Advanced Customer Analytics: Targeting, Valuing, Segmenting and Loyalty Techniques [Pehme köide]

  • Formaat: Paperback / softback, 264 pages, kõrgus x laius x paksus: 234x157x14 mm, kaal: 418 g, black & white illustrations
  • Sari: Marketing Science
  • Ilmumisaeg: 30-Nov-2016
  • Kirjastus: Kogan Page Ltd
  • ISBN-10: 0749477156
  • ISBN-13: 9780749477158
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  • Formaat: Paperback / softback, 264 pages, kõrgus x laius x paksus: 234x157x14 mm, kaal: 418 g, black & white illustrations
  • Sari: Marketing Science
  • Ilmumisaeg: 30-Nov-2016
  • Kirjastus: Kogan Page Ltd
  • ISBN-10: 0749477156
  • ISBN-13: 9780749477158
Teised raamatud teemal:
Advanced Customer Analytics provides a clear guide to the specific analytical challenges faced by the retail sector. The book covers: the nature and scale of data obtained in transactions, relative proximity to the consumer, and the need to monitor customer behavior across multiple channels. The book advocates a category management approach, taking into account the need to understand the consumer mindset through elasticity modelling and discount strategies, as well as targeted marketing and loyalty design.

A practical approach to complex scenarios is taken throughout, breaking down tasks into easily digestible steps. The use of a fictional retail analyst 'Scott' helps to provide accessible examples of practice. Advanced Customer Analytics addresses the complexities of this subject and offers support to steer retail marketers towards making the right choices for analyzing their data.

Expert guidance in a direct and conceptual style on the analytic steps to take to resolve data-heavy retail marketing questions, taking into account consumer behaviour and multi-channel marketing scenarios.
01 Overview
1(4)
What is retail?
1(1)
What is analytics?
2(1)
Who is this book for?
3(1)
Why focus on retail?
3(1)
Why am I making these suggestions?
3(1)
How is this book organized?
4(1)
02 Regression and factor analysis: an introduction
5(10)
Introduction
5(2)
Regression 101: What is regression?
7(1)
Assumptions of classical linear regression
8(1)
Why is regression important and why is it used?
9(1)
Factor analysis
10(1)
Exploratory vs. confirmatory factor analysis
11(1)
Using factor analysis
11(2)
Conclusion
13(2)
03 Retail: industry uniqueness
15(9)
Introduction to retail
15(1)
Brief history of retail
16(2)
Retail analytics
18(1)
Orientation: because retail is... this book is
19(3)
Retail culture and corporate agility
22(1)
Conclusion
22(2)
04 Retail: data uniqueness
24(11)
Which CRM systems are used?
24(1)
Sources of retail data
25(2)
What is Big Data?
27(2)
Is it important?
29(1)
What does it mean for analytics? For strategy?
29(1)
Why is it important?
30(1)
Surviving the Big Data panic
30(2)
Big Data analytics
32(1)
Conclusion
33(2)
INTERLUDE
35(207)
05 Understanding and estimating demand
37(15)
Introduction
38(1)
Business objective
39(1)
Using ordinary regression to estimate demand
39(1)
Properties of estimators
40(2)
A note on time series data: autocorrelation
42(2)
Dummy variables
44(1)
Business case
44(6)
Conclusion
50(2)
06 Price elasticity and discounts
52(19)
Introduction to elasticity
53(3)
Modelling elasticity
56(6)
Business case
62(7)
Conclusion
69(2)
07 Valuing marketing communications (marcomm)
71(9)
Business case
73(5)
Conclusion
78(2)
08 Forecasting future demand
80(10)
Autocorrelation
81(1)
Dummy variables and seasonality
82(1)
Business case
83(5)
Conclusion
88(2)
09 Targeting the right customers
90(10)
Introduction
90(1)
Business case
91(1)
Results applied to the model
92(6)
A brief procedural note
98(1)
Variable diagnostics
98(1)
Conclusion
99(1)
10 Maximizing the impact of mailing
100(6)
Introduction
100(1)
Lift charts
101(1)
Scoring the database with probability formula
102(3)
Conclusion
105(1)
11 The benefits of product bundling
106(7)
What is a market basket?
107(1)
How is it usually done?
107(1)
Logistic regression
108(1)
How to estimate/predict the market basket
108(1)
Business case
109(3)
Conclusion
112(1)
12 Estimating time of purchase
113(11)
Introduction
113(1)
Conceptual overview of survival analysis
114(1)
More about survival analysis
115(3)
A procedure suggestion and pseudo-fit
118(1)
Business case
118(2)
Model output and interpretation
120(2)
Conclusion
122(2)
13 Investigating the time of product purchase
124(3)
Competing risks
125(1)
Conclusion
126(1)
14 Increasing customer lifetime value
127(14)
Descriptive analysis
128(1)
Predictive analysis
129(2)
Introduction to tobit analysis
131(1)
Business case
132(7)
Conclusion
139(2)
15 Modelling counts (transactions)
141(5)
Business case
142(3)
Conclusion
145(1)
16 Quantifying complexity of customer behaviour
146(16)
Introduction
147(1)
What are simultaneous equations?
147(1)
Why go to the trouble to use simultaneous equations?
148(3)
Business case
151(9)
A brief note on missing value imputation
160(1)
Conclusion
161(1)
17 Designing effective loyalty programmes
162(12)
Introduction to loyalty
163(1)
Is there a range or spectrum of loyalty?
164(1)
What are the 3Rs of loyalty?
164(1)
Why design a programme with earn--burn measures?
165(5)
Business case
170(2)
Conclusion
172(2)
18 Identifying loyal customers
174(11)
Structural equation modelling (SEM)
174(5)
Business case
179(5)
Conclusion
184(1)
19 Introduction to segmentation
185(12)
Overview
186(1)
Introduction to segmentation
186(1)
What is segmentation? What is a segment?
186(1)
Strategic uses of segmentation
187(2)
A priori or not?
189(1)
Conceptual process
190(6)
Conclusion
196(1)
20 Tools for successful segmentation
197(11)
Overview
197(1)
Metrics of successful segmentation
198(1)
General analytic techniques
198(1)
CHAID
199(7)
Conclusion
206(2)
21 Drawing insights from segmentation
208(18)
Business case
208(4)
Analytics
212(8)
Comments/details on individual segments
220(5)
Conclusion
225(1)
22 Creating targeted messages
226(5)
Overview
226(3)
Conclusion
229(2)
23 RFM vs. segmentation
231(7)
Introduction
232(1)
What is RFM?
232(2)
What is behavioural segmentation?
234(2)
What does behavioural segmentation provide that RFM does not?
236(1)
Conclusion
237(1)
24 Marketing strategy: customers not competitors
238(4)
Customer-centricity
238(3)
Conclusion
241(1)
References and further reading 242(1)
Index 243
Mike Grigsby has been involved in marketing science for more than 25 years. He was marketing research director at Millward Brown and has held leadership positions at Hewlett-Packard and the Gap. With a wealth of experience at the forefront of marketing science and analytics, he now heads up the strategic retail analysis practice at Targetbase. Mike is also known for academic work, having written articles for academic and trade journals and taught at graduate and undergraduate levels. He is a regular speaker at trade conventions and seminars.

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