Research Proposal
Understanding Customer Relationship Management:
Exploring the Implications of CRM Fit, Market Knowledge
Competence, and Market Orientation
A Research Proposal by: Jounghae Bang (Kris)
Doctoral Candidate
College of Business Administration University of Rhode Island
[email protected] Faculty Advisor: Nik Dholakia, Ph.D.
Professor in Marketing, E-Commerce, Information Systems Areas College of Business Administration
University of Rhode Island Kingston, RI 02881
Contact Information: Jounghae Bang (Kris) College of Business Administration
University of Rhode Island Kingston, RI 02881 Phone: (401) 267-0116 Email: [email protected]
TABLE OF CONTENT CHAPTER 1. INTRODUCTION ... 4 CHAPTER 2. LITERATURE REVIEW ... 10 CHAPTER 3.
CONCEPTUAL FRAMEWORK AND PROPOSITIONS ... 33 CHAPTER 4.
RESEARCH METHODOLOGY ... 55 CHAPTER 5.
DISCUSSION AND IMPLICATIONS ... 66 REFERENCES ... 68
FIGURE FIGURE 1.
A FRAMEWORK OF CRM...16 FIGURE 2.
CRM AND KDD PROCESS LINKAGE...24 FIGURE 3. RESEARCH MODEL...34 TABLE TABLE 1. DEFINITIONS OF CRM...19 TABLE 2.
CUSTOMER RELATIONSHIP RELATED DATA ANALYSIS AND DATA
MINING TOOLS...27 TABLE 3.
CRM SYSTEMS REQUIREMENTS AND CATEGORIES...39 TABLE 4.
RELATIONSHIP CYCLE...43 TABLE 5.
PERSPECTIVES OF FIT...47 TABLE 6.
PROPOSITIONS AND STUDY STRUCTURE...55 TABLE 7.
DEFINITIONS OF VARIABLES...57 TABLE 8.
POTENTIAL ITEMS FOR CRM SYSTEM...58 TABLE 9.
DEFINITIONS OF MARKET ORIENTATION AND MARKET KNOWLEDGE COMPETENCE...61 TABLE 10.
MEASURES FOR MARKET ORIENTATION...62 TABLE 11.
MEASURES FOR MARKET KNOWLEDGE COMPETENCE...63 TABLE 12.
CHAPTER 1.
INTRODUCTION
1.1 Background
Well-managed customer relationship management (CRM) systems have clear economic payoffs. Customer acquisition costs, which are 5 to 7 times higher than customer retention costs (Kotler, 1997), can be held in check; while profits can be boosted by 25% to 85% by reducing customer defections by a mere 5% (Reichheld and Sasser, 1990). For these reasons, customer relational capability has started receiving attention as one of the resources to gain competitive advantages (Day, 2002).
In addition, CRM technologies offer significant possibilities for creating and sustaining ideal, highly satisfying customer relationships (Goodhue 2002; Ives 1990). With the help of new information technologies, managing customer relationships is feasible even as people became more mobile, cities grow, and companies become larger (Goodhue, 2002). Information technology can provide the ability to identify and track individual customers, to monitor service levels by company representatives, and to assist customers in specifying, acquiring, fixing, or returning products (Ives 1990). Through CRM and
database technologies, companies can improve their customer relations, while enhancing their competitive positions (Day, 2002; Ives 1990).
On the flip side, however, implementation of CRM is still far short of ideal (Abbott 2001; Winer 2001). Despite several years of experience, Web-based companies were not able to fulfill many Christmas orders in 2000 and customers continue to have difficulties returning defective products. Most customers have had bad experiences such as poor customer service and unanswered emails (Tweney, 2001). In fact, the 1999 email failure
rate was actually worse than the failure rate in 1998, which Jupiter pegged at 38 percent (Tweney, 2001). According to the Gartner Group, nearly 55% of CRM projects during 2002-2006 are expected to fail (Caufield, 2001).
CRM systems cost an average of $35,000 per call-center agent to deploy, with setup and maintenance adding $28,000 and $40,000 per salesperson over a three-year cycle (Caulfield 2001). Given such high costs of deployment and maintenance, the drastic failure rates represent huge financial risks for most CRM adopters. To make matters worse, 20% of these CRM failures end up souring long-standing customer relationships (Mello, 2002).
1.2 Issues raised
What, then, are the problems that often derail CRM implementations? With regard to the causes of the CRM failure rates, a Gartner study indicated that, through 2004, up to 80 percent of enterprises do not understand how customer relationship management creates value in their customer base (Kirkby, 2002). Because of this lack of understanding, firms have failed to develop unifying CRM strategies to build up their relationship assets.
In a similar vein, many consulting companies and experts point out that the real challenges lie in the “softer” aspects of CRM such as coordinating employees,
understanding customers, and managing organizational environment rather than the technical aspects. For example, CMO Consulting International pinpointed that businesses usually do not understand the customer’s perspective of relationships. Caulfield (2001) indicated that since CRM projects usually involve a variety of departments, the lack of explanation and agreements among the organization members about the project, and therefore the lack of high-level cooperation, are among the reasons for failure. Dubois
(2002) found that data quality issues account for up to 70% of failure rates of data warehouse and contribute to a 55-to-70% failure rate for CRM projects.
Such problems are pervasive in the CRM cycle, starting from defining a customer and customer relationship to managing systems and data quality. Under ideal conditions, CRM systems should ensure that the processes of defining the customer and the customer relationship, and the identification and integration of the business processes to support and serve the customer, are done right. CRM systems should ensure that it is the organization and its customers, and not the technology, which are at the center of the CRM practice, and yet, technology and databases cannot be overlooked due to budgetary concerns.
Therefore, in order to provide better insight of CRM practice, one needs to scrutinize CRM practice from multi-disciplinary perspectives. Such integrated views of CRM will provide better theoretical background to enhance the understanding of as well as improve the practice of CRM.
Little rigorous academic research, however, exists on CRM. Even the definition of CRM is subject to multiple interpretations (Goodhue, Wixom, and Watson, 2002; Winer, 2001; Wright, 2002).
Several studies in MIS area have been examined on the effect of information systems on organization performance, but there have not been convergent findings
regarding the positive effect of IT on performance. Many authors claimed that IT spending has failed to generate significant productivity gains and that the benefits of IT are not satisfying (Franke 1987; Roach 1991; Strassman 1990; Weill 1992). In other words, IT spending has also been linked to significant productivity improvements (Brynjolfsson and Hitt 1993, 1995; Osterman, 1986).
Several studies on service quality, loyalty, and customer retention have also been conducted in the relationship marketing area. Few studies, however, focus on the linkage between the marketing concepts and IT. Even though CRM relies heavily on information technology, the effects of technology have not been clearly found to contribute to the CRM goals. Additionally, it is not clear how technology can be managed to improve the customer relationships in CRM practice.
