Data quality in customer relationship management (CRM): Literature review

Abstract

The aim of this paper is to examine challenges that organizations face when they start to deal with quality of customer data more seriously in order to manage their customer relationships better. Research extracted from the literature review has identified some problems with the quality of customer data as well as suggestions for their solutions. The author found that challenges regarding the quality of data used in customer relationship management are reflected in: decentralized data storage, inconsistencies in input and storage, inadequate integration of different data sources, different data defects, and their tendency in quality deterioration over time. In addition, problems have been identified in the high costs of maintaining data quality, as well as new challenges in the form of big data and open data. Possible improvement solutions have been suggested through a number of tools and frameworks by different authors.

Full text

STRATEGIC MANAGEMENT, Vol. 25 (2020), No. 2, pp. 040-047
DOI: 10.5937/StraMan2002040P
Received: November 22, 2019
Accepted: February 10, 2020
Data quality in customer relationship
management (CRM) – literature review
Marijana Petrović
ICT College of Applied Studies, Serbia
Abstract
The aim of this paper is to examine challenges that organizations face when they start to deal with quality of
customer data more seriously in order to manage their customer relationships better. Research extracted from
the literature review has identified some problems with the quality of customer data as well as suggestions for
their solutions. The author found that challenges regarding the quality of data used in customer relationship
management are reflected in: decentralized data storage, inconsistencies in input and storage, inadequate
integration of different data sources, different data defects, and their tendency in quality deterioration over
time. In addition, problems have been identified in the high costs of maintaining data quality, as well as new
challenges in the form of big data and open data. Possible improvement solutions have been suggested
through a number of tools and frameworks by different authors
Keywords
Data quality, customer relationship management, DQ.
Introduction
A key source of the company's competitive
advantage lies in its ability to dynamically
respond to changes (Adamik et al., 2018). As a
result of changes in the digital environment,
companies are increasingly facing large amount of
data stored in diverse and often inconsistent
databases. The main necessity for the quality of
data used to manage customer relationships is the
need for accurate information that will serve to
manage campaigns and determine customer value
(Zahay, Peltier & Krishen, 2011). A 2015
Experian report identified that nearly 80% of
organizations lack sophisticated access to data
quality. Gartner's report predicted that “by 2017,
33% of Fortune 100 organizations will experience
an information crisis, due to their inability to
evaluate, manage and trust their information”.
This is followed by evidence suggesting that
many organizations are unaware of their data
quality problems and either ignore them or don’t
prioritize them (O 'Brien, 2015).
The subject of this paper is the set of
challenges that organizations face when they
become more serious about data quality (DQ) in
the context of customer relationship management
(CRM). The research question addressed in the
paper is: What are the problems organizations
face when dealing with the quality of their
customer data and how can they be solved? The
author has tried to present an overview of the
most common problems in the field of data
quality that organizations face, with an outline of
possible solutions.
After explaining the concepts of CRM and DQ
and their interrelation in Chapters 1 and 2, the
author will refer to data quality issues in CRM
and their potential solutions in response to the
Research Question in Chapter 3. Subsequently,
concluding considerations will be presented.
1. Customer relationship management
(CRM)
CRM is a strategic approach to systematically
target, monitor, communicate and transform
relevant customer data into information that
underlies strategic decision making and action
(Missi, Alshawi & Fitzgerald, 2005). The goal of
CRM is to improve operational efficiency,

Marijana Petrović Data quality in customer relationship management (CRM) – literature review 41
STRATEGIC MANAGEMENT, Vol. 25 (2020), No. 2, pp. 040-047
achieve an acceptable level of customer
connectivity (Reid & Catterall, 2014), manage
customer relationships within the company and
increase their satisfaction (Tu & Yang, 2013), and
as a consequence of all this - increase revenue
(Negahban, Kim & Kim, 2016). CRM enables
organizations to identify and attract new
customers, as well as increase retention of
profitable customers through developing,
strengthening and managing relationships with
them, considering sustainable company growth
(Sharma, Goyal & Mittal, 2010). In today's
competitive business environment, key problems
relate to the quality of organizational data and
their integration and it is necessary to capture
customer information in real time (Missi et al.,
2005). Unfortunately, the reality is that CRM
classification models are outdated, unbalanced,
and noisy (Natchiar & Baulkani, 2014), and
customer-stored data is often located in separate
departments and not linked throughout the
company’s CRM (Missi et al. 2005). The problem
with data quality only occurs when an
organization wants to correct anomalies in one
data source or when it wants to integrate data
coming from different sources into one new data
source. Due to the tendency of organizations to
avoid or ignore the importance of data quality and
their integration process, we often witness the
failure of CRM projects (Missi et al., 2005).
