Original Article
A hierarchical IMC data integration
and measurement framework and
its impact on CRM system quality
and customer performance
Received (in revised form): 18th January 2013
James Peltier
is a Professor of Marketing and Director of the Institute of Sales Excellence at the University of Wisconsin-Whitewater, and
President of Applied PhD Research and Marketing. His research on database marketing and interactive marketing has been widely
published in the
Journal of Advertising Research, Industrial Marketing Management, European Journal of Marketing, Journal of
Interactive Marketing
, International Journal of Advertising, Communications of the ACM, Journal of Database Marketing & Customer
Strategy Management
, and is the Co-Editor of the International Journal of Integrated Marketing Communications. As a Consultant,
he regularly works with businesses on how to incorporate customer data in the marketing decision process, including new product
development, customer-centric strategies and tactics, and marketing communications.
Debra Zahay
is an Associate Professor of Interactive Marketing at Northern Illinois University, where she heads the Interactive Marketing
Program. She holds her Doctorate in Marketing from the University of Illinois. She researches how firms manage customer
information for competitive advantage. Her publications include the
Journal of Product Innovation Management, Decision Sciences,
The Journal of Interactive Marketing
and Industrial Marketing Management. She has served on the editorial board of the Journal of
Database Marketing & Customer Strategy Management
, will serve on the board of the Journal of Marketing Analytics, and serves as
the Editor-in-Chief of the
Journal of Research in Interactive Marketing.
Anjala S. Krishen
is an Associate Professor of Marketing at University of Nevada, Las Vegas since 2013. She completed a BS in Electrical Engineering
from Rice University in 1990, an MBA from Virginia Tech in 1996, and an MS and PhD in Marketing from Virginia Tech in 2007. Prior
to academia, she worked full-time for 13 years. She researches consumer decision making and has published in journals including
Journal of Business Research
, Journal of Advertising Research, Information & Management and European Journal of Marketing.
Correspondence:
Debra Zahay, Department of Marketing, Northern Illinois University, Journal of Research in Interactive
Marketing, DeKalb, Illinois 60115, USA
E-mail: zahay@niu.edu
ABSTRACT
Marketers and advertisers seek to get close to customers through data analytics
procedures that allow for the measurement of personalized messages delivered across
multiple communication touchpoints. This article tests a hierarchical integrated marketing
communications data integration framework that utilizes customer information (transactional,
demographic and psychographic) to develop personalized communication and communication
campaigns distributed across multiple interactive customer touchpoints. Our model
posits that by using basic customer data we can increase the priority for collecting other types
of data needed to get close to customers. Our findings show that customer data needs are
hierarchically ordered and that the sequential interaction between these variables impacts
customer relationship management system quality and measurement of performance.
Journal of Marketing Analytics
(2013) 1, 32–48. doi:10.1057/jma.2013.1
Keywords:
marketing analytics; customer data; integrated marketing communications
(IMC); data quality; customer relationship management (CRM); marketing metrics
&
2013 Macmillan Publishers Ltd. 2050-3318 Journal of Marketing Analytics Vol. 1, 1, 32–48
www.palgrave-journals.com/jma/
INTRODUCTION
Throughout the last few decades, marketers,
advertisers, and even consumers, have held
much hope for improving theory and best
practice in integrated marketing
communications (IMC) (Kerr
et al, 2008;
Kitchen
et al, 2008). Recently, the merging of
advanced marketing and advertising channels
with more traditional communication media
has altered the fabric of IMC (Sasser
et al,
2007; Zigmond and Stipp, 2010), more
specifically with regard to measuring ongoing
and real-time ‘interactive’ buyer–seller
relationships (Schultz and Patti, 2009;
Hipperson, 2010; Acker
et al, 2011).
Interactive IMC has not only impacted the
way marketers communicate with customers
and prospects, but has also placed greater
value on bringing together multiple data
touchpoints, media and messages to deliver
personalized marketing communications that
maximize return on investment (Swain, 2004;
Micu
et al, 2011; Abdul-Muhmin, 2012).
IMC has thus evolved from simply creating a
consistent message to increasing the value of
traditional and emerging media. To achieve
maximum value, appropriate data analytics
and smart marketing must construct synergies
for enhancing customer loyalty and lifetime
value (Assael, 2011; Stewart and Hess, 2011).
Developing personalized contact strategies
places greater emphasis on amassing customer
data from multiple sources (Zahay
et al,
forthcoming). As a proprietary resource,
customer data offers marketers the
opportunity to acquire competitive
advantages by developing multi-channel
initiatives designed to acquire and maintain
close relationships with customers. In the
last decade, because of the proliferation
and adaptation of customer relationship
management (CRM) systems and
sophisticated marketing metrics, firms are
increasingly focused on the value of customer
analytics as a key organizational asset
(Reimann
et al, 2010; LaPointe, 2012).
However, a CRM strategy based on quality
data requires companies to organize and
analyze every touchpoint so that the
customer’s value to the firm can be readily
determined. Utilizing customer profiling,
firms can then implement interactive IMC
campaigns that maximize this value over
time (Abdul-Muhmin, 2012). Through
appropriate resource allocation and marketing
mix optimization (Kumar and George, 2007),
the anticipated outcomes of personalized,
data-driven IMC programs include increased
retention, share of wallet, customer lifetime
value and profitability (Peltier
et al, 2013).
Although marketing scholars are calling
for research that increases the understanding
of effective methods for collecting, storing,
analyzing and utilizing different types of
customer data (Precourt, 2011), measurement
and data analytic problems abound. In many
cases, technological advances have outpaced
our ability to measure the effectiveness of
IMC efforts in today’s multi-channel, multitouchpoint
communication environment
(Hallward, 2008; Precourt, 2009b; Wind and
Sharp, 2009). As Wurtzel (2009, p. 263)
noted ‘it’s the crisis in measurement. You
can’t sell what you can’t measure, and,
unfortunately, our measurement systems are
not keeping up with either technology or
consumer behavior’. Customer and prospect
information can thus be misused or
underutilized when marketers fail to have
a data framework in place to maximize the
power of interactive IMC initiatives. As a
consequence, there is increasing evidence that
many cross-platform IMC initiatives have not
lived up to their potential (Kitchen
et al,
2008; Kliatchko, 2008).
Given the inadequate state of IMC metrics
(Wurtzel, 2009; Smit and Neijens, 2011) and
mounting data integration concerns, research
that develops mechanisms and methodologies
for designing and measuring effective crossmedia
campaigns is warranted (Precourt,
2009a; Pettit, 2010). Despite this need, and
although CRM has received increased
coverage in both academic and popular press,
few firms implement relational frameworks
IMC data integration and measurement framework
&
2013 Macmillan Publishers Ltd. 2050-3318 Journal of Marketing Analytics Vol. 1, 1, 32–48 33
that provide a 360
1 view of their customers’
transactional, attitudinal and psychographic
profiles (Peltier
et al, 2013). Moreover, most
firms do not have a clear vision for how data
collected from various touchpoints can be
used singularly and in combination for
launching personalized marketing strategies
(O’Regan
et al, 2011). Even fewer have
metrics in place for measuring the impact that
their interactive IMC programs have on
customer retention and long-term
profitability (Lee and Park, 2007). Now
more than ever we have the ability to utilize
real-time marketing analytics as a means of
merging customer data from multiple
customer touchpoints (Hipperson, 2010;
Acker
et al, 2011).
