A Short Guide to Marketing Model Alignment & Design: Advanced Topics in Goal Alignment - Model Formulation
Marketing Models are neither just Statistics nor just Marketing, but a synthesis of the information sources creating a cohesive predictive system. If you're looking for a book that talks about the "logic of marketing" and the "design of statistical models" in an integrated way to increase model accuracy and improve business profits, then this book was written for you. Nevertheless, anyone who's worked around Marketing Models at all will have heard people talk about modifying models for "statistical reasons" or modifying them for "business reasons" as though the two sets of criteria are from Mars and Venus, respectively. In this book, I try to help readers develop a deeper understanding of the reasoning behind both sets of rules to put themselves in a better position to weigh the value of all evidence and define the most applicable business goals for their models to address. And after defining those goals, design the best models for achieving them. If you'd like to better understand: • how to define dependent variables to maximize business goals; • how business logic should influence your model design; • when lower R-Squared statistics can represent better models; • how much information you can reasonably expect from your data; • how to safely work with imperfect data that may offer partial information but that shouldn't be naively relied upon, and; • ultimately how to create models offering superior business value … then read on.
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A Short Guide to Marketing Model Alignment & Design: Advanced Topics in Goal Alignment - Model Formulation
Marketing Models are neither just Statistics nor just Marketing, but a synthesis of the information sources creating a cohesive predictive system. If you're looking for a book that talks about the "logic of marketing" and the "design of statistical models" in an integrated way to increase model accuracy and improve business profits, then this book was written for you. Nevertheless, anyone who's worked around Marketing Models at all will have heard people talk about modifying models for "statistical reasons" or modifying them for "business reasons" as though the two sets of criteria are from Mars and Venus, respectively. In this book, I try to help readers develop a deeper understanding of the reasoning behind both sets of rules to put themselves in a better position to weigh the value of all evidence and define the most applicable business goals for their models to address. And after defining those goals, design the best models for achieving them. If you'd like to better understand: • how to define dependent variables to maximize business goals; • how business logic should influence your model design; • when lower R-Squared statistics can represent better models; • how much information you can reasonably expect from your data; • how to safely work with imperfect data that may offer partial information but that shouldn't be naively relied upon, and; • ultimately how to create models offering superior business value … then read on.
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A Short Guide to Marketing Model Alignment & Design: Advanced Topics in Goal Alignment - Model Formulation

A Short Guide to Marketing Model Alignment & Design: Advanced Topics in Goal Alignment - Model Formulation

by David Young
A Short Guide to Marketing Model Alignment & Design: Advanced Topics in Goal Alignment - Model Formulation

A Short Guide to Marketing Model Alignment & Design: Advanced Topics in Goal Alignment - Model Formulation

by David Young

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$9.99 

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Overview

Marketing Models are neither just Statistics nor just Marketing, but a synthesis of the information sources creating a cohesive predictive system. If you're looking for a book that talks about the "logic of marketing" and the "design of statistical models" in an integrated way to increase model accuracy and improve business profits, then this book was written for you. Nevertheless, anyone who's worked around Marketing Models at all will have heard people talk about modifying models for "statistical reasons" or modifying them for "business reasons" as though the two sets of criteria are from Mars and Venus, respectively. In this book, I try to help readers develop a deeper understanding of the reasoning behind both sets of rules to put themselves in a better position to weigh the value of all evidence and define the most applicable business goals for their models to address. And after defining those goals, design the best models for achieving them. If you'd like to better understand: • how to define dependent variables to maximize business goals; • how business logic should influence your model design; • when lower R-Squared statistics can represent better models; • how much information you can reasonably expect from your data; • how to safely work with imperfect data that may offer partial information but that shouldn't be naively relied upon, and; • ultimately how to create models offering superior business value … then read on.

Product Details

ISBN-13: 9781543915563
Publisher: BookBaby
Publication date: 10/18/2017
Sold by: Barnes & Noble
Format: eBook
Pages: 60
File size: 1 MB

About the Author

David Young has 25 years of experience in Marketing Modeling applied to Marketing Mix Modeling, Choice Modeling, Market Segmentation, Propensity Models, etc. He currently works as a Senior Group Director at Neustar in McLean, VA managing a team of 10 modelers whom optimize about 2 billion dollars of annual advertising dollars across multiple, often multinational clients. A native of the USA, he also lived in Madrid, Spain for ten years and speaks Spanish passably well. He's an invited speaker at various Data Science venues.

Table of Contents

Acknowledgements 3

How this book is different from other Marketing Modeling books and what you can get from it 4

1 Sources of Model Alignment Error 6

2 Objective Definition 8

Model Goal Definition 8

Accuracy vs. Precision 9

Dependent Definitions: Exacting versus General 13

3 Information Adequacy Assessment 19

How much data is enough? 20

Response Rates 20

Data Requirements Vary by Coefficient 22

Measuring Small Impacts 24

Small Media Can't be Accurately Measured Unless Big Effects are Controlled 24

Measuring Small Impacts in Practice 26

Measuring Long Slow Impacts 27

Measuring Long Slow Impacts in Practice 30

Bounding Trend Parameters 31

Special Case: Long Term Changes to Brand Value 35

Evolutionary Patterns 36

Measuring Short Unique Impacts 38

Event Uniqueness 38

Metric Comparability 40

Incremental Impact 42

Proper Experimental Design for Proper In-Market Tests - A Real-World Case 43

4 Planning for Future Analytics 46

Case Study: Modeling Brand Awareness 48

Awareness Modeling Challenges: 48

Recommended Approaches: 55

About the Author 65

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