Companies misunderstand that it is necessary to
have large amounts of customer data, and in fact it
is much more important to have quality data
(Zahay et al., 2011).
In order for CRM to be successful, it is
necessary to integrate three key components:
business processes, the human factor and
technology (Negahban et al., 2016). Business
processes need to be streamlined (because
sometimes complex processes cause data
complexity) (Foss, Henderson, Murray & Stone,
2002), employees need to be motivated by senior
management and organizational culture to pay
more attention to the quality of the data collected
(Reid & Catterall, 2014), (Peltier, Zahay &
Lehmann, 2013) and all this should be supported
by the use of technology that will optimize
customer interaction. As a large percentage of
customer interactions will occur rather on the
Internet than with employees, technology must
adapt to a changing and unpredictable market
(Chen & Popovich, 2003), but even the most
sophisticated IT or business systems will not
succeed if they rely on data of insufficient quality
and if they are not structured for the purpose for
which they are applied (Sharma et al., 2010). In
some organizations, CRM is a simple technology
solution that enhances customer targeting efforts
through the use of a separate database and sales
automation tools, while other organizations see it
as a tool specifically designed for 1-on-1 customer
communication, which is the responsibility of
sales, call centers and marketing departments
(Chen & Popovich, 2003).
Missi et al. (2005) cite the basic types of data
that organizations collect about customers:
demographics (gender, age, marital status,
education level, home ownership, etc.) that are
very stable and not very expensive, but the
problem is that we can hardly get them on an
individual basis with a high level of accuracy;
behavioral data (types of purchases, payments,
customer service activities, etc.) that are the
easiest to predict, but are the most difficult and
expensive to obtain from external sources;
psychographic data (opinions, lifestyle, personal
values, etc.) that can lead to improvement and be
used to determine a customer's life stage, but the
weakness is that they indicate behavior that may
be highly, partially, or marginally related to the
right behavior (Missi et al., 2005). In addition to
these types of data, Zahay et al. (2011) also
emphasizes the contact information of the users,
which forms the basis for marketing efforts, as
well as personalization i.e. the ability to tailor
marketing communications to the individual
customer. Personalized communication strategies
can be developed by using demographic
information with psychographic profiles to
achieve interactive communication with users
(Zahay et al., 2011), in order to create, elaborate
and reinforce meaning in customers’ relationship
with the company (Ferreira et al., 2019).
It can be concluded that the quality of
customer data is very important and that
information about customers should be carefully
collected, as it is one of the main, if not the main
factor that will affect the performance of CRM
systems. Having the right information at the right
time is essential to a successful CRM strategy
(Sharma et al., 2010).
2. Data quality (DQ)
Peltier et al. (2013) provided a definition of
quality data: "Customer data are of high quality
when the information collected across multiple
transactions, touchpoints, and channels accurately
reflects the behavior and sentiments of customers,

42 Marijana Petrović Data quality in customer relationship management (CRM) – literature review
STRATEGIC MANAGEMENT, Vol. 25 (2020), No. 2, pp. 040-047
both collectively and individually" (Peltier et al.,
2013). The high quality of well integrated
customer data is the foundation of successful
CRM projects. If the data quality problem is not
resolved on time, low data quality can affect
operating costs, customer satisfaction, effective
decision making, and CRM workers' confidence.