Owing to these concerns, the goal of our
article is to develop and test an exploratory
hierarchical IMC data integration and
measurement framework that focuses on using
customer information (transactional,
demographic and psychographic) to develop
personalized communication and marketing
campaigns that can then be distributed via
various interactive customer touchpoints.We
extend recent work by Zahay
et al (2012) and
Peltier
et al (2006) to propose an IMC data
continuum. Our framework moves from data
needed to profile customers, to data needed to
develop personalized communications and
offers, and finally to data needed to metricize
how customers respond to marketing efforts
across multiple contact points. Our model
posits that the collection of basic customer data
leads to placing higher priority on collecting
other types of data needed to get close to
customers and to nurture relationships.
Responding to a call for research that links
CRM initiatives to performance, we also
assess the impact that these interactive IMC
campaigns have on two marketing metrics:
(i) the quality of the CRM database and
(ii) customer performance. Our findings
contribute to existing literature by offering a
framework for how customer data can be
used to design personalized and profitable
communication strategies and tactics.
We begin with a brief review of the CRM
and IMC literatures, then develop and test
our IMC data integration framework, and
close with a discussion of key strategic
implications.
THEORETICAL BACKGROUND
CRM defined
Relevant to our IMC data integration
framework, Payne and Frow (2005, p. 168)
define CRM as a strategic process
‘... concerned with creating improved
shareholder value through the development
of appropriate relationships with key
customers and customer segments
yCRM
provides enhanced opportunities to use data
and information to both understand
customers and co-create value with them’.
This data-driven orientation requires the
‘cross-functional integration of processes,
people, operations and marketing capabilities
that is enabled through information,
technology and applications’. Following this
logic, Even
et al (2010) and Verhoef et al
(2010) contend that the use of CRM as a tool
for developing effective data-driven
interactive marketing tactics requires an
analytic-driven and holistic view of customers
across multiple transactions, channels and
customer touchpoints. Echoing this
perspective, Boulding
et al (2005, p. 157)
advance the notion of CRM as a strategic
mechanism for ‘... managing the dualcreation
of value, the intelligent use of data
and technology, the acquisition of customer
knowledge and the diffusion of this
knowledge’, for the purpose of developing
personalized relationships and enhanced
customer value.
Interactive CRM data
Although CRM systems could logically
contain an extensive array of IMC data types,
we focus on those outlined by Zahay
et al
(2004, 2012) that are captured from
Peltier
et al
34
& 2013 Macmillan Publishers Ltd. 2050-3318 Journal of Marketing Analytics Vol. 1, 1, 32–48
interactive customer touchpoints (that is,
Internet, email, telephone and personal
service encounters), transactional data
(that is, purchase history, credit history,
payment history), psycho-demographics
(that is, loyalty programs, satisfaction surveys)
and customer lifetime value data (that is,
retention, share of wallet). We adopt Peltier
et al
’s (2013) definition of high-quality
customer data, which claims that information
should be collected across multiple
transactions, touchpoints and channels so that
it accurately reflects the behavior and
sentiments of customers, both collectively
and individually. From this definition, it
follows that a customer database becomes a
means by which a firm can create a customer
knowledgebase and make marketing
decisions. As we focus on IMC data
categories and interactive customer
touchpoints, we omit mass media from
our model.
CRM and IMC
The IMC literature places emphasis on two
interrelated components of the IMC
construct: (i) the use of multiple
communication media and (ii) the
consistency of messages achieved across these
media. Specific to the former, effective IMC
programs mandate a clear understanding of all
sources of a brand’s contact with consumers
(Kitchen and Schultz, 2009). Regarding the
latter, IMC requires clarity and consistency
across multiple platforms, including a
common message strategy, voice and look.
More recently, IMC has been viewed as an
opportunity for creating and sustaining
consumer–marketer dialogue brought on by
the use of sophisticated databases and CRM
system applications (McGrath, 2010).
On the basis of the notion of interactive IMC
(Peltier
et al, 2006), this approach involves
the development of communication strategies
for delivering personalized messages and
offers to prospects and customers over a
range of dual-dialogue channels (Thomas and
Sullivan, 2005). In this regard, IMC requires
sound interactive marketing strategies driven
by customer needs across the relationship
lifecycle, beginning with the capture of new
prospects and all the way through to customer
valuation and retention strategies.
Data quality and customer
analytics
Although the importance of having a quality
CRM database is relatively undisputed,
methods by which to measure data inputs
across a diverse set of CRM systems needs is
still unclear (Zahay
et al, 2012). Despite this
uncertainty,
access to information collected
and utilized across multiple transactions,
channels and customer touchpoints is viewed
as a minimum requisite for developing
effective interactive IMC initiatives
(Even
et al, 2010; Verhoef et al, 2010).
As such, creating insightful data analytic
initiatives necessitates a corporate-wide
commitment to collecting customer
information at all points of the relationship
lifecycle, from the capture of new customers
through to customer valuation and retention
(Peltier
et al, 2003). Along these lines, Zahay
et al
(2004) focus on analytic competencies
across multiple sources including customer
touchpoints, transaction data, loyalty/
satisfaction data and customer lifetime value
data. They find that having higher data
quality relates to each type of data and
ultimately is positively associated with
customer and business performance, a finding
corroborated by Zahay and Peltier (2008) and
Zahay
et al (2012).
FRAMEWORK AND
HYPOTHESES
Zahay
et al (2012, 2004) argue that CRM
data quality and customer performance may
be explained through an examination of the
hierarchical ordering of the value of different
types of IMC data. Extending this work, our
interactive IMC data integration and
IMC data integration and measurement framework
&
2013 Macmillan Publishers Ltd. 2050-3318 Journal of Marketing Analytics Vol. 1, 1, 32–48 35
measurement framework is shown in
Figure 1. We examine how the data collected
from various acquisition points directly and
indirectly impacts CRM data quality and
customer performance. Building on previous
research by Peltier
et al (2006), Peltier et al
(2003) and Zahay
et al (forthcoming), these
IMC data acquisition points are useful for
creating personalized marketing offers and
messages that are delivered via diverse
interactive touchpoints. As given in Figure 1,
we present the IMC data via a sequential
ordering process: (i) transactional and psychodemographic
data are first used to create
target segments and customer profiles;
(ii) personalization data are then used to
deliver and track the efficacy of messaging
and offer tactics targeted to different
segments; and lastly (iii) touchpoints
represent target-specific data and outcomes
collected from various interactive
communication channels.
Our proposed interactive IMC data
analytic framework brings together
behavioral and psychographic data to develop
target-specific and personalized messages and
marketing offers delivered via interactive
response channels. Although our framework
includes a variety of direct effects, we are
particularly interested in understanding the
most effective ordering of these different
types of data and determining how they
indirectly impact CRM data quality and
customer performance.
Transactional and RFM data –
Direct effects
Understanding customers’ past transactional
history is a cornerstone metric driving
successful CRM and IMC initiatives (Taylor,
2010) and is an important component of
ARF’s 360 human-centric advertising model
(Romaniuk and Gugel, 2010). Transactional
data are a key element for explaining why
customer segments differ in their present
contribution to the firm and are the
dominant mechanism used by many
interactive marketers to assess customer
lifetime value and future potential
(Homburg
et al, 2008).
