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

A Short Guide to Marketing Model Alignment & Design

Copyright © 2017 by David Young.

All rights reserved. No part of this book may be used or reproduced in any manner, distributed in any form, or stored in a database or retrieval system without the prior written consent of the author except in the case of brief quotations embodied in critical articles and reviews.

Printed in the United States of America.

ISBN: 978-1-54391-556-3

Contents

Acknowledgements

How this book is different from other Marketing

Modeling books and what you can get from it

1. Sources of Model Alignment Error

2. Objective Definition

Model Goal Definition

Accuracy vs. Precision

2 Key Modeling Points

Statistical Precision – Judgmental Accuracy

Trading Accuracy for Precision

Dependent Definitions: Exacting versus General

Exacting Definitions

Examples of Problematic Dependents Lacking Precision:

How exact is exact enough?

General, or Less, Goal Congruent Definitions

Sparse Dependent Problem

Noisy Dependent Problem

3. Information Adequacy Assessment

How much data is enough?

Response Rates

Noteworthy Sizing Implications

Data Requirements Vary by Coefficient

Measuring Small Impacts

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

Measuring Small Impacts in Practice

Measuring Long Slow Impacts

Measuring Long Slow Impacts in Practice

Bounding Trend Parameters

Proportional Relationships

Industry Volume

Distribution

Market Size

Logically Nested Factors

Special Case: Long Term Changes to Brand Value

Evolutionary Patterns

BASS Diffusion Model

Measuring Short Unique Impacts

Event Uniqueness

Preplanned Measurement

Post Event Measurement

Metric Comparability

Incremental Impact

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

4. Planning for Future Analytics

Case Study: Modeling Brand Awareness

Awareness Modeling Challenges:

Challenge 1: Modeling Smooth Lines

Challenge 2: Survey Sampling Error

Challenge 3: Expected Changes Due to Many Causal Variables

Challenge 4: Conceptual Confusion with Awareness, Preference, Choice, and Equity

Recommended Approaches:

Improving the Dependent

Larger surveys

Better Define Your Goals

Measure Where the Response is

Suggested Advantages

A last but important thought on brand trackers

Conclusion

Low Cost Alternative: GQV Share

About the Author

Acknowledgements

In the editing and review of this book I asked several Marketing, Modeling, and Business friends and professionals to offer their feedback and recommendations. While all remaining errors, faults of logic, and half-baked ideas are of my own invention, I’d like to thank the many people whom donated their time and talents to contribute to this work. They are all professionals whose opinions I value and to whom I owe a debt of gratitude.

Karl Lendenman

Natalie Robb

Panos Ventikos

Randy Bartlett

Gurkan Sener

Chris Checco

Chris Cornell

Damian Fernandez

Michael Wolfe

Randy Guse

Sean Gonzalez

Jack Yang

How this book is different from other Marketing

Modeling books and what you can get from it

This book integrates Marketing and Statistical concepts and is written for Marketing Modelers, Model Users, and Purchasers of marketing models.

Most books on Marketing Modeling are slightly modified statistical texts.

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.

Marketing Models are neither just Statistics nor just Marketing, but a synthesis of the information sources creating a cohesive predictive system, and the broader you cast your net for useful information the better your models will be.

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.

Too often, modeling objectives are defined quickly and without much thought. In many cases, standard statistical techniques are pulled from the tool box and applied without aim. Oftentimes the models resulting from that process are reasonable, although typically not optimal. Other times they’re clearly lacking, however the business users (and sometimes competent modelers) are fooled by the façade of science surrounding the modeling process. Hence, criticism is often deflected and