The Power of Predictive - Predictive Analytics

2. AGENDA
WHO WE ARE
• Welcome
• Setting the scene
• Data science
• China Mobile
• IBM
• The Blues
• NZ Customs
• Mi Predict
• Questions

4. 3 in 10

5. 7 in 10

6. 2000 sensors

7. Solved by listening
A single cracked wheel can cause a derailment

8. Pattern Recognition

11. TALENT GAP
THE NEED…

12. DATA FOR DATA’S SAKE
THE NEED…

13. DATA AND DATA EVERYWHERE
THE NEED…

14. INFIGHTING
THE NEED…

15. AIMING TOO HIGH
THE NEED…

16. • Email / chat transcripts
• Call center notes
• Web click-streams
• In person dialogues
INTERACTION
DATA
USUALLY UNSTRUCTURED

17. • Opinions
• Preferences
• Needs & desires
• Survey results
• Social media
ATTITUDINAL
DATA
SEMI STRUCTURED

18. • Attributes
• Characteristics
• Self-declared info
• (Geo)demographics
DESCRIPTIVE
DATA
STRUCTURED

19. • Orders
• Transactions
• Payment history
• Usage history
BEHAVIORAL
DATA
STRUCTURED

20. DATA
MINING
Process of discovering meaningful correlations, patterns and
trends by sifting through large amounts of data stored in
repositories.
Data mining employs pattern recognition technologies, as
well as statistical and mathematical techniques

21. • Evolution of data analysts
• Amalgamation of Mathematics,
Computer Science, Applied
Statistics and Business
Knowledge
• Imagine driving…
DATA SCIENCE

22. APPLICATIONS
Acquire customers:
• Understand who your best
customers are
• Connect with them in the right
ways
• Take the best action maximize
what you sell to them
PREDICTIVE CUSTOMER ANALYTICS

23. Grow customers:
• Understand the best mix of things
needed by your customers and
channels
• Maximize the revenue received
from your customers and channels
• Take the best action every time to
interact
APPLICATIONS
PREDICTIVE CUSTOMER ANALYTICS

24. Retain customers:
• Understand what makes your
customers leave and what
makes them stay
• Keep your best customers
happy
• Take action to prevent them
from leaving
PREDICTIVE CUSTOMER ANALYTICS
APPLICATIONS

25. APPLICATIONS
PREDICTIVE OPERATIONAL ANALYTICS
Manage operations:
• Maximize the usage of your assets
• Make sure inventory and resources are in the
right place at the right time
• Identify the impact of investment

26. APPLICATIONS
PREDICTIVE OPERATIONAL ANALYTICS
Maintain infrastructure:
• Understand what causes
failure in your assets
• Maximize uptime of assets
• Reduce costs of upkeep

27. Maximize capital efficiency:
• Improve the efficiency and
effectiveness of your assets
• Reduce operational costs
• Drive operational excellence
in all phases: procurement,
development, availability and
distribution
APPLICATIONS
PREDICTIVE OPERATIONAL ANALYTICS

28. APPLICATIONS
Monitor environments:
• Identify leaks
• Increase compliance
• Leverage insights in critical
business functions
PREDICTIVE THREAT AND FRAUD ANALYTICS

29. Detect suspicious activity:
• Identify fraudulent patterns
• Reduce false positives
• Identity collusive and fraudulent
merchants and employees
• Identify unanticipated transaction
patterns
APPLICATIONS
PREDICTIVE THREAT AND FRAUD ANALYTICSPREDICTIVE THREAT AND FRAUD ANALYTICS

30. Control outcomes:
• Take action in real-time to
prevent abuse
• Reduce Claims Handling Time
• Alert clients of transaction fraud
APPLICATIONS
PREDICTIVE THREAT AND FRAUD ANALYTICSPREDICTIVE THREAT AND FRAUD ANALYTICS