The trouble is that even when problems are
noticed at an early stage, they are still difficult to
address. That is why it is important to create a
comprehensive data quality management strategy
at the beginning of CRM implementation (Reid &
Catterall, 2014) and to understand data quality
management (DQM) as a continuous process
(Even, Shankaranarayanan & Berger, 2010).
Customer information is usually heterogeneous
data collected from different sources, mostly
informal, unlimited and in different formats
(numeric and categorical) (Tu & Yang, 2013).
They can contain a large amount of redundant and
irrelevant information that affects the performance
of CRM (Natchiar & Baulkani, 2014). The most
common sources of "dirty" data are: legacy
systems that contain poorly documented and
outdated data, the distribution of data across
databases in different departments with a lack of
data coding standards, typing errors, poor data
entry, missing data, etc. (Reid & Catterall, 2014).
Data quality is both a technical and
organizational problem, it also requires
understanding the types of information required
and understanding how this information is used to
make sound marketing decisions (Peltier et al.,
2013). Most authors have tried to improve the
quality of data using mathematical and
programming solutions (Sharma et al., 2010).
Technological developments have allowed new
data mining (DM) approaches to analyze
customer data to be applied to find the best CRM
strategies (Natchiar & Baulkani, 2014). DM
represents a large group of algorithms and
methods that are used to analyse large data
volumes (Dusmanescu et al., 2016) in order to
extract comprehensible, hidden and useful
information from data, to find unexpected
connections between them (even predictive
information that experts may miss because they
are beyond their expectations) and to predict
trends and behavior based on them. The process
consists of observing specific examples in order to
define general conceptual definitions (Vukelić,
Stanojević & Anđelić, 2015). For DM tools to
assist in CRM, appropriate data quality is crucial
(Sharma et al., 2010), but it is not possible to
establish generally acceptable procedures for DM
classification, as it is difficult to find a single
methodology that will address all DM problems
that CRM data yields: heterogeneity,
dimensionality, serious anomalies on data,
unbalanced classification, data encryption, etc.
(Tu & Yang, 2013). DM tools can provide
answers to business questions that are time
consuming and complicated to solve; it is only
necessary to research which tools would be
appropriate in which situation and apply them
accordingly to improve data quality (Sharma et
al., 2010).
The failure of the CRM system has been
attributed to the inability to facilitate and improve
the organization-level transfer of customer
information (Peltier et al., 2013). The company
needs to know the state of its data to know what
needs to be improved and what benefits it will
bring, and because of the difficulty of managing
large amounts of data, companies will sometimes
leave these problems to firms specializing in this
(Foss et al., 2002). Higher quality targeting
typically increases the value of a data set, but can
involve a lot of costs. The elements that the data
describe can change over time, such as changing a
customer's address, their profession, marital
status, etc., which means that their quality may
deteriorate over time. Maintaining data at a high
level of quality involves significant costs
associated with efforts to detect and correct
defects, set up management, redesign processes
and invest in quality monitoring tools (Even et al.,
2010). Eppler & Helfert (2004) split the costs into
those caused by low data quality (verification, re-
entry, compensation, low reputation, wrong
decisions, sunk costs) and those to improve data
quality accuracy (training, monitoring,
development and usage standards, analysis,
reporting, plan repair and implementation) (O
'Brien, 2015).
3. Data quality management –
problems and solutions
By analyzing the extracted literature, an answer to
the Research Question was formed, which
presented the problems of different quality
dimensions in CRM and potential solutions to
some of the mentioned problems.
3.1. Solutions:
 Sharma et al. (2010) suggest that there
should be a ranking of allowable and

Marijana Petrović Data quality in customer relationship management (CRM) – literature review 43
STRATEGIC MANAGEMENT, Vol. 25 (2020), No. 2, pp. 040-047
expected data quality in CRM systems,
which depends on the specific data
element. Some data must be perfect, such
as unique keys, internal security
information, and anything audited.
However, some other data may be
estimates or even missing, making it easier
to maintain bases and reduce their costs.
