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082	0	0	_a658.8302856312 LE PR
100	1		_aLeventhal, Barry _918381
245	1	0	_aPredictive analytics for marketers : _busing data mining for business advantage _cBarry Leventhal
260			_aLondon : _bKoganPage, _cc2018.
300			_ax, 251 p. : _bill. ; _c24 cm.
504			_aIncludes bibliographical references and index.
505			_aMachine generated contents note: 01.How can predictive analytics help your business? Introduction What is predictive analytics? The analytical model `AH models are wrong, but some are useful' Two types of model predictive and descriptive The profitability seesaw Applying predictive analytics to e-mail marketing Making a difference 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 models 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 03.Managing the data for predictive analytics The roles of data The useful data for predictive analytics Data sources that can be leveraged Having the right data Contents note continued: Types of data structured and unstructured Data quality checks the data audit Data preparation 04.The analytical modelling toolkit Types of techniques Widely used predictive models Widely used descriptive methods The Bayesian approach Which is the right technique to use? Combining models together 05.Software solutions for predictive analytics The architecture required for data mining Software for analytical modelling Communicating models between development and deployment Model management Scalable analytics in the Cloud 06.Predicting customer behaviour using analytical models Overview building and deploying predictive models Defining the business requirements Framing the business problem The timelines for model development and deployment The sample size required Contents note continued: Preparing the analytic dataset Building the model Assessing model performance Planning model deployment From testing to implementation 07.Predicting lifetimes from customers to machines 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 models Applications to asset management 08.How to build a customer segmentation Principles of segmentation Potential business applications Steps in developing and implementing customer segmentation Some useful segmentation approaches 09.Accounts, baskets, citizens or businesses applying predictive analytics in various sectors Applications in retail banking Analytics in mobile telecoms Contents note continued: Customer analysis in retail Use of advanced analytics in the public sector Analysing businesses 10.From people to products using predictive analytics in retail An overview of retail applications Price optimization Markdown pricing Forecasting base demand 11.How to benefit from social network analysis Analysing social networks of customers Business applications of SNA Applying SNA to learn more about customers Extending network analysis to social media 12.Testing the benefits of predictive models and other marketing effects The purpose of testing Golden rules Planning a marketing test Advanced experimental design Constructing and running the test Analysing test results Testing in the online world 13.Top tips for gaining business value from predictive analytics Contents note continued: Reprise of main messages Final tips.
520			_aPredictive 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 has been used to successfully achieve a range of business purposes.
650		0	_aMarketing research _9123
650		0	_aConsumer behavior _9633
650		0	_aData mining _9312
942			_2ddc _cREGULAR