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Analytics asYour
Business Edge
Srinath Perera, Ph.D
VP Research, WSO2
Member, Apache Foundation
@srinath_perera
A Day in Your Life
Success Stories
•  Money Ball ( Baseball drafting)
•  Nate Silver predicted outcomes in 49 of
the 50 states in the 2008 U....
If you collect data about your business, and feed it to a Big Data
system, you will find useful insights that will provide...
Putting Analytics to Work
§  What happened? And
Why? ( Hindsight)
§  What is Happening
right now?
( oversight)
§  What wil...
Value Preposition
Let the Analytics Lead the
Charge
§  Keep Your
Customers
§  Get New Customers
§  Improve Operations
§  Monetize your data
KeepYour Customers
§  Churn Prediction
§  Telco (E.g. Is account in use)
§  Customer Context
§  In Branch Interactions ( u...
Customer
Context
with BLE
•  Track people through BLE via
triangulation
•  Higher level logic via Complex
Event Processing...
Get New Customers
§  Brand Awareness
§  Who mention my brand
§  What are their sentiments
§  What affects my brand?
§  Mar...
Predict Promising Customers
•  Typical website can get millions of users
•  Only very small fraction coverts
•  Each user,...
Improve Operations
§  Understand cost center and
ROI
§  Day to day Operations
§  Where is most friction?
§  Ask what if?
§...
Predict Wait-time in the Airport
•  Predicting the time to go
through airport
•  Real-time updates and
events to passenger...
Fight the Fraud
§  Fraud are cause for major
risk and friction
§  Often done via human
authored rules (e.g. more
than 10k ...
Data is the New Oil
•  Best example is Google, Facebook
( most valued companies)
•  Some operations can be justified just
...
Challenges: Causality
•  Correlation does not imply Causality!! ( send a
book home example [1])
•  Causality
–  do repeat ...
Curious Case of Missing Data
http://www.fastcodesign.com/1671172/how-a-story-from-world-war-ii-shapes-facebook-today, Pic ...
Actionable Insights
are the Key!!
•  Significant event that warrant
attention ( e.g. more than two
technical issues would ...
Summary
•  Role of Big Data and Impact
•  Keep Your Customers
•  Get New Customers
•  Improve Operations
•  Monetize your ...
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Webinar: Analytics as Your Business Edge

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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.

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Webinar: Analytics as Your Business Edge

  1. 1. Analytics asYour Business Edge Srinath Perera, Ph.D VP Research, WSO2 Member, Apache Foundation @srinath_perera
  2. 2. A Day in Your Life
  3. 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. 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. 5. Putting Analytics to Work §  What happened? And Why? ( Hindsight) §  What is Happening right now? ( oversight) §  What will happen? (Foresight)
  6. 6. Value Preposition
  7. 7. Let the Analytics Lead the Charge §  Keep Your Customers §  Get New Customers §  Improve Operations §  Monetize your data
  8. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 19. Summary •  Role of Big Data and Impact •  Keep Your Customers •  Get New Customers •  Improve Operations •  Monetize your data •  Use your common sense

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