Even et al. (2010a) have proposed a model
that allows different levels of quality to be
set for different records, so that optimal
quality varies depending on the records.
 Reid & Catterall (2014) proposed
simplification of the database architecture
in order to make data quality support
easier and more cost effective.
 Foss et al. (2002) state that it is sometimes
better to focus on process simplification
than on case integration.
 Practitioners of CRM classification require
a standardized framework with simplified
DM processes that could produce
satisfactory results for CRM data in
general, with all the DM challenges
mentioned previously (Tu & Yang, 2013).
 Ahmed et al. (2016) have shown that using
Java programming and SQL can define
appropriate constraints and get 100%
accurate data. Data from different users
can be standardized for more accurate
information and its processing. Using SQL
servers will help address key data quality
tasks, such as profiling, cleaning and
refining, as well as auditing. This will
create approaches that will reduce system
integration costs, develop benefits, and
mitigate data risks.
 The ability to update data throughout the
system would be preferable in order to
avoid problems when the country code is
changed or when data is integrated from
different sources. Consistency issues
should be addressed at an early stage of
integration by defining data standards and
data rules within the organization (Jaya et
al., 2007).
 Given the common legacy of poor data
quality from a previous system being
ported to a new system, the solution may
be to invest in a data cleaner that will
reform the data before being transferred to
a new database so that it stores pure data
only (Reid & Catterall, 2014).
 Using the right tools has a direct impact on
the performance of the adopted CRM
(Alshawi et al., 2011). As different types
of errors can exist in the same data set, we
often need to implement more than one
error detection tool (Abedjan et al., 2016).
Missi et al., (2005) cite a variety of tools
that can be used to achieve data quality
and integration: tools that provide insight
into one relational access to data, tools
that transform non-relational into
relational data; tools that develop, test, and
perform transformations in databases and
automatically generate code that makes it
easy to manage even the most complex
transformations of all types of data and
applications; tools for converting data
among hundreds of formats and
applications; tools for consolidation,
verification, standardization, real-time
data profiling; a tool that records, models,
and maintains metadata from various
sources, stores numerous models and
versions.
 Data quality can be improved by e.g.
automating data collection, continuous
inspection, correction and cleaning of data
(Even et al., 2010a).
 Data completeness is best addressed by
improving the process of data entry by
including data verification processes. In
the case of mobile CRM, this can be
automated or, in the case of traditional
CRM, should involve an expert who will
verify data entry, which will help improve
accuracy in the organization (Jaya et al.,
2007). Even et al. (2010a) propose that
older data should be ignored, and that
companies should invest in the quality of
the newer data. It is only necessary to
determine what percentage of the data
should be captured.
 To ensure that eCRM uses the latest user
data, organizations can apply rules that
require the use of different date formats
from the same source, so the one with the
most recent date will be selected and
saved (Ahmed et al., 2016). The mobile
CRM system is explicitly designed to
work with data from a central CRM
system, which provides sufficient data
accuracy. Scripts can be implemented to
check the length of the attribute values
(restrictive rules for checking country
codes and a limited number of digits /

44 Marijana Petrović Data quality in customer relationship management (CRM) – literature review
STRATEGIC MANAGEMENT, Vol. 25 (2020), No. 2, pp. 040-047
characters in mobile number / user name).
A security identification scheme can be
used during the registration process to
verify the validity of the user's identity
and his / her phone number. Application
usage can also be monitored to find out
which users are actively using the
application and what messages they are
interested in (Hable & Aglassinger, 2013).
3.2. Problems:
 Data is often stored in separate departments
and it’s not linked across the entire
company’s CRM (Missi et al., 2005).
 Lack of agreement on a standard set of
dimensions that contribute to high data
quality (Jaya, Sidi, Ishak & Affendey,
2007).
 Problems of logical consistency of data
entry (in many organizations there is no
common language of logically compatible
data that would affect CRM (Alshawi,
Missi & Irani, 2011), as well as
inconsistencies in how information is
stored in different units, which occurs
because in CRM, almost everyone in the
organization is in touch with the
application. This results in a greater
likelihood that data quality will be poor
given the large number of people who
interact with the data (Reid & Catterall,
2014).
