Data-driven decision-making in churn prevention and crew scheduling
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| 100 | 1 | |a Gattermann-Itschert, Theresa |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Data-driven decision-making in churn prevention and crew scheduling |c Theresa Gattermann-Itschert |
| 264 | 1 | |a Berlin |b Logos Verlag |c [2022] | |
| 300 | |a IX, 119 Seiten |b Illustrationen |c 21 cm | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 0 | 7 | |a Entscheidungsfindung |0 (DE-588)4113446-1 |2 gnd |9 rswk-swf |
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| 650 | 0 | 7 | |a Datenanalyse |0 (DE-588)4123037-1 |2 gnd |9 rswk-swf |
| 653 | |a Machine learning | ||
| 653 | |a Optimization | ||
| 653 | |a Churn prediction | ||
| 653 | |a Churn prevention | ||
| 653 | |a Crew schedulin | ||
| 655 | 7 | |0 (DE-588)4113937-9 |a Hochschulschrift |2 gnd-content | |
| 689 | 0 | 0 | |a Prozessoptimierung |0 (DE-588)4176074-8 |D s |
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Datensatz im Suchindex
| _version_ | 1804185186711109632 |
|---|---|
| adam_text | CONTENTS
LIST
OF
FIGURES
V
LIST
OF
TABLES
VII
LIST
OF
ABBREVIATIONS
IX
1
INTRODUCTION
1
1.1
MOTIVATION
............................................................................................
1
1.2
OUTLINE
AND
CONTRIBUTION
....................................................................
2
2
HOW
MULTI-SLICING
IMPROVES
PERFORMANCE
IN
CHURN
PREDICTION
7
2.1
ABSTRACT
...............................................................................................
8
2.2
INTRODUCTION
.........................................................................................
8
2.3
LITERATURE
REVIEW
................................................................................
10
2.3.1
CHURN
PREDICTION
WITH
CLASSIFICATION
TECHNIQUES
....................
10
2.3.2
MODEL
TESTING
APPROACHES
.....................................................
11
2.3.3
MODEL
TRAINING
WITH
DATA
FROM
MULTIPLE
PERIODS
.................
12
2.4
METHODOLOGY
......................................................................................
13
2.4.1
TIME
SLICES
..............................................................................
13
2.4.2
MULTI-SLICING
..............................................................................
14
2.4.3
CROSS-VALIDATION
WITH
TIME
SLICES
............................................
15
2.5
EMPIRICAL
STUDY
...................................................................................
17
2.5.1
DATASET
....................................................................................
17
2.5.2
CHURN
DEFINITION
.....................................................................
18
2.5.3
FEATURES
....................................................................................
19
2.6
EXPERIMENTAL
SETTING
.........................................................................
20
2.6.1
FEATURE
SELECTION
.....................................................................
21
2.6.2
EVALUATION
METRICS
..................................................................
21
2.6.3
MODEL
TRAINING
........................................................................
22
2.6.4
CROSS-VALIDATION
AND
STATISTICAL
TESTING
................................
24
2.6.5
SINGLE
AND
MULTI-SLICING
VERSIONS
.........................................
24
I
2.7
RESULTS
.....................................................................................................
25
2.7.1
CLASSIFIER
SELECTION
..................................................................
25
2.7.2
PERFORMANCE
ENHANCEMENT
WITH
MULTI-SLICING
.......................
27
2.7.3
EFFECTS
OF
SAMPLE
SIZE
AND
INCLUDING
SAMPLES
FROM
MULTIPLE
TIME
SLICES
.................................................................................
28
2.7.4
IMPACT
OF
NUMBER
OF
TIME
SLICES
..............................................
29
2.8
CONCLUSION
...............................................................................................
29
2.
A
FEATURES
..................................................................................................
32
2.B
DETAILS
ON
FEATURE
SELECTION
...................................................................
33
2.C
FEATURE
IMPORTANCES
............................................................................
34
3
PROACTIVE
RETENTION
MANAGEMENT
BASED
ON
CHURN
PREDICTION
37
3.1
ABSTRACT
..................................................................................................
