Here is a potential case study on "AI application in marketing" with more than 10 references: Case study: How AI is transforming digital marketing Introduction Artificial intelligence (AI) and machine learning are revolutionizing how companies market to customers. By analyzing massive amounts of customer data, AI can help brands personalize experiences, target ads more effectively, and automate repetitive tasks. This case study examines how several leading companies are applying AI to digital marketing. Personalized experiences (references 1, 2) Online retail giant Amazon uses AI to power product recommendations on its site. By analyzing a customer's purchase history along with data from millions of other shoppers, Amazon's algorithms can suggest additional items the customer might like. This

1. Evolution of Data Sciences and
Marketing Analytics
Prof. M J Xavier
Email: xavier.mj@gmail.com

2. Historic
Development
of Business
Analytics
• When did Analytics Begin?
• With the invention of wheel – 3500 BC
• To calculate the circumference to fit a rim – π
• Number system and astronomy
• Kings used numbers for collecting and accounting
taxes
• Probability theory got developed for games (6th
century BC)
• Statistical methods were developed in 5th
Century BC.
• In 1660s statistics was used for census and
demographic studies
• Bayes theorem was invented in 1761
• In 1747 design of experiments was used to find a
cure for scurvy
• Design of experiments were extensively used in
Agriculture

3. Other
Developments
• Operations Research started in 1930s
to solve problems of military
operations.
• After Word war – II these experts
started solving business problems.
• Critical Path Method – Project
Management
• Shortest route problems -
Transportation
• Warehouse location
• Replacement Problems
• Inventory Management
• Simplex algorithm for LP was
developed in 1947

4. My
Encounter
With
Analytics
• Operations Research in Chemical Engineering at REC
Warangal
• LP for Petroleum products
• Dynamic programming for Chemical plant design
• Doctorate in IIM Calcutta
• ITC problem
• My PhD Work
• Experience at MODE – Data Analysis
• TNS and GFK
• iCRM Projects
• Shopper’s Stop
• Online Travel company
• Recent Works
• Model for Credit worthiness
• Emotion Analysis for customer satisfaction

5. DS 0.0 –
User of
Numbers
• Numbers for bookkeeping and to
measure performance
• Sales
• Finance

6. DS 1.0 –
Operations
Research
• Operation Research Models
for Optimal Decisions
• Critical Path Method – Project
Management
• Shortest Route problems -
Transportation
• Warehouse location
• Replacement Problems
• Inventory Management

7. DS 1.1- Statistical Tools
• Data Analysis
• Use of statistical tools
• Factor Analysis
• Conjoint Analysis
• Multidimensional Scaling
• Correspondence Analysis
VST Study

8. DS 2.0 –
Electronic
Data
Processing
• Use of Computers for storing and
retrieving data
• EDP department (SPIC sales in
regions)

9. DS 2.1 – MIS
• MIS and DSS
• Metrics
• Dashboards
• Knowledge Management

10. DS 3.0 –
Business
Intelligence
• Business Intelligence
• Data Warehousing
• OLTP and OLAP
• Data Marts

11. Metadata
Data Sources Data Management Access
Complete Warehouse Solution Architecture
Operational Data
Legacy Data
The Post
VISA
External Data
Sources
Enterprise
Data
Warehouse
Organisationally
structured
Extract
Transform
Maintain
Data Information Knowledge
Asset Assembly (and Management) Asset Exploitation
Data
Mart
Data
Mart
Departmentally
structured
Data
Mart

12. Data
Mart and
Metadata
• Data marts are subsets of a data
warehouse, which are designed to
meet the unique needs of specific
functional or operating groups.
• Metadata is data about data. The
metadata for a specific database may
be likened to a book’s table of content;
it identifies what is stored where
within the database.

13. What is Online Analytic Processing (OLAP)?
•“OLAP enables an end user to rapidly analyze a data
warehouse from any perspective in an ad-hoc
environment”
Regional Analysis
BOMBAY
DELHI
January February
Cosmetics
Perfumes
Clothing
Accessories
Cosmetics
Perfumes
Clothing
Accessories

14. The
Questions
OLAP
Answers
• Comparisons
• Revenue increase this quarter
compared to same period last year
• Marketshare
• Show Brand A sales as a percentage
of total sales
• Top and bottom N, top and bottom
N%
• Show top 10 products based on last
week’s sales in the Eastern region

15. DS 3.1 – Data Analytics
• Data Analytics
• Patterns and Trends in the
Data - Tesco
• Clustering and Classification

16. What Is
Data
Analytics
“Simply put, data analytics is used to
discover patterns and relationships
in the data in order to help
managers make
better business decisions and exploit
new opportunities.”

