CUSTOMER KNOWLEDGE MANAGEMENT:
IMPLEMENTATION AND ADDED-VALUE
Rim Jallouli and Faten Raboudi
High School of E-commerce, University of Manouba, Tunisia
Yamen Koubaa
The Brittany School of Business, France
Abstract
The case presents first the customer relationship management and its utility in the current era of marketing
dominance. The case follows by showing the relevance and the potential great add-value an efficient
management of the data available about customers, grace to the abundance of IT technologies, can bring to
the firm. It presents hence the concepts of knowledge management and customer knowledge management.
These processes of data accumulation, knowledge creation and knowledge management are illustrated by
an empirical case of a Tunisian manufacturer and retailer of pharmaceutical products.
The case is appropriate for marketing classes in particular those dealing with the customer knowledge
management. It helps educators introduce the concepts of knowledge management and customer
knowledge management and their relative contribution to the firm’s success. It allows learners elucidate
these tools and get to their practical utilities through the empirical illustration. It suits better to master level
students.
This case is written by Rim Jallouli and Faten Raboudi from the Higher School of E‐commerce, University of
Mannouba, Tunisia and Yamen Koubaa from the ESC Bretagne Brest (the Brittany School of Business),
France. The case is intended to illustrate the efficiency of the customer knowledge management. It was
compiled from published sources and an empirical investigation. It was made possible with the
cooperation of EM company.
“©2012 Rim Jallouli, High School of E‐commerce, University of Manouba, Tunisia. All rights reserved”
“©2012 Faten Raboudi, High School of E‐commerce, University of Manouba, Tunisia. All rights reserved”
“©2012 Yamen Koubaa, ESC Bretagne Brest. All rights reserved”
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Overview
Enterprises evolve in the economy of « knowledge » which makes the information and
knowledge key assets of the competitiveness for enterprises. Knowledge is omnipresent
and concerns all enterprises and all industries starting from small and medium-sized
firms to multinationals.
In the new economy, knowledge is proved to be a critical resource and a competitive
factor for all kinds of businesses. The obesity Info and the information overload impel
managers to come up with procedures to efficiently manage these quantities of
information and then to bring value out of them. Information management and
knowledge management make echo in the world of marketing and management in
particular customers’ management.
The synergy between the customer relationship management (CRM) process and the
Knowledge Management (KM) approach defines the concept of customer knowledge
management (CKM). The identification of the implementation’s steps of the CKM
process is indispensable to maximize the chances of positive returns on the firm’s market
performance. The case identifies the steps of implementing a CKM process in a Tunisian
company called (EM) and follows by showing the importance of the CKM for firms to
optimize their clustering and targeting and improve their market performance.
Customer relationship management, knowledge management and
customer knowledge management
During the time of scan and of the « without paper », Knowledge may be conducted by
several oblique, notably by the CRM which is a coherent and complete set of individuals,
process and technologies directed at understanding clients. One of the concerns of the
CRM is to maintain a record of the interactions with clients and to share this knowledge
with different channels of communication.
The concept underlying knowledge which will be discussed in this paper is the
knowledge management, called also knowledge capitalization and more known under the
Anglo-Saxon acronym KM. Given the broad dimension of the concept of knowledge
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mainly in the context of customer relationship, enterprises are more and more focused on
« knowledge-centric » acquaintances. One of the problems that a given enterprise may
face nowadays is « the obesity info ». Indeed, there is too much information about the
enterprise’s clients (internal or external) and managers should find the ways to transform
it into knowledge.
The challenge that knowledge managers must take up is how to identify quality
information to be processed for decision making purposes. The abundance of data may
lead to information overload or to the reliance on less important pieces of information in
detriment of the more important ones. It is in this respect that Business Intelligence (BI)
takes its entire dimension and importance. Thus, BI is a tool with a high added value.
Given the fact that it makes information exploitation under different forms possible, it
provides teams with the possibility of optimizing their tasks combinations and hence
enables them to gain on market performance and profitability.
Customer relationship management (CRM)
New technologies allow enterprises to learn about their customer’s needs and then to
respond accordingly. New information technologies lead to the emergence of new
marketing concepts such as “one to one marketing”, the “CRM” and more recently the
“E-CRM”; and change segmentation practices and customer relationship management.