38
3.2
INTRODUCTION
............................................................................................
38
3.3
CASE
STUDY
...............................................................................................
40
3.3.1
CUSTOMER
CHURN
.........................................................................
41
3.3.2
CUSTOMER
RETENTION
...................................................................
42
3.4
CHURN
PREDICTION
MODEL
......................................................................
43
3.4.1
MODEL
TRAINING
AND
EVALUATION
................................................
43
3.4.2
FEATURE
ENGINEERING
...................................................................
45
3.4.2.1
RECENCY,
FREQUENCY
AND
MONETARY
VALUE
.............
45
3.4.2.2
FEATURES
SPECIFIC
FOR
B2B
RELATIONSHIPS
................
46
3.4.3
PERFORMANCE
MEASURES
............................................................
49
3.4.3.1
THRESHOLD-DEPENDENT
MEASURES
............................
49
3.4.3.2
THRESHOLD-INDEPENDENT
MEASURES
.........................
49
3.4.3.3
DOMAIN-SPECIFIC
MEASURES
......................................
50
3.4.4
PREDICTION
RESULTS
.....................................................................
50
3.4.5
FEATURE
IMPORTANCES
...............................................................
51
3.5
EXPERIMENTAL
SETTING
............................................................................
53
3.6
EXPERIMENTAL
DESIGN
............................................................................
55
3.7
RESULTS
AND
DISCUSSION
.........................................................................
56
3.7.1
CHURN
PREDICTION
.....................................................................
57
3.7.2
CHURN
PREVENTION
.....................................................................
59
3.7.3
REVENUE
CHANGE
........................................................................
59
3.8
CONCLUSIONS
............................................................................................
60
3.8.1
THEORETICAL
IMPLICATIONS
.........................................................
61
3.8.2
MANAGERIAL
IMPLICATIONS
.........................................................
62
3.8.3
LIMITATIONS
...............................................................................
63
II
3.
A
DETAILS
ON
MODEL
TRAINING
................................................................
65
4
LEARNING
PLANNERS
PREFERENCES
IN
CREW
SCHEDULING
OPTIMIZATION
67
4.1
ABSTRACT
...............................................................................................
68
4.2
INTRODUCTION
.........................................................................................
68
4.3
LITERATURE
REVIEW
................................................................................
70
4.4
PROBLEM
SETTING
...................................................................................
71
4.5
INTEGRATION
APPROACH
.........................................................................
72
4.6
METHODOLOGY
......................................................................................
73
4.6.1
DATA
COLLECTION
........................................................................
73
4.6.2
DATA
PREPARATION
.....................................................................
74
4.6.3
MODEL
BUILDING
........................................................................
76
4.6.4
MODEL
EVALUATION
.....................................................................
77
4.7
RESULTS
..................................................................................................
78
4.7.1
PREDICTIVE
PERFORMANCE
............................................................
78
4.7.2
MODEL
INTERPRETATION
...............................................................
79
4.7.2.1
FEATURE
IMPORTANCES
...............................................
79
4.7.2.2
PARTIAL
DEPENDENCE
PLOTS
.........................................
80
4.7.2.3
SHAP
VALUES
............................................................
82
4.8
CONCLUSION
.............................................................................................
84
5
INCLUDING
PLANNERS
PREFERENCES
IN
CREW
SCHEDULING
OPTIMIZATION
87
5.1
ABSTRACT
...............................................................................................
88
5.2
INTRODUCTION
.........................................................................................
88
5.3
LITERATURE
REVIEW
...............................................................................
90
5.3.1
CREW
SCHEDULING
........................................................................
90
5.3.2
PREFERENCE
LEARNING
..................................................................
91
5.3.3
MACHINE
LEARNING
AND
COMBINATORIAL
OPTIMIZATION
.............
91
5.4
PROBLEM
SETTING
...................................................................................
93
5.5
SOLUTION
APPROACH
................................................................................