 The status of existing customer databases
created in the previous period when not
much thought was given to the quality of
the data being collected (Alshawi et al.,
2011).
 Inadequate integration of different data
sources, so each product has unique
identifiers in the database and storage. In
order to see the relevant data of the user's
order, it is necessary to access the files in
the database (Ahmed, Amroush & Ben
Maati, 2016).
 Data collected and stored can have defects
such as: incorrect (typing errors,
misspelling, etc.) and missing values
(initially blank values, changing and
adding new ones) (Even et al., 2010),
(Even, Shankaranarayanan & Berger,
2010a), duplicate records (Reid &
Catterall, 2014), noise records, and
unbalanced datasets (Natchiar & Baulkani,
2014), insignificant values (e.g. an
attribute that preserves the value of a bank
employee in charge of a customer) (Hable
& Aglassinger, 2013). Defective data, in
addition to misleading organizations about
their customers, can compromise the
performance of DM's data quality tools if
they are not filtered out, because customer
information is heterogeneous and with
different scales, with many irrelevant
features. (Tu & Yang, 2013).
 A unique value violation, whereby the
same user can be stored in the database
multiple times with a different user
number (Hable & Aglassinger, 2013). It
brings poor pairing of individual customer
records and thus the inability of the
company to determine how many
customers it actually has because it has
stored the same customer several times in
the database (Reid & Catterall, 2014).
Alshawi et al. (2011) and Reid & Catterall
(2014) mention problems with the unique
customer identifier, where one of such
problems is cited by the lack of a postal
code in European countries.
 Syntax violation. For example, it should be
ensured that phone numbers are of a
certain format, that they have a limited
number of non-numeric characters, so that
records with incorrect values are not
stored (Hable & Aglassinger, 2013).
 Outdated values in user profiles (Even et
al., 2010a). Values that were correct may
not be true anymore. For example, the user
may have changed the phone number
(Hable & Aglassinger, 2013).
 Data quality deteriorates over time. The
elements that the information describes
can change over time, such as changing a
customer's address, their profession,
marital status, etc. (Even et al., 2010). If
Amazon has 60 million active users per
year, it begs the question whether it is
economically logical to maintain all
records at a high level or whether it should
be limited to a specific subset (Even et al.,
2010a).
 High costs for maintaining a new database
(Reid & Catterall, 2014). Maintaining data
at a high level of quality involves
significant costs associated with efforts to
detect and correct defects, set up
management, redesign processes and
invest in quality monitoring tools (Even et

Marijana Petrović Data quality in customer relationship management (CRM) – literature review 45
STRATEGIC MANAGEMENT, Vol. 25 (2020), No. 2, pp. 040-047
al., 2010).
 New challenges in data quality
management resulting from new
technologies - big data and open data
(Jaya et al., 2007). Armeanu, Andrei,
Lache & Panait, (2017) stated that
although ”it is not possible to determine a
priori whether this huge amount of data
should be entirely used in the decision
making process”, one cannot ignore
complex relationships that are based on
the correlations between variables and
output (Armeanu, Andrei, Lache & Panait,
2017).
 For a better view, the identified problems
and suggestions for their solution are
presented in Table 1.
Table 1 Identified problems and solutions of customer data quality
No. Problems Solutions
1 Lack of agreement on a standard set of data
quality dimensions There are no suggested solutions to overcome this problem.
2 Decentralized data storage Missi et al., (2005) mention possible tools to overcome this problem.
3 Inconsistency in data entry and storage
The authors Tu & Yang (2013), Ahmed et al. (2016) and Jaya et al.
(2007) state that it is necessar
y
to have a framework for standardizin
g
data,
and also that it is necessary to include the process of automatic or expert
verification of data.
4 The unsatisfactory condition of existing
customer databases
The solution is to simplify the database architecture and invest in a data
cleanup tool that will reform the data before it is transferred to a new
database (Reid & Catterall, 2014).