Cinquin et al. (2002) attest that the promotion of Internet has allowed new opportunities
to marketing managers in terms of what they called the “industrialization” of the
customer relationship. The customer has become the main topic of the organization
engagement and the market share is becoming one of the most important measures of the
company performance. Day et Van Den Bulte (2002) define CRM as “a cross-functional
process for achieving a continuing dialogue with customers, across all their contact and
access points, with personalized treatment of the most valuable customers, to increase
customer retention and the effectiveness of marketing initiatives”. In addition to the
marketing approach, Grabner-Kraeuter and Moedritsher (2002) emphasize on the
technology dimension of the CRM. The concept of CRM reflects the efforts of the
company to refocus its efforts and resources around its most profitable customers to build
with these durable and customized relationships. The technology allows the
centralization of all customer information to better monitor their needs and desires.
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The CRM includes three components namely the operational CRM, the analytical CRM
and the collaborative CRM. The operational CRM is the integration and the automation
of the interaction processes of the company with its clientele such as call centers,
customer databases, software, and automation of customer services, marketing activities
and the sales force. The analytical CRM (referred to as business intelligence) is the
analysis and the exploitation of raw data by relying on smart technology to enable
business decisions, improve understanding of customers, and extend the knowledge and
the dissemination of information across business processes. This area is well connected
to technologies so-called Data Warehouse (DW) and Data Mining (DM). These
technologies have been emerged with the increasing volume of data to facilitate the
decision making process. In a data exchange system, there may be integration between
the operational CRM and the analytical CRM. Indeed, the analytical CRM makes sense
of data and thus provides knowledge to the operational part. The collaborative CRM
includes all channels for communication (e-mail, e-conferencing, electronic mail,
electronic catalogs and brochures) with the client or between all partners who interact
with the client. This multi-channel technique provides the advantage of the one to one
marketing (Crosby and Johnson, 2001). Its role is essential to optimize the customer
contact, to convey the right message at the right time by the appropriate channel to meet
customer expectations and improve their loyalty and the enterprises’ profitability.
Knowledge management (KM)
Nonoka (1994) believes that in an economy where the only certainty is uncertainty, the
only reliable source of sustainable competitive advantage is knowledge. Despite the
numerous writings, the KM definition remains ambiguous. Skyrme (1999) suggests that
Knowledge Management is the explicit and systematic management of knowledge and
processes associated with. Knowledge management includes creation, collection,
organization, dissemination and use of knowledge. KM requires the transformation of
personal knowledge to collective knowledge which can be shared widely within an
organization. Knowledge Management is a process of capture, dissemination and
effective use of knowledge (Firestone, 2001). Knowledge is seen as a process, a complex
set of skills, know-how, an expertise or skills that are evolving. Malhotra (2000) argues
that Knowledge Management is a discipline that should benefit from the synergy of the
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human creativity and innovativeness with the powerful information technologies to help
organizations to survive in their environment which is more and more complex and
competitive. The KM’s purpose is to promote collective processes of learning and
innovation (Swan and al (1999). According to Ramon (2001), KM is the process through
which a company uses its collective intelligence to accomplish its strategic objectives.
The main features of KM are related to organizational dynamics, human and cultural
resources, process engineering and technology Gold et al (2001).
Knowledge management is therefore a process of knowledge creation and enrichment via
the accumulation, treatment and dissemination of information that involves all
stakeholders in the organization mainly consumers and suppliers.
Customer knowledge management (CKM)
CKM which is a combination of knowledge and customer relationship management is a
key factor for the firm’s CRM long term success. In this sense, Garcia-Murillo and
Annabi (2002) stipulates « CKM has drawn much attention by the combining of both the
technology-driven and data oriented approaches in CRM and the people-oriented
approach in KM, with a view to exploit their synergy potential »(p5). Lin and al (2005)
define CKM as the identification, capture, selection, storage, sharing, creation and re-use
of customer knowledge. Likewise, Gibbert and al. (2002) remark that « KM and CRM
focused on gaining knowledge about the customer, managing customer knowledge is
geared towards gaining knowledge directly from the customer» (p464). The authors add
« CKM is the strategic process by which cutting-edge companies emancipate their
customers from passive recipients of products and services, to empowerment as
knowledge partners » (p2). In this line Su and al, (2005) have distinguished between
three categories of knowledge in the field of CKM; first, knowledge "for" customers
aiming to meet customer needs regarding products, services and market. Second,
knowledge "about" clients studies the main motivations and customer preferences for
products or services. Finally, the knowledge "of" customers, this concept is relevant to
the consumer’s needs and / or the experiences of product or service consumption. CKM
is a pretty combination of human excellence and technology empowerment. The human
excellence is related to the skills and the ability of managers in the organization to
manage information technology (Broadbent and Weill, 1997). The technical
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empowerment includes all data, applications and technologies (Broadbent et al, 1996).