Advanced information technology
innovation has increased the ability of firms to
capture an expanding array of transactional
Figure 1:
Interactive IMC data integration and measurement framework.
Peltier
et al
36
& 2013 Macmillan Publishers Ltd. 2050-3318 Journal of Marketing Analytics Vol. 1, 1, 32–48
data from diverse customer contact sources
needed for generating important metrics such
as customer lifetime value (CLV) and
customer equity (Du
et al, 2007). Most
frequently, transactional data have been
investigated in terms of the impact that
RFM (Recency (last purchase), Frequency
(number of transactions) and Monetary
(value of transactions)) have on CLV. Despite
an overwhelming consensus by direct and
interactive marketers that RFM data have
predictive power in determining CLV, little
research examines how it impacts the quality
of interactive CRM systems and the extent to
which these data motivate the capture of
other forms of customer information
necessary for developing effective IMC
initiatives (Zahay
et al, 2012). Transactional
and RFM data represent the base of our IMC
model. We thus hypothesize that:
Hypothesis 1:
Increased use of transactional
data is positively related to CRM system
quality.
Psycho-demographic data –
Direct effects
Whereas transactional data measures
customer behavior, psychographic-based
data focuses on understanding customers
in terms of their values, buying motivations,
attitudes, beliefs and lifestyles. Psychographic
data merged with common demographic
data such as age, gender, income, marital
status and family size allows marketers to
appeal to the underlying motivations and
lifestyles of different customer and prospect
target audiences (Smith
et al, 2010).
Psycho-demographic data are most often
generated internally from a customer
satisfaction and needs survey, and externally
from commercially acquired information
about customers and prospects, which
would then be appended to internal data
files.
Although many studies have contributed
to explaining consumer behavior, few have
sought to utilize customers’ psychodemographics
for segmenting customers
using data mining techniques. The reason for
this omission is that the psychographic data
that are needed for data mining are stored in
customers’ minds, and not well integrated
with demographic information, which is
stored in a well-formed IMC database. In
some ways, psycho-demographics are seen as
static elements, yet when coupled with
dynamic CRM data, such as transactional
information, they can aid in the formation of
a longitudinal view of the customer. Despite
the logical connection between customer
psycho-demographics and relational
outcomes, scant research has empirically
tested how their use impacts CRM data
quality and customer performance. We posit:
Hypothesis 2:
Increased use of psychodemographic
data is positively related to
CRM system quality.
Personalization delivery and
tracking data – Direct effects
Both marketing and advertising literature
have long acknowledged that customers have
diversified needs. These needs not only
represent the product and service offers
customers desire, but also the relevant
messages that they receive and respond to
(Zahay and Griffin, 2003; McCoy and
Hargie, 2007). Broadly, personalization
is the ability to individualize customer
communications and marketing offers
(Zahay
et al, 2004). The creation and
delivery of personalized marketing offers
and communications move away from a
one-size-fits-all strategy to the realization that
customers are not faceless entities but rather
are distinct individuals with different
behavioral and psycho-demographic profiles
(Chakraborty
et al, 2003). Personalization is
more than the mere identification and
delivery of messages and offers; successful
tracking of personalization efforts is
also necessary in quality CRM systems
IMC data integration and measurement framework
&
2013 Macmillan Publishers Ltd. 2050-3318 Journal of Marketing Analytics Vol. 1, 1, 32–48 37
(Jackson, 2007). This tracking measures the
extent to which customers receive the right
offers and messages at the right time and
place (Li
et al, 2011).
Although there is scant research that
examines the relationship between the
amount of personalization data an
organization collects and its performance,
Zahay
et al (2012) find that the data used
to personalize buyer–seller relationships has
the greatest impact on perceived data quality.
Therefore, we hypothesize that:
Hypothesis 3:
Increased use of offer and
message personalization data is positively
related to CRM system quality.
Hypothesis 4:
Increased use of
personalization tracking data is
positively related to CRM system quality.
Customer touchpoint data – Direct
effects
The success of an IMC campaign is
contingent in part on how well advertising
messages and offers are delivered across
multiple interactive touchpoints (Hallward,
2008). Although there are varied definitions
of customer touchpoints, most agree that they
refer to a point of contact specific to the
delivery and reception of communications
and offers. In this regard, touchpoint planning
is a comprehensive approach for designing,
delivering, managing and measuring
personalized customer relationships across
communication channels (Jenkinson, 2007).
Interactive
customer touchpoints include
information captured via the Internet, email
clickthroughs, service encounters, telephone
call centers and other channels offering
dual-direction communication.
Research suggests that the development
and management of highly valued customer
relationships is impacted by the degree to
which firms collect and integrate behavioral
data at the point of information delivery
(Davis, 2005). This value is expected to be
higher when marketing communications
work in tandem with other touchpoints to
maximize customer connections. A core
element of CRM systems is the ability to
track where and how communications/offers
are delivered, which in turn are assigned to
individual customer files (Romaniuk and
Gugel, 2010). Moreover, as a final link in our
hierarchical IMC data process, we expect that
using relevant customer touchpoints will
positively impact the quality of CRM
systems.
Hypothesis 5:
Increased use of customer
touchpoint data is positively related to
CRM system quality.
Hypothesis 6:
Increased use of customer
touchpoint data is positively related to
customer performance.
Indirect hypotheses
Zahay
et al (2012) conceptualized a customer
data pyramid relevant to understanding CRM
system quality and customer performance.
They contend that the value of a firm’s
customer data is tied to the ease by which it
can be collected for use in CRM systems.
The authors speculate that transactional
history data would be at the bottom of the
pyramid, followed by psycho-demographic
data, personalization data and customer
touchpoint data. Whereas they present no test
of the ordering of these data categories and
how they impact data categories higher in the
pyramid, our IMC data framework posits the
existence of an IMC data hierarchy, with data
lower on the pyramid leading to increased
collection of higher-order IMC data.
Transactional and psycho-demographic data
have been used extensively to develop
customer segments. Zahay
et al (2004) note
that psycho-demographic data are more
powerful than transactional data and are
appended to transaction-based segments as a
means of creating a picture of the profiles of
target customers. Thus:
Hypothesis 7:
Increased use of RFM/
transactional data is positively related to
Peltier
et al
38
& 2013 Macmillan Publishers Ltd. 2050-3318 Journal of Marketing Analytics Vol. 1, 1, 32–48
the collection of psycho-demographic
data.
Targeted messaging and offer
personalization uses data that are transformed
by behavioral segmentation and profiling
models (Peltier
et al, 2003; Dutta-Bergman,
2006). The more basic data elements
such as customer transactions and
psycho-demographic data serve as inputs
into the personalization process that requires
a matching of what customers want and
who they are with appropriate offers and
brand messages (Jackson, 2007). In practice,
effective database managers also append
data collected from multiple touchpoints
to a customer’s behavioral and
psycho-demographic profile as a means
of providing a richer understanding of
the relationship. Thus, we hypothesize
that:
Hypothesis 8:
Increased use of RFM/
transactional data is positively related
to the collection of (i) offer and
message personalization data; and
(ii) personalization tracking data.
