Data mining and market intelligence for optimal marketing returns:
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Amsterdam [u.a.]
Elsevier/Butterworth-Heinemann
2008
|
| Ausgabe: | 1. ed. |
| Schlagworte: | |
| Online-Zugang: | lizenzfrei Inhaltsverzeichnis |
| Beschreibung: | XII, 280 S. graph. Darst. |
| ISBN: | 0750682345 9780750682343 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV035007135 | ||
| 003 | DE-604 | ||
| 005 | 20080918 | ||
| 007 | t | ||
| 008 | 080818s2008 d||| |||| 00||| eng d | ||
| 020 | |a 0750682345 |9 0-7506-8234-5 | ||
| 020 | |a 9780750682343 |9 978-0-7506-8234-3 | ||
| 035 | |a (OCoLC)254314513 | ||
| 035 | |a (DE-599)GBV563410981 | ||
| 040 | |a DE-604 |b ger | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-945 |a DE-384 |a DE-M347 |a DE-188 | ||
| 082 | 0 | |a 658.83 | |
| 084 | |a QP 650 |0 (DE-625)141922: |2 rvk | ||
| 100 | 1 | |a Chiu, Susan |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Data mining and market intelligence for optimal marketing returns |c Susan Chiu ; Domingo Tavella |
| 250 | |a 1. ed. | ||
| 264 | 1 | |a Amsterdam [u.a.] |b Elsevier/Butterworth-Heinemann |c 2008 | |
| 300 | |a XII, 280 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 4 | |a Marketingpolitik / Marktforschung / Data Mining | |
| 650 | 0 | 7 | |a Data Mining |0 (DE-588)4428654-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Marketingforschung |0 (DE-588)4200055-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Marketingaudit |0 (DE-588)4114516-1 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Business Intelligence |0 (DE-588)4588307-5 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Marketingforschung |0 (DE-588)4200055-5 |D s |
| 689 | 0 | 1 | |a Data Mining |0 (DE-588)4428654-5 |D s |
| 689 | 0 | |5 DE-604 | |
| 689 | 1 | 0 | |a Marketingaudit |0 (DE-588)4114516-1 |D s |
| 689 | 1 | 1 | |a Data Mining |0 (DE-588)4428654-5 |D s |
| 689 | 1 | |5 DE-604 | |
| 689 | 2 | 0 | |a Marketingforschung |0 (DE-588)4200055-5 |D s |
| 689 | 2 | 1 | |a Data Mining |0 (DE-588)4428654-5 |D s |
| 689 | 2 | 2 | |a Business Intelligence |0 (DE-588)4588307-5 |D s |
| 689 | 2 | |5 DE-188 | |
| 700 | 1 | |a Tavella, Domingo |e Verfasser |4 aut | |
| 856 | 4 | |u http://www.gbv.de/dms/zbw/563410981.pdf |z lizenzfrei |3 Inhaltsverzeichnis | |
| 856 | 4 | 2 | |m HBZ Datenaustausch |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016676438&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-016676438 | ||
Datensatz im Suchindex
| _version_ | 1804137926180732928 |
|---|---|
| adam_text | Contents
Preface %i
Biographies xiii
1 Introduction 1
Strategic importance of metrics, marketing research and
data mining in today s marketing world 3
The role of metrics 4
The role of research 4
The role of data mining 6
An effective eight-step process for incorporating metrics,
research and data mining into marketing planning
and execution 6
Step 1: identifying key stakeholders and their
business objectives 6
Step 2: selecting appropriate metrics to measure
marketing success 7
Step 3: assessing the market opportunity 8
Step 4: conducting competitive analysis 8
Step 5: deriving optimal marketing spending and media mix 9
Step 6: leveraging data mining for optimization and getting
early buy-in and feedback from key stakeholders 9
Step 7: tracking and comparison of metric goals and results 9
Step 8: incorporating the learning into the next round of
marketing planning 10
Integration of market intelligence and databases 10
Cultivating adoption of metrics, research and data mining
in the corporate structure 11
Identification of key required skills 12
Creating an effective engagement process 14
Promoting research and analytics 15
2 Marketing Spending Models and Optimization 19
Marketing spending model 21
Static models 23
Dynamic models 34
Contents
Marketing spending models and corporate finance 35
A framework for corporate performance marketing
effort integration 36
3 Metrics Overview 39
Common metrics for measuring returns and investments 41
Measuring returns with return metrics 42
Measuring investment with investment metrics 42
Developing a formula for return on investment 43
Common ROI tracking challenges 44
Process for identifying appropriate metrics 45
Identification of the overall business objective 45
Understanding the impact of a marketing effort on target
audience migration 46
Selection of appropriate marketing communication
channels 49
Identification of appropriate return metrics by stage in the
sales cycle 56
Differentiating return metrics from operational metrics 61
Multi-channel Campaign Performance Reporting and