The proposed research starts with a key question: How can a firm manage customer relationships well using information technology while reducing the risk of CRM failure? This study will be one of the first attempts to provide a systematic investigation of CRM from multi-disciplinary perspectives.
1.3 Research Objectives
Customer relationships and their management are undoubtedly important. Even customers with whom “no relationship” is established can be viewed as members of a category called “null” relationships. CRM systems can manage null relationships as well as any other category.
This study focuses on CRM as a continuous process to satisfy customers and maintain good relationships (including null relationships) with customers rather than as a short-term project.
The purpose of this study is to answer the very basic question: how to establish
and maintain good relationships (either relational or transactional) with customers by using CRM technologies. To answer this question, the proposed study seeks to:
Achieve a comprehensive understanding of CRM practice: what is CRM, how it works, and how can it be made to work better?
Arrive at one integrated framework for CRM.
Identify critical success factors of CRM implementation, how these factors work and interrelate, and what would be the effects of these factors on customer retention and satisfaction, and ultimately on the performance of the CRM-implementing organization.
1.4 Theoretical Basis
This study will achieve its goals by drawing from and blending multiple disciplinary perspectives. Knowledge Discovery in Databases (KDD), the
Task-Technology Fit (TTF) model from Management Information Systems (MIS) and Computer Science, and Market Orientation and Market Knowledge Competence from Marketing and Business Strategy literatures are adopted and adapted for this study.
Market Orientation explains cultural norms of a firm toward a market. Market Knowledge Competence focuses on organizational processes to generate market knowledge from an organizational aspect, while KDD focuses on technological aspects of such
knowledge (Li & Calantone, 1998). In line with Slater and Narver’s (1995) notion that it is important to understand how features of the organization’s culture facilitate these
processes, the cultural aspects (Market Orientation) and systematic processes (Market Knowledge Competence) will be examined together in the context of CRM.
The Task-Technology Fit (TTF) model from MIS provides a way to explain how CRM systems could lead to increased customer retention and satisfaction. Technologies that fit their intended tasks lead to salutary performance impacts (Goodhue, 1995). Moreover, it is not technology in isolation that affects performance – organizational characteristics also come into play (Goodhue, Klein, & March, 2000). These notions are
appropriate for the proposed study of CRM practice: even though technology is the enabler of CRM, it is factors beyond technology that bring success to CRM practice (CMO, 2002).
1.5 Organization of the Dissertation
The remainder of the dissertation will be organized as follows: Chapter 2 reviews the literature on CRM and other related issues. The third chapter introduces a conceptual framework that guides the research along with a set of propositions. Chapter 4 details the research design and methodology. The results of the data analysis are reported in Chapter 5. The dissertation ends with a discussion of the results along with implications,
CHAPTER 2.
LITERATURE REVIEW
2.1 Customers in CRM
To answer the question, “What is customer relationship management?” we need to first define the customer. Questions such as “What is a customer? Who is a customer? What are customer relationships?” need to be addressed before getting into the details of Customer Relationship Management (CRM) technologies, processes, and issues. Many different answers exist to these questions, and these answers vary according the disciplines and perspectives producing the answers.
In Relationship Marketing discipline, not only external customers but also internal customers are included in the customer definition. Internal customers refer to employees and suppliers. (Gamble 1999). Some researchers have argued that satisfying the needs of internal customers improves the capability for satisfying the needs of external customers. Greenberg (2002) also emphasized the importance of the internal customers. He argued that employees are also customers in terms of the service provided and the fee to be charged.
Greenberg (2002) also demarcated customers from clients. Customers are distinguished from clients in terms of the context of business-to-business (B2B) or of business-to-consumer (B2C). That is, in B2B settings, the customers are usually referred to as ‘clients,’ while in the B2C settings, the consumers are called as ‘customers.’ However, he comes to the conclusion that, even without distinguishing client and customer, there are four different types of customers. The four are (1) paying clients, (2) employees, (3) supplier/vendor, and (4) partner.
With the rising importance of partnering and channel management, research studies on the Partner Relationship Management and Channel Relationship Management have picked up steam. For the purposes of this chapter, however, the definition of customer is limited to buyers of the products and services of the firm. Having narrowed the focus of the term “customer” to the product/service buyer, understanding what is CRM and what
elements constitute CRM are the next steps.
2.2 Framework of CRM
Understanding what is CRM and what elements constitute CRM is essential for further investigation of CRM. Many researchers have studied CRM. CRM, however, has connoted different things to researchers in the various disciplines (Goodhue, Wixom, and Watson, 2002; Winer, 2001; Wright, 2002), and, therefore, CRM is being implemented in different ways. For example, to some, CRM means direct emails or database marketing. For others, it refers to OLAP (online analytical processing) and CICs (customer interaction centers). Wright (2002) argued that the understanding of definitions such as ‘customer retention’ and ‘cross-selling’ and their application in practice is often weak (Wright 2002).
Several frameworks have been provided to understand CRM, and most of the frameworks highlight not only the information technology but also the managerial aspects of CRM practice including strategy and people who use the technology. The frameworks are, however, still not clearly integrated. In this chapter, these models will be reviewed and one integrated model will be proposed. Based on the integrated framework, CRM will be defined.
2.2.1 Industry experts
Most of the CRM experts and research point out that CRM should be viewed as an organizational strategy, and therefore, its structure should start from the organization’s goal. For example, Onyx Software viewed the structure of CRM as a pyramid, on the apex of which Business Objectives are placed, with Programs and Metrics, and individual Department Plans at levels below the apex; and at the bottom, there is the foundation of Technology (reported in Greenberg 2002). CMO Group viewed CRM as a strategy and proposed the Integrated CRM (ICRM) framework, spanning the range from internal databases to the market place. ICRM analyzes the data in a company’s database based on the planned relationship structure and develops CRM strategies under conditions of market competition (CMO 2002). Front Line Solution Inc. viewed CRM as a business strategy to select and manage customers to optimize long-term value. According to this view, CRM requires a customer-centric business philosophy and culture to support effective marketing, sales, and service processes. They believe that CRM must start with a business strategy, which drives changes in the organization and work processes, which are in turn enabled by information technology. They emphasized the flow (sequences) because the reverse does not work (reported in Greenberg 2002).
In sum, CRM is viewed as a disciplined business strategy to create and sustain long-term, profitable customer relationships. Successful CRM initiatives start with a business strategy and philosophy that aligns company activities around customer needs. CRM technology is a critical enabler of the processes required to turn strategy into business results.
2.2.2 Academic Studies
2.2.2.1 Winer’s modelWiner (2001) proposed a basic model for CRM. He argued that his CRM basic model shows about what managers should know about their customers and how to use information to develop a complete CRM perspective (Winer 2001).