Hypothesis 9:
Increased use of transactional
data is positively related to the
collection of customer touchpoint data.
Hypothesis 10:
Increased use of
psycho-demographic data is positively
related to collection of (i) offer and
message personalization data; and
(ii) personalization tracking data.
As provided in Figure 1, we alter the
ordering of Zahay
et al’s (2012) data pyramid
by switching the sequencing of
personalization data and touchpoint data.
Specifically, because a firm’s personalized
messages and offers are distributed via
selected touchpoints, touchpoint data
logically holds the final position in the
data hierarchy framework. This notion
is in line with Jenkinson (2007), who
proposes a model for distributing
personalized customer communications
and experiences across multiple touchpoints
and media platforms.
Hypothesis 11:
Increased use of offer
and message personalization data is
positively related to the collection
of personalization tracking data.
Hypothesis 12:
Increased use of (i) offer
and message personalization data; and
(ii) personalization tracking data is
positively related to the collection of
customer touchpoint data.
CRM data quality and customer
performance
Closing the loop in our interactive IMC data
framework, we assess the relationship
between the quality of IMC data within a
CRM system and customer performance.
A growing stream of research shows that
effective CRM implementation and use
contributes to improved customer
performance (for example, Homburg
et al,
2008). Although evidence for the effect that
CRM system data quality on customer
performance is scant, we hypothesize that:
Hypothesis 13:
The quality of IMC data in
a CRM system is positively related to
customer performance.
RESEARCH METHODOLOGY
Sample and data collection
A total of 525 executives in the financial
services industry were selected from Hoover’s
database as the pool of potential respondents.
Three data collection waves were conducted;
two mail waves (including a US$2 bill as an
incentive) and a follow-up telephone call.
Respondents were given the option of
mailing the questionnaire back or completing
the questionnaire online via the attached
URL. A second mailing was later sent to
non-respondents (id codes were used to
determine respondents) approximately
14 days after the mailing was delivered.
IMC data integration and measurement framework
&
2013 Macmillan Publishers Ltd. 2050-3318 Journal of Marketing Analytics Vol. 1, 1, 32–48 39
Finally, two graduate assistants called the
remaining non-respondents, either speaking
with them personally or leaving a voice mail
message. In total, 170 questionnaires were
returned. All but a few respondents used the
online survey option, eliminating question
non-response (responses were largely
mandatory). Four respondents were removed
in model development due to non-response,
leaving a sample of 166. This resulted in an
overall response rate of 32 per cent, which
compares favorably with response rates
typically received from business executives.
Table 1 contains the profile of respondents.
Approximately 45 per cent of the businesses
are B2B and about 40 per cent are B2C, with
the remainder accounted for by other trade
relationships. The respondents reported that
50 per cent of their business was conducted at
retail or branch banking locations and relied
on outside sales personnel for 27 per cent of
their business. The majority of the respondents
(63 per cent) were 45 years or older, suggesting
that the sample had substantial industry
experience. Online business was a little over
10 per cent of their sales, consistent with
the industry average. Most of the firms
(68.3 per cent) reported at least $250 million
in sales/assets under management.
Possible biases of informants were
controlled for by requiring informants to
be: (i) knowledgeable in their area; (ii) have
a great deal of business experience; and
(iii) have a significant amount of background
in their industry. In addition, a Harmon’s
one-factor test revealed that common
method bias was not an issue in the data. In
addition,
T-tests comparing the responses
of early responders to late responders did
not provide any evidence of response bias.
Measures and validation
On the basis of prior work in the CRM and
organizational learning literatures, scales were
developed for the five independent variables in
our model. Because our hypotheses posit that
the increased use of these data types will lead to
higher quality CRM systems, all variables were
assessed using multi-item 5-point scales
measuring the percentage of time that these
data are collected for inclusion in their
customer database (0 per cent, 25 per cent,
50 per cent, 75 per cent, 100 per cent). A
summed average score was calculated for each.
Transactional/RFM Data
(a¼0.83) was
measured by five items:
(i) Customers’ last purchase date,
(ii) Revenue by product or product line,
(iii) Frequency of purchase,
(iv) Total revenue from customer and
(v) Length of time as customer.
Psycho-Demographic Data
(a¼0.75) was
assessed by three items:
(i) Customer lifestyle data,
(ii) Customer psychographics and
(iii) Customer demographics.
Message/Offer Personalization Data
(a¼0.82)
was measured by three items:
(i) Tailor marketing offers to customers,
(ii) Tailor communications to customers and
(iii) Tailor communications to prospects.
Personalization Tracking Data
(a¼0.89) was
measured by three items:
(i) Tracking marketing offers/messages made
to customers,
(ii) Tracking marketing offers/messages
customers responded to and
Table 1
: Demographic profile of respondents
Percentage of sales Mean
B2B sales percentage 45.2
B2C sales percentage 39.6
Retail sales percentage 50.2
Online sales percentage 10.3
External sales percentage 26.9
Sales/assets under management Per cent
o
50 million 13.0
51–250 million 18.7
250.1 million–1 billion 20.1
1.1–5 billion 29.5
4
5 billion 18.7
Respondent age Per cent
o
35 9.1
35–44 27.9
45–54 38.2
55
þ 24.8
Peltier
et al
40
& 2013 Macmillan Publishers Ltd. 2050-3318 Journal of Marketing Analytics Vol. 1, 1, 32–48
(iii) Tracking method of contact for marketing
offers/messages.
Customer Touchpoint Data
(a¼0.76) was
assessed by three point of contact items:
(i) Email communications,
(ii) Personal service contacts and
(iii) Internet communications/sales.
The two dependent variables in the model
are Overall CRM system quality and
Customer Performance. In our model,
Overall CRM system quality is an antecedent
of Customer Performance.
Overall CRM System Quality
(a¼0.76) was
assessed by four items related to multi-touchpoint
CRM system implementation:
(i) Overall Quality of Internet and Email Data,
(ii) Overall Quality of Loyalty/Retention Data,
(iii) Overall Quality of Contact Management
Data and
(iv) Overall Quality of CRM Data
Capabilities.
The scale ranged from 1
¼poor to
5
¼excellent.
Customer Performance
(a¼0.76) was measured
by three items that reflect elements of
long-term customer profitability:
(i) Customer Retention on an annual basis,
(ii) Cross-Selling and
(iii) ROI on a customer basis.
Customer performance was stated as:
‘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
¼lower to 5¼higher scale.
Items from the survey were subjected to
an exploratory factor analysis, followed by an
item to total correlation analysis. The method
utilized was that suggested by McDonald
(1999) where the CFA is guided and informed
by the EFA results. Items with low item to total
correlations were eliminated. Table 2 provides
the reliability and factor loadings for the
final independent variables. The coefficient
a
’s range from 0.75 to 0.90, indicating
satisfactory levels of reliability for the measures.
We next conducted a confirmatory factor
analysis. A global CFA for discriminant
validity was not conducted because the data
did not meet the five observations per
indicator variable threshold (Hair
et al, 2010).
