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

A Conceptual Framework for Managing Customer

Experience and AnalyticsTMF TAW

Lisbon

January 2010

Dr. Lorien Pratt

Quantellia, LLC

[email protected]

Blog: www.lorienpratt.com

mailto:[email protected]

U.S. Patent Pending. All rights reserved. Copyright © 2010 Quantellia Inc.


MCEAnalyti

cs

Agenda


MCE

Agenda


Holistic MCE Models


Holistic MCE Models


Holistic MCE Models


Holistic MCE Models


Holistic MCE Models


Key Factor Analysis (TR148, TR149)

Process

$

People

Touchpoint

Loyalty Profitability


MCEAnalyti

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Agenda


Analytics

Agenda


Examples of Analytics in MCE

• Measuring order fallout

• Predictive resource allocation

• Installation process optimization

- Offer design and analysis

- Customer segmentation / marketing

- Upsell triggering

- Serviceability analysis

- Lifetime Customer Value prediction

- Recommendations

- Personali-zation

- Advertising- Direct

Marketing- Retail

Placement

• CDR analysis

• Payment, credit, cash flow forecasting

• Leakage identification

• Personalization

• 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

Predictive Analytics

Reporting

Data Management (including collection, ETL, deduplication, aggregation, correlation, data migration, data quality, data

modeling)



Adaptive 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: NoCombination: 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



Adaptive Analytics


Reporting

Data Management

(including data migration,

data quality,

data modeling)


Decision Engineering: Unifies manual and automated decision making

• Measuring order fallout

• Predictive resource allocation

• Installation process optimization

- Offer design and analysis

- Customer segmentation / marketing

- Upsell triggering

- Serviceability analysis

- Lifetime Customer Value prediction

- Recommendations

- Personali-zation

- Advertising- Direct

Marketing- Retail

Placement

• CDR analysis

• Payment, credit, cash flow forecasting

• Leakage identification

• Personalization

• 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?

Data

Data

Gap

www.quantellia.com

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.” Revenu

e 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?”

Reduce Time We Spend on Customer Care Telephone Calls

Lower Customer Care Costs

Improved Contribution Margin

Unhappier Customers

Reduced Knowledge of our Customers

Greater Customer 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




Strategic CEM Decision Engineering Process





• 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





Measure effectson customer behavior, costs

and revenues.

Customers reactto external effects ofnew initiative.

Create/updateinitiatives 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


MCEAnalyti

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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

[email protected]

+1 650 943 2444

Blog: www.lorienpratt.com

mailto:[email protected]

http://www.lorienpratt.com/

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

Technology

mce analytics agenda

data migration

data quality

data modeling

examples of analytics

nonlinear analytics

mce agenda

lorien pratt quantellia