5 Inadequate integration of different data sources
Hable & Aglassinger (2013), Ahmed et al. (2016) and Missi et al. (2005)
state that it is necessary to use tools that will allow adequate integration of
data of different formats.
6
Data defects (incorrect and missing data,
unique value violations, syntax violations,
outdated values in user profiles)
Data quality can be improved e.g. automating data collection, continuous
inspection, correction and cleaning of data (Even et al., 2010a). Defect
prevention has been addressed in greater detail by Missi et al. (2005),
Even et al. (2010), Hable & Aglassinger (2013), Reid & Catterall (2014),
and Ahmed et al. (2016) in their works, where they outlined various tools
that can be used to achieve data quality and integration.
7 Data quality is deteriorating over time
It would be desirable to be able to update data throughout the system
(Jaya et al., 2007). Even et al. (2010a) propose that older data be ignored
and that the quality of the newer ones be invested.
8 High costs for securing and maintaining data
quality
Cost reduction can be achieved by addressing key quality problems
(Ahmed et al., 2016) by different algorithms (Even et al., 2010). It is
necessary to reconcile maximizing economic benefits with the optimum
level of data quality (Even et al., 2010).
9 New challenges - big data and open data
The improvement of computer systems and the expansion of databases
can be used to partially overcome big data problems (Erceg, Šereš &
Zoranović, 2019).
Source: Authors’ research
CRM systems make it easy to build long-term
customer relationships by creating centralized
databases and enabling sales force automation.
This minimizes duplication of data, retains
customer knowledge, institutionalizes links
between users, helps manage numerous products /
services, and increases revenue while allowing
firms to cross-sell. Mobile CRM enables
employees and managers to access real-time data
and make better decisions (Negahban et al.,
2016). In addition to the aforementioned
proposals for solving certain data quality
problems, it is essential that employees are
supported by senior management and motivated to
manage the data well. Human resource
management, methods and processes, software
and guiding principles should be combined to
ensure efficient data management and the ability
to transmit them (Foss et al., 2002).

46 Marijana Petrović Data quality in customer relationship management (CRM) – literature review
STRATEGIC MANAGEMENT, Vol. 25 (2020), No. 2, pp. 040-047
Conclusion
CRM has become one of the focal points for many
industries such as banking, retail,
telecommunications, insurance, etc. (Natchiar &
Baulkani, 2014). As CRM relies on data in its
operation, it is very important that the quality of
the data is appropriate so that the organization can
have coordinated CRM responses to today's
business needs (Alshawi et al., 2011). Increasing
numbers of structured data, better tools and big
economic drivers are pushing organizations to
aggregate and use data from different sources
(Bidlack & Wellman, 2010). New challenges are
emerging in managing data quality resulting from
new technologies - big data and open data, with
their ability to collect large amounts of data from
different sources and store them as different types
of data - structured, unstructured and semi-
structured (Jaya et al., 2007).
Data defects can prevent managers and
analysts from having a real picture of customers
and their purchasing preferences, which can
significantly affect marketing efforts that will not
produce the expected results and lead to poor
decisions (Even et al., 2010), and potential
financial risks i.e. financial losses that affect
company’s prosperity (Valaskova, Kliestik &
Kovacova, 2018). They can also affect a
company’s inability to determine how many
customers it actually has (Reid & Catterall, 2014).
When looking at the quality of data used in CRM,
we can conclude that bigger is not always better,
because increasing the number of records that are
monitored, maintaining more attributes and
achieving perfect quality may have technical and
functional merit, but it is not profitable (Even et
al., 2010).
The analysis of the extracted works pointed to
certain problems regarding the quality of
customer data, as well as suggestions for their
solutions. We can conclude that the challenges
regarding the quality of data used in CRM are
reflected in: decentralized storage of data,
inconsistency of their input and storage,
inadequate integration of different data sources,
different number of data defects, and their
tendency to have quality that gets worse over
time. In addition, problems were identified in the
high costs of maintaining data quality, as well as
new challenges in the form of big data and open
data. Possible solutions have been suggested
through a variety of tools and frameworks to
improve them.
