An Integrated IMC Data Framework
DMEF Direct/Interactive Marketing Research Summit: October 13-14,
2012
Las Vegas, NV
James Peltier
Professor of Marketing
University of Wisconsin, Whitewater
Marketing Department
College of Business and Economics
Debra Zahay
Associate Professor of Interactive Marketing
Marketing Department
Northern Illinois University
Anjala S. Krishen
Assistant Professor
Department of Marketing, Lee Business School
University of Nevada, Las Vegas
Agenda
•
•
•
•
•
•
Background/Motivation
Prior Work
Hypotheses
Method
Results
Implications
Managerial View of the Learning Organization
Competitive Advantage
Learning Activities
Use
4. Interpret
Move
Store
Get
3. Disseminate
2. Remember
1. Generate
Zahay, Peltier and Krishen (2012) Examined a
Hierarchical Framework for IMC Data
Personalization
Customer
Touchpoint
Psycho-Demographic
Transactional/RFM Data
Customer Contact Information
Relationships Supported in General, Transactional,
Contact Data More Important in Relation to Customer
Data Quality, Touchpoint Data Less So
Personalization
=.39
Transactional/RFM =.32
Offers and
Communications
Psycho-Demographic =.30
Customer Contact Information = .24
Customer Touchpoint =.13
Customer Info and
Collection Points
The Strategic CRM Context (Payne and
Frow 2005)
Improves Shareholder Value by:
Developing
relationships with
key customers and
segments
Requires Data-Driven Cross Functional
Integration of:
Processes
Enabled by:
People
Uses data and
information to cocreate value with
customers
Operations
Marketing
Capabilities
Information
Technology
Applications
Multi-Stage Research Method & Analysis
•
•
•
•
Qualitative Study
Pre-Test
Final Survey
Exploratory Factor analysis, correlation
analysis, Coefficient Alphas, CFA
• SEM to determine relationship between use
of customer data types and CRM Data
Quality
Method: Survey Data Collection
• Data Collection:
– 525 mailed
– Three waves, one mail wave, one including $2 bill and
one telephone follow up wave
– 32 % response rate
• 170 Executives in Financial Services
– 50% primarily b2b and 40% b2c, rest other trade
relationships
– 50% had retail relationships, 27% relied on outside sales
– 10% online sales
– Executives had typically twenty years of experience
• 166 useable surveys
• Response: Percent of Time Data Collected
What is CRM System Quality (α = .76)?
Overall, Data is of high quality when it reflects perceived
reality. In our context, we measured managers’
perception of:
1.
2.
3.
4.
Overall Quality of Internet and Email data
Overall Quality of Loyalty/Retention Data
Overall Quality of Contact Management Data and
Overall Quality of CRM data capabilities
• 5-Point Scale
• 5=Strongly Agree
• 1=Strongly Disagree
Customer Performance Measured by
Long-Term Customer Profitability
Customer Performance (α = .76):
1. Customer Retention on an annual basis,
2. Cross-Selling, and
3. ROI on a customer basis.
“To the best of your knowledge, please rate your
business unit’s performance in the past 2-3 years
relative to the competition” on a 1 = lower to 5 = higher
scale.
• 5-Point Scale
• 5=Higher
• 1=Lower
Independent Variables Measured Percent of
Time Data Types Collected
•
•
•
Personalization/Tracking Data (α = .89):
1.
Tracking marketing offers/messages
made to customers,
2.
Tracking marketing offers/messages
responded to by customers
3.
Tracking method of contact for
marketing offer
Pyscho-Demographic Data (α = .75):
1.
Lifestyle data
2.
Psychographics
3.
Demographics
Customer Touchpoint Data (α = .76) :
1.
Email
2.
Service
3.
Internet
4.
Telephone
• RFM/Transactional Data (α = .83):
1. Last purchase date,
2. Revenue by product,
3. Total Revenue from Customers
4. Length of time as customer
• Message/Offer Personalization Data
(α = .82):
1. Tailor marketing offers to
customers
2. Tailor communications to
customers
3. Tailors communications to
prospects
SEM RESULTS, all paths except RFM/ Transactional to
CRM System Quality (p < .055) significant at p <.05, one tailed
Model Fit: RMSEA=.01, GFI =.995
→ CRM System Quality
→ CRM System Quality
→ CRM System Quality
H4
H5
H6
H7
H8a
RFM/Transactional
Psycho-Demographic
Offer/Message
Personalization
Personalization Tracking
Customer Touchpoints
Customer Touchpoints
RFM/Transactional
RFM/Transactional
Std
Coef
.110
.248***
.144*
→
→
→
→
→
.303***
.139*
.293***
.419***
.140*
4.22
2.20
3.96
5.93
2.06
H8b
H9
H10a
RFM/Transactional
RFM/Transactional
Psycho-Demographic
→
→
→
n.s.
.201**
.275***
n.s.
2.60
3.67
H10b
H11
H12
Psycho-Demographic
Psycho-Demographic
Offer/Message
Personalization
Personalization Tracking
CRM System Quality
→
→
→
.230***
n.s.
.452***
3.29
n.s.
7.06
.165*
.211***
2.12
3.96
H1
H2
H3
H13
H14
CRM System Quality
CRM System Quality
Customer Performance
Psycho-Demographic
Offer/Message
Personalization
Personalization Tracking
Customer Touchpoints
Offer/Message
Personalization
Personalization Tracking
Customer Touchpoints
Personalization Tracking
→ Customer Touchpoints
→ Customer Performance
t-Value
1.60
3.50
1.87
Implications
• Performance link from Data Quality
• Firms need to be more vigilant than ever in
tracking transactional and psych-demographic
data
• Personalization and Content data leads to
increased customer touchpoint data
• Touchpoint data eventually leads to CRM
system Quality, CRM Performance, completing
the loop, validating the cycle
Contacts and Questions
James Peltier
University of Wisconsin, Whitewater
262-472-5474
peltierj@uww.edu
Debra Zahay
Northern Illinois University
815-753-6215
zahay@niu.edu
Anjala S. Krishen
University of Nevada, Las Vegas
540-588-3961
anjala.krishen@unlv.edu
“Building the foundation for customer data quality
in CRM Systems for financial services firms,”
Journal of Database Marketing and
Strategy Management, Volume 19, Number 1, pages
5-16
Peltier, J.W., Zahay, D.L. and Lehmann, D.L. (2012
Forthcoming), "Organizational Learning and CRM
Success: A Model for Linking Organizational
Practices, Customer Data Quality, and Performance,"
Journal of Interactive Marketing.