Analytics as Your 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.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)

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])

Putting Analytics to Work

§  What happened? And Why? ( Hindsight)

§  What is Happening right now? ( oversight)

§  What will happen? (Foresight)

Value Preposition

Let the Analytics Lead the Charge

§  Keep Your Customers

§  Get New Customers §  Improve Operations §  Monetize your data

Keep Your 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)

GeorgeCalebBingham,1846

Customer Context with BLE

•  Track people through BLE via triangulation

•  Higher level logic via Complex

Event Processing

•  Traffic Monitoring

•  Smart retail

•  Airport management

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?

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.

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

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

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

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/

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/

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?

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)

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