Predictive analytics for marketers : using data mining for business advantage / Barry Leventhal.

Dr Barry Leventhal is a leading UK authority on geodemographics and a marketing analytics expert. He is Emeritus Chair of the Census and Geodemographics Group (CGG), which is an advisory board of The Market Research Society (MRS) and a leading voice in the UK information industry. He was recently awarded the MRS Gold Medal Award - the association's rarest accolade, presented for the first time since 2008 - in recognition of his lifetime of exceptional achievement and contribution to the research profession.

Includes bibliographical references (pages 241-245) and index.

Cover; Contents; About the author; Contributors' 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. 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. 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. 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. 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.

Predictive analytics has revolutionized marketing practice. It involves using many techniques from data mining, statistics, modelling, machine learning and artificial intelligence, to analyse current data and make predictions about unknown future events. In business terms, this enables companies to forecast consumer behaviour and much more. Predictive Analytics for Marketers will guide marketing professionals on how to apply predictive analytical tools to streamline business practices. Including comprehensive coverage of an array of predictive analytic tools and techniques, this book enables readers to harness patterns from past data, to make accurate and useful predictions that can be converted to business success. Truly global in its approach, the insights these techniques offer can be used to manage resources more effectively across all industries and sectors.

Written in clear, non-technical language, Predictive Analytics for Marketers contains case studies from the author's more than 25 years of experience and articles from guest contributors, demonstrating how predictive analytics can be used to successfully achieve a range of business purposes.