Optimization 63
Multi-channel campaign performance reporting 65
Multi-channel campaign performance optimization 67
Uncovering revenue-driving factors 71
5 Understanding the Market through Marketing Research 73
Market opportunities 75
Market size 75
Factors that impact market-opportunity dynamics 76
Market growth trends 80
Market share 80
Basis for market segmentation 81
Market segmentation by market size, market growth, and
market share: case study one 82
Using market research and data mining for building a
marketing plan 85
Marketing planning based on market segmentation and
overall company goal: case study two 85
Target-audience segmentation 88
Target-audience attributes 88
Types of target-audience segmentation 89
Contents
Understanding route to market and competitive landscape
by market segment 91
Routes to market 91
Competitive landscape 93
Competitive analysis methods 95
Overview of marketing research 100
Syndicated research versus customized research 101
Primary data versus secondary data 105
Surveys 106
Panel studies 108
Focus groups 109
Sampling methods 109
Sample size 110
Research report and results presentation 112
Structure of a research report 112
6 Data and Statistics Overview 115
Datatypes 117
Overview of statistical concepts 117
Population, sample, and the central limit theorem 118
Random variables 118
Probability, probability mass, probability density, probability
distribution, and expectation 118
Mean, median, mode, and range 120
Variance and standard deviation 120
Percentile, skewness, and kurtosis 121
Probability density functions 122
Independent and dependent variables 126
Covariance and correlation coefficient 126
Tests of significance 130
Experimental design 134
7 Introduction to Data Mining 137
Data mining overview 139
An effective step by step data mining thought process 141
Step one: identification of business objectives and goals 141
Step two: determination of the key focus business areas and
metrics 142
Step three: translation of business issues into technical
problems 142
Step four: selection of appropriate data mining techniques
and software tools 143
Step five: identification of data sources 143
Contents
Step six: conduction of analysis 144
Step seven: translation of analytical results into actionable
business recommendations 145
Overview of data mining techniques 145
Basic data exploration 146
Linear regression analysis 146
Cluster analysis 151
Principal component analysis 163
Factor analysis 165
Discriminant analysis 166
Correspondence analysis 168
Analysis of variance 172
Canonical correlation analysis 175
Multi-dimensional scaling analysis 176
Time series analysis 179
Conjoint analysis 186
Logistic regression 188
Association analysis 190
Collaborative filtering 190
8 Audience Segmentation 193
Case study one: behavior and demographics segmentation 195
Model building 196
Model validation 201
Case study two: value segmentation 205
Model building 207
Model validation 208
Case study three: response behavior segmentation 208
Model building 209
Validation 210
Case study four: customer satisfaction segmentation 210
Model building 212
Validation 213
Data Mining for Customer Acquisition, Retention,
and Growth 219
Case study one: direct mail targeting for new customer
acquisition 221
Purchase model on prospects having received a catalog 222
Purchase model based on prospects not having received
a catalog 224
Prospect scoring 226
Modeling financial impact 226
Contents
Case study two: attrition modeling for customer retention 227
Case study three: customer growth model 229
10 Data Mining for Cross-Selling and Bundled Marketing 233
Association engine 235
Case study one: e-commerce cross-sell 236
Model building 237
Model validation 239
Case study two: online advertising promotions 241
Model building 242
Model validation 243
11 Web Analytics 245
Web analytics overview 247
Web analytic reporting overview 248
Brand or product awareness generation 248
Web site content management 249
Lead generation 250
E-commerce direct sales 252
Customer support and service 253
Web syndicated research 253
12 Search Marketing Analytics 255
Search engine optimization overview 257
Site analysis 259
SEO metrics 262
Search engine marketing overview 263
SEM resources 263
SEM metrics 264
Onsite search overview 265
Visitor segmentation and visit scenario analysis 265
Index 269
|
| adam_txt |
Contents
Preface %i
Biographies xiii
1 Introduction 1
Strategic importance of metrics, marketing research and
data mining in today's marketing world 3
The role of metrics 4
The role of research 4
The role of data mining 6
An effective eight-step process for incorporating metrics,
