The document discusses the intersection of data science and marketing, emphasizing the importance of data intelligence and analytics in driving business value. It outlines various data sources and predictive models for applications such as hiring, fraud detection, and personalized marketing. Additionally, it touches on advanced visualization techniques and the significance of consumer behavior insights for competitive advantage.

1. Data Science for Marketing
Dr. Komes Chandavimol
December 27, 2020

2. My DBA Journey

3. Data Science for Business
The overview

4. Today Data

5. Today Data

6. 2020
Today Data (Machine + Human)

7. Advance of Technology

8. Advance of Technology

9. How to combine (Big) Data, Advance
of Technology to bring value?

10. http://dataofthings.blogspot.com/2014/04/the-bbbt-sessions-hortonworks-big-data.html
Copyright 2019 Komes Chandavimol. All Rights Reserved
More Data, More Value, become Intelligence

11. Copyright 2020 Komes Chandavimol. All Rights Reserved
Data Intelligence by business value
4 Levels of Analytics

12. Data Intelligence to Data Science

13. Data
New Analytic Insights
(Information, knowledge, data story)
Data Product
+ VisualizationMass Analytic Tools
Data Mining/Machine Learning
Recommender systems
Complex Event Processing Data Science Team
Data Scientist
Datafication
Copyright 2020 Komes Chandavimol. All Rights Reserved
13
“The ability to take data — to be able to understand it, to process it, to extract VALUE from it, to
visualize it, to communicate it — that’s going to be a hugely important skill in the next decades.”
Data Science

14. People Analytics: Hiring, Reskills, Churn
Data sources: Historical hiring attributes
Data products: Predictive model – recruiting, Personalized Development,
Churn Prediction, Talent Identification
Behavioral Test
Situational Test
GPA
Brain Teaser
Good School
http://www.kornferryinstitute.com/briefings-magazine/summer-2014/big-data-predictive-analytics-and-hiring

15. Fraud Detection
Data sources: historical pattern of transaction data
Data products: predictive models – fraud/non-fraud,
Anomaly Detection
https://bluefishway.com/2013/09/13/panic-oh-no-not-again/
http://blogs.wsj.com/cio/2015/08/25/paypal-fights-fraud-with-machine-learning-and-human-detectives/

16. Predictive Maintenance
Data sources: IoT Sensors in factory
Data products: predictive maintenance models
http://www.electrex.it/en/news/600-automated-energy-management-system-a-enms-for-cement-production-plants.ht
http://www.digitalistmag.com/digital-economy/2015/12/01/iot-digitization-reinforce-cement-industry-03814141

17. Fuel Saving
Data sources: Telematics (sensor), GPS
Data products: Prescriptive analytics – route
optimization, predictive maintenance
(parts/malfunction)
http://www.cnet.com/news/ups-turns-data-analysis-into-big-savings/
http://www.cnet.com/news/ups-turns-data-analysis-into-big-savings/ Credit: Jarun Ngamvirojcharoen

18. Recommendation System
Data sources: Click Streams, Customer Behaviors
Data products: Prescriptive analytics – Personalized Marketing,
Recommended Pages
Personalized Shopping Experience
Data sources: Click Steam, Customer History, Call Center ID
Data products: Omni- Channel Prediction, Customer Journey

19. Rolls-Royce Data Labs
Data Scientist, Visualization Specialist, Data Engineer, Business Analytics
https://www.youtube.com/watch?v=AOdH9aZVdaE

20. Data Science Tools: Machine Learning

21. Data Science Tools & Platforms
2000
2019
https://twitter.com/Chuck_Moeller/status/1341060434800107527?s=20

22. Data Science Tools & Platforms

23. Data Science Tools & Platforms

24. Data Science for Marketing

27. Joseph Rivera (2019)

28. Insights
An insight has to contain new information
An insight has to quantify causality
An insight must focus on understanding
consumer behaviors
An insight has to provide a competitive advantage
An insight must generate financial implications

29. • What Drive Demand?
• Who is most likely to buy and how do I target
them?
• When are my customers most likely to buy?
https://tambbideas.web.app/w-vs-v-recovery.html

30. What Drive Demand?
Marketing problems: determining and
quantifying those things that drive demand.
https://tambbideas.web.app/w-vs-v-recovery.html

31. Technical discussion

32. Data Science Life Cycles
Data Sources Data Preparation & Engineering
Modeling

33. Reference Papers (Advance Topics)

34. Who is most likely to buy and
how do I target them?
The next marketing question is around targeting,
particularly who is likely to buy.
http://www.experian.com/blogs/marketing-forward/2014/06/24/high-definition-customer-profiles-a-
clapperboard-for-marketers/

35. Technical discussion

36. Data Science Workflow
Data Sources Data Preparation & Engineering
Modeling

37. Reference Papers (Advance Topics)

38. When are my customers most
likely to buy?
• The next marketing question is ‘WHEN’ is an
event (purchase, response, churn, etc)
zzz

39. Technical discussion

40. Technical discussion

41. Data Science Life Cycles
Data Sources
Data Preparation &
Engineering Modeling

42. Reference Papers (Advance Topics)

43. MARKETING + DATA + SCIENCE
Future Topics for Research

47. Cioffi, R., 2019. DATA-DRIVEN MARKETING: Strategies, metrics and infrastructures to optimize the
marketing performances (Doctoral dissertation, Politecnico di Torino).

49. Visualization
Foundation Methods
1. Perception Mapping
– Multidimension scaling (MDS)
– Join Space Mapping
2. Feature Grouping
– Factor Analysis

50. Visualization: Perception Mapping
. Multidimension scaling (MDS) vs Join Space Mapping
https://feedbackjuice.com/marketing-research-analysis/multidimmensional-scaling/
https://www.perceptualmaps.com/map-format/

51. Visualization: Feature Mapping
Factor Analytics

52. Visualization: Feature Mapping
https://www.datacamp.com/community/tutorials/introduction-factor-analysis

53. Visualization
Advanced Methods
1. Frequency based perceptual mapping
– Correspondence analysis
2. Feature importance in marketing
– Conjoint analysis
3. Feature importance (text )
– LDA: Latent Dirichlet allocation
4. Temporal weighting scheme

54. Visualization: Correspondence Analysis
http://www.sthda.com/english/articles/31-principal-component-methods-in-r-
practical-guide/113-ca-correspondence-analysis-in-r-essentials/

55. Visualization: Correspondence Analysis
http://www.sthda.com/english/articles/31-principal-component-methods-in-r-
practical-guide/113-ca-correspondence-analysis-in-r-essentials/

56. Visualization: Conjoint Analysis
https://www.questionpro.com/blog/what-is-conjoint-analysis/

57. Visualization
Big Data and Analytics
1. Perception Mapping in Big Data
2. Customer Relationship Management
3. Parallel coordinates approach
4. OpinionSeer

58. Parallel coordinates approach
https://www.serendipidata.com/posts/visualizing-high-dimensional-data

59. Perception Mapping in Big Data
https://link.springer.com/article/10.1007/s40558-015-0033-

60. CRM in Big Data

61. Parallel coordinates approach

62. OpinionSeer

63. Segmentation

64. Segmentation
RFM (Recency, Frequency, Monetary)
https://clevertap.com/blog/rfm-analysis/

65. Segmentation
K-Mean
Reference: https://www.softnix.co.th/

66. Segmentation
Latent Class Analysis

67. Reference