The model contains following 7 components. A database of customer activity Analyses of the database
Given the analyses, decisions about which customers to target Tools for targeting the customers
How to build relationship with the targeted customers Privacy Issues
Metrics for measuring the success of the CRM program
In this model, the first necessary step is the construction of a customer database in which the data should ideally contain transactions, customer contacts, descriptive
information, and response to marketing stimuli. Second, for the analysis of the database, he pointed out that there is increased attention being paid to understanding each customer and what s/he can deliver to the company in terms of profits. In turn, the concept of “Lifetime Customer Value” has been introduced to marketers, which urges that each customer in the database should be analyzed in terms of current and future profitability to the firm. Third, in the customer selection step, the goal is to use the customer profitability analysis to separate valuable customers from those that are currently hurting profits. This allows the managers to “fire” customers that are too costly to serve relative to the revenues being produced (Zeithaml, Rust, and Lemon, 2001). Fourth, with the advance of new information technology, consultants such as Peppers and Rogers (1993) have urged companies to begin
to dialogue with their customers through these targeted approaches rather than talking “at” customers with mass media. Fifth, the overall goal of relationship programs is to deliver a higher level of customer satisfaction than competing firms deliver. Customer satisfaction is the ultimate goal of the programs. Sixth, privacy issues take place. Many consumers and advocacy groups are concerned about the amount of personal information that is contained in databases and how it is being used particularly with the popularity of the Internet. Opt in and Opt out options are included in the Privacy issues. Lastly, metrics come into play. Increased emphasis is being placed on developing measures that are customer-centric and give managers a better idea of how their CRM policies and programs are working.
2.2.2.2 Ang and Buttle’s Model
Ang and Buttle (2002) conceptualized CRM as three levels of abstraction: strategic, operational and analytical. At a strategic level, CRM is seen as a core business strategy. In their view, CRM is consistent with customer centric or market oriented. At an operational level, CRM is concerned with automating chunks of the enterprise. CRM vendors have developed products that enable automation of selling, marketing, and service functions. A major driver of CRM implementations has been channel integration. Most CRM projects involve a number of smaller projects, which are also very challenging, such as: systems integration, data quality improvement, process reengineering, data analytics, and market segmentation. At an analytical level, CRM is concentrated on exploitation of customer data to drive more highly focused sales and marketing campaigns. Analytical tools such as decision trees, neural networks and clustering can be used to improve the effectiveness and efficiency of customer acquisition, customer development and customer retentions
strategies.
Ang and Buttle defined CRM from these three perspectives; CRM is the core business strategy that integrates internal processes and functions and external business networks to create and deliver value to targeted customers at a profit. It is grounded on high quality customer data and enabled by information technology.
2.2.2.3 Goodhue, Wixom, and Watson’s model – Technical Architecture
Goodhue, Wixom, and Watson (2002) defined CRM as “any application or
initiative designed to help an organization optimize interactions with customers, suppliers, or prospects via one or more touch points – such as a call center, salesperson, distributor, store, branch office, Web, or e-mail – for the purpose of acquiring, retaining, or cross-selling customers.”
They viewed CRM’s technical architecture from two sides; analytical side and operational side. On the analytical side, a data warehouse typically maintains historical data that supports generic applications, such as reporting, queries, online analytical processing (OLAP), and data mining, as well as specific applications such as campaign management, churn analysis, propensity scoring, and customer profitability analysis.
On the operational side, data must be captured, integrated, and stored from all in-bound touch points, including the Web, call centers, stores, and ATMs.
2.2.2.4 Greenberg’s model – Types of CRM technology
Greenberg (2002) highlighted three components of CRM technologies: Operational CRM, Analytical CRM, and Collaborative CRM. Operational CRM is the “ERP-like” segment of CRM. He indicated the possibility of integrating operational CRM with the financial and human resources functions of the enterprise resource planning (ERP)
applications. With this integration, end-to-end functionality from lead management to order tracking can be implemented. Analytical CRM is the capture, storage, extraction,
processing, interpretation, and reporting of customer data to a user. The value of the application is not just in the algorithms and storage, but also in the ability to individually personalize the response using the data. Collaborative CRM is almost an overlay. It is the communication center that provides the neural paths to the customer and his suppliers. It could be any CRM function that provides a point of interaction between the customer and the channel itself.
2.2.3 Integrated Framework of CRM
Based on the analysis of existing models, one integrated model is proposed. In this model, CRM can be viewed as three different levels (shown in Figure 1). One is the management level, which contains goals, strategy, plans and metrics. The second level is the technological structure, which contains analytical, operational, and collaborative technology. The third level is customer. Each level has to be coordinated.
First, as an organizational strategy (Ang and Buttle 2002; Day and Van den Bulte 2002; Smith 2001), CRM systems should deal with various management levels. Strategies should be established to accomplish corporate-level goals. Specific plans have to be crafted and the performance of these plans has to be tracked and evaluated thoroughly. These goals, strategies, and plans should reflect the corporate philosophy regarding customer orientation and inculcate a customer-responsive corporate culture.
Figure 1. A Framework of CRM
Second, the technological structure needs to be worked out, including analytical CRM systems, operational CRM systems, and collaborative CRM systems.
Analytical CRM systems help a firm to analyze the huge amount of customer data so that the firm can find some patterns of customers’ purchasing behavior (Goodhue, Wixom, and Watson, 2002). Operational CRM systems entail the integration of all the front-end customer-facing functions of the business. For example, since the sales process depends on the cooperation of multiple departments performing different functions, the systems to support the business processes must be configurable to meet the needs of each department (Earl, 2003; Greenberg, 2002). Collaborative CRM systems refer to CRM functions that provide points of interaction between the customer and the channel – the so-called “touchpoints” (Greenberg, 2002).
Third and finally, the raison d’être of any CRM system is the customer. Customer service and related issues must be included in the design, implementation, and operation of any CRM system. Davids (1999) emphasized that viewing CRM as a sales or customer service solution is the surest way to fail. The only way to benefit the organization is to first benefit their customers (Davids, 1999). CRM software needs to pay attention to not only users within the implementing organization, but also to the end customer (Earl, 2003).
Goal Strategy Plan Metrics
Analytical CRM systems Analytical CRM systems Collabora -tive CRM systems Collabora -tive CRM systems Operational CRM systems Operational CRM systems Custo mers
While enhancing the operational efficiency of the organization is an important goal of using CRM technology, servicing and delighting the customers are the ultimate end-goals as well as the ultimate determinants of success.
Each level has to be coordinated for successful CRM implementation and
performance outcomes. It is important to note that placing customers in the center should be the first. And then every other activity can be done to understand and satisfy the customers.
2.3 Definition of CRM
Many researchers and experts have defined CRM and Table 1 shows some of the recent definitions.