17. Analysis
procedures
• Association: looking for patterns where
one event is connected to another event
• Sequence or path analysis: looking for
patterns where one event leads to a later
event
• Classification: looking for new patterns
• Clustering: finding and visually
documenting groups of facts not previously
known
• Scoring models: developing propensity
scores for individual customers
• Forecasting/Predictions: discovering
patterns in data that can lead to reasonable
predictions about the future

18. Fraud
and
Crime
Detection
• Look for Outliers – Stock Market
• Map the network of criminals

19. Jialun Qin, Jennifer J. Xu, Daning Hu,
Marc Sageman and Hsinchun Chen,
“Analyzing Terrorist Networks: A Case Study
of the Global Salafi Jihad Network” Lecture
Notes in Computer Science,
Publisher: Springer-Verlag GmbH, Volume
3495 / 2005 , p. 287.

20. DS 3.2 – Web Analytics
• Google Analytics (IFMR)
• Semrush
• Kissmetrics
• Heatmaps
• New metrics
• Look to Book
• Abandonment rate
• Click Through Rate

21. DS 4.0 – Big Data

22. Speed of data
generation

23. Activity data •

24. Conversation
Data
• Our conversations are now
digitally recorded. It all started
with emails but nowadays most of
our conversations leave a digital
trail. Just think of all the
conversations we have on social
media sites like Facebook or
Twitter. Even many of our phone
conversations are now digitally
recorded.

25. Photo
and
Video
Image
Data
• Just think about all the pictures
we take on our smart phones or
digital cameras. We upload and
share 100s of thousands of them
on social media sites every
second. The increasing amounts of
CCTV cameras take video images
and we up-load hundreds of hours
of video images to YouTube and
other sites every minute .

26. Sensor Data
• We are increasingly surrounded by sensors that collect and
share data. Take your smart phone, it contains a global
positioning sensor to track exactly where you are every
second of the day, it includes an accelometer to track the
speed and direction at which you are travelling. We now have
sensors in many devices and products.

27. IoT Data
• We now have smart TVs that are able to
collect and process data, we have smart
watches, smart fridges, and smart
alarms. The Internet of Things, or
Internet of Everything connects these
devices so that e.g. the traffic sensors on
the road send data to your alarm clock
which will wake you up earlier than
planned because the blocked road means
you have to leave earlier to make your
9am meeting…
Telematics for Cars and Trucks

28. Characteristics
of Big Data
• With the datafication comes big
data, which is often described using
the four Vs:
• Volume
• Velocity
• Variety
• Veracity

29. Volume
• …refers to the vast amounts of data
generated every second. We are not
talking Terabytes but Zettabytes or
Brontobytes. If we take all the data
generated in the world between the
beginning of time and 2000, the same
amount of data will soon be generated
every minute. New big data tools use
distributed systems so that we can store
and analyse data across databases that
are dotted around anywhere in the
world.

30. Velocity • …refers to the speed at which new data is
generated and the speed at which data
moves around. Just think of social media
messages going viral in seconds. Technology
allows us now to analyse the data while it is
being generated (sometimes referred to as
inmemory analytics), without ever putting it
into databases.

31. Variety
• …refers to the different types of data we can now use. In the
past we only focused on structured data that neatly fitted into
tables or relational databases, such as financial data. In fact,
80% of the world’s data is unstructured (text, images, video,
voice, etc.) With big data technology we can now analyse and
bring together data of different types such as messages,
social media conversations, photos, sensor data, video or
voice recordings.

32. Veracity
• …refers to the messiness or
trustworthiness of the data. With
many forms of big data quality and
accuracy are less controllable (just
think of Twitter posts with hash tags,
abbreviations, typos and colloquial
speech as well as the reliability and
accuracy of content) but technology
now allows us to work with this type
of data.

33. Gaps
Analyzable
Data
Analyzed
Data
Total Data
Tools Gap
Application Gap

34. Turning Big Data Into Value

35. Big Data
Applications

36. Better
understand
and target
customers
• To better understand and target
customers, companies expand their
traditional data sets with social media
data, browser, text analytics or sensor data
to get a more complete picture of their
customers. The big objective, in many
cases, is to create predictive models. Using
big data, Telecom companies can now
better predict customer churn; retailers
can predict what products will sell, and car
insurance companies understand how well
their customers actually drive.

37. Understand
and
Optimize
Business
Processes
• Big data is also increasingly used to
optimize business processes.
Retailers are able to optimize their
stock based on predictive models
generated from social media data,
web search trends and weather
forecasts. Another example is supply
chain or delivery route optimization
using data from geographic
positioning and radio frequency
identification sensors.

38. Improving
Health
• The computing power of big data analytics
enables us to find new cures and better
understand and predict disease patterns.
We can use all the data from smart watches
and wearable devices to better understand
links between lifestyles and diseases. Big
data analytics also allow us to monitor and
predict epidemics and disease outbreaks,
simply by listening to what people are
saying, i.e. “Feeling rubbish today - in bed
with a cold” or searching for on the
Internet, i.e. “cures for flu”.