Because of the small sample size, the
dependent and independent variables were
analyzed separately. However, using AMOS,
separate CFAs were conducted on the
independent and dependent variables. Fornell
and Larcker’s (1981) criterion is that evidence
of discriminant validity is shown if the
average variance extracted (AVE) is greater
than the square of the construct’s correlations
with the other factors, squared inter-item
correlation (SIC). The results of an AVE
analysis demonstrate that the AVE of the
items in the scale are greater than the SIC,
providing evidence of discriminate validity in
the constructs.
The fit indices of the dependent variable
CFA indicate a good fit, especially for the
Table 2
: Reliability and factor loadings for
independent variables
Factor
loading
Transactional/RFM data (
a¼0.83)
Customers’ last purchase date 0.81
Frequency of purchase 0.76
Total revenue from customer 0.67
Revenue by product or product line 0.65
Psycho-demographic data (
a¼0.75)
Customer lifestyle data 0.90
Customer psychographics/personality 0.86
Customer demographics 0.67
Message/offer personalization data (
a¼0.82)
Tailor communications to customers 0.92
Tailor marketing offers to customers 0.92
Tailor marketing offers to prospects 0.84
Personalization tracking data (
a¼0.9)
Tracking marketing messages/offers
made to customers
0.84
Tracking marketing messages/offers
customers responded to
0.79
Tracking method of contact for
marketing offer
0.70
Customer touchpoint data (
a¼0.76)
Email communications 0.88
Service contacts 0.77
Internet communications/sales 0.77
IMC data integration and measurement framework
&
2013 Macmillan Publishers Ltd. 2050-3318 Journal of Marketing Analytics Vol. 1, 1, 32–48 41
relatively small sample size, with
w2 50.45
(DF
¼10), normed fit index (NFI)¼0.935,
incremental fit index (IFI)
¼0.958,
comparative fit index (CFI)
¼0.958, the
Tucker-Lewis index (TLI)
¼0.938 and the
root mean square error of approximation
(RMSEA)
¼0.10. The fit indices of the
independent variable CFA also indicated a
reasonable fit, again especially for the small
sample size, with
w2 181.21 (DF¼94),
NFI
¼0.872, IFI¼0.934, CFI¼0.933, the
TLI
¼0.914 and the RMSEA¼0.075.
Having conducted these tests for discriminate
validity, the final scales were created as
summed mean scores of the individual items.
The correlation matrix along with the means
and standard deviations of our summed
dimensions are reported in Table 3. Whereas
the correlation matrix demonstrates some of
the primary relationships such as the strong
relationship between both customer
touchpoints, CRM data quality and
performance, the SEM as fit demonstrates the
complex relationships of the variables.
Analysis and results
The hypothesized direct and indirect
relationships were tested in a combined SEM
model using AMOS 19. Both the Goodness
of Fit Index (GFI 0.995) and Adjusted
Goodness of Fit Index (AGFI
¼0.982),
which measure the fit of the combined
measurement and structural model to data
(
w2¼2.64) were greater than 0.90
(Baumgartner and Homburg, 1996). The
Root Mean Residual, which assesses the
correlations between the residual variance of
the model items, and should be less than
0.05 for a close fit, is 0.027 (Bagozzi and
Yi, 1988). The Steiger-Lind RMSEA, a
non-centrality measure of the square root of
an estimate of the population discrepancy
divided by the degrees of freedom that should
be as close to 0 as possible, is 0.001. CFI,
a normed comparative fit index that should
be as close to 1 as possible, was 0.95
(Bentler, 1990).
The results of the hypotheses testing based
on the model are summarized in Table 4.
Alternate models were tested that eliminated
variables and/or paths and that reversed the
hypothesized directional relationships. None
of these alternate models fit better than the
model reported in Figure 1, nor had as much
explanatory power. Because the one-tailed
test is most appropriate for these data
(being that negative responses were not
allowed or appropriate), all paths except
RFM/transactional data to offer/message
personalization data and psychodemographic
data to customer touchpoints
were significant at
Po0.05. The path from
RFM/Transactional data to CRM system
quality was significant at
Po0.055.
Mediation tests
As CRM system quality is the key construct
in this research, several mediation tests were
conducted using the approach advocated by
Zhao
et al (2010) via an SPSS script file
Table 3
: Item correlations and reliabilities
Variables PSYCH RFM TOUCH MESSAGE PERS CRM PERF
Psycho-Demographic 1 — — — — — —
RFM/Transactional 0.419
1 — — — — —
Customer Touchpoints 0.154
0.255 1 — — — —
Message/Offer Personalization 0.275
0.204 0.107 1 — — —
Personalization Tracking 0.410
0.326 0.231 0.540 1 — —
CRM System Quality 0.456
0.357 0.270 0.484 0.476 1 —
Customer Performance 0.129 0.111 0.334
0.082 0.1 0.241 1
Mean 2.68 3.63 3.67 3.64 3.17 2.99 3.33
Standard Deviation 1.15 1.17 1.11 1.1 1.17 0.77 0.75
Peltier
et al
42
& 2013 Macmillan Publishers Ltd. 2050-3318 Journal of Marketing Analytics Vol. 1, 1, 32–48
developed by Hayes (Preacher and Hayes,
2008). We examined the relationships in the
model to determine if CRM system quality
mediated the relationship between the
antecedent variables and the dependent
performance variable, Customer
Performance. We anticipated the finding that
CRM system quality would be critical to the
model, and reinforced by these tests.
Indeed, the mediation tests in
Table 5 show that CRM system quality
mediates the effect of the collection of,
Psycho-demographic, Personalization/
Tracking, Offer/Message Personalization on
performance. In the cases of these types of
data – Psycho-demographic, Personalization/
Tracking, Offer/Message Personalization –
the indirect effects are larger than the direct
effect and the direct effect becomes
insignificant when the mediator is included
in the equation, consistent with direct
mediation. In other words, the organization’s
business and customer performance is not
achieved directly from data collection but
through paying attention to CRM system
quality.
Table 5 also shows that CRM system
quality partially mediates (complementary
mediation in the language of Zhao
et al, 2010)
the impact of the collection of RFM/
Table 5
: Results mediation tests
Independent
variable
Dependent
variable
Mediator Direct effect (standard
regression coefficients
(significance level)
Indirect effect (standard
regression coefficients
(significance level)
Result
RFM/
Transactional
Customer
Performance
CRM system
quality
0.1129 (0.0028)
0.2361 (0.0124) Partial
Mediation
Psychodemographic
Customer
Performance
CRM system
quality
0.0830 (0.1008) ns 0.3068 (0.0046)
Mediation
Customer
Touchpoints
Customer
Performance
CRM system
quality
0.1879 (0.0431)
0.2384 (0.000) Partial
Mediation
Personalization
Tracking
Customer
Performance
CRM system
quality
0.0705 (0.1819) ns 0.3355 (0.0028)
Mediation
Offer/Message
Personalization
Customer
Performance
CRM system
quality
0.0546 (0.2724) ns 0.3193 (0.0019)
Mediation
All tests are two tailed.