For example, Goodhue, Wixom, and Watson (2002) defined CRM as “any application or initiative designed to help an organization optimize interactions with customers, suppliers, or prospects via one or more touch points – such as a call center, salesperson, distributor, store, branch office, Web, or email – for the purpose of acquiring, retaining, or cross-selling customers.” Wright, Stone, and Abbott (2002) followed Kleindl (2001)’s definition that CRM systems “combine software and management practices to serve the customer from order through delivery and after-sales service.” Ang and Buttle (2002) defined CRM as the core business strategy that integrates internal processes and functions and external business networks to create and deliver value to targeted customers at a profit. It is grounded on high quality customer data and enabled by information technology. Dyché (2002) defined CRM as “the infrastructure that enables the delineation
of and increase in customer value, and the correct means by which to motivate valuable customers to remain loyal – indeed, to buy again”.
Table 1. Definitions of CRM Authors Definition
Goodhue, Wixom, and Watson (2002)
Any application or initiative designed to help an organization optimize interactions with customers, suppliers, or prospects via one or more touch points – such as a call center, salesperson, distributor, store, branch office, Web, or email – for the purpose of acquiring, retaining, or cross-selling customers
Wright, Stone, and Abbott (2002) Kleindl (2001)
Combination of software and management practices to serve the customer from order through delivery and after-sales service Ang and Buttle
(2002)
A core business strategy that integrates internal processes and functions and external business networks to create and deliver value to targeted customers at a profit. It is grounded on high quality customer data and enabled by information technology.
Dyché (2002) The infrastructure that enables the delineation of and increase in customer value, and the correct means by which to motivate valuable customers to remain loyal – indeed, to buy again
Kellen (2002) A business strategy aimed at gaining long-term competitive advantage by optimally delivering customer value and extracting business value simultaneously
Kim, Suh, and
Hwang (2003) Managerial efforts to manage business interactions with customers by combining business processes and technologies that seek to understand a company’s customers.
Kirkby (2002) A blueprint for turning for an enterprise’s customers into an asset by building up their value.
Rembrandt (2002) A good CRM program enables customers to easily access the information they need at any time and includes a 24-by-7 web-site, fast email tools and the ability to discuss problems with a human being rather than an electronic answering system.
Smith (2001) A business strategy combined with technology to effectively manage the complete customer life cycle.
Most earlier studies viewed CRM as a database marketing method or a sales orientated IT system. Most of them also viewed CRM as a strategy of an organization rather than as a new information systems implementation project.
Therefore, based on Ang and Buttle (2002)’s definition and the components of integrated CRM framework, CRM can be defined as follows:
CRM is a core business strategy that integrates internal processes and functions and external business networks to interact, create, and deliver value with personalized treatment to targeted customers to improve customer satisfaction and customer retention at a profit. It is grounded in high quality customer data and enabled by information technology.
2.4 CRM and Information Technology
Moe and Fader (2001) argued that the Internet provides managers with an
enormous amount of customer information that was previously unavailable, and therefore, the new struggle has been to manage and use this information accurately and efficiently to somehow measure customers, trends, and performance (Moe 2001). In this section, the issues related to the technical structure of CRM will be reviewed.
2.4.1 Analytical CRM and Data mining and Knowledge Discovery in
Database
2.4.1.1 Data mining and Knowledge Discovery in Database
Since multiple data formats and distributed nature of knowledge on the web make it a challenge to collect, discover, organize and manage CRM-related customer data (Shaw et
al., 2001), knowledge discovery in databases (KDD) methods are receiving attention in
relationship marketing contexts (Mackinnon 1999; Fayyad, Piatetsky-Shapiro, and Smyth, 1996). Massive databases are commonplace, and they are even growing and changing, heterogeneous, and various in types (Mackinnon and Glick, 1999). Data mining and KDD anticipate database that are not only massive but also growing and changing (Mackinnon
and Glick, 1999). Systematic combining of data mining and knowledge management techniques can be the basis for advantageous customer relationships (Shaw et al., 2001).
Knowledge discovery in databases (KDD) is defined as the iterative process of data selection, sampling, pre-processing, cleaning, transformation, dimension reduction,
analysis, visualization, and evaluation (Mackinnon, 1999). As a component of KDD (Fayyad, Piatetsky-Shapiro, and Smyth, 1996), data mining can be viewed defined as the process of searching and analyzing data in order to find latent but potentially valuable information (Berry and Linoff, 1997; Fayyad, Piatetsky-Shapiro, and Smyth, 1996a; Frawley, Piatetsky-Shapiro, and Matheus, 1992; Shaw et al., 2001).
KDD constitutes the overall process of extracting useful knowledge from databases. It is a multidisciplinary activity with following stages (Brachman et al. 1996; Bruha et al. 2000; Fayyad, Piatetsky-Shapiro, and Smyth, 1996)
• Selecting the problem area and choosing a tool for representing the goal to be achieved
• Collecting the data and choosing tools for representing objects (observations) of the dataset
• Preprocessing of the data: integrating and cleaning data
• Data mining: extracting pieces of knowledge
• Postprocessing of the knowledge derived: testing and verifying, interpreting, and applying the knowledge to the problem area at hand. Data mining can be seen as an extension of traditional data analysis and statistical approaches since it incorporates analytical techniques drawn from a range of disciplines including numerical analysis, pattern matching and areas of artificial intelligence (Jackson 2002).
Data mining methods can be divided into two categories: the use of statistical models and leading-edge artificial intelligence (machine learning) methods. The latter include neural networks, decision trees, and genetic algorithms, rule induction, fuzzy logic,
pattern recognition, case-based reasoning, and rough set theory (Chen, Sakaguchi, and Frolick, 2000).
As a source of information, a data warehouse can be used for KDD and data mining (Gray and Watson, 1998). It entails data cleaning and data integration, which can be
viewed as an important pre-processing step for data mining (Jackson 2002).
As the new channel for distribution of goods, promotion of products, handling of transactions, and coordination of business processes, the Web is emerging as an important and convenient source of customer data (Shaw, 2001).In web-based relationship
marketing, three distinct categories of data mining have emerged: web content mining, web structure mining, and web usage mining (Jackson, 2002).
Web content mining describes the discovery of useful information from the web content/data/documents. Essentially, the Web content data consists of the data the web page was designed to convey to the users, including text, image, audio, video, metadata, and hyperlinks. Web structure mining is a tool to discover the model underlying the link structure of the Web while web usage mining tries to make sense of the data generated by the Web Surfer’s sessions or behaviors. Web usage mining is also referred to as clickstream analysis (Edelstein 2001).
Clickstream data can be viewed as a Web visitor’s trail around the site. Marketers can examine a customer’s navigation patterns and guess about which actions to take. As well, they can combine those patterns with more specific customer data – customer’s previous purchases in that product category, key demographic and psychographic data, or her/his lifetime value score – to provide a holistic view of that customer’s value and interest (Dyche 2002). Therefore, valuable information hidden in the clickstream data of many commerce sites can provide sharp diagnostics and accurate forecasts, allowing
e-22
Cleaning
Data Mining
commerce sites to profitably target and reach key customers (Moe 2001). Such Web-based CRM systems require large, integrated data repositories and advanced analytical capability. Even though there are many success stories, a Web-based CRM project is still an expensive and risky undertaking.