31. PREDICTIVE
ANALYTICS
PRESCRIPTIVE
ANALYTICS
BIG DATA
ANALYTICS
DATA
EXPERTISE
CAPABILITIES

32. SUCCESS STORIES
• Telco Customer Segmentation and Churn
Prediction
– Zong China Mobile
• Predictive Maintenance and Quality – internal
IBM project
• Injury Prevention and Talent Identification
– Auckland Blues Rugby Franchise

33. CUSTOMER BEHAVIOR SEGMENTATION AND
CHURN PREDICTION ZONG CHINA MOBILE
Zong China Mobile’s key focus
was to build it’s advanced
analytics capability which could
enable them explore more
revenue channels, reduce the
increasing churn and monetise
their existing assets

34. Business challenge
• The client was facing a high customer
churn rate and the success of this project
was critical for client’s market dominance
• Churn models with 70% accuracy for
predicting timely churn were required
• The data provided for modeling was limited
and noisy
CUSTOMER BEHAVIOR SEGMENTATION AND
CHURN PREDICTION ZONG CHINA MOBILE

35. Solution
• Churn Prediction models for prepaid, post-paid and early
churn subscribers
• Customer Segmentation for identifying Behavioral
(Revenue, Usage, Dormancy) clusters
• Social network analysis – churn based on influencers
• Identification of Leaders and distinct User Groups
• Revenue Forecasting for zero balance subscribers and the
monthly value Migration model for complete subscriber
base.
• Best Bundle Prediction
CUSTOMER BEHAVIOR SEGMENTATION AND
CHURN PREDICTION ZONG CHINA MOBILE

36. Benefits
• Identify indicators of customer churn using CDRs
and call center data for tailoring marketing efforts
as a result
• 50% improvement in churn detection and an
initial reduction in average gross churn per month
of 0.43% (approx. 1 million customers).
• Churn reduction resulted saving on potential
revenue loss of half a million USD.
Components
• IBM SPSS Modeler
50%
CUSTOMER BEHAVIOR SEGMENTATION AND
CHURN PREDICTION ZONG CHINA MOBILE

37. IT OPERATIONAL ANALYTICS
INTERNAL IBM PROJECT
Enable data-based decisions to
guide automation and
optimisation decisions to improve
operations, quality and customer
satisfaction

38. Business challenge
• Prevent outages from prescribing balanced
configuration of IT assets
• Intercept outages from detecting operational
patterns
• Optimise client experience by prioritisation of
requests
• Root cause isolate problems rapidly and identify
remediation
IT OPERATIONAL ANALYTICS
INTERNAL IBM PROJECT

39. Solution
• Incident Reduction: Analyse structured and
unstructured tickets data to link incidents to top drivers by
usage type for remediation actions
• Defect Prevention: Analyse ticket data of various types
(i.e., change and incident) to identify patterns and triggers
that can be proactively fixed to prevent similar defects to
propagate
• Voice Operation Issues: Analyse Voice operations ticket
to identify issues from current operations.
IT OPERATIONAL ANALYTICS
INTERNAL IBM PROJECT

41. Benefits
INCREASED MEAN TIME
BETWEEN FAILURE
IT OPERATIONAL ANALYTICS
INTERNAL IBM PROCESS - GTS

42. REDUCED MEAN TIME
TO RESOLVE
Benefits
IT OPERATIONAL ANALYTICS
INTERNAL IBM PROCESS - GTS

43. COST REDUCTION
Benefits
IT OPERATIONAL ANALYTICS
INTERNAL IBM PROCESS - GTS

44. HARDWARE
COST DOWN
Benefits
IT OPERATIONAL ANALYTICS
INTERNAL IBM PROCESS - GTS

45. Components
IBM SPSS Modeler, ‘R’ and Python
IT OPERATIONAL ANALYTICS
INTERNAL IBM PROCESS - GTS

46. AUCKLAND BLUES
INJURY PREVENTION MODEL
Using sports performance tracking
devices, coupled with medical data
and well-being, Auckland Blues are
predicting the likelihood of a player
getting injured during the game and
practicing off-season. This helps Blues
to modify the training loads and
overcoming performance thresholds of
players.