93
5.6
PREDICTION
MODEL
................................................................................
94
5.7
EMPIRICAL
STUDY
...................................................................................
94
5.7.1
DATA
SET
.....................................................................................
95
5.7.2
BENCHMARK
AND
EVALUATION
METHOD
......................................
96
5.8
RESULTS
..................................................................................................
96
5.8.1
PLANNER
ACCEPTANCE
PROBABILITY
.............................................
97
5.8.2
COST
OBJECTIVE
............................................................................
98
5.8.3
DUTY
CHARACTERISTICS
..................................................................
99
III
5.9
CONCLUSION
..................................................................................................
101
6
CONCLUSION
105
6.1
SUMMARY
OF
KEY
RESULTS
............................................................................
105
6.2
CRITICAL
REVIEW
AND
FUTURE
RESEARCH
......................................................
107
BIBLIOGRAPHY
110
IV
|
| adam_txt |
CONTENTS
LIST
OF
FIGURES
V
LIST
OF
TABLES
VII
LIST
OF
ABBREVIATIONS
IX
1
INTRODUCTION
1
1.1
MOTIVATION
.
1
1.2
OUTLINE
AND
CONTRIBUTION
.
2
2
HOW
MULTI-SLICING
IMPROVES
PERFORMANCE
IN
CHURN
PREDICTION
7
2.1
ABSTRACT
.
8
2.2
INTRODUCTION
.
8
2.3
LITERATURE
REVIEW
.
10
2.3.1
CHURN
PREDICTION
WITH
CLASSIFICATION
TECHNIQUES
.
10
2.3.2
MODEL
TESTING
APPROACHES
.
11
2.3.3
MODEL
TRAINING
WITH
DATA
FROM
MULTIPLE
PERIODS
.
12
2.4
METHODOLOGY
.
13
2.4.1
TIME
SLICES
.
13
2.4.2
MULTI-SLICING
.
14
2.4.3
CROSS-VALIDATION
WITH
TIME
SLICES
.
15
2.5
EMPIRICAL
STUDY
.
17
2.5.1
DATASET
.
17
2.5.2
CHURN
DEFINITION
.
18
2.5.3
FEATURES
.
19
2.6
EXPERIMENTAL
SETTING
.
20
2.6.1
FEATURE
SELECTION
.
21
2.6.2
EVALUATION
METRICS
.
21
2.6.3
MODEL
TRAINING
.
22
2.6.4
CROSS-VALIDATION
AND
STATISTICAL
TESTING
.
24
2.6.5
SINGLE
AND
MULTI-SLICING
VERSIONS
.
24
I
2.7
RESULTS
.
25
2.7.1
CLASSIFIER
SELECTION
.
25
2.7.2
PERFORMANCE
ENHANCEMENT
WITH
MULTI-SLICING
.
27
2.7.3
EFFECTS
OF
SAMPLE
SIZE
AND
INCLUDING
SAMPLES
FROM
MULTIPLE
TIME
SLICES
.
28
2.7.4
IMPACT
OF
NUMBER
OF
TIME
SLICES
.
29
2.8
CONCLUSION
.
29
2.
A
FEATURES
.
32
2.B
DETAILS
ON
FEATURE
SELECTION
.
33
2.C
FEATURE
IMPORTANCES
.
34
3
PROACTIVE
RETENTION
MANAGEMENT
BASED
ON
CHURN
PREDICTION
37
3.1
ABSTRACT
.
38
3.2
INTRODUCTION
.
38
3.3
CASE
STUDY
.
40
3.3.1
CUSTOMER
CHURN
.
41
3.3.2
CUSTOMER
RETENTION
.
42
3.4
CHURN
PREDICTION
MODEL
.
43
3.4.1
MODEL
TRAINING
AND
EVALUATION
.
43
3.4.2
FEATURE
ENGINEERING
.
45
3.4.2.1
RECENCY,
FREQUENCY
AND
MONETARY
VALUE
.
45
3.4.2.2
FEATURES
SPECIFIC
FOR
B2B
RELATIONSHIPS
.