In order for organizations to take full
advantage of the customer data they possess for
the purpose of adequately analyzing and
evaluating their desires, preferences, behaviors
and thereby gaining a competitive advantage on
the basis of valuable, hard-to-imitate data, it is
imperative to align economic benefits with the
optimum level of data quality (Even et al.,
2010).SM
References
Abedjan, Z., Chu, X, Deng, D, Fernandez, R.C., Ilyas, I. F.,
Ouzzani, M., Papotti, P., Stonebraker, M., & Tang, N.
(2016). Detecting Data Errors: Where are we and what
needs to be done? Proceedings of the VLDB
Endowment, 9 (12), 993-1004.
https://doi.org/10.14778/2994509.2994518
Adamik, A., Nowicki, M., & Szymanska, K. (2018).
Openness to co-creation as a method of reducing the
complexity of the environment and dynamizing
companies' competitive advantages, Management &
Marketing – Challenges for the Knowledge Society,
13(2), 880-896.
Ahmed, B., Amroush, F., & Ben Maati, M. L. (2016). The
implementation Trends of Intelligence E-CRM in
Business process, 2016 4th IEEE International
Colloquium on Information Science and Technology
(CiSt).
https://doi.org/10.1109/CIST.2016.7805080
Alshawi, S., Missi, F., & Irani, Z. (2011). Organisational,
technical and data quality factors in CRM adoption -
SMEs perspective, Industrial Marketing Management,
40, 376-383.
https://doi.org/10.1016/j.indmarman.2010.08.006
Armeanu, D., Andrei, J., V., Lache, L., & Panait, M. (2017).
A multifactor approach to forecasting Romanian gross
domestic product (GDP) in the short run, PloS ONE 12
(7), 1-23.
https://doi.org/10.1371/journal.pone.0181379
Bidlack, C. R., & Wellman, M. P. (2010). Exceptional Data
Quality using Intelligent Matching and Retrieval. AI
Magazine, 31 (1), 65-73.
https://doi.org/10.1609/aimag.v31i1.2280
Chen, I. J.,& Popovich, K. (2003). Understanding Customer
Relationship Management (CRM) People, process and
technology, Business Process Management Journal, 9,
(5), 272-292.
https://doi.org/10.1108/14637150310496758
Dusmanescu, D., Andrei, J., Popescu, G. H., Nica, E., &
Panait, M. (2016). Heuristic methodology for estimating
the liquid biofuel potential of a region. Energies, 9 (9),
703.
https://doi.org/10.3390/en9090703
Erceg, V., Šereš, L., & Zoranović, T. (2019). Data quality
assessment, International Conference on Applied
Internet and Information Technologies – AIIT 2019,
October 3-4 2019, Zrenjanin, Serbia, Conference
Proceedings, ISBN 978-86-7672-327-0, 112-116.
Even, A., Shankaranarayanan, G., & Berger P. D. (2010).
Evaluating a model for cost-effective data quality
management in a real-world CRM setting, Decision
Support Systems, 50, 152-163.
https://doi.org/10.1016/j.dss.2010.07.011

Marijana Petrović Data quality in customer relationship management (CRM) – literature review 47
STRATEGIC MANAGEMENT, Vol. 25 (2020), No. 2, pp. 040-047
Even, A., Shankaranarayanan, G., & Berger, P. D. (2010a).
Mapping the Quality of Marketing Data: Cost/benefit
Tradeoffs and Optimal Configuration, Journal of
Interactive Marketing, 24, 209-221.
https://doi.org/10.1016/j.intmar.2010.01.001
Ferreira, P., Rodrigues, P., & Rodrigues, P. (2019). Brand
Love as Mediator of the Brand Experience-Satisfaction-
Loyalty Relationship in a Retail Fashion Brand.
Management & Marketing – Challenges for the
Knowledge Society, 14 (3), 278-291.
https://doi.org/10.2478/mmcks-2019-0020
Foss, B., Henderson, I., Murray, D., & Stone, M. (2002).