OLAP stands for On-Line Analytical Processing. OLAP is used to describe the various types of query-driven analysis that are undertaken when analyzing the data in a database or a data warehouse (Berry and Linoff, 2000). It involves in generating an online report, analyzing the results, and submitting a more detailed query in order to understand the result data (Dyche 2002). Therefore, data mining and OLAP can be seen as
complementary tools (Jackson 2002). Both Web-based CRM systems and OLAP, in general, involve vast volumes of both structured and unstructured data. One common challenge with managing this data is to incorporate unstructured data into a data
warehouse, which typically poses a problem because traditional database systems are not designed for unstructured data.
Research in KDD in general is intended to develop methods and techniques to process a large volume of unstructured data in order to retrieve valuable knowledge (which is ‘hidden’ in these databases) that would be compact and abstract; yet understandable and useful for further applications (Bruha et al. 2000).
2.4.1.2 CRM and KDD
There are two aspects to the link between CRM and KDD: process and issue. In the process aspect, the KDD process and types of CRM systems are addressed. Issue aspect reviews different relationship marketing issues and the data mining tools.
23
Figure 2.
CRM and KDD process Linkage
Figure 2 explains the process aspect of linkage. As explained above, the importance of gaining knowledge has been well recognized. In line with this notion, CRM starts with understanding customers and gaining more knowledge about the customers. Therefore, the link between KDD and CRM takes place on an analytical CRM part of CRM systems and customer knowledge discovery in database process of overall KDD process as shown in Figure 2. Collaborative CRM systems would help collecting accurate information from customers while operational CRM can capitalize on the result of analyses. Problem definition stage of KDD process can be done also in the management dimension of CRM. Following the definition of KDD and DM, data mining techniques are included under the analysis stage of KDD. In sum, gaining customer knowledge becomes critical for managing customer relationship and is benefited by systematic knowledge generating process. For
24 Operational CRM Operational CRM CRM KDD Process Information Flow Analytical CRM Analytical CRM Collaborative CRM Collaborative CRM C U S T O M E R S C U S T O M E R S
Interpreting Analyzing Cleaning Defining problem & Collecting
Cluster analysis Neural networks Regression analysis Decision Trees Discrimination analysis Correlation analysis Association Rules, etc. Cluster analysis Neural networks Regression analysis Decision Trees Discrimination analysis Correlation analysis Association Rules, etc.
Data Mining
Techniques
effective customer-centric marketing strategies, the discovered knowledge has to be managed in a systematic manner.
Different relationship marketing issues have been emerged and those rely heavily on technologies, especially for thorough analysis. Various data mining techniques and KDD process exists and provide the right tools to solve the problems. Among others, database marketing and one-to-one marketing methods have come to the fore. The strategic goal of database marketing is to use collected information to identify customers and
prospects as individuals and build continuing personalized relationships with them, leading to greater benefits for the individuals and greater profits for the corporation (Kahan 1998). Database marketing anticipates customer behavior over time and reacts to changes in the customer’s behavior. Database marketing identifies unique segments in the database reacting to specific stimuli such as promotions (McKim 2002).
One-to-One marketing represents the ultimate expression of target marketing – market segments with just one member each – or at least one at a time (Pitta 1998). It relies on a two-way communication between a company and its customers to enhance a true relationship and allows customers to truly express the desires that the company can help fulfill (Dyche, 2002). A promising solution to implementing one-to-one marketing is the application of data mining techniques aided by information technology. Data mining allows organizations to find patterns within their internal customer data. Whatever patterns are uncovered can lead to target segmentations. Armed with such information, organizations can refine their targets and develop their technology to achieve true one-to-one marketing (Pitta 1998).
As an extension of one-to-one marketing, the concept of permission marketing is focused on seeking customers’ agreement about desired marketing methods. Customers not
only needs to be communicated with as individuals, they themselves should be able to stipulate how and when they wish to be approached (Newell 2003). One-to-one and
permission marketing rely heavily on information technology to track individual customers, understand their differences, and acknowledge their interaction preferences (Dyche, 2002).
Data mining methods allow marketers to sift through growing volumes of data and to understand their customers better. Shaw et al. (2001) introduced three major areas of application of data mining for knowledge-based marketing – (1) customer profiling, (2) deviation analysis, and (3) trend analysis. Also, Jackson (2002) noted that data mining can be used as a vehicle to increase profits by reducing costs and/or raising revenue. Some of the common ways to use data mining in customer relationship context include:
• Eliminating expensive mailings to customers who are unlikely to respond to an offer during a marketing campaign
• Facilitating one-to-one marketing and mass customization opportunities in customer relationship management.
Also, by determining characteristics of good customers (profiling), a company can target prospects with similar characteristics. By profiling customers who bought a
particular product a firm can focus attention on similar customers who have not bought that product (cross-selling). Profiling also enables a company to act to retain customers who are at risk for leaving (reducing churn or attrition), because it is usually far less expensive to retain a customer than acquire a new one (Berry and Linoff, 2000)
Therefore, many organizations use data mining to help manage all phases of the customer lifecycle, and CRM systems can benefit from well-managed data analysis based on data mining. Table 2 illustrates the each relationship marketing issues, and includes the possible customer analyses and potential data mining techniques for the analyses.
Table 2.
Customer relationship related data analysis and Data Mining Tools
CUSTOMER RELATIONSHIP MARKETING ISSUES
Database Marketing One-to-One Marketing Permission Marketing Issue Understanding
customers with the database on customer behavior over time including reactions to changes
Communicating with customers as individuals Developing custom products and tailored messages based on customers’ unspoken needs.
Seeking customers’ agreement about desired marketing methods.
Challenge Identifies unique segments in the database
Find patterns within the internal customer data. Track individual customers Understand their differences Track individual customers Understand their differences Acknowledge their interaction preferences Stimulate the customer’s response Possible analysis Segmentation Classification Prediction Classification Dependency Analysis Data mining Technique most likely used Descriptive and visualization Cluster Analysis Neural networks Regression analysis Neural networks Decision Trees Discriminant Analysis Descriptive and visualization Neural networks Regression analysis Correlation Analysis Decision Trees Discriminant Analysis Case-Based Reasoning Association Rules While companies are eager to learn about their customers by using data mining technologies, it is very difficult to choose the most effective algorithms for the diverse range of problems and issues that marketers face (Kim, Kim, and Lee 2002). Data mining studies, however, have focused on the techniques and the development of the better techniques while customer relationship studies have focused on the interface to the customer and the strategies to manage customer interactions (Shaw et al., 2001). More in-depth research on the development of the better techniques from the marketing and customer relationship aspects is needed.
