This presentation slide introduces Data Science to Maketing Professionals. This intent to explain how to think like a data scientist in term of marketing concept which focus on consumer behaviors and new set of big data (web, social, location.. etc). The reference books are at the end of the slides.
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
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“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
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
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/
47. Cioffi, R., 2019. DATA-DRIVEN MARKETING: Strategies, metrics and infrastructures to optimize the
marketing performances (Doctoral dissertation, Politecnico di Torino).
57. Visualization
Big Data and Analytics
1. Perception Mapping in Big Data
2. Customer Relationship Management
3. Parallel coordinates approach
4. OpinionSeer