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El. knyga: Marketing Analytics: A Practical Guide to Improving Consumer Insights Using Data Techniques

3.69/5 (37 ratings by Goodreads)
  • Formatas: 336 pages
  • Išleidimo metai: 03-Dec-2022
  • Leidėjas: Kogan Page Ltd
  • Kalba: eng
  • ISBN-13: 9781398608207
  • Formatas: 336 pages
  • Išleidimo metai: 03-Dec-2022
  • Leidėjas: Kogan Page Ltd
  • Kalba: eng
  • ISBN-13: 9781398608207

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Confidently apply marketing analytics techniques to improve consumer insights and marketing performance, so you can compete more effectively in the marketplace.

Who is most likely to buy and what is the best way to target them? How can I use both consumer analytics and modelling to improve the impact of marketing campaigns? Marketing Analytics takes you step-by-step through these areas and more.

Marketing Analytics
enables you to leverage predictive techniques to measure and improve marketing performance. By exploring real-world marketing challenges, it provides clear, jargon-free explanations on how to apply different analytical models for each purpose. From targeted list creation and data segmentation, to testing campaign effectiveness, pricing structures and forecasting demand, it offers a complete resource for how statistics, consumer analytics and modelling can be put to optimal use.

This revised and updated third edition of Marketing Analytics contains new material on forecasting, customer touchpoints modelling, and a new focus on customer loyalty. With accessible language throughout, methodologies are simplified to ensure the more complex aspects of data and analytics are fully accessible for any level of application. Supported by a glossary of key terms and supporting resources consisting of datasets, presentation slides for each chapter and a test bank of self-test question, this book supplies a concrete foundation for optimizing marketing analytics for day-to-day business advantage.

Recenzijos

"In Marketing Analytics, Mike Grigsby takes passionate marketing strategists on a practical, real-life journey for solving common marketing challenges. By combining the concepts and knowledge areas of statistics, marketing strategy and consumer behaviour, Grigsby recommends scientific and innovative solutions to common marketing problems in the current business environment. I highly recommend reading this book as it adds a completely new dimension to marketing science." * Kristina Domazetoska, Project Manager and Implementation Consultant at Insala Talent Development and Mentoring Solutions * "Grigsby's book is the right blend of theory applied to the real-world large-scale data problems of marketing. It's exactly the book I wish I'd had when I started out in this field." * Jeff Weiner, Senior Director, Analytics, One10 *