Managing the quality and completeness of customer
data, Journal of Database Marketing 2002, 10 (2), 139-
158.
https://doi.org/10.1057/palgrave.jdm.3240105
Hable, R., & Aglassinger, T. (2013). Strategies for
Enhancing Data Quality in Mobile CRM, 27th
International Conference on Advanced Information
Networking and Applications Workshops.
https://doi.org/10.1109/WAINA.2013.46
Jaya, I., Sidi, F., Ishak, I., & Affendey, L. S. (2007). A review
of data quality research in achieving high data quality
within organisation, Journal of Theoretical and Applied
Information Technology, 95 (12), 2647-2657.
Missi, F., Alshawi, S., & Fitzgerald, G. (2005). Why Crm
Efforts Fail? A Study of the Impact of Data Quality and
Data Integration, Proceedings of the 38th Hawaii
International Conference on System Sciences – 2005.
Natchiar, S. U., & Baulkani, S. (2014). Customer
Relationship Management Classification Using Data
Mining Techniques, 2014 International Conference on
Science Engineering and Management Research
(ICSEMR).
https://doi.org/10.1109/ICSEMR.2014.7043662
Negahban, A., Kim, D. J., & Kim, C. (2016). Unleashing the
Power of mCRM: Investigating Antecedents of Mobile
CRM Values from Managers’ Viewpoint, International
Journal of Human-Computer Interaction.
https://doi.org/10.1080/10447318.2016.1189653
O' Brien, T. (2015). ‘Accounting’ for data quality in
enterprise system, Procedia Computer Science 64,
442-449.
https://doi.org/10.1016/j.procs.2015.08.539
Peltier, J. W., Zahay, D., & Lehmann, D. H. (2013).
Organizational Learning and CRM Success: A Model
for Linking Organizational Practices, Customer Data
Quality, and Performance, Journal of Interactive
Marketing 27, 1-13.
https://doi.org/10.1016/j.intmar.2012.05.001
Reid, A., & Catterall M. (2014). Hidden Data Quality
Problems in CRM Implementation, Marketing,
Technology and Customer Commitment in the New
Economy, 184-189.
https://doi.org/10.1007/978-3-319-11779-9_67
Sharma, S., Goyal, D. P., & Mittal, R. K. (2010). Imperative
relationship between data quality and performance of
datamining tools for CRM, Int. J. Business Competition
and Growth, 1, (1), 45-61.
https://doi.org/10.1504/IJBCG.2010.032828
Sundararaman, A. (2011). A Framework for Linking Data
Quality to Business Objectives in Decision Support
Systems, 3rd International Conference on Trendz in
Information Sciences & Computing (TISC2011).
https://doi.org/10.1109/TISC.2011.6169110
Tu, Y., & Yang, Z. (2013). An enhanced Customer
Relationship Management classification framework with
Partial Focus Feature Reduction, Expert Systems with
Applications, 40, 2137-2146.
https://doi.org/10.1016/j.eswa.2012.10.028
Valaskova, K., Kliestik, T., & Kovacova, M. (2018).
Management of financial risks in Slovak enterprises
using regression analysis. Oeconomia Copernicana, 9
(1), 105-121
https://doi.org/10.24136/oc.2018.006
Vukelić, G., Stanojević, S., & Anđelić, Z. (2015). Quality of
Research Results in Agro-Economy By Data Mining,
Economics of Agriculture, 62, 1137-1146
https://doi.org/10.5937/ekoPolj1504137V
Zahay, D., Peltier, J. V., & Krishen, A. (2011). Building the
Foundation for Customer Data Quality in CRM Systems
for Financial Services Firms, Database Marketing &
Customer Strategy Management, 19 (1), 5-16.
https://doi.org/10.1057/dbm.2012.6
 Correspondence
Marijana Petrović
ICT College of Applied Studies
Zdravka Celara 16, Belgrade, Serbia
E-mail: marijana.petrovic@ict.edu.rs