Transcript Slide 1

BGS
Customer Relationship Management
Chapter 5
CRM and Data Management
Thomson Publishing 2007 All Rights Reserved
Introduction
• Data management is a key CRM enabler
• Data integration is a series of steps
• Critical path to create a single accurate view
of the customer
• Manage the customer interaction
Managing Customer Interactions
• Customer perceptions driven by interaction
experiences
• Digital age has enabled the customer to expect
more
• Improper management undermines
relationship between the organization and the
customer
• Proper management requires a centric view of
the customer
Managing Customer Interactions
• Successful management allows organizations to
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Create and sustain loyalty
Differentiate itself from competition
Grow relationships
Increase favorable customer word-of-mouth
• Attaining a centric view of the customer requires data
integration across the enterprise
Customer Data Integration (CDI) Problem
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Data capture and storage process variances
Disparate databases
Real-time customer interaction
Data latency
Lack of standards
Data inaccuracy
CDI Definition and Requirements
• CDI: A data management process where all customer
and prospect data is consolidated to create a single
accurate view of the customer
• All organization points of customer interaction must
have access to an accurate and current customer
centric view
• Data to be distributed accurately to points of
interaction in a timely manner
CDI Definition and Requirements
• CDI requires:
– Enabling technology to manage initial and ongoing
data integration efforts
– Customer linkage capability
– Organization-wide adoption of technology and
customer linkage
House-Holding Concepts
• Individuals living at the same address and
having the same last name are considered to
be in the same household
• House-holding allows an organization to view
a customer at several levels
– Individual level
– Household level
• Mechanics for creating households are similar
for consumers and businesses
House-Holding Concepts
• Consumer house-holding considerations
– Children have temporary address while attending
college
– People have secondary residences (e.g., snowbirds)
– Changing norms (e.g., home office, nontraditional
family)
– Ethnic names
House-Holding Concepts
• Business house-holding is more complex
– Different addresses and different names but same
organization
– People within organization are new, promoted,
transferred, and leave the organization
– Virtual offices
CDI Steps
Identify Touch Points
• Any area that an interaction can occur between an
organization and a customer or prospect
• Interaction medium may vary
– Human to human (e.g., POS)
– Human to human with technology as enabler (e.g., customer
communicating via Web chat session)
– Human to technology (e.g., customer interfacing with
computer telephony)
– Technology to technology (e.g., Web transaction)
CDI Steps
Identify Touch Points
• Position in value chain can present challenges
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Mfg. not having access to retail transaction
Retailer not having access to mfg. warranty transaction
Outsourced Web hosting and data capture quality issues
Retailer not having access to subcontractors for delivery and
customer service
– Organizations not capturing informal data from outsourced
telemarketing firm
• B2C business interface examples – POS, order processing,
customer service, distribution, repair/maintenance, PR, survey,
promotion response, unsolicited communication from consumer,
noncountry of origin
CDI Steps
Identify Touch Points
• B2B business interface examples –
procurement, accounts payable and receivable,
sales, technical support, order processing,
customer service, distribution,
repair/maintenance, PR, survey, promotion
response, investing community, value chain
partners, unsolicited communication
CDI Steps
Define How Data is Collected
• Technology
– Web forms, Web free form text, computer
telephony, kiosks, self-service POS, fax
• Human
– Verbal, written, observation
CDI Steps
Establish Data Collection Rules
• Identify data variables to be collected (e.g.,
demographic, psychographic, geographic,
behavioral, transaction)
• Define priority scheme for data variable
capture based on source
Consumer Data Rule Construction
Data
Element
Income
Age
Occupation
Homeowner
Touch Point
A
Touch
Point B
Touch
Point C
Touch
Point D
Data Used
