)

Opposite: deductive learning,i.e. analyze and modifyknowledge in knowledge bases

Tree looks like a tree

Leafs indicate classes Nodes specify tests

Induction tree? (Quinlan 1993)


Chart 19

Rule Generation through Induction Trees II

multi-dimensional point set

miles

class

-

---

-

--

--

-

+++

++

+

+ +

++

+ID attr. 1

classattr. 2miles

targetno-show

1 5 18.5 +2 2 17.9 -...

.

.

.

.

.

.

.

.

.

N 12 32.6 +

PNR-table


Chart 20

Rule Generation through Induction Trees IIIa

Known facts: Historical PNR data

Build a model of the dataset Induction tree

Quantify the model

R+ : p = 15/17 = 88%

R- : p = 20/25 = 80%

R- : p = 12/15 = 80%

R- : p = 25/31 = 81%

R+ : p = 23/27 = 85%

+

+

-+ +-

-

+ +

--

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+

+ +

++

--

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+

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-

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-+

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- --

--

----

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--

--

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-+

+ ++

+

+

+

+

++

+-

-

-

-

++ +-+

+ + ++

+

+

+- -

class

miles


Chart 21

Rule Generation through Induction Trees IIIb Substitute the data by the model for fast predictions

Model prediction: Datum (class = 2, miles = 18.5) is no-show with p = 88%

Query the model: Is datum (class = 2, miles = 18.5) a no-show?

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- --

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miles

class

-+ -

- +

88 %

80 %

80 % 81 %

85 %

18.5

2

+


Chart 22

Rule Generation through Induction Trees IV

miles 0 -16miles 0 -16

class 1-3class 1-3 class 4-6

class 4-6

+-

miles 0-12miles 0-12 miles 13-36

miles 13-36

+

class

miles

rootall datarootall data


class 7-12

miles 17-32miles 17-32



class 7-12

miles 17-32miles 17-32

-

+ -

+-

+

+

--

-


Chart 23

Rule Generation through Induction Trees V

Why rules?

Induction trees can be cumbersome, complex and inscrutable.

Each node has a specific context.

Individual subconcepts can be fragmented.

Corresponding rule set is as complex as the tree is.

Generate rules and simplify them


Chart 24

Rule Generation through Induction Trees VIa

From trees to rules every path (root leaf)

gives one initial rule -

Class 7-12Class 7-12

+

Miles 13-36Miles 13-36

+-

Miles 0-12Miles 0-12

class 1-6class 1-6

miles 0 -16miles 0 -16 miles 17-32

miles 17-32


class 4-6

+-

+ -



Chart 25

Rule Generation through Induction Trees VIb

-

class 1-6class 1-6

miles 0 -16miles 0 -16 miles 17-32

miles 17-32

class 1-3class 1-3

+-

+


IF [class in range 1-6]

AND [miles in range 17-32]

THEN [pax is no-show (88 %)]

From trees to rules

THEN [pax shows up (80 %)]

AND [miles in range 0-16]

IF [class in range 1-6]

AND [class in range 1-3]

88 %

80 %


Chart 26

Rule Generation through Induction Trees VII

KDD

Passenger attributes

Passenger

is no-show

cancels

shows up

Rule 1: IF … THEN ...



Rule set

PNR data


Chart 27




Results


Motivation



Chart 28

Integration in Revenue Management Process I

KnowledgePost-Processing

Human ResourceIdentification

ProblemSpecification

DataProspecting

Domain KnowledgeElicitation

DataPre-Processing

PatternDiscovery

Refinement

permanent

KnowledgeDiscovery

FC - Engine

KnowledgeDiscovery

FC - Engine

MethodologyIdentification

(Anand 98b)


Chart 29

Integration in Revenue Management Process IIa

1

Get all customer data ...

