Synopses & Reviews
Publisher Comments
The book makes three major propositions:
1. Big Data brings all the data to the table leading to a lot more detailed analysis and discovery. We can get a lot more understanding about consumer behavior.
2. Automation and Social Media drives a lot more influence on the decisions. We can converse with the customers as they make decisions and influence their decision-making using a series of sophisticated marketing tools.
3. The market leaders will integrate their resources and investment to optimize across a number of instruments in ways we have never seen before.
These changes have tremendous impact on our marketing processes and capabilities. In the first half of the book, using a series of examples from big data pioneers, such as PF Chang's, Best Buy, Google, and IBM, Sathi describes how each marketing function is undergoing fundamental changes:how personalized advertising is delivered using online channels where the marketers identify the specific customer and tailor their messaging based on customer behavior, context, and intention;how customer behaviors are collected from a variety of sources across many industries and examined to identify micro segments; and how online and physical stores collaborate to provide a unified shopping experience and deliver product information. The second half of the book examines the tools and techniques for marketing science in support of these capabilities including statistical techniques, qualitative reasoning, and real-time pattern detection, to name a few. Based on these changes, the book prescribes the changes needed to update our skill and tools for Marketing Analytics.
About the Author
Prior to joining IBM, Dr. Sathi was the pioneer in developing knowledge-based solutions for CRM at Carnegie Group. At BearingPoint, he led the development of Enterprise Integration, MDM, and Operations Support Systems/Business Support Systems (OSS/BSS) solutions for the communications market and also developed horizontal solutions for communications, financial services, and public services. At IBM, Dr. Sathi has led several Information Management programs in MDM, data security, business intelligence, advanced analytics, big data and related areas and has provided strategic architecture oversight to IBM's strategic accounts. He has also delivered a number of workshops and presentations at industry conferences on technical subjects including MDM and data architecture, and he holds two patents in data masking. His first book 'Customer Experience Analytics' was released by MC Press in October 2011 and the second book 'Big Data Analytics' was released in October 2012. He has also been a contributing author in a number of Data Governance books written by Sunil Soares.
Table of Contents
PART I: INTRODUCTION
1. Why this Book?
2.Data Sources
3. Audience
4.Book Overview
PART II: DISRUPTIVE FORCES
5.Introduction
6. Social Media and the Empowered Customer
7. Emergence of Big Data
8. Advanced Analytics
9. Public and Private Clouds and the Data Bazaar
10. Summary
PART III: PIONEERS
11.Introduction
12. Consumer Research
13. Advertising
14. Promotions
15. Shopper Tracking
16. Order Tracking
17. Summary
PART IV: PROPOSITION 1 - BIG DATA MEANS A LOT MORE OBSERVATIONS
18.Introduction
19. Data
20.Analytics
21. Change with Big Data
22. Summary
PART V: PROPOSITION 2 - AUTOMATION AND SOCIAL MEDIA
23.Listening
24. Conversations
25. Participation in Games
26. Endorsements
27. Ambassadors and Advisors
28. Summary
PART VI: PROPOSITION 3 - PERSONALIZED MARKETING
29.Introduction
30. Micro Segmentation
31. Directed Communication
32. Privacy Management
33. Single Customer View
34. Cross Channel Management
35. Organizational Implications
36. Summary
PART VII: TECHNOLOGICAL ENABLERS
37.Introduction
38. Unstructured Data Analysis
39. Pattern Discovery
40. Experiment Design
41. Customer Identity Resolution
42. Real-time Bidding
43. Summary
PART VIII: SKILLS REQUIREMENTS - DATA SCIENTISTS AND DATA ENGINEERS
44.Introduction
45. Data Warehouse and Business Intelligence Skills
46. Data Scientist
47. Data Engineer
48. Analytics as a Service
49. Evolution vs. Revolution
50. Summary
PART IX: WHERE ARE WE HEADED?
51. Introduction
52. What did we cover?
53. What is the impact?
54. How can we get ready for the changes?
55. Conclusions