Predictive analytics for marketers : using data mining for business advantage / Barry Leventhal.
Understand how to apply predictive analytics to better manage a company and its resources more effectively, with this revolutionary book for marketing professionals.
Saved in:
| Online Access: |
Full Text (via EBSCO) |
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| Main Author: | |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
London ; New York :
Kogan Page,
2018.
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| Subjects: |
MARC
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| 100 | 1 | |a Leventhal, Barry, |e author. | |
| 245 | 1 | 0 | |a Predictive analytics for marketers : |b using data mining for business advantage / |c Barry Leventhal. |
| 263 | |a 1802 | ||
| 264 | 1 | |a London ; |a New York : |b Kogan Page, |c 2018. | |
| 264 | 4 | |c ©2018 | |
| 300 | |a 1 online resource (xvi, 251 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
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| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Cover; Contents; About the author; Contributorsâ#x80;#x99; biographies; Foreword; Preface and acknowledgements; 01 How can predictive analytics help your business?; Introduction; What is predictive analytics?; The analytical model; â#x80;#x98;All models are wrong, but some are usefulâ#x80;#x99;; Two types of model â#x80;#x93; predictive and descriptive; The profitability seesaw; Applying predictive analytics to e-mail marketing; Making a difference â#x80;#x93; eight examples of useful models; Generating customer knowledge; Competing on analytics; Data protection and privacy issues; Conclusion; Notes. | |
| 505 | 8 | |a 02 Using data mining to build predictive modelsIntroduction; What is data mining?; Who are the stakeholders?; The data-mining process; Involvement of the stakeholders; The relationship between data mining, data science and statistics; Conclusion; 03 Managing the data for predictive analytics; Introduction; The roles of data; The useful data for predictive analytics; Data sources that can be leveraged; Having the right data; Types of data â#x80;#x93; structured and unstructured; Data quality checks â#x80;#x93; the data audit; Data preparation; Conclusion; 04 The analytical modelling toolkit; Introduction. | |
| 505 | 8 | |a Types of techniquesWidely used predictive models; Widely used descriptive methods; The Bayesian approach; Which is the right technique to use?; Combining models together; Conclusion; 05 Software solutions for predictive analytics; Introduction; The architecture required for data mining; Software for analytical modelling; Communicating models between development and deployment; Model management; Scalable analytics in the Cloud; Conclusion; 06 Predicting customer behaviour using analytical models; Introduction; Overview â#x80;#x93; building and deploying predictive models. | |
| 505 | 8 | |a Defining the business requirementsFraming the business problem; The timelines for model development and deployment; The sample size required; Preparing the analytic dataset; Building the model; Assessing model performance; Planning model deployment; From testing to implementation; Conclusion; 07 Predicting lifetimes â#x80;#x93; from customers to machines; Introduction; Importance of the customer lifecycle; Survival analysis applications; Key concepts of this technique; Describing customer lifetimes; Predicting survival times; Applications to customer management. | |
| 505 | 8 | |a Differences between survival and churn modelsApplications to asset management; Conclusion; 08 How to build a customer segmentation; Introduction; Principles of segmentation; Potential business applications; Steps in developing and implementing customer segmentation; Some useful segmentation approaches; Conclusion; 09 Accounts, baskets, citizens or businesses â#x80;#x93; applying predictive analytics in various sectors; Introduction; Applications in retail banking; Analytics in mobile telecoms; Customer analysis in retail; Use of advanced analytics in the public sector; Analysing businesses. | |
| 520 | |a Understand how to apply predictive analytics to better manage a company and its resources more effectively, with this revolutionary book for marketing professionals. | ||
| 588 | 0 | |a Print version record. | |
| 650 | 0 | |a Marketing research. | |
| 650 | 0 | |a Consumer behavior. | |
| 650 | 0 | |a Data mining. | |
| 650 | 7 | |a Consumer behavior |2 fast | |
| 650 | 7 | |a Data mining |2 fast | |
| 650 | 7 | |a Marketing research |2 fast | |
| 758 | |i has work: |a Predictive analytics for marketers (Text) |1 https://id.oclc.org/worldcat/entity/E39PCGT9pmmMxpcQq6rkmjVGf3 |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 776 | 0 | 8 | |i Print version: |a Leventhal, Barry. |t Predictive analytics for marketers. |d London ; New York : Kogan Page, 2018 |z 9780749479930 |w (DLC) 2017050352 |
| 856 | 4 | 0 | |u https://colorado.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&site=ehost-live&AN=1690586 |z Full Text (via EBSCO) |
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