Table 4
: Results and hypothesis testing structural equation model
Standard coefficient t-value
H1 RFM/Transactional
- CRM System Quality 0.110w 1.60
H2 Psycho-Demographic
- CRM System Quality 0.248 3.50
H3 Offer/Message Personalization
- CRM System Quality 0.144 1.87
H4 Personalization Tracking
- CRM System Quality 0.303 4.22
H5 Customer Touchpoints
- CRM System Quality 0.139 2.20
H6 Customer Touchpoints
- Customer Performance 0.293 3.96
H7 RFM/Transactional
- Psycho-Demographic 0.419 5.93
H8a RFM/Transactional
- Offer/Message Personalization 0.140 2.06
H8b RFM/Transactional
- Personalization Tracking n.s. n.s.
H9 RFM/Transactional
- Customer Touchpoints 0.201 2.60
H10a Psycho-Demographic
- Offer/Message Personalization 0.275 3.67
H10b Psycho-Demographic
- Personalization Tracking 0.230 3.29
H11 Psycho-Demographic
- Customer Touchpoints n.s. n.s.
H12 Offer/Message Personalization
- Personalization Tracking 0.452 7.06
H13 Personalization Tracking
- Customer Touchpoints 0.165 2.12
H14 CRM System Quality
- Customer Performance 0.211 3.96
w
P 0.10, Po0.05, Po0.01, Po0.001 (one-tailed tests).
Notes
: Model fit: w2 (166)¼2.64, GFI¼0.995, AGFI¼0.982, CFI¼0.95, RMSEA¼0.001.
IMC data integration and measurement framework
&
2013 Macmillan Publishers Ltd. 2050-3318 Journal of Marketing Analytics Vol. 1, 1, 32–48 43
Transactional data and Customer Touchpoint
data on Customer Business Performance. The
indirect paths from Organizational Culture to
performance are stronger than the direct,
consistent with mediation, but both the
indirect and direct effects are significant.
These results indicate that CRM system
quality does mediate the path from collection
of RFM/Transactional data and Customer
Touchpoint data to Performance. Again, the
organization’s business and customer
performance is not achieved directly from
data collection but through using the
information gained from data collection to
improve CRM system quality. However,
because the relationship is not reflective of
full mediation, there might be another factor
to consider in future analyses. As both RFM/
Transactional data and Customer Touchpoint
data are complex concepts, it is not surprising
that these constructs might need to be
expanded to increase understanding of the
mediation effects.
DISCUSSION
This model extends and focuses previous
work and shows the importance of several
types of customer information and their
ultimate impact not only on personalized
communications, but of equal value, on
CRM system quality and customer
performance. Our model empirically
demonstrates the fact that CRM System
Quality leads to enhanced customer
performance, showing that a strategic data
system is not only important for personalized
communications and customer touchpoints,
but can eventually yield higher returns and
loyalty from customers. As shown in Figure 2,
our findings highlight the fact that easier-tocollect
customer data impacts the extent to
which other higher-level customer data are
collected and utilized for getting close to
customers.
Our hierarchical IMC model provides
guidance in a world where managers are
grappling to understand ‘big data’ and how to
manage and integrate disparate customer
databases across an exploding number of
media channels. In this context, targeted
media campaigns that span multiple media
types, especially in light of the dynamic
nature of technology such as the social media
digital space, need to be tightly integrated
(Wakolbinger
et al, 2009) in order to make
them advantageous for firms. Such crossmedia
campaigns can only be developed with
clean segmentation and profiling data in
combination with personalized tracking
information. In recent years, the channels
of sales have grown, allowing for the
ability to reach customers not only through
bricks-and-mortar and e-commerce, virtually
via v-commerce (Krishen
et al, forthcoming),
and also through mobile environments or
kiosks (Bui
et al, 2012). In combination
with multiple media formats, these channels
not only allow for truly integrated
communications, but also present an even
more pressing challenge for firms
as they struggle to optimize and organize
their customer information.
With such opportunities in digital
advertising, e-marketing, viral marketing
and social media marketing, there is an
even more pressing need for firms to
remain vigilant in tracking transactional
and psycho-demographic data now than ever
before; as the model here shows, this data can
Figure 2:
Data pyramid.
Peltier
et al
44
& 2013 Macmillan Publishers Ltd. 2050-3318 Journal of Marketing Analytics Vol. 1, 1, 32–48
be used to personalize delivery and track it
and improve customer touchpoints. The
model clearly suggests that firms pay a
performance price for not collecting critical
types of information and not using it for
personalized communications. In fact, it
is primarily through the collection of
Psycho-Demographic data and both
Message/Content Personalization and
Personalization Tracking Data and the
increase in CRM system quality that results
that performance is achieved in this context.
Substantiating this finding, current research
in the area of digital television advertising
indicates that firms now have even greater
opportunities to personalize message
qcontent in interactive platforms (Lekakos,
2009). In spite of the increased capability
for personalization, the connection of
personalization with performance remains
under-researched. This study extends the
research of Zahay and Griffin (2004) in
which a link from message personalization
to performance was established, by focusing
more sharply on the role of CRM data quality
in creating firm performance.
This model also suggests that increased
personalization message and content data
delivery and tracking leads to increased
customer touchpoint data. In other words,
customers are more likely to follow up and
return contact to a firm when the materials
they receive are personalized and the content
is delivered in a timely manner. Our findings
also support the idea that firms will pay a
performance price without a data collection
process at customer touchpoints that is both
efficient and effective. In fact, in line with this
notion, Lautman and Pauwels (2009) find that
advertising and promotion awareness can be
termed a ‘metric that matters’ and can lead to
not only base sales, but incremental sales of a
product as well. Moreover, the longevity
factor of the customer relationship cycle is of
utmost importance; making an initial
impression on a customer can drive a spike
in sales, but without an ongoing and
personalized communication plan, the
customer can eventually switch to another
product or firm.
With updated data banks, firms can
accurately profile customers and target their
personalized communications to those with
high wallet share through proper touchpoint
data. As our model shows, this touchpoint
data eventually leads to CRM System Quality
and ultimately to CRM performance, hence
completing the feedback loop. To further
validate this cycle, Baldinger
et al (2002)
conduct a longitudinal study and suggest that
continued loyalty to a brand leads to increased
market share and that customer retention is
essential to grow in a competitive market.
Hence, the role of CRM System Quality
and how it leads to improved customer
performance is essential for firms to
eventually increase market penetration and
performance.
LIMITATIONS AND FUTURE
RESEARCH
As with all research, there are limitations.
Given the exploratory nature of this study,
more work needs to be done on larger sample
sizes in diverse industries and to understand
how CRM quality can lead to customer
performance. As firms are increasingly
globalizing, future research should test our
model from a cross-cultural perspective and
identify ways in which it is impacted by
self-construal. For example, research spanning
multiple cultures shows that knowledge
management, when combined with a
customer focus, can create a very effective
model for the deployment of CRM (called
KCRM) efforts (Lin
et al, 2006). In essence,
their framework suggests that customer
information must be managed through efforts
that include knowledge identification, capture,
selection, storage, sharing, application,
creation and selling.
The impact of different types of data we
identified in our model on KCRM, then, is
also a fruitful area of future research. To that
end, one aspect of knowledge management,
IMC data integration and measurement framework
&
2013 Macmillan Publishers Ltd. 2050-3318 Journal of Marketing Analytics Vol. 1, 1, 32–48 45
customer knowledge orientation, requires
that companies keep marketing databases
up-to-date, utilize internal database
marketing information, monitor the accuracy
of information in marketing databases and
utilize performance-based reward systems
(Stein and Smith, 2009). Stein and Smith
(2009) find that customer knowledge
orientation leads directly to more use of
CRM, which then enhances firm
performance. Thus, accurate customer
knowledge ultimately leads to better firm
performance. Our model also finds this
important linkage.