39. Security Services
• Security services use big data analytics to foil terrorist plots
and detect cyber attacks. Police forces use big data tools to
catch criminals and even predict criminal activity and credit
card companies use big data analytics it to detect fraudulent
transactions.

40. Improving
Sports
Performance
• Most elite sports have now embraced big
data analytics. Many use video analytics
to track the performance of every player
in a football or baseball game, sensor
technology is built into sports equipment
such as basket balls or golf clubs, and
many elite sports teams track athletes
outside of the sporting environment –
using smart technology to track nutrition
and sleep, as well as social media
conversations to monitor emotional
wellbeing.

41. Improving
and
Optimizing
Cities and
Countries
• Big data is used to improve many aspects of
our cities and countries. For example, it
allows cities to optimize traffic flows based
on real time traffic information as well as
social media and weather data. A number
of cities are currently using big data
analytics with the aim of turning
themselves into Smart Cities, where the
transport infrastructure and utility
processes are all joined up. Where a bus
would wait for a delayed train and where
traffic signals predict traffic volumes and
operate to minimize jams.

43. Business Intelligence Vs Business
Analytics

44. 4 Types of Data Analytics

45. Data Analysis Vs Data Analytics

47. DS 4.1 AI, ML & Deep
Learning

48. Human Brain
and Natural
Intelligence
Human brain is a graph of 100 Billion nodes and 700 Trillion Edges

49. Artificial Intelligence

50. AI Assistants
Microsoft Azure
Apple Amazon Web Services

52. Components
of AI
Natural Language processing
(Understands Human Language)
Big Data (Large data base of past
experiences)
Learning algorithm (Learns to
respond to Queries)

54. Machine learning is a field of computer science that gives computers the
ability to learn without being explicitly programmed
Methods that can learn from and make predictions on data
Labeled Data
Labeled Data
Machine Learning
algorithm
Learned model Prediction
Training
Prediction
Machine Learning Basics

55. Regression
Supervised: Learning with a labeled training set
Example: email classification with already labeled emails
Unsupervised: Discover patterns in unlabeled data
Example: cluster similar documents based on text
Reinforcement learning: learn to act based on feedback/reward
Example: learns better to classify based on reward
Types of Learning
class A
class A
Classification Clustering
http://mbjoseph.github.io/2013/11/27/measure.html

58. A machine learning subfield of learning representations of data. Exceptional
effective at learning patterns.
Deep learning algorithms attempt to learn (multiple levels of) representation by
using a hierarchy of multiple layers
If you provide the system tons of information, it begins to understand it and
respond in useful ways.
What is Deep Learning (DL) ?
https://www.xenonstack.com/blog/static/public/uploads/media/machine-learning-vs-deep-learning.png

59. So, 1. what exactly is deep learning ?
And, 2. why is it generally better than other methods on
image, speech and certain other types of data?
The short answers
1. ‘Deep Learning’ means using a neural network
with several layers of nodes between input and output
2. the series of layers between input & output do
feature identification and processing in a series of stages,
just as our brains seem to.

60. hmmm… OK, but:
3. multilayer neural networks have been around for
25 years. What’s actually new?
we have always had good algorithms for learning the
weights in networks with 1 hidden layer
but these algorithms are not good at learning the weights for
networks with more hidden layers
what’s new is: algorithms for training many-later networks

65. Tools for Deep
Learning
• Matlab
• R
• Amazon Rekognition
• IBM Watson
• Python
• Azure

67. Amazon Rekognition Pricing

68. Deep Learning in
Education

69. Robots in
Education
Image Courtesy: Indus International
School, Bangalore

70. More AI Applications
• Attendance Taking
• Class participation Marking
• Proctoring of online Exams
• Paper correction
• Chatbots for doubt clearing
• Robot teachers
• Precision teaching
• Authoring books and academic
articles

71. Precision Learning
• Just like precision medicine and precision
agriculture, learning will become
personalized and customized.
• Future will belong to those who develop
platforms for customized, personalized and
individualized course offerings using
Artificial Intelligence.
• Metacog is an App for IIT JEE coaching
on Google Playstore that serves lessons
based on prior knowledge of the student’s
aptitude for different subjects and speed of
learning.
• A time will come, for example, MBA program
itself will run on artificial intelligence
platform that would customize the content
based on the ability and the aptitude of the
learner.

72. Downside
of AI
https://www.impactbnd.com/blog/youtubes-top-stats-for-2020-infographic

73. What Next
https://www.impactbnd.com/blog/youtubes-top-stats-for-2020-infographic

74. Where are we headed?
0 Write a case study on “AI application in marketing”
0 Give sources from which you collected the information (Min 10
references)
0 This is a group assignment that should be submitted before 18th
october.

75. This Photo by Unknown Author is licensed under CC BY-NC