$65K-$70K
$120K
N/A
X
$65K
35-40
37
37
X
37
Professional
Other
Unskilled
X
Professional
N/A
N/A
Yes
X
Yes
N/A
2(4-8 yrs)
1 @ 4 yrs,
1 @ 3yrs
1 @ 4 yrs,
1 @ 3yrs
2 (4-8 yrs)
Children
Business Data Rule Construction
Data
Element
Industry
Research
Web Sites
Sales
Personnel
Trade
Publications
Data Used
Annual
Revenue
$10 MM
$11-13 MM
$10 MM
N/A
$10 MM
4,000
5,500
3,500 –4,000
3,800 - 4,200
4,000
Plant Square
Footage
CDI Steps
Manage Input Process after Collection
• Timing
– Process step dependency
– Data flow and scheduling
• Security
– Corrupted or lost
– Unauthorized use or access
• Inconsistency worse than inaccuracy
CDI Steps
Place Data in Common Formats
• Provides for:
– Efficient data hygiene processing
– Enhances matching logic for postal, linkage, and
enhancement processing
• Is required by some software processing
• May not always be necessary depending on
software capability in handling dynamic
processing
CDI Steps
Split Linkage Data
• Split data into two categories: nonlinkage and
linkage
• Nonlinkage data can contain many variables and is
not needed for data hygiene, postal, matching, and
secondary data enhancement processing
• Linkage data is required for the data hygiene, postal,
matching, and enhancement steps
• Splitting the data increases processing efficiency and
reduces data management efforts
CDI Steps
Standardize and Correct Linkage Data
• Ensure address variables from all touch
points are in a standard format for an
optimal address correction process
• Utilize commercial software to correct
address components
– Outsource or acquire software and process
internally
– Process in real-time or batch mode
CDI Steps
Postal Processing
• Varies by country
• Mandatory for certain types of mailing in
United States.
• Usually an outsourced function
• Required to ensure most current addresses
are on file for customers and prospects,
which supports the relationship build and
sustain strategy
CDI Steps
Postal Processing
• 18-20 percent of the population change
addresses annually
• LACS – Locatable Address Conversion
System
• NDI – National Deliverability Index
• DSF2 – Delivery Sequence File Second
Generation
CDI Steps
Customer Linkage Identification
• Sometimes referred to as deduplication or
merge/purge
• Objective – identify each appearance of an
individual or business and assign an
identifier to each linkage record
CDI Steps
Customer Linkage Identification
• Record linkage categories include Manual,
Deterministic, or Probabilistic
– Manual is not feasible for large files
– With deterministic, individuals or companies are
said to be the same if there is a match on certain
variables
– Probabilistic uses weights and probability
algorithms to determine if two or more records
are the same person or company
CDI Steps
Customer Linkage Identification
• Phonemic name compression
• Customer linkage approaches
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Tight versus loose matching
Industry nuances
Some industries prone to less accuracy
Organizational structure influences business rule
definitions
– Changing linkage rules require extensive rework
to database contents
CDI Steps
Data Enhancement
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Nonprimary sources of data
Highly dependent upon linkage capability
Can be costly
Source credibility must be determined
Determine if the process will be outsourced
CDI Steps
Data Suppression
• Purpose
– Avoid contact with nonprofitable customer
– Adhere to customer request for no interaction
– Legal and ethical conformance (e.g., deceased, young
children, prisons, military, fraud detection)
– Optimize marketing investments
• Perform using internal information as basis for
suppression (e.g., opt-out, fraud detection, avoid
nonprofitable customers)
• Use external suppression files (Table 5.10)
CDI Steps
Consolidate Linkage and Non-Linkage Data
• Match linkage to non-linkage data on
sequence numbers
• Consolidate and aggregate appropriate
variables
• Prepare data for update process to respective
Database entities
• Data is not actionable knowledge
CDI Ancillary Benefits
• Fraud detection
• Data anomalies
• Identify data collection areas that need
improvement or that present new
opportunities
• Identify business process areas that may need
improvement, are unnecessary, or that are
missing
Summary
• CDI is a CRM key success factor
• CDI is dynamic due to technology changes,
business objective changes, new best
• CDI can vary by industry or country
• CDI has ancillary benefits