research and data mining into marketing planning
and execution 6
Step 1: identifying key stakeholders and their
business objectives 6
Step 2: selecting appropriate metrics to measure
marketing success 7
Step 3: assessing the market opportunity 8
Step 4: conducting competitive analysis 8
Step 5: deriving optimal marketing spending and media mix 9
Step 6: leveraging data mining for optimization and getting
early buy-in and feedback from key stakeholders 9
Step 7: tracking and comparison of metric goals and results 9
Step 8: incorporating the learning into the next round of
marketing planning 10
Integration of market intelligence and databases 10
Cultivating adoption of metrics, research and data mining
in the corporate structure 11
Identification of key required skills 12
Creating an effective engagement process 14
Promoting research and analytics 15
2 Marketing Spending Models and Optimization 19
Marketing spending model 21
Static models 23
Dynamic models 34
Contents
Marketing spending models and corporate finance 35
A framework for corporate performance marketing
effort integration 36
3 Metrics Overview 39
Common metrics for measuring returns and investments 41
Measuring returns with return metrics 42
Measuring investment with investment metrics 42
Developing a formula for return on investment 43
Common ROI tracking challenges 44
Process for identifying appropriate metrics 45
Identification of the overall business objective 45
Understanding the impact of a marketing effort on target
audience migration 46
Selection of appropriate marketing communication
channels 49
Identification of appropriate return metrics by stage in the
sales cycle 56
Differentiating return metrics from operational metrics 61
Multi-channel Campaign Performance Reporting and
Optimization 63
Multi-channel campaign performance reporting 65
Multi-channel campaign performance optimization 67
Uncovering revenue-driving factors 71
5 Understanding the Market through Marketing Research 73
Market opportunities 75
Market size 75
Factors that impact market-opportunity dynamics 76
Market growth trends 80
Market share 80
Basis for market segmentation 81
Market segmentation by market size, market growth, and
market share: case study one 82
Using market research and data mining for building a
marketing plan 85
Marketing planning based on market segmentation and
overall company goal: case study two 85
Target-audience segmentation 88
Target-audience attributes 88
Types of target-audience segmentation 89
Contents
Understanding route to market and competitive landscape
by market segment 91
Routes to market 91
Competitive landscape 93
Competitive analysis methods 95
Overview of marketing research 100
Syndicated research versus customized research 101
Primary data versus secondary data 105
Surveys 106
Panel studies 108
Focus groups 109
Sampling methods 109
Sample size 110
Research report and results presentation 112
Structure of a research report 112
6 Data and Statistics Overview 115
Datatypes 117
Overview of statistical concepts 117
Population, sample, and the central limit theorem 118
Random variables 118
Probability, probability mass, probability density, probability
distribution, and expectation 118
Mean, median, mode, and range 120
Variance and standard deviation 120
Percentile, skewness, and kurtosis 121
Probability density functions 122
Independent and dependent variables 126
Covariance and correlation coefficient 126
Tests of significance 130
Experimental design 134
7 Introduction to Data Mining 137
Data mining overview 139
An effective step by step data mining thought process 141
Step one: identification of business objectives and goals 141
Step two: determination of the key focus business areas and
metrics 142
Step three: translation of business issues into technical
problems 142
Step four: selection of appropriate data mining techniques
and software tools 143
Step five: identification of data sources 143
Contents
Step six: conduction of analysis 144
Step seven: translation of analytical results into actionable
business recommendations 145
Overview of data mining techniques 145
Basic data exploration 146
Linear regression analysis 146
Cluster analysis 151
Principal component analysis 163
Factor analysis 165
Discriminant analysis 166
Correspondence analysis 168
Analysis of variance 172
Canonical correlation analysis 175
Multi-dimensional scaling analysis 176
Time series analysis 179
Conjoint analysis 186
Logistic regression 188
Association analysis 190
Collaborative filtering 190
8 Audience Segmentation 193
Case study one: behavior and demographics segmentation 195
