008 |
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200820s2021 vaua b 001 0 eng |
010 |
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|a 2020037900
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020 |
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|a9780813945156|q(hbk.) :|cNT$1118
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020 |
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|a9780813945163|q(ebook)
|
040 |
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|aDLC|beng|cDLC|dDLC|dTMUE|eaacr2
|
042 |
|
|apcc
|
050 |
00
|
|aHF5415.127|b.V453 2021
|
082 |
00
|
|a658.8/3|223
|
095 |
|
|aTIANMU|bTIA06|cA0336836|pB|d658.83|eV461|y2021|tDDC|r1118
|
100 |
1
|
|aVenkatesan, Rajkumar.
|
245 |
10
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|aMarketing analytics :|bessential tools for data-driven decisions /|cRajkumar Venkatesan, Paul W. Farris, and Ronald T. Wilcox.
|
260 |
|
|aCharlottesville :|bDarden Business Publishing, University of Virginia Press,|c2021.
|
300 |
|
|ax, 294 p. :|bill. ;|c24 cm.
|
504 |
|
|aIncludes bibliographical references (p. [259]-271) and index.
|
505 |
0
|
|aResource Allocation -- Cluster Analysis -- Conjoint Analysis -- Linear Regression -- Customer Lifetime Value -- Marketing Experiments -- Paid Search Advertising -- Text Analytics -- Logistic Regression -- Recommendation Systems -- Automation of Marketing Models -- Implementing Marketing Analytics.
|
520 |
|
|a"A textbook on marketing analytics, this book provides practical predictive models in marketing. It strives to strike a balance between highly sophisticated models and managerial relevance. Through the diverse business cases in the book, readers learn how to connect marketing inputs to customer behavior and how to use historic information, experiments, or heuristics to develop forward-looking what-if scenarios and predictive models. The real-life cases include real data, enabling readers to take a hands-on approach to the analysis"--|cProvided by publisher.
|
650 |
0
|
|aMarketing|xManagement.
|
650 |
0
|
|aMarketing|xStatistical methods.
|
650 |
0
|
|aMarketing research.
|
700 |
1
|
|aFarris, Paul W.
|
700 |
1
|
|aWilcox, Ronald T.
|