47. Business challenge
• Decrease soft tissue injuries
• Study the thresholds of
performance for each player and
positions they play at
• Root cause analysis for known
injuries
AUCKLAND BLUES
INJURY PREVENTION MODEL

48. Solution
• Used rfid based tracking modules to
calculate the acceleration, distance
traveled, intensity and other factors to
track performance along with medical
data, past injuries, wellbeing and
demographic data to predict the likelihood
of injuries in players.
• Root cause analysis was done on the
known injuries to ensure future prevention
of injuries.
Components
• IBM SPSS Modeler – Batch Mode
AUCKLAND BLUES
INJURY PREVENTION MODEL

49. Gillian Warren
Manager Data Analytics
Intelligence, Investigations &
Enforcement

51. The Customs context
• Facilitate trade and travel;
• Collect revenue (duties
and taxes);
• Protect the border
(together with MPI and
Police)

52. Risk Assessment
Increasing complexity of risks
• Globalisation
• Increased cross-border
movement by people and
goods
• Cross-border connectivity

53. Why an advanced analytics capability?
• Border transactions and passenger
movements continue to climb exponentially
• Do not have infinite resources at the frontline
• Must facilitate trade and travel as well as
“protect the border”

54. Increase in trade transactions
Imports: from approx $40b in 2007 to estimated $60b in 2018

55. Travel volumes are growing
0
50,000
100,000
150,000
200,000
250,000
2006/07 2007/08 2008/09 2009/10 2010/11 2011/12 2012/13 2013/14
Total arriving cruise ship passengers and crew processed by Customs
Cruise passengers: from approx 60,000 in 2006/07 to 235,000 in 2013/14

56. …and are forecast to increase
0
500,000
1,000,000
1,500,000
2,000,000
2,500,000
3,000,000
3,500,000
20002001200220032004200520062007200820092010201120122013201420152016201720182019
Other
US
UK
Korea
Japan
Germany
China
Canada
Australia
Passenger arrival volumes (historical and forecast) by nationalities (excluding New
Zealand passport holders)
Air Arrivals: from 1.7m in 2000 to estimated 3.2m in 2019

57. Current risk assessment process
Current system:
• Ahead of its time but now challenged by
volumes of data
• Relies on experience and knowledge of
officers to create alerts and profiles for
further examination purposes

58. Potential benefits?
Opportunity with big data tools to:
• Enlist tools to do the work human brains
cannot (e.g. correlate and interpret big data)
• Gain insights not currently available
• Use information strategically to prioritise work
and ensure effective interventions

59. Lessons we are learning along the way…
Understanding and preparing Customs for:
• How analytics is going to change the way we work, and even the
way we organise ourselves
• Making sure key resources are available to support analytics:
• Factor in resource for data wrangling/data infrastructure
• Right mix of staff capabilities for this organisation
• Staff are properly trained – on our data
Data data data!
• Change processes to improve the quality of our data – e.g. use
mandatory fields
• Data culture: not just in the backroom

60. INTRODUCING

61. DATA SCIENTIST…
IN A BOX!

62. • Investment up-front software
• Solution architecture and services fees
• Hardware cost
• On-going maintenance and updates
• Data Scientist headcount
• Investment between 250k – 750k*
TRADITIONAL
APPROACH
BUYING AND DEPLOYING SOLUTION

63. • Industry specific solutions
• Customer Analytics, Predictive Maintenance, Operational
Analytics and more
• Automated solutions (batched and real time) and available as
web service
• Embedded into existing infrastructure of your business
MI PREDICT
CLOUD HOSTED AND MANAGED SOLUTION

64. DEMO – MI PREDICT

65. DEMO – MI PREDICT

66. QUESTIONS