46
3.4.3
PERFORMANCE
MEASURES
.
49
3.4.3.1
THRESHOLD-DEPENDENT
MEASURES
.
49
3.4.3.2
THRESHOLD-INDEPENDENT
MEASURES
.
49
3.4.3.3
DOMAIN-SPECIFIC
MEASURES
.
50
3.4.4
PREDICTION
RESULTS
.
50
3.4.5
FEATURE
IMPORTANCES
.
51
3.5
EXPERIMENTAL
SETTING
.
53
3.6
EXPERIMENTAL
DESIGN
.
55
3.7
RESULTS
AND
DISCUSSION
.
56
3.7.1
CHURN
PREDICTION
.
57
3.7.2
CHURN
PREVENTION
.
59
3.7.3
REVENUE
CHANGE
.
59
3.8
CONCLUSIONS
.
60
3.8.1
THEORETICAL
IMPLICATIONS
.
61
3.8.2
MANAGERIAL
IMPLICATIONS
.
62
3.8.3
LIMITATIONS
.
63
II
3.
A
DETAILS
ON
MODEL
TRAINING
.
65
4
LEARNING
PLANNERS
'
PREFERENCES
IN
CREW
SCHEDULING
OPTIMIZATION
67
4.1
ABSTRACT
.
68
4.2
INTRODUCTION
.
68
4.3
LITERATURE
REVIEW
.
70
4.4
PROBLEM
SETTING
.
71
4.5
INTEGRATION
APPROACH
.
72
4.6
METHODOLOGY
.
73
4.6.1
DATA
COLLECTION
.
73
4.6.2
DATA
PREPARATION
.
74
4.6.3
MODEL
BUILDING
.
76
4.6.4
MODEL
EVALUATION
.
77
4.7
RESULTS
.
78
4.7.1
PREDICTIVE
PERFORMANCE
.
78
4.7.2
MODEL
INTERPRETATION
.
79
4.7.2.1
FEATURE
IMPORTANCES
.
79
4.7.2.2
PARTIAL
DEPENDENCE
PLOTS
.
80
4.7.2.3
SHAP
VALUES
.
82
4.8
CONCLUSION
.
84
5
INCLUDING
PLANNERS
'
PREFERENCES
IN
CREW
SCHEDULING
OPTIMIZATION
87
5.1
ABSTRACT
.
88
5.2
INTRODUCTION
.
88
5.3
LITERATURE
REVIEW
.
90
5.3.1
CREW
SCHEDULING
.
90
5.3.2
PREFERENCE
LEARNING
.
91
5.3.3
MACHINE
LEARNING
AND
COMBINATORIAL
OPTIMIZATION
.
91
5.4
PROBLEM
SETTING
.
93
5.5
SOLUTION
APPROACH
.
93
5.6
PREDICTION
MODEL
.
94
5.7
EMPIRICAL
STUDY
.
94
5.7.1
DATA
SET
.
95
5.7.2
BENCHMARK
AND
EVALUATION
METHOD
.
96
5.8
RESULTS
.
96
5.8.1
PLANNER
ACCEPTANCE
PROBABILITY
.
97
5.8.2
COST
OBJECTIVE
.
98
5.8.3
DUTY
CHARACTERISTICS
.
99
III
5.9
CONCLUSION
.
101
6
CONCLUSION
105
6.1
SUMMARY
OF
KEY
RESULTS
.
105
6.2
CRITICAL
REVIEW
AND
FUTURE
RESEARCH
.