CustomerData

and feed the FC-Engine

KnowledgeDiscovery

FC - Engine

KnowledgeDiscovery

FC - Engine


Chart 30

Integration in Revenue Management Process IIb

1 2

Generate different forecast models for the same problem

MA MB MJ MKModels

PA PJPB PKPNR data

KnowledgeDiscovery

FC - Engine

KnowledgeDiscovery

FC - Engine


Chart 31

Integration in Revenue Management Process IIc

1 2 3

Rank predictions for all forecast models

Normalization and Ranking Process

A J KB Probabilities

MA MB MJ MK Models

CustomerData

KnowledgeDiscovery

FC - Engine

KnowledgeDiscovery

FC - Engine


Chart 32

Integration in Revenue Management Process IId

2 31 4

Link models for prediction application

BMBAMA JMJ KMK

KnowledgeDiscovery

FC - Engine

KnowledgeDiscovery

FC - Engine


Chart 33

Integration in Revenue Management Process IIe

2 31

Use the best forecast mix for actual customer request

4

and feed the Optimizer

RevenueManagement

Optimizer

RevenueManagement

Optimizer

forecast

5

KnowledgeDiscovery

FC - Engine

KnowledgeDiscovery

FC - Engine

BMBAMA JMJ KMK

PNR-data


Chart 34

Integration in Revenue Management Process IIf

In the meanwhile go to step 1 and refine 2 3 4 51

KnowledgeDiscovery

FC - Engine

KnowledgeDiscovery

FC - Engine

RevenueManagement

Optimizer

RevenueManagement

Optimizer

BMBAMA JMJ KMK

forecastPNR-data


Chart 35




Results


Motivation



Chart 36

Results I

All results based on Lufthansa real world data

Computation of no-show rate

based on PNRs (new method) based on historical data (standard method) based on combination of both methods

Comparison with respect to

flights booking classes compartments

Calculation of no-show rate errors

using mean absolute deviation (MAD) using mean square error (MSE)


Chart 37

Results IIa

Relative no-show rate error

Comp 87 % * Bkd Standard PNR-based combined

F 15.2 % 16.4 % 10.7 % 11.6 %

C 10.5 % 9.6 % 8 % 7.6 %

M 12 % 11.2 % 9.7 % 9.3 %

Average 12.5 % 12.4 % 9.5 % 9.5 %


Chart 38

Results IIb

Relative no-show rate error compared to standard method

Comp 87 % * Bkd Standard PNR-based combined

F 7 % 0 % 34.8 % 29.3 %

C -9.3 % 0 % 16 % 20.8 %

M -5.3 % 0 % 22.3 % 25.9 %

Average -0.8 % 0 % 23.4 % 23.4 %


Chart 39

Results III

Significance of attributes with respect to no-show forecast

Influence of “day of week” is highly overrated

“It is futile to do with more what can be done with fewer.” (Ockham)


Chart 40




Results


Motivation



Chart 41


Introduction of PNR-based no-show forecast

Induction Trees

Performance superior to standard methods

New insight into significance of attributes

Easy integration in existing revenue management process

Other methods, e.g.

CHAID analysis, Logit model Artificial Neural Networks

Transfer to cancellations forecast: first results

Introduction of fuzzy logic approaches: upcoming


Chart 42

References

(Anand 98a)Anand, S.S., Hughes, J.G.: Hybrid Data Mining Systems: The Next Generation. In: Wu, X., Kotagiri, R., Korb, K.B. (Eds.): Research and Development in Knowledge Discovery and Data Mining. Springer-Verlag, Berlin, 1998.

(Anand 98b)Anand, S.S., Patrick, A.R., Hughes, J.G., Bell, D.A.: A Data Mining methodology for cross-sales. Knowledge-Based Systems 10 (1998) pp. 449-461.

(Fayyad et al 96)Fayyad, U., Piatetsky-Shapiro, G., Smyth, P., and Uthurusamy, R.: Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, Cambridge, 1996.

(Hoffmann 97)Hoffmann, R., Minkin, V.I., Carpenter, B.K.: Ockham’s Razor and Chemistry. HYLE - An International Journal for the Philosophy of Chemistry, http://www.uni-karlsruhe.de/~philosophie/hyle.html 3 (1997) pp.3-28.

(McGill 99)McGill, J.I., van Ryzin, G.J.: Revenue Management: Research Overview and Prospects. Transportation Science 33 (1999) 2, pp. 233-256.

(OLAP Council 95)http://www.olapcouncil.org, glossary of terms: http://www.olapcouncil.org/research/glossaryly.htm

(Quinlan 93)Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, 1993.

(Wrobel 98)Wrobel, S.: Data Mining und Wissensentdeckung in Datenbanken. Künstliche Intelligenz 1 (1998) pp. 6-10.

Thank you for your attention!Any questions?

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