1. BDML Ecommerce

2. What is Big Data?
•
“Big data," is a group of data technologies that
are making the storage, manipulation and
analysis of large volumes of data cheaper and
faster than ever.
•
Types of “Big data”
–
Transactional Data
–
Data from mobile app
• Location data , Profiles
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3. Big Data Challenge
•
Managing the three “V”s of big data
–
Volume
–
Velocity
• The speed at which data is coming and changing
–
Variety
• Text, Audio, Video
•
Big Data is mainly unstructured data
3
•
Technology to store big data

4. The Business Needs
•
Traditionally business wanted answers to Five
Questions
•
Traditional BI answers two of those questions
–
What Happened? – Reports and Ad-hoc Queries
–
Why it Happened? – Analytics, Cubes
•
Dash Boards and Score Cards Answer the third
–
What is happening Now?
• 4

5. Big Data Opportunity
•
The relational databases has limitations
–
Data needs to be modeled
–
Need to know the business needs to create good
data models
–
Data needs to be structured to support queries
•
Can we do analytics on big data and answer all
Five business questions?
5

6. Value Potential of Big Data
6

7. Pattern-Based Strategy Model
7

8. Patterns for Competitive
Advantage
8

9. Examples: Zara (Retail Clothing)
9

10. Major Appliance Retailer
10

11. Enterprise Hadoop Solutions Rating Q1 2012
11

12. Big Data Opportunities
•
McKinsey projects that in the U.S. alone, there will
be a need by 2018 for 140,000 to 190,000 “data
scientists”
•
Steep technical learning curves and a lack of
qualified technical staff create barriers to adoption
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13. Big Data Opportunities
•
Need for another 1.5 million data-literate
managers
–
Formal training in predictive analytics and
statistics.
•
The technologies in the big data area are not
Analyst Friendly
–
Need Programmers with knowledge of Hadoop,
Statistics and analytics
• Companies Retraining programmers and13
database

14. McKinsey Predicts the Magnitude
of Big Data Potential Across
Sectors
14

15. How Big Data is going to change BI
and Analytics – MIT Research
15

16. Billion dollar idea
16

17. DMA Campaign Response Rates
•
2010 rate of 3.72% and an
Email to a house list averaged a 19.47% open rate, a 6.64% click-through rate,
and a 1.73% conversion rate, with a bounce-back
unsubscribe rate of 0.77%.
•
Direct mail: Letter-sized envelopes had a response rate this year of 3.42% for a
house list and 1.38% for a prospect list.
•
Catalogs had the lowest cost per order of $47.61, just ahead of inserts at
$47.69, email at $53.85, and postcards $75.32.
•
Outbound telemarketing to prospects had the highest cost per order of
$309.25, but it also had the highest response rate from prospects of 6.16%.
•
Paid search had an average cost per click of $3.79, with a 3.81% conversion
rate. The conversion rate (after click) of Internet display advertisements was
slightly higher at 4.43%.
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18. 18

19. Mobile Marketing and Purchase
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20. Improving Offer Acceptance Rate: Algorithms to
Personalize Offers
•
K-Means Clustering for clustering Users
–
Cluster users based on brand preferences and
demographics
–
Most popular Clustering Algorithm
•
Logistic regression for finding the probability
of accepting an offer
•
SVD (Single Value Decomposition) to reduce
dimensionality of data and to reduce noise
–
Reducing the dimensions to a few improves
performance and reduce accuracy 20

21. Logistic Regression for Click
Prediction
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22. How Does The Model Work?
–
Classification Algorithms learns from Examples in
a process known as Training
–
Need Training Data and Decide on Training
Algorithm 22

23. Choosing Products for customer and Ordering
Customer
Details
Click Prediction
Sale Items Model for Product
Items Display
Chosen Order
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24. Conclusion
•
On the basis of our on-line surveys, face-to-
face survey and analysis of studies done by
others we conclude that the opportunity for a
Marketing application based on Big data and
Machine Learning is great. In a scale of 1-10
we rate this opportunity at 9
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