107
BIBLIOGRAPHY
110
IV |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Gattermann-Itschert, Theresa |
| author_facet | Gattermann-Itschert, Theresa |
| author_role | aut |
| author_sort | Gattermann-Itschert, Theresa |
| author_variant | t g i tgi |
| building | Verbundindex |
| bvnumber | BV048957571 |
| classification_rvk | QP 327 |
| ctrlnum | (OCoLC)1373709724 (DE-599)DNB1250593840 |
| dewey-full | 658.515 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.515 |
| dewey-search | 658.515 |
| dewey-sort | 3658.515 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| format | Book |
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| genre | (DE-588)4113937-9 Hochschulschrift gnd-content |
| genre_facet | Hochschulschrift |
| id | DE-604.BV048957571 |
| illustrated | Illustrated |
| index_date | 2024-07-03T21:59:30Z |
| indexdate | 2024-07-10T09:51:14Z |
| institution | BVB |
| institution_GND | (DE-588)1065538812 |
| isbn | 9783832554309 3832554300 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-034221382 |
| oclc_num | 1373709724 |
| open_access_boolean | |
| owner | DE-N2 |
| owner_facet | DE-N2 |
| physical | IX, 119 Seiten Illustrationen 21 cm |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Logos Verlag |
| record_format | marc |
| spelling | Gattermann-Itschert, Theresa Verfasser aut Data-driven decision-making in churn prevention and crew scheduling Theresa Gattermann-Itschert Berlin Logos Verlag [2022] IX, 119 Seiten Illustrationen 21 cm txt rdacontent n rdamedia nc rdacarrier Entscheidungsfindung (DE-588)4113446-1 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Prozessoptimierung (DE-588)4176074-8 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Machine learning Optimization Churn prediction Churn prevention Crew schedulin (DE-588)4113937-9 Hochschulschrift gnd-content Prozessoptimierung (DE-588)4176074-8 s Datenanalyse (DE-588)4123037-1 s Maschinelles Lernen (DE-588)4193754-5 s Entscheidungsfindung (DE-588)4113446-1 s DE-604 Logos Verlag Berlin (DE-588)1065538812 pbl X:MVB text/html http://deposit.dnb.de/cgi-bin/dokserv?id=4365081a32134479a7b522f20ea11003&prov=M&dok_var=1&dok_ext=htm Inhaltstext B:DE-101 application/pdf https://d-nb.info/1250593840/04 Inhaltsverzeichnis DNB Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034221382&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis 1\p dnb 20230316 DE-101 https://d-nb.info/provenance/plan#dnb 2\p dnb 20230316 DE-101 https://d-nb.info/provenance/plan#dnb |
| spellingShingle | Gattermann-Itschert, Theresa Data-driven decision-making in churn prevention and crew scheduling Entscheidungsfindung (DE-588)4113446-1 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Prozessoptimierung (DE-588)4176074-8 gnd Datenanalyse (DE-588)4123037-1 gnd |
| subject_GND | (DE-588)4113446-1 (DE-588)4193754-5 (DE-588)4176074-8 (DE-588)4123037-1 (DE-588)4113937-9 |
| title | Data-driven decision-making in churn prevention and crew scheduling |
| title_auth | Data-driven decision-making in churn prevention and crew scheduling |
| title_exact_search | Data-driven decision-making in churn prevention and crew scheduling |
| title_exact_search_txtP | Data-driven decision-making in churn prevention and crew scheduling |
| title_full | Data-driven decision-making in churn prevention and crew scheduling Theresa Gattermann-Itschert |
| title_fullStr | Data-driven decision-making in churn prevention and crew scheduling Theresa Gattermann-Itschert |
| title_full_unstemmed | Data-driven decision-making in churn prevention and crew scheduling Theresa Gattermann-Itschert |
| title_short | Data-driven decision-making in churn prevention and crew scheduling |
| title_sort | data driven decision making in churn prevention and crew scheduling |
| topic | Entscheidungsfindung (DE-588)4113446-1 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Prozessoptimierung (DE-588)4176074-8 gnd Datenanalyse (DE-588)4123037-1 gnd |
| topic_facet | Entscheidungsfindung Maschinelles Lernen Prozessoptimierung Datenanalyse Hochschulschrift |
| url | http://deposit.dnb.de/cgi-bin/dokserv?id=4365081a32134479a7b522f20ea11003&prov=M&dok_var=1&dok_ext=htm https://d-nb.info/1250593840/04 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=034221382&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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