As well, Shaw et al. (2001) mentioned that the process of choosing the target goals of knowledge discovery and techniques for data mining on a specific set of data is still unstructured and based on judgment. That is, there has not been yet the systematic method established. It is important to realize that even though the machine provides the outcomes from the data analysis, the interpretation and application are still on people.
Therefore, in different organizational environment, how to manage KDD process and customer relationship with the knowledge generated by sharing the knowledge will be the next problems to solve.
2.4.2 Operational CRM and Integration
Operational CRM technology can be seen as the systems, which start from ordering to delivering the product to the customers. For this operational CRM, following two issues are addressed: business process reengineering and enterprise resource planning.
In order to manage and enhance customer relationships, business process improvements are as important as data analysis. Dyche (2002) pointed out that every successful CRM program involves a process improvement of some kind. All the CRM-related business processes should be designed around the customer’s perspective with the ultimate goal of improving the customer’s experience.
However, it is important to distinguish between operational CRM and ERP. The concept of enterprise resource planning (ERP) is the integration of all office functions so that any interruptions and breaks in the processes were smoothed out and the
incompatibilities of any applications were eliminated or reduced. When the corporate system is seen to have two distinct chains: the supply chain and the demand chain
(Greenberg, 2002), ERP is in the supply chain and CRM is in the demand chain. The supply chain covers the back office to external suppliers and distributors while the demand chain extends the front office to the customers and the channel. Operational CRM is more toward the front office functions dealing with customers while ERP is a highly integrated system of back-office functions that are integrally customized and linked to all existing office business processes. Even though the natures of CRM and ERP are different, the ideal of seamless integration between CRM and ERP becomes closer to a reality due to the development of Internet architectures and Web (Greenberg, 2002).
There are several studies conducted on this area. For example, for the e-business process, Wang, Hidvegi, Bailey Jr., and Whinston (2000) proposed a verification method that determines and checks whether a system satisfies certain specifications under all circumstances. They demonstrated that model checking has potential in economically checking for certain flaws (Wang 2000). El Sawy and Bowles (1997) noted that customer support and service is becoming one of the most critical core business processes. They attempted to provide insights for redesigning IT-enabled customer support processes. To meet the demanding requirements of the emerging electronic economy in which fast response, shared knowledge creating and inter-networked technologies are the dynamic enablers of success (El Sawy 1997). Holweg and Pil (2001) argued that as companies rely more on the forecast, they lose more sight of real customer requirements, and it is harder to handle a customer order when it comes along. They introduced three dimensions of a successful build-to-order strategy, which are process flexibility, product flexibility, and volume flexibility. They stressed out that the three dimensions should be optimized across the entire value chain, rather than in select parts, and companies and their suppliers must first understand what customers want (Holweg 2001).
2.4.3 Collaborative CRM
Collaborative CRM is the communication center (Greenberg, 2002). It can be any CRM function that provides a point of interaction between the customer and the channel.
Even before the Internet arrived, companies were under pressure to serve their customers with more varied channels (e.g. toll free call center), and the Internet also became as one of the channels to customers (Johnson 2002). However, it is important to note that companies should employ the Internet in a way to ensure that the technology enhances all their other channels. All the channels should be skillfully managed to avoid potential channel conflict in ways that allow channels to complement one another (Johnson 2002).
In the similar vein, Butler (2000) also noted that the online channel can suppress the growth of other channels. It is still possible that the traditional channel may be a best way to offer the product or services. In regard to the online channel, Butler (2000) pointed out that the online channel is so much more than a showcase or a communications tool and that the online channel can be as expensive as, if not more expensive than, the other channels. Therefore, companies must plan the online channel to increase their visibility, accessibility, and sales to the growing customer base on the Internet, and to enhance customer
relationships. As well, they have to plan for analytics as they integrate the online channel into CRM (Butler 2000).
One study, however, found that over 50 per cent of the respondents (who were in the UK industry) said that there would be no changes to channels linked to the
implementation of CRM (Abbott 2001).
Some of the articles for this collaborative CRM are about the Website. The effects of the designs of web pages and web portal are mostly main investigation topics. For example, Mandel and Johnson investigated the effect of visual primes on the choices of experts and novices of Web (Mandel 2002). They noted that the finding confirmed online atmospherics in electronic environments could have a significant influence on consumer choice.
Yuan (2002) argued that agents, which are the catalysts for commerce on the Web, are mostly price-dominated and unreflective of the nature of supplier/consumer
differentiation, or the changing course of differentiation over time. To overcome those problems, Yuan (2002) proposed a personalized and interactive comparison-shopping engine. The author argued that these engines are able to leverage the interactive power of the Web for a more accurate understanding of consumer’s preferences by combining the agent-enabled customization of contents and the agents-enabled behavior analysis of interactions (Yuan 2002).
Joh and Lee (2002) pointed out that most of the e-market places for B2B electronic commerce are seller-centric rather than buyer-centric. Each e-marketplace organizes the directory of e-catalogs for the items it handles, and for the buyers, these external directories are not efficient to integrate with the internal e-procurement systems of the buyers. In order to overcome this problem and inconvenience, they propose the logic programming
approach and a top-down algorithm (Joh 2002).
Kang and Han (2002) also introduced the agent based e-marketplace system for more fair and efficient transaction. They noted that many users are still unfamiliar with the system and find it difficulty buying and selling products in the cyber marketplace despite the rapid emergence of Internet-based electronic transactions. They suggests a broker-based
synchronous transaction algorithm that would guarantee a more faire and efficient transaction deal for both sellers and buyers (Kang 2002).
Some of the studies more focus on the design of the application through which consumers interact with the businesses. Balasubramanian, Ma, and Yoo (1995) proposed the systematic approach to designing a WWW application (Balasubramanian 1995). As well, Isakowitz, Stohr and Balasubramanian (1995) noted that hypermedia projects are different since they may involve people with very different skill sets, and the design of hypermedia applications involves capturing and organizing the structure of a complex domain and making it clear and accessible to users. In their study, they attempted to develop a methodology for structured hypermedia design (Isakowitz 1995).
In sum, analytical, operational, and collaborative CRM systems and the related issues and studies have been reviewed. Abundant studies have been conducted for each different area of CRM systems, and specific techniques and tools have been developed. However, overall CRM systems effectiveness or managing CRM systems remains for further investigation. From MIS field, there are only a few studies on overall CRM information systems, and yet they are focused on implementation of the system and the effectiveness based on the user of systems (e.g. Gefen and Ridings, 2002). Therefore, the investigation from multidisciplinary perspectives is needed to provide better understanding of CRM. Next chapter, an integrated conceptual framework will be proposed and
discussed. 32 CRM Performance CRM Performance
CHAPTER 3.
CONCEPTUAL FRAMEWORK AND PROPOSITIONS
3.1 Conceptual Framework
Figure 3 outlines the proposed model of factors that drive CRM performance and success. As the framework of CRM indicates, the proposed model includes three
dimensions – Management (Market Knowledge Competence), Technology (CRM Fit), and Customer (Market Orientation).