Finally, even though our model does not
measure loyalty outcomes, opportunity exists
to further this type of research along those
lines. The ability to contact appropriate
customers based on accurate profiling over
time is a necessity to guarantee an ongoing
relationship. Research indicates that customer
retention is enhanced when customers are
satisfied and their complaints are handled in
an efficient and appropriate manner; to
mitigate such complaints and handle
customer communications effectively, firms
must have a quality CRM system and strategy
in place (Zineldin, 2006). The link to
customer loyalty that firms can make when
they have high CRM System Quality enables
them to continue relationships with
customers, thus creating a feedback loop for
our IMC model. In essence, by retaining
customers, firms are able to update
transactional, psycho-demographic and
personalization tracking data on an
ongoing basis.
ACKNOWLEDGEMENTS
We are grateful to the Marketing Science
Institute, the Direct Marketing Policy Center
at the University of Cincinnati and the
University of Wisconsin, Whitewater for
their financial support of the data collection
phase of this research.
REFERENCES
Abdul-Muhmin, A.G. (2012) CRM technology use and
implementation benefits in an emerging market.
Journal of
Database Marketing & Customer Strategy Management
19(2):
82–97.
Acker, O., Gro¨ne, F., Blockus, A. and Bange, C. (2011) Inmemory
analytics – Strategies for real-time CRM.
Journal of
Database Marketing & Customer Strategy Management
18(2):
129–136.
Assael, S. (2011) From silos to synergy: A fifty-year review of
cross-media research shows synergy has yet to achieve its full
potential.
Journal of Advertising Research 51(1): 42–58.
Bagozzi, R.P. and Yi, Y. (1988) On the evaluation of structural
equation models.
Journal of the Academy of Marketing Science
16(1): 74–94.
Baldinger, A.L., Blair, E. and Echambadi, R. (2002) Why
brands grow.
Journal of Advertising Research 42(1): 7–14.
Baumgartner, H. and Homburg, C. (1996) Applications of
structural equation modeling in marketing and consumer
research: A review.
International Journal of Research in
Marketing
13(2): 139–161.
Bentler, P.M. (1990) Comparative fit indexes in structural
models.
Psychological Bulletin 107(2): 238–246.
Boulding,W., Staelin, R., Ehret, M. and Johnston,W.J. (2005)
A customer relationship management roadmap: What is
known, potential pitfalls, and where to go.
Journal of
Marketing
69(4): 155–166.
Bui, M., Krishen, A. and Latour, M. (2012) Kiosk retailing
promotions: Effects of gender on ad credibility and product
expectations.
Journal of Advertising Research 51(3): 1–18.
Chakraborty, G., Lala, V. and Warren,W. (2003) What do
customers consider important in B2B Websites?
Journal of
Advertising Research
43(1): 50–61.
Davis, S. (2005) Marketers challenged to respond to changing
nature of brand building.
Journal of Advertising Research
45(2): 198–200.
Du, R.Y., Kamakura, W.A. and Mela, C.F. (2007) Size and
share of customer wallet.
Journal of Marketing 71(2): 94–113.
Dutta-Bergman, M.J. (2006) The demographic and
psychographic antecedents of attitude toward advertising.
Journal of Advertising Research
46(1): 102–112.
Even, A.S., Shankaranarayan, G. and Berger, P. (2010)
Inequality in the utility of customer data: Implications for
data management and usage.
Journal of Database Marketing &
Customer Strategy Management
17(1): 19–35.
Fornell, C. and Larcker, D.F. (1981) Evaluation of structural
equations models with unobservable variables and measurement
error.
Journal of Marketing Research 18(1): 39–50.
Hair, J., Black, W., Babin, B. and Anderson, R. (2010)
Multivariate Data Analysis
, Upper Saddle River, NJ:
Prentice-Hall.
Hallward, J. (2008) Make measurable what is not so: Consumer
mix modeling for the evolving media world.
Journal of
Advertising Research
48(3): 339–351.
Hipperson, T. (2010) The changing face of data insight –
And its relationship to brand marketing.
Journal of
Database Marketing & Customer Strategy Management
17(4):
262–266.
Homburg, C., Droll, M. and Totzek, D. (2008) Customer
prioritization: Does it pay off, and how should it be
implemented?
Journal of Marketing 72(5): 110–130.
Peltier
et al
46
& 2013 Macmillan Publishers Ltd. 2050-3318 Journal of Marketing Analytics Vol. 1, 1, 32–48
Jackson, T.W. (2007) Personalization and CRM.
Journal of
Database Marketing & Customer Strategy Management
15(1):
24–36.
Jenkinson, A. (2007) Evolutionary implications for touchpoint
planning as a result of neuroscience: A practical fusion of
database marketing and advertising.
Journal of Database
Marketing & Customer Strategy Management
14(3): 164–185.
Kerr, G.F., Schultz, D., Patti, C. and Ilchul, K. (2008) An
inside-out approach to integrated marketing
communication: An international analysis.
International
Journal of Advertising
27(4): 511–548.
Kitchen, P.J. and Schultz, D.E. (2009) IMC: New horizon/
false dawn for a marketplace in turmoil?
Journal of Marketing
Communications
15(2-3): 197–204.
Kitchen, P.J., Kim, I. and Schultz, D.E. (2008) Integrated
marketing communication: Practice leads theory.
Journal of
Advertising Research
48(4): 531–546.
Kliatchko, J.G. (2008) Revisiting the IMC construct.
International Journal of Advertising
27(1): 133–160.
Krishen, A.S., Hardin, A.M. and LaTour, M.S. (forthcoming)
Virtual world experiential promotion.
Journal of Current
Issues & Research in Advertising
, accepted.
Kumar, V. and George, M. (2007) Measuring and maximizing
customer equity: A critical analysis.
Journal of the Academy of
Marketing Science
35(2): 157–171.
LaPointe, P. (2012) The dog ate my analysis: The hitchhiker’s
guide to marketing analytics.
Journal of Advertising Research
52(4): 395–396.
Lautman, M.R. and Pauwels, K. (2009) Metrics that matter:
Identifying the importance of consumer wants and needs.
Journal of Advertising Research
49(3): 339–359.
Lee, D.H. and Park, C.W. (2007) Conceptualization and
measurement of multidimensionality of integrated
marketing communications.
Journal of Advertising Research
47(3): 222–236.
Lekakos, G. (2009) It’s personal: Extracting lifestyle indicators
in digital television advertising.
Journal of Advertising Research
49(4): 404–418.
Li, S., Sun, B. and Montgomery, A.L. (2011) Cross-selling the
right product to the right customer at the right time.
Journal
of Marketing Research
48(4): 683–700.
Lin, Y., Su, H.Y. and Chien, S. (2006) A knowledge-enabled
procedure for customer relationship management.
Industrial
Marketing Management
35(4): 446–456.
McCoy, M. and Hargie, O. (2007) Effects of personalization
and envelope color on response rate, speed and quality
among a business population.
Industrial Marketing
Management
36(6): 799–809.
