Weitere ähnliche Inhalte Ähnlich wie How can Analytics Drive Customer Values? (20) Mehr von SAS Asia Pacific (8) Kürzlich hochgeladen (20) How can Analytics Drive Customer Values?1. How can Analytics Drive
Customer Values?
Franklin So
Regional Analytic Practice, SAS AP
June 28, 2011
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3. My new iPhone 4… FREE!!!
Guess from whom????
3
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5. 1 week later
She moved to another telco
operator in early June...
5
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6. SORRY THIS ATM IS
TEMPORARY
OUT FO CASH
6
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8. Leave my comments in Facebook
for these promotion offer…
8
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9. =
All these involved Analytics …
9
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10. Analytics to manage
CHURN customers
10
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12. Churn Scores for prediction…
12
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13. Social network for viral marketing
Linking individuals and measuring the
strength of their relationships.
•Identify communities based on behavioral
relationships between customers
•Measure and segment customers based on
social influence (e.g. “leaders”, “followers”,
“marginals” and “outliers”)
•Target customers based on community
status and behavioral changes within
communities (e.g. when a community
“leader” changes, target his/her “followers”)
Leader
Follower
13
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14. SORRY THIS ATM IS
TEMPORARY
OUT FO CASH
Analytics to forecast demand
e.g. cash demand in ATM machines
14
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15. ATM Replenishment Forecasting and Optimization
SAS Forecast Server
SAS/OR
Bank ATM system transaction log
Demand Forecasting for each ATM
SAS BI
Optimization of
the Replenish
schedule by
denominations
Executive report on ATM
Replenishment Status and
Manual Overrides 15
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16. Business Forecasting and scenario (“What-if analysis”)…
Red line represents the
forecast of transactions
of the ATM.
White dots represent
the history of ATM
transaction.
16
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17. Optimized replenishment schedule tells you which ATM to replenish at what time
• Reduced ATM down-time due to cash out
• Optimized no. of ATM replenishment trips
• Improved operational efficiency of maintaining
ATM Network
• Increase customer satisfaction for reducing
the cash out events
17
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18. Simulate the operational process and time required
for queuing the teller…
How many queues are required?
One queue for one teller or one single queue for all
tellers
18
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19. Analytics to identify customer strategy
who to sell what, on when, and how…
19
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20. Who should be targeted on what, when, and what’s
NEXT…
Deposits Bank
Knows savings
balance and some
demographic details
Credit Card
Knows income
, purchasing & payment
behavior
Insurance
Life stage and some
Customer asset and liability
details
Investments
Knows investments
Channels 20
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21. Segmenting customer based on behavioral attributes results in segments which are:
Identifiable – customer behavioral data are much richer.
Actionable – behavioral attributes tell us more about the needs of a customer.
relationship product holdings
customer tenure, number of what products does the customer
accounts/products, recency of have with the bank?
account, branch, etc.
financials
transaction pattern Customer
average, trend, variance,
what type of transactions, how often, etc. for balances,
amount per transactions, etc. interest, fees, etc.
channel
preference & usage
what channels does the
customer own, access and
how often, etc.
21
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22. Customer Value
22
Loyalty Score
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23. Acquisition
Retention & Retention
Customer Value
Cross
selling
23
Loyalty Score
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27. And finally Offer Optimization
27
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28. Analytics to analyze Social Media
and Unstructured Data
28
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29. WHAT’s Important to
Overall Brand Your Business
What’s the
►Service, Price, Network
Sentiment?
Topics Relevant to Your Organization Performance, Marketing Program
Media Types WHERE You’re Being Talked
About
Positive Negative
• Internal: Portal, Call Centre
Neutral
Media Sources
External:
What’s the
Bloggers
Bloggers WHO’s Talking about You and What Value?
They’re Saying
i.e. Harvey West ► the top Twitter influencer
Commentary
with 300,000 followers
….What’s the Trend…. Time….
Historical Forecasted Future
29
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31. Statistical Process Control
Analysis of Variance Categorical Data Analysis
Social Network Analysis
Spectral Analysis
Statistical Analysis
R Integration
Process Capability Analysis
Forecasting Scheduling
Reliability Analysis
Nonlinear Programming
Design of Experiments
Cluster Analysis Sentiment Analysis Linear Programming
Data Visualization
Network Flow Models
Predictive Modeling
Econometrics
Vector Autoregressive Models
Discrete Event Simulation
Exploratory Data Analysis Mixed-Integer Programming Sample Size Computations
Nonparametric Analysis
Interactive Matrix Programming ARIMA Models
Matrix Programming
X11 & X12 Models
Neural Networks
Scoring Acceleration
Ensemble Models
Bayesian
Data Mining Survival Analysis D-Optimal
High Performance Forecasting
Text Analytics
Decision Trees
SAS leads advanced analytics market by wide margin (IDC, June 2011) Psychometric Analysis Information Theory
Statistics
Descriptive Modeling Mixed Models Multivariate Analysis
Quality Improvement
Text Mining
Multinomical Discrete Choice Study Planning
Gradient Boosting Machines
Predictive Analytics Analysis of Means
Interior-Point Models
Random Forrests
Survey Data Analysis Content Categorization
Genetic Algorithms
Operations Research
Discrete Event Simulation Content Categorization
Automated Scoring Simulation
Ontology Management
Time Series Analysis
Model Management
Association & Sequence Analysis
Constraint Programming Fractional Factorial
Ontology Management
Large-Scale Forecasting Regression 32
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