Model building 196
Model validation 201
Case study two: value segmentation 205
Model building 207
Model validation 208
Case study three: response behavior segmentation 208
Model building 209
Validation 210
Case study four: customer satisfaction segmentation 210
Model building 212
Validation 213
Data Mining for Customer Acquisition, Retention,
and Growth 219
Case study one: direct mail targeting for new customer
acquisition 221
Purchase model on prospects having received a catalog 222
Purchase model based on prospects not having received
a catalog 224
Prospect scoring 226
Modeling financial impact 226
Contents
Case study two: attrition modeling for customer retention 227
Case study three: customer growth model 229
10 Data Mining for Cross-Selling and Bundled Marketing 233
Association engine 235
Case study one: e-commerce cross-sell 236
Model building 237
Model validation 239
Case study two: online advertising promotions 241
Model building 242
Model validation 243
11 Web Analytics 245
Web analytics overview 247
Web analytic reporting overview 248
Brand or product awareness generation 248
Web site content management 249
Lead generation 250
E-commerce direct sales 252
Customer support and service 253
Web syndicated research 253
12 Search Marketing Analytics 255
Search engine optimization overview 257
Site analysis 259
SEO metrics 262
Search engine marketing overview 263
SEM resources 263
SEM metrics 264
Onsite search overview 265
Visitor segmentation and visit scenario analysis 265
Index 269 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Chiu, Susan Tavella, Domingo |
| author_facet | Chiu, Susan Tavella, Domingo |
| author_role | aut aut |
| author_sort | Chiu, Susan |
| author_variant | s c sc d t dt |
| building | Verbundindex |
| bvnumber | BV035007135 |
| classification_rvk | QP 650 |
| ctrlnum | (OCoLC)254314513 (DE-599)GBV563410981 |
| dewey-full | 658.83 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.83 |
| dewey-search | 658.83 |
| dewey-sort | 3658.83 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| edition | 1. ed. |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02087nam a2200517 c 4500</leader><controlfield tag="001">BV035007135</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20080918 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">080818s2008 d||| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">0750682345</subfield><subfield code="9">0-7506-8234-5</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780750682343</subfield><subfield code="9">978-0-7506-8234-3</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)254314513</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)GBV563410981</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-945</subfield><subfield code="a">DE-384</subfield><subfield code="a">DE-M347</subfield><subfield code="a">DE-188</subfield></datafield><datafield tag="082" ind1="0" ind2=" "><subfield code="a">658.83</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">QP 650</subfield><subfield code="0">(DE-625)141922:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Chiu, Susan</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Data mining and market intelligence for optimal marketing returns</subfield><subfield code="c">Susan Chiu ; Domingo Tavella</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">1. ed.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Amsterdam [u.a.]</subfield><subfield code="b">Elsevier/Butterworth-Heinemann</subfield><subfield code="c">2008</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">XII, 280 S.</subfield><subfield code="b">graph. Darst.</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Marketingpolitik / Marktforschung / Data Mining</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Data Mining</subfield><subfield code="0">(DE-588)4428654-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Marketingforschung</subfield><subfield code="0">(DE-588)4200055-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Marketingaudit</subfield><subfield code="0">(DE-588)4114516-1</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Business Intelligence</subfield><subfield code="0">(DE-588)4588307-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Marketingforschung</subfield><subfield code="0">(DE-588)4200055-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Data Mining</subfield><subfield code="0">(DE-588)4428654-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="689" ind1="1" ind2="0"><subfield code="a">Marketingaudit</subfield><subfield code="0">(DE-588)4114516-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2="1"><subfield