A Conceptual Framework for Managing Customer Experience and Analytics (using Decision Engineering)

A Conceptual Framework for
Managing Customer
Experience and Analytics
TMF TAW
Lisbon
January 2010
Dr. Lorien Pratt
Quantellia, LLC
Lorien.pratt@quantellia.com
Blog: www.lorienpratt.com

MCE Analytics
Agenda

MCE
Agenda

Holistic MCE Models

Key Factor Analysis (TR148, TR149)
Process
$
People
Touch
point
Loyalty Profitability

Analytics
Agenda

Examples of Analytics in MCE
• Measuring
order fallout
• Predictive
resource
allocation
• Installation
process
optimization
- Offer design
and analysis
- Customer
segmentatio
n /
marketing
- Upsell
triggering
- Serviceability
analysis
- Lifetime
Customer
Value
prediction
- Recommend
ations
- Personali-
zation
- Advertising
- Direct
Marketing
- Retail
Placement
• CDR
analysis
• Payment,
credit, cash
flow
forecasting
• Leakage
identification
• Personalizati
on
• Fraud
identification
• Parental
Control
• VOD
Purchasing
Behavior
• Clickstream
analysis’
• Relationship
building /
loyalty
Acquisition Fulfillment Usage Support
Optimi-
zation
• SLA Analysis
• Multi-level
support
• Retention
• Next-best
offer

Decision
Engineering
Adaptive
Analytics
PredictiveAnalytics
Reporting
Data Management (including collection, ETL,
deduplication, aggregation, correlation, data
migration, data quality, data modeling)

Decision
Engineering
Adaptive
Analytics
Predictive Analytics
Reporting
Data Management (including data
migration, data quality, data modeling)

One-slide predictive/adaptive analytics
overview
Will this customer churn?
Yes/No data: If customer has an open trouble ticket: Yes, otherwise: No
Real-Valued: If customer age < 30: Yes, otherwise: No
Combination: If customer age <30
AND has an open trouble ticket: Yes, otherwise: No
Linear Combination: If 2.3 x Age + 4.4 x Income > 40: Yes, otherwise: No
Predictive Analytics: Obtain these numbers by analyzing historical data
Adaptive Analytics: Update your historical data, and re-derive the numbers
periodically to take changing situations into account.
Nonlinear Analytics:
age
Income vs.
age
Income
Pattern
4.1 2.1 3

Decision Engineering: Unifies manual and automated
decision making
• Measuring
order fallout
• Predictive
resource
allocation
• Installation
process
optimization
- Offer design
and analysis
- Customer
segmentatio
n /
marketing
- Upsell
triggering
- Serviceability
analysis
- Lifetime
Customer
Value
prediction
- Recommend
ations
- Personali-
zation
- Advertising
- Direct
Marketing
- Retail
Placement
• CDR
analysis
• Payment,
credit, cash
flow
forecasting
• Leakage
identification
• Personalizati
on
• Fraud
identification
• Parental
Control
• VOD
Purchasing
Behavior
• Clickstream
analysis’
• Relationship
building /
loyalty
Acquisition Fulfillment Usage Support
Optimi-
zation
• SLA Analysis
• Multi-level
support
• Retention
• Next-best
offer
Many decisions are made
manually. Why:
- When the future is not like
the past, analytics is not
enough
- Missing data
- Uncertain data
- Changing situation
- Complex situation
We cannot wait for complete
data to support MCE decision
making.
Where should I invest my MCE dollars?

DataData
Gap
The Decision Support Problem

Systematic Decision Making Problems*
• “We focus on only one measure, when there are really multiple
objectives.”
• “We make decisions that assume a predictable unchanging future.”
• “Our focus is on short-term goals,
ignoring long-term ones.”
• “We are unable to reason about long
cause-and-effect chains.”
• “We ignore intangibles like morale, reputation, trust, and brand.
• “We plan for only a single future scenario when radically different
courses of action may be appropriate, depending on how the future
unfolds.”
Revenue
Community
Service
Cost
“Five years from now, the market
for our product will have grown by
30%”
“I can barely plan for next quarter,
how can I think about the future,
too?”
ReduceTimeWe Spend on
Customer CareTelephone Calls
Lower Customer Care Costs
Improved Contribution Margin
Unhappier Customers
Reduced Knowledge of our
Customers
GreaterCustomer ChurnSmaller Profits
Brand
*High Performance Decision Making. Pratt and
Zangari, 2009

Tactical CEM Decision
Engineering Process
Define Objectives
and Specifications
Analyze Data Needs
& Availability
Design Decision
Model
Determine Inputs
and Outcomes
Clearly specify:
• Terminology.
• What is to be
achieved.
• What are the
constraints.
Is historical data
relevant? Or will
this initiative
change internal or
external behavior
to make past data
misleading?
Construct the
appropriate
decision model:
• Extrapolate
from past data,
or
• Model new
system
Use decision
model to
determine
execution
parameters and
baseline
performance
metrics.
Strategic
Decision
Engineering
Define Objectives
and Specifications
Analyze Data Needs
& Availability
Design Decision
Model
Determine Inputs
and Outcomes

Strategic CEM Decision
Engineering Process
Define Objectives
and Specifications
Analyze Data Needs
& Availability
Design Decision
Model
Determine Inputs
and Outcomes
• Compare outputs of
decision design
process for different
alternative courses
of action.
• Determine which
option best meets
the company’s
business needs.
• Begin Execution.
Execution

CEM Execution
Define Objectives
and Specifications
Analyze Data Needs
&Availability
Design Decision
Model
Determine Inputs
and Outcomes
Measure effects
on customer
behavior, costs
and revenues.
Customers react
to external effects of
new initiative.
Create/update
initiatives based on
analysis of models
and data
New initiatives
have internal and
external effects
Unexpected effects
may require re-plan.

Design versus monitoring
KPI #1
• Like automobile design
• Key competency: being able to
understand how the system will
work
• Key competency: using
judgment where data is missing
• Like monitoring a working
vehicle
• Key competency: detecting
problems accurately and quickly
• Key competency: diagnosis

√
√
√
√
√
√
A comprehensive
analytics strategy
addresses many
MCE issues

MCE Analytics
Conclusion
• MCE has moved
from individual
touchpoints to a
holistic approach
• Data management
and analytics
support several
parts of this
process
• To be “Actionable”, CEM data must support decisions
• These decisions are tactical and strategic, and can include
investment / ROI decisions
• Data must support both manual and automated decision
making
• When the future is not like the past, a “computer aided
decision design” approach is helpful

THANK YOU
Dr. Lorien Pratt
Lorien.pratt@quantellia.com
+1 650 943 2444
Blog: www.lorienpratt.com

A Conceptual Framework for Managing Customer Experience and Analytics (using Decision Engineering)

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