Introduction 1(1)
PART ONE How can marketing analytics help you?
1(34)
1 Overview of statistics
9(11)
Measures of central tendency
9(2)
Measures of dispersion
11(3)
The normal distribution
14(1)
Confidence intervals
15(1)
Relations among two variables: covariance and correlation
16(2)
Probability and the sampling distribution
18(1)
Conclusion
18(1)
Checklist: You'll be the smartest person in the room if you...
19(1)
2 Consumer behaviour and marketing strategy
20(9)
Introduction
20(1)
Consumer behaviour as the basis for marketing strategy
21(1)
Overview of consumer behaviour
22(2)
Overview of marketing strategy
24(3)
Conclusion
27(1)
Checklist: You'll be the smartest person in the room if you...
28(1)
3 What is an insight?
29(6)
Introduction
29(1)
Insights tend not to be used by executives
29(1)
Is this an insight?
30(1)
So, what is an insight?
31(1)
Ultimately, an insight is about action-ability
32(2)
Checklist: You'll be the smartest person in the room if you...
34(1)
PART TWO Dependent variable techniques
35(108)
4 Modelling demand and elasticity
37(27)
Introduction
37(1)
Dependent equation type vs interrelationship type statistics
38(1)
Deterministic vs probabilistic equations
38(1)
Business case
38(6)
Results applied to business case
44(1)
Modelling elasticity
44(3)
Technical notes
47(4)
Highlight: Segmentation and elasticity modelling can maximize revenue in a retail/medical clinic chain: field test results
51(1)
Abstract
51(1)
The problem and some background
52(1)
Description of the dataset
53(1)
First: segmentation
53(2)
Then: elasticity modelling
55(6)
Last: test vs control
61(1)
Discussion
61(1)
Conclusion: why is elasticity modelling so rarely done?
62(1)
Checklist: You'll be the smartest person in the room if you...
63(1)
5 Polynomial distributed lags
64(9)
What is PDL?
64(2)
An example
66(2)
Business case
68(3)
Conclusion
71(1)
Checklist: You'll be the smartest person in the room if you...
72(1)
6 Using Poisson regression
73(6)
When to use Poisson regression
73(1)
Technical note
74(1)
Business case
75(2)
Conclusion
77(1)
Checklist: You'll be the smartest person in the room if you...
78(1)
7 Logistic regression and market basket analysis
79(23)
Introduction
79(1)
Conceptual notes
80(1)
Business case
81(1)
Results applied to the model
82(3)
Lift charts
85(1)
How deep to mail
86(4)
Using the model - collinearity overview
90(4)
Variable diagnostics
94(2)
Highlight: Using logistic regression for market basket analysis
96(1)
Abstract
96(1)
What is a market basket?
96(1)
How is it usually done?
96(1)
Logistic regression
97(1)
How to estimate/predict the market basket
97(1)
An example
98(2)
Conclusion
100(1)
Checklist: You'll be the smartest person in the room if you...
101(1)
8 Survival modelling and lifetime value
102(21)
Introduction
102(1)
Conceptual overview of survival analysis
103(1)
Business case
104(2)
More about survival analysis
106(2)
Model output and interpretation
108(4)
Note: The only way to do churn modelling
112(2)
Conclusion
114(1)
Highlight: Lifetime value: how predictive analysis is superior to descriptive analysis
115(1)
Abstract
115(1)
Descriptive analysis
115(1)
Predictive analysis
116(2)
An example
118(4)
Checklist: You'll be the smartest person in the room if you...
122(1)
9 Panel regression and same store sales
123(10)
Introduction
123(3)
What is panel regression?
126(1)
Panel regression: details
126(2)
Business case
128(1)
Insights about marcom (direct mail, email and SMS)
128(1)
Insights about time period (quarters)
129(1)
Insights about cross-sections (counties)
130(1)
Brief note on modelling same store sales
130(1)
Conclusion
131(1)
Checklist: You'll be the smartest person in the room if you...
132(1)
10 Introduction to forecasting
133(10)
Overview
133(1)
Forecasting demand
134(1)
Autocorrelation
134(1)
Dummy variables and seasonality
135(2)
Business case
137(4)
Conclusion
141(1)
Checklist: You'll be the smartest person in the room if you...
142(1)
PART THREE Interrelationship techniques
143(78)
11 Simultaneous equations
145(11)
Introduction
145(1)
What are simultaneous equations?
146(1)
Why go to the trouble of using simultaneous equations?
147(1)
Desirable properties of estimators
148(3)
Business case
151(3)
Conclusion
154(1)
Checklist: You'll be the smartest person in the room if you...
155(1)
12 Principal components and factor analysis
156(6)
Interrelationship techniques
156(1)
What is factor analysis?
157(2)
What is PCA?
159(1)
Similarities between PCA and factor analysis
159(1)
Differences between PCA and factor analysis
159(1)
Conclusion
160(1)
Checklist: You'll be the smartest person in the room if you...
161(1)
13 Segmentation overview
162(22)
Introduction
162(1)
Introduction to segmentation
163(1)
What is segmentation? What is a segment?
163(1)
Why segment? Strategic uses of segmentation
164(2)
The four Ps of strategic marketing
166(2)
Criteria for actionable segmentation
168(1)
A priori or not?
168(1)
Conceptual process
169(6)
Highlight: Using segmentation to improve both strategy and predictive modelling
175(1)
Introduction
175(1)
Segmentation is a strategic, not an analytic, process
175(1)
Why would segmentation improve predictive modelling accuracy?
176(1)
Segmenting variables for model improvement
177(1)
Example: churn modelling
177(2)
Interpretation and insights
179(2)
What if there was no segmentation?
181(1)
Conclusion
182(1)
Checklist: You'll be the smartest person in the room if you...
183(1)
14 Tools of segmentation
184(37)
Overview
184(1)
Metrics of successful segmentation
185(1)
General analytic techniques
185(8)
Segmentation techniques summary
193(1)
Business case
194(3)
Analytics
197(4)
Profile and output
201(2)
Comments/details on individual segments
203(7)
K-means compared to LCA
210(4)
Highlight: Why go beyond RFM?
214(1)
Abstract
214(1)
What is RFM?
214(2)
What is behavioural segmentation?
216(2)
What does behavioural segmentation provide that RFM does not?
218(1)
Conclusion
219(1)
Checklist: You'll be the smartest person in the room if you...
220(1)
PART FOUR Focus on media and loyalty
221(58)
15 Modelling marcom value
223(7)
Introduction
223(1)
Value of marcom model
223(3)
Business case
226(3)
Checklist: You'll be the smartest person in the room if you...
229(1)
16 Media mix modelling
230(9)
Overview of MMM
230(1)
Adstock models
231(1)
Single equation and PDLs
231(1)
Simultaneous equations
232(1)
Business case
233(4)
Conclusion
237(1)
Checklist: You'll be the smartest person in the room if you...
238(1)
17 Overview of loyalty
239(14)
Introduction to loyalty
239(1)
Is there a range or spectrum of loyalty?
240(1)
What are the three Rs of loyalty?
241(1)
Why design a programme with earn-burn measures?
242(4)
Business case
246(2)
A note on programme valuation
248(2)
Conclusion
250(2)
Checklist: You'll be the smartest person in the room if you...
252(1)
18 Loyalty with SEM
253(10)
Structural equation modelling (SEM)
253(4)
Business case
257(4)
Conclusion
261(1)
Checklist: You'll be the smartest person in the room if you...
262(1)
19 The customer loyalty journey
263(16)
Introduction
263(1)
Background
264(1)
Segmentation
265(1)
Elasticity modelling
266(1)
Simultaneous equations
267(1)
The experiences questionnaire
268(9)
Conclusion
277(1)
Checklist: You'll be the smartest person in the room if you...
278(1)
PART FIVE More important topics for everyday marketing
279(24)
20 Statistical testing
281(11)
Everyone wants to test
281(1)
Sample size equation: use the lift measure
282(2)
A/B testing and full factorial differences
284(2)
Business case
286(5)
Checklist: You'll be the smartest person in the room if you...
291(1)
21 Introduction to Big Data
292(11)
Introduction
292(1)
What is Big Data?
292(2)
Is Big Data important?
294(1)
What does it mean for analytics? For strategy?
294(1)
So what?
295(1)
Surviving the Big Data panic
295(2)
Big Data analytics
297(1)
Big Data - exotic algorithms
298(3)
Conclusion
301(1)
Checklist: You'll be the smartest person in the room if you...
302(1)
Conclusion: The finale 303(8)
References 311(2)
Further reading 313(2)
Index 315
Mike Grigsby, based in Orlando, Florida, has more than 30 years' experience in the field of marketing analytics. He was formerly vice president of customer insights and advanced analytics at Brierley and Partners and of strategic business analysis and advanced analytics at Targetbase and has also held leadership positions at Hewlett-Packard and Gap. Previously an adjunct professor at the University of Texas at Dallas, he taught analytics at both graduate and undergraduate levels. He is the author of Advanced Customer Analytics, also published by Kogan Page.