McDonald, R.P. (1999)
Test Theory, Mahwah, NJ: Erlbaum.
McGrath, J.M. (2010) Using means-end analysis to test
integrated marketing communications effects.
Journal of
Promotion Management
16(4): 361–387.
Micu, A.C.
et al. (2011) Guest editorial: The shape of
marketing research in 2021.
Journal of Advertising Research
51(1): 213–221.
O’Regan, M., Ashok, K., Maksimova, O. and Reshetin, O.
(2011) Optimizing market segmentation for a global mobile
phone provider for both targeting and insight.
Journal of
Advertising Research
51(4): 571–577.
Payne, A. and Frow, P. (2005) A strategic framework for
customer relationship management.
Journal of Marketing
69(4): 167–176.
Peltier, J.W., Schibrowsky, J.A. and Schultz, D.E. (2003)
Interactive integrated marketing communication:
Combining the power of IMC, the new media and database
marketing.
International Journal of Advertising 22(1): 93–116.
Peltier, J.W., Schibrowsky, J.A. and Schultz, D.E. (2006)
Interactive IMC: The relational-transactional continuum
and the synergistic use of customer data.
Journal of
Advertising Research
46(2): 146–159.
Peltier, J.W., Zahay, D. and Lehmann, D.R. (2013)
Organizational learning and CRM success: A model for
linking organizational practices, customer data quality, and
performance.
Journal of Interactive Marketing 27(1): 1–13.
Pettit, R. (2010) The march toward quality: The ARF’s
quality-enhancement process.
Journal of Advertising Research
50(2): 120–1240.
Preacher, K.J. and Hayes, A.F. (2008) Asymptotic and
resampling strategies for assessing and comparing indirect
effects in multiple mediator models.
Behavior Research
Methods
40(3): 879–891.
Precourt, G. (2009a) Editorial: The innovation issue.
Journal of
Advertising Research
49(3): 253–255.
Precourt, G. (2009b) Editorial: Why empirical generalizations
matter.
Journal of Advertising Research 49(2): 113–114.
Precourt, G. (2011) Editorial: New models for a new age of
research.
Journal of Advertising Research 51(2): 333–334.
Reimann, M., Schilke, O. and Thomas, J.S. (2010) Toward an
understanding of industry commoditization: Its nature and
role in evolving marketing competition.
International Journal
of Research in Marketing
27(2): 188–197.
Romaniuk, J. and Gugel, C. (2010) The ARF 360 model:
Update to a human-centric approach.
Journal Of Advertising
Research
50(3): 334–343.
Sasser, S.L., Koslow, S. and Riordan, E.A. (2007) Creative and
interactive media use by agencies: Engaging an IMC media
palette for implementing advertising campaigns.
Journal of
Advertising Research
47(3): 237–256.
Schultz, D.E. and Patti, C.H. (2009) The evolution of IMC:
IMC in a customer-driven marketplace.
Journal of Marketing
Communications
15(2-3): 75–84.
Schultz, D.E., Davis, J., Schibrowsky, J.A. and Peltier, J.W.
(2002) Interactive psychographics: Cross-selling in the
banking industry.
Journal of Advertising Research 42(2):
7–22.
Smit, E.G. and Neijens, P.C. (2011) The march to reliable
metrics: A half-century of coming closer to the truth.
Journal of Advertising Research
51(2): 124–135.
Smith, J.A., Boyle, B.A. and Cannon, H.M. (2010) Surveybased
targeting fine-tunes television media planning a case
for accuracy and cost efficiency.
Journal Of Advertising
Research
50(4): 428–439.
Stein, A. and Smith, M. (2009) CRM systems and
organizational learning: An exploration of the relationship
between CRM effectiveness and the customer information
orientation of the firm in industrial markets.
Industrial
Marketing Management
38(2): 198–206.
Stewart, D.W. and Hess, M. (2011) How relevancy, use,
and impact can inform decision making the uses of
quantitative research.
Journal of Advertising Research 51(2):
195–204.
Swain, W.N. (2004) Perceptions of IMC after a decade of
development: Who’s at the wheel, and how can we measure
success?
Journal of Advertising Research 44(1): 46–65.
IMC data integration and measurement framework
&
2013 Macmillan Publishers Ltd. 2050-3318 Journal of Marketing Analytics Vol. 1, 1, 32–48 47
Taylor, C.R. (2010) Editorial: Measuring return on investment
from advertising: ‘Holy grail’ or necessary tool?
International
Journal of Advertising
29(3): 345–348.
Thomas, J.S. and Sullivan, U.Y. (2005) Managing marketing
communications with multichannel customers.
Journal of
Marketing
69(4): 239–251.
Verhoef, P.C., Venkatesan, R., McAlister, L., Malthouse, E.C.,
Krafft, M. and Ganesan, S. (2010) CRM in data-rich
multichannel retailing environments: A review and future
research directions.
Journal of Interactive Marketing 24(2):
121–137.
Wakolbinger, L.M., Denk, M. and Oberecker, K. (2009) The
effectiveness of combining online and print advertisements:
Is the whole better than the individual parts?
Journal of
Advertising Research
49(3): 360–372.
Wind, Y. and Sharp, B. (2009) Advertising empirical
generalizations: Implications for research and action.
Journal
of Advertising Research
49(2): 246–252.
Wurtzel, A. (2009) Viewpoint: Now or never – An urgent call
to action for consensus on new media metrics.
Journal of
Advertising Research
49(3): 263–265.
Zahay, D. (2008) Successful B2B customer database management.
Journal of Business & Industrial Marketing
23(4): 264–272.
Zahay, D. and Griffin, A. (2003) Information antecedents of
personalization and customization in business-to-business
service markets.
Journal of Database Marketing 10(3): 255–271.
Zahay, D. and Griffin, A. (2004) Customer learning
processes, strategy selection, and performance in
business-to-business service firms.
Decision Sciences 35(2):
169–203.
Zahay, D., Peltier, J., Schultz, D.E. and Griffin, A. (2004) The
role of transactional versus relational data in IMC programs:
Bringing customer data together.
Journal of Advertising
Research
44(1): 3–18.
Zahay, D., Peltier, J. and Krishen, A.S. (2012) Building the
foundation for customer data quality in CRM system
quality for financial services firms.
Journal of Database
Marketing & Customer Strategy Management
19(1): 5–16.
Zahay, D.L., Peltier, J., Krishen, A.S. and Schultz, D.E.
(forthcoming) Organizational processes for B2B services
IMC data quality.
Journal of Business & Industrial Marketing.
In press.
Zhao, X., Lynch, J.G. and Chen, Q. (2010) Reconsidering
Baron and Kenny: Myths and truths about mediation
analysis.
Journal of Consumer Research 37(2): 197–206.
Zigmond, D. and Stipp, H. (2010) Assessing a new advertising
effect: Measurement of the impact of television
commercials on internet search queries.
Journal of Advertising
Research
50(2): 162–168.
Zineldin, M. (2006) The royalty of loyalty: CRM, quality
and retention.
Journal of Consumer Marketing 23(7):
430–437.
Peltier
et al48 & 2013 Macmillan Publishers Ltd. 2050-3318 Journal of Marketing Analytics Vol. 1, 1, 32–48
No comments:
Post a Comment