Since Ang and Buttle (2002) noted that the strategic thrust of CRM is “market-oriented,” Market Orientation is included in the proposed model to inject aspects of corporate culture that support strong customer focus. Since it enables an organization to understand customer needs and offer products and services that meet those needs, market orientation is a means to developing a competitive advantage (Jaworski and Kohli, 1993). The relationship between market orientation and performance has been examined
(Deshpande, 1999) and several studies have found support for the fundamental market orientation-performance relationship (Narver, Jacobson, and Slater, 1999; Narver and Slater, 1990; Nobel, Sinha, & Kumar, 2002; Pelham, 2000; Pelham and Wilson 1996; Slater and Narver, 1994). However, Jaworski and Kohli (1993) argued that technological turbulence – the rate of technological change – will moderate the relationship between a market orientation and business performance. Since CRM practice relies heavily on technology, market orientation itself may not be enough to explain the effects and impacts of CRM practice. Therefore not only Market Orientation, but also CRM Fit and Market Knowledge Competence are included in the model. Figure 3 shows the relationships between the factors.
33
CRM CRM
Figure 3. Research Model
Information technology plays a critical role in the CRM practice. And in fact, huge investments in technology characterize the contemporary practice of CRM. To explain how CRM systems could lead to increased customer retention and satisfaction, one of the valuable frameworks is the Task-Technology Fit (TTF) model from MIS. The TTF model highlights the importance of task-technology fit in explaining how technology leads to performance impacts (Goodhue, 1995).
These notions are appropriate for the proposed study of CRM practice since many CRM experts have claimed that, even though technology is the enabler of CRM, it is not just technology that brings success to the CRM practice (CMO, 2002). Sisodia and Wolfe (2000) note that to conquer the biggest challenges marketing faces, “neither the data in IT systems nor the computer is the solution (authors stressed this point),” and argued that relationship marketing requires more complex information systems than does product-driven or transaction-product-driven marketing because of increased intimacy among providers, channel clients, and consumers. In relationship marketing, information about consumers and players in marketing channels is gathered on an individual basis and used to tailor products, product distribution, and marketing messages.
Therefore, investigation of CRM practice requires the understanding of both information technology and the goals and tasks of CRM. For this reason, not merely Technology, but CRM Fit is proposed in this model. The concept of CRM Fit captures the idea that along with technology, organizational factors play a crucial role in the success of CRM systems. 34 CRM Performance CRM Performance Technology Technology Market Knowledge Competence Market Knowledge Competence Tasks of CRM Tasks of CRM Organization Characteristics Organization Characteristics CRM Fit CRM Fit Market Orientation Market Orientation
Furthermore, CRM is not a single project but a continuing process. Market
Knowledge Competence is treated as the process-focused knowledge generating capability of the organization. CRM systems rely heavily on technology, and a customer knowledge management process with appropriate technology is critical for understanding customers.
In the next section, each factor that influences CRM performance and exploratory research propositions flowing from that factor are discussed.
3.2 CRM Performance
In the effort to understand CRM, it is important to examine the link of CRM to performance outcomes. CRM performance can be discussed along two distinct dimensions: employing subjective vs. objective measures, and employing financial vs. nonfinancial measures.
First, organization’s performance can be categorized into financial company performance and nonfinancial company performance (Homburg et al. 2002). Financial company performance measures are profitability measures (e.g. ROI) while market-based performance (nonfinancial company performance) relates to the effectiveness of an organization’s marketing activities with the variables such as customer satisfaction, customer retention, customer benefit, and market share (Menon, Bharadwaj, and Howell, 1996; Morgan and Piercy 1996). Customer satisfaction occurs as a result of a customer’s interactions with the firm over time (Anderson, Fornell and Lehmann 1994; Crosby, Evans and Cowles 1990), and most of prior research has found that satisfaction has a positive effect on customer loyalty (Bloemer and Ruyter, 1998; Rust and Zahorik 1993; Szymanski and Henard, 2001). Gronholdt, Martensen and Kristersen (2000), in particular, found the
significant customer satisfaction – customer loyalty relationship at the organizational level. Therefore, excellent work in CRM is expected to lead to higher customer satisfaction rate and retention rate.
In addition, previous research supports that nonfinancial performance leads to improved financial performance (Rust, Zahorik, and Keiningham, 1995).
Therefore, CRM performance can be seen as two-dimensional: financial and nonfinancial performance.
Second, issues of subjective and objective judgmental assessment of performance have been raised (Noble, Sinha, & Kumar, 2002). In the studies of Pelham and Wilson (1996) and Jaworski and Kohli (1993), significant results were found when using a subjective relative performance measure.
Therefore, in the proposed study, CRM performance refers to how well CRM practice executes in terms of subjective assessment such as customer satisfaction, retention, and customer benefit, and objective assessment (e.g. market share). As well, the financial profitability is included in the CRM performance.
In the following section, the factors expected to influence the CRM performance are discussed.
3.3 CRM Fit
In order to explain the effect of CRM information systems, TTF model from MIS is adopted and adapted. Task-Technology Fit in the TTF model is defined as the degree to which a technology assists an individual in performing his/her portfolio of tasks (Goodhue and Thompson, 1995). Following the TTF definition, for the purposes of this study CRM
Fit is defined as the degree to which CRM systems match well to the tasks and goals of CRM.
TTF model assumes that the performance impacts are dependent on the fit between three constructs: technology characteristics, task requirements, and individual abilities. Thus it emphasizes that it is not the technology in isolation that affects performance – organizational characteristics also come into play (Goodhue et al. 2000).
In fact, there have been similar findings reported in CRM research. Even though technology itself is well recognized as an important player in the CRM practice, the direct effect of technology on CRM performance has been found not significant (Croteau and Li 2003; IDC, 2000). Croteau and Li (2003) found that the relationship between technological readiness and CRM impact was not significant. Rather, the indirect effect of technological readiness was found significant through the knowledge management capabilities. IDC (2000) found that a large proportion of CRM technology deployments do not perform up to expectations. Furthermore, TTF model studies showed that the fit has significant
relationship with individual performance (Goodhue et al. 2000). Therefore, CRM Fit, rather than technology itself, is expected to affect CRM performance. Therefore the following relationship is expected:
P1: CRM Fit is positively related to CRM performance.
3.4 Antecedents of CRM Fit and the Relationship
In this section, the CRM systems and CRM tasks are addressed. The task and technology are viewed as the antecedents of TTF in the TTF model. Here in the proposed
study, the quality of CRM systems and the characteristics of CRM tasks and goals are viewed as antecedents of CRM Fit.
3.4.1 CRM systems
In the TTF model, technologies are viewed as tools for carrying out organizational tasks (Goodhue and Thompson 1995). The tools can be computer systems (hardware, software, and data) and user support services (training, help lines, etc.).
