To view recording of this webinar please use the below URL: http://wso2.com/library/webinars/2016/08/analytics-as-your-business-edge/ Data is the new oil! For most enterprises, data is the oil you’ve been sitting on without realizing its value. You can gain many useful insights from data that lead to new and better products and operations, enables new user experiences, allows better understanding of customers, makes interactions seamless and enables new pay per use business models and dynamic pricing models. Furthermore, data itself can be monetized. Enterprises can broker interactions between end users as done in digital advertising or sell insights to third parties in anonymized forms. Just like in Google and Facebook, data can be a primary asset that organizations collect as part of their operations.

1. Analytics asYour
Business Edge
Srinath Perera, Ph.D
VP Research, WSO2
Member, Apache Foundation
@srinath_perera

2. A Day in Your Life

3. Success Stories
• Money Ball ( Baseball drafting)
• Nate Silver predicted outcomes in 49 of
the 50 states in the 2008 U.S. Presidential
election
• Cancer detection from Biopsy cells ( Big
Data find 12 patterns while we only knew
9), http://go.ted.com/CseS
• Bristol-Myers Squibb reduced the time it
takes to run clinical trial simulations by
98%
• Xerox used big data to reduce the attrition
rate in its call centers by 20%.
• Kroger Loyalty programs ( growth in 45
consecutive quarters)

4. If you collect data about your business, and feed it to a Big Data
system, you will find useful insights that will provide competitive
advantage
– (e.g. Analysis of data sets can find new correlations to "spot business
trends, prevent diseases, combat crime and so on”. [Wikipedia])

5. Putting Analytics to Work
§ What happened? And
Why? ( Hindsight)
§ What is Happening
right now?
( oversight)
§ What will happen?
(Foresight)

6. Value Preposition

7. Let the Analytics Lead the
Charge
§ Keep Your
Customers
§ Get New Customers
§ Improve Operations
§ Monetize your data

8. KeepYour Customers
§ Churn Prediction
§ Telco (E.g. Is account in use)
§ Customer Context
§ In Branch Interactions ( use Bacons to
know when customer is in the branch,
tell him waiting time proactively)
§ Customer’s Own Statistics ( Can you
help him plan his life?)
§ E.g. Bank, Grocery
§ Customer Segmentation ( not all
customers are created equal, do special
treatment for who really matters)
George Caleb Bingham, 1846

9. Customer
Context
with BLE
• Track people through BLE via
triangulation
• Higher level logic via Complex
Event Processing
• Traffic Monitoring
• Smart retail
• Airport management

10. Get New Customers
§ Brand Awareness
§ Who mention my brand
§ What are their sentiments
§ What affects my brand?
§ Marketing Campaigns
§ Does marketing $$ spent efficiently?
§ Where are outcomes?
§ Ask hard questions?
§ Who are non Customers in
the site?
§ What new services existing
customers looking at?

11. Predict Promising Customers
• Typical website can get millions of users
• Only very small fraction coverts
• Each user, we know what he access, where
is works, country, what browser, OS, etc.
• Problem is to predict what users will
covert
• Used Logistic regression, Random Forest,
Survival Modeling etc.

12. Improve Operations
§ Understand cost center and
ROI
§ Day to day Operations
§ Where is most friction?
§ Ask what if?
§ Alternative modes of interactions: Can
customer make an appointment via his
phone, and give feedback also via phone?
§ Predictive Maintenance
§ Employee Hiring and Churn
Prediction
§ Fraud and Risk Analysis

13. Predict Wait-time in the Airport
• Predicting the time to go
through airport
• Real-time updates and
events to passengers
• Let airport manage by
allocate resources
• Implemented using
linear regression

14. Fight the Fraud
§ Fraud are cause for major
risk and friction
§ Often done via human
authored rules (e.g. more
than 10k at midnight)
§ Machine Learning can
learn those rules and
adept
See White Paper,
Fraud Detection and
Prevention: A Data Analytics
Approach

15. Data is the New Oil
• Best example is Google, Facebook
( most valued companies)
• Some operations can be justified just
to get the data
• Monetize your data
• Retailers could be paying major US banks
$1.7 billion a year by 2015 to send targeted
discount offers to customers (Aite Group)
• Telcos send targeted advertisements
h5p://dupress.com/ar>cles/data-as-the-new-
currency/

16. Challenges: Causality
• Correlation does not imply Causality!! ( send a
book home example [1])
• Causality
– do repeat experiment with identical test
– If CAN’T do a randomized test (A/B test)
– With Big data we cannot do either
• Option 1: We can act on correlation if we can
verify the guess or if correctness is not critical
(Start Investigation, Check for a disease,
Marketing )
• Option 2: We verify correlations using A/B
testing or propensity analysis
[1] http://www.freakonomics.com/2008/12/10/the-blagojevich-upside/
[2] https://hbr.org/2014/03/when-to-act-on-a-correlation-and-when-not-to/

17. Curious Case of Missing Data
http://www.fastcodesign.com/1671172/how-a-story-from-world-war-ii-shapes-facebook-today, Pic from http://
www.phibetaiota.net/2011/09/defdog-the-importance-of-selection-bias-in-statistics/
• WW II, Returned Aircrafts
and data on where they
were hit?
• How would you add
Armour?

18. Actionable Insights
are the Key!!
• Significant event that warrant
attention ( e.g. more than two
technical issues would lead
customer to churn)
• Can identify the context associated
with the insight ( e.g. operators can
see though history of customers
who qualify)
• Decision makers can do
something about the insight ( e.g.
can work with customers to
reassures and fix)

19. Summary
• Role of Big Data and Impact
• Keep Your Customers
• Get New Customers
• Improve Operations
• Monetize your data
• Use your common sense