code="a">Data Mining</subfield><subfield code="0">(DE-588)4428654-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="1" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="689" ind1="2" ind2="0"><subfield code="a">Marketingforschung</subfield><subfield code="0">(DE-588)4200055-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="2" ind2="1"><subfield code="a">Data Mining</subfield><subfield code="0">(DE-588)4428654-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="2" ind2="2"><subfield code="a">Business Intelligence</subfield><subfield code="0">(DE-588)4588307-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="2" ind2=" "><subfield code="5">DE-188</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Tavella, Domingo</subfield><subfield code="e">Verfasser</subfield><subfield code="4">aut</subfield></datafield><datafield tag="856" ind1="4" ind2=" "><subfield code="u">http://www.gbv.de/dms/zbw/563410981.pdf</subfield><subfield code="z">lizenzfrei</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">HBZ Datenaustausch</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016676438&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-016676438</subfield></datafield></record></collection> |
| id | DE-604.BV035007135 |
| illustrated | Illustrated |
| index_date | 2024-07-02T21:42:37Z |
| indexdate | 2024-07-09T21:20:03Z |
| institution | BVB |
| isbn | 0750682345 9780750682343 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-016676438 |
| oclc_num | 254314513 |
| open_access_boolean | |
| owner | DE-945 DE-384 DE-M347 DE-188 |
| owner_facet | DE-945 DE-384 DE-M347 DE-188 |
| physical | XII, 280 S. graph. Darst. |
| publishDate | 2008 |
| publishDateSearch | 2008 |
| publishDateSort | 2008 |
| publisher | Elsevier/Butterworth-Heinemann |
| record_format | marc |
| spelling | Chiu, Susan Verfasser aut Data mining and market intelligence for optimal marketing returns Susan Chiu ; Domingo Tavella 1. ed. Amsterdam [u.a.] Elsevier/Butterworth-Heinemann 2008 XII, 280 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Marketingpolitik / Marktforschung / Data Mining Data Mining (DE-588)4428654-5 gnd rswk-swf Marketingforschung (DE-588)4200055-5 gnd rswk-swf Marketingaudit (DE-588)4114516-1 gnd rswk-swf Business Intelligence (DE-588)4588307-5 gnd rswk-swf Marketingforschung (DE-588)4200055-5 s Data Mining (DE-588)4428654-5 s DE-604 Marketingaudit (DE-588)4114516-1 s Business Intelligence (DE-588)4588307-5 s DE-188 Tavella, Domingo Verfasser aut http://www.gbv.de/dms/zbw/563410981.pdf lizenzfrei Inhaltsverzeichnis HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016676438&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Chiu, Susan Tavella, Domingo Data mining and market intelligence for optimal marketing returns Marketingpolitik / Marktforschung / Data Mining Data Mining (DE-588)4428654-5 gnd Marketingforschung (DE-588)4200055-5 gnd Marketingaudit (DE-588)4114516-1 gnd Business Intelligence (DE-588)4588307-5 gnd |
| subject_GND | (DE-588)4428654-5 (DE-588)4200055-5 (DE-588)4114516-1 (DE-588)4588307-5 |
| title | Data mining and market intelligence for optimal marketing returns |
| title_auth | Data mining and market intelligence for optimal marketing returns |
| title_exact_search | Data mining and market intelligence for optimal marketing returns |
| title_exact_search_txtP | Data mining and market intelligence for optimal marketing returns |
| title_full | Data mining and market intelligence for optimal marketing returns Susan Chiu ; Domingo Tavella |
| title_fullStr | Data mining and market intelligence for optimal marketing returns Susan Chiu ; Domingo Tavella |
| title_full_unstemmed | Data mining and market intelligence for optimal marketing returns Susan Chiu ; Domingo Tavella |
| title_short | Data mining and market intelligence for optimal marketing returns |
| title_sort | data mining and market intelligence for optimal marketing returns |
| topic | Marketingpolitik / Marktforschung / Data Mining Data Mining (DE-588)4428654-5 gnd Marketingforschung (DE-588)4200055-5 gnd Marketingaudit (DE-588)4114516-1 gnd Business Intelligence (DE-588)4588307-5 gnd |
| topic_facet | Marketingpolitik / Marktforschung / Data Mining Data Mining Marketingforschung Marketingaudit Business Intelligence |
| url | http://www.gbv.de/dms/zbw/563410981.pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=016676438&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT chiususan dataminingandmarketintelligenceforoptimalmarketingreturns AT tavelladomingo dataminingandmarketintelligenceforoptimalmarketingreturns |