The technical architecture of CRM can include multiple applications: performing analytical, operational, and collaborative functions. In the CRM technical structure, on the analytical side, a data warehouse typically maintains historical data that supports generic applications such as reporting, queries, online analytical processing (OLAP), and data mining as well as specific applications such as campaign management, churn analysis, propensity scoring, and customer profitability. On the operational and collaborative sides, data must be captured from the in-bound touch points, including the Web, call centers, stores, and ATMs; as well as outbound touch points such as email, direct mail,
telemarketing, and mobile devices (Goodhue, Wixom, and Watson, 2002).
Three different targets of CRM technical structure also have been identified. The three CRM targets are applications, infrastructure, and transformation (Goodhue 2002). All these three targets are supposed to be addressed by CRM systems but, in practice, most of the companies can be categorized as focusing primarily on one of these three CRM targets.
The unique requirements of CRM system should be identified. Dyché (2002) provides the possible requirements for CRM systems for firms attempting to choose such systems.
• Integration and connection requirements: The ability of the tool to integrate into
the company’s unique technology infrastructure from a hardware, software, and networking perspective.
• Processing and Performance requirements: The ability to support and control
required operations
• Security requirements: The ability to limit user access
• Reporting requirements: The versatility to provide company and user-requested
information.
• Usability requirements: Enabling end users to easily and intuitively accomplish
required tasks.
• Function – enabling features: The way in which the tool provides certain required
functionality
• Performance requirements: Laying out acceptable turnaround time for CRM
activities or reporting response time
• Availability requirements: The acceptable level of system availability (Dyche, 2002)
These requirements can be grouped into four basic categories: Integration and Connection, Functionality, Security, and Usability. Based on these four categories of basic CRM technology requirements, the CRM system will be investigated. The categories and the CRM technology requirements are summarized in Table 3.
Due to the various conditions prevailing in organizations, however, organizations can choose diverse sets of application packages with different targets. Software packages and systems tend to vary considerably. The decision on the applications and systems an organization chooses is totally dependent on the situation. Therefore, pre-recognition of all the possible CRM tools seems not only impossible but also undesirable.
Furthermore, information system quality concepts cannot be used to investigate the CRM technologies since the quality concept contains the underlying idea that how well a system performs is based on the tasks given.
Table 3.
CRM Systems Requirements and Categories Category CRM Technology
Requirement
Integration and
connection requirement
Integration and connection requirements
The ability of the tool to integrate into the company’s unique technology infrastructure from a hardware, software, and networking perspective.
Functionality requirement
Processing and
Performance requirements
The ability to support and control required operations
Function – enabling features
The way in which the tool provides certain required functionality
Reporting requirements The versatility to provide company and user-requested information
Performance requirements Laying out acceptable turnaround time for CRM activities or reporting response time
Availability requirements The acceptable level of system availability Security
requirement
Security requirements The ability to limit user access Usability
requirement
Usability requirements Enabling end users to easily and intuitively accomplish required tasks
Source: Author’s research
Therefore, technology readiness of the organization, rather than the lists of the tools used in the firm, is more suitable for the purpose of this study. Technological readiness refers to the level of sophistication of information technology (IT) usage and IT
management in an organization (Croteau and Li 2003; Iacovou et al. 1995). Sophisticated technology-ready organizations are (1) less likely to feel intimidated by technology, (2) possess a superior corporate view of data as an integral part of overall information
management, (3) have access to the required technological resources (Iacovou et al. 1995). CRM systems, in this study, refer to the level of sophistication of information technology that the organization possesses in terms of the CRM technology requirements.
3.4.2 CRM Goals and Tasks
Tasks in the TTF model are broadly defined as the actions carried out by
individuals in turning inputs into outputs (Goodhue and Thompson, 1995). Likewise, in 40
this proposed study, the CRM tasks can be viewed as the activities and goals carried out by organization for better customer relationship.
The goals and objectives of CRM first can be derived from the definition of CRM. Viewed as a core business strategy, the main goal of CRM practice is to improve profitability by providing better services leading to customer satisfaction and retention. To achieve enhanced customer retention and satisfaction, a firm has to interact with customers, and create and deliver value with personalized treatment to targeted customers by
integrating internal processes and functions and external business networks. This requires high quality customer data and information technology. Each individual activity described here has been viewed as a task for CRM, and many applications and software packages have been developed and commercialized to support such tasks. For example, data mining techniques and respondent tools are available for creating value with personalized
treatment to targeted customers, and call center packages allow for better interaction with customers.
Implementing such tools in an organization to support individual activity, however, does not guarantee the success of CRM. It is important to ensure that the tools – including the techniques and technology – fit well and satisfy the specific technical requirements of each organization. Thus, it is important to find the meaningful fit between the specific organizational requirements and the tools rather than the fit between the broadly defined goals and tools.
In fact, each organization has different types of strengths and weaknesses, and the customer relationship is likely to be managed based on the particular strengths of an organization. The specific CRM focus may therefore differ for each organization.
Therefore, the tasks of CRM, as an antecedent of CRM Fit, are addressing how well these goals and activities are set up in terms of three dimensions:
1. Clarity of Goals and objectives 2. Scope of CRM focus
3. Design of Business process
In the proposed study, the tasks and goals of CRM are identified through the relationship cycle (Gamble 1999). The stages of the relationship cycle are Welcome / qualification program, Getting to know, Customer development, Problem management, and Win-back. Each stage in the cycle is summarized and described in Table 4.
Table 4. Relationship Cycle
Stage Description Opportunity Challenge
Welcome / Qualificatio n Program
Beginning of the relationship building
Providing initial benefit to customers
Gains customer information
Understand what the customer might be inclined to buy
What they can afford
How they want to be managed
It creates first professional image in the customer’s mind
Getting to
Know Learning Promote higher value products/services for the same category of purchase or try and increase the frequency or volume of purchases. (Up-selling)
Gradual customer education on the benefits of products Incentives Customer Develop -ment Account management: Database and contact strategy
Loyalty program and cross-selling (Difficult to the products, where short duration, intermittent or infrequent purchase patterns are inherent in the product/services)
Data driven contact activities: DB analysis aimed to identify potential problems or opportunities and route information to the right contact channel for action.
Problem Manage -ment
Complaint management
Good customers don’t complain without cause. Individual complaints vary in severity. The severity depends on whether it is justifiable, whether it is the result of a previously unresolved complaint, who actually makes it and the frequency with which it is made.
Record the data of present and planned
contacts, trace dates (when follow-up action is needed), and feedback code that identifies future required actions.
All complaints have to be recorded. Problem management has to be designed to ensure that all activities or contacts remain on an action list until they have been solved.
Winback Reactivating inactive customers Identifying customers who are becoming inactive before they lapse.
Reactivating customers who lapsed some time ago.
Data on inactive customers can be tested or revalidated through telemarketing.
Source: Adapted and summarized from Gamble, Stone, and Woodcock (1999)
