Non-Fiction Books:

Predictive Analytics for Marketers

Using Data Mining for Business Advantage

Format

Paperback

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Predictive Analytics for Marketers by Barry Leventhal
$55.99
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Description

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.

Author Biography

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.
Release date NZ
February 3rd, 2018
Pages
272
Country of Publication
United Kingdom
Imprint
Kogan Page Ltd
Dimensions
156x234x14
ISBN-13
9780749479930
Product ID
26799371

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