The technology helps in categorizing the data and establishing possible links worth of
producing knowledge. It transforms somewhat abstract understandings of people into
formal results and propositions which enable managers identify the right information at
the right time with the right situation and come up with the most appropriate decision.
CKM and market performance
Market performance refers to the optimization of customers’ management tasks. Market
performance implies an efficient management of the amount of data available with the
firm about its current and potential clientele as well as the availability of means able to
collect and generate new pieces of information. The CKM allows marketing managers in
particular process large amount of customers’ data to detect consumption trends and
fluctuations, anticipate behaviors and react effectively and efficiently to meet customers’
needs and desires before the move of competitors. The efficiency of the market oriented
measures (e.g., promotional campaign, personal communication, and after sale services)
determines the market performance level of the firm. CKM is believed to have a positive
impact on the efficiency of these measures as it enables managers to better diagnostic the
problem and come up with the appropriate reactions at the needed time with the best
forms. CKM is key driver of the firm’s market performance.
The firm EM
EM is a wholesaler and a manufacturer which produces and retails paramedical products
in Tunisia since 1995. The company uses several methods to prospect clients namely
appointments, fax, phone, e-mailing and the firm’s website. EM classifies its clientele
into six groups namely:
- Hyper and super-markets (e.g., Carrefour, Géant, Carrefour market and
Monoprix)
- Pharmacies
- Wholesalers of paramedical products
- Parapharmacies
- Gyms
- Ordinary customers (Individual patients)
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Due to an increasing competition recent years, the firm is willing to increase its market
performance. The management believes that a better market performance passes
necessarily through a deep understanding of the firm’s customers which is obviously the
fruit of an efficient CRM. To do so, the company fixed two objectives: first to learn
deeper about each of the above groups to offer a customized package of products and
services and second to identify the most promising one. After consultation, the
management was convinced that an implementation of a CKM would push the firm
forward in achieving its objectives and thus decided to tackle the experience.
CKM’s implementation steps
As information is the most essential ingredient in any CKM, EM was constrained to start
first by gathering and organizing all the information it has about its clientele. Then, links
among these pieces of information will be analyzed to detect relationships and trends.
Hence, a data warehouse was designed. A data warehouse is a subject-oriented,
integrated, time-variant and non-volatile collection of data in support of management's
decision making processes. It is then necessary to duplicate it in a common place with an
ETL (Elaboration, Transformation, Load) tool to build and/or consolidate the data model.
A data warehouse is ‘Subject-Oriented’ because a data warehouse can be used to
analyze a particular subject area. For example, "sales" can be a particular subject; is
‘Integrated’ because it integrates data from multiple data sources. For example, source
A and source B may have different ways of identifying a product, but in a data
warehouse, there will be only a single way of identifying a product; is ‘Time-Variant’
because historical data is kept in a data warehouse. For example, one can retrieve data
from 3 months, 6 months, 12 months, or even older data from a data warehouse. This
contrasts with a transactions system, where often only the most recent data is kept. A
transaction system may hold the most recent address of a customer while a data
warehouse can hold all addresses associated with a customer; and is ‘Non-volatile’
because once data is in the data warehouse, it will not change. So, historical data in a data
warehouse should never be altered.
The data warehouse has to be designed and then inspected for quality before it can be
used for knowledge generation.
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Design of the data warehouse
The design of a data warehouse involves at least six steps which are:
- Requirement gathering,
- Physical environment setup,
- Data modeling,
- ETL,
- Reports development, and
- Incremental enhancements
Requirement gathering refers to those tasks determining the necessary conditions for a
new product and taking into account all possible conflicting requirements of the users of
the data warehouse. Physical environment setup refers to the setup of physical servers
and databases according to the process that will be used for data transformation. Data
modeling is the establishment of logical links among the various types of data included in
the data warehouse so the software can follow to produce logical relationships. ETL
which is extract, transform and load; refers to the extraction of data from external sources
(E), its transformation to fit operational needs such as fixing the quality level (T) and its
loading into the end target which is the data warehouse (L). Reports development refers
to the form and the content of the reports to be developed via the system. Incremental
enhancements are all possible enhancements of the system through the application of
parsimonious changes.
Enrich the data warehouse
The location of practice tests can be a centralized data repository (Data warehouse) or
decentralized and more specialized one called Datamart, depending on whether the goal
is global or functional. An end user can analyze data directly from the central data
warehouse through queries. But sometimes the data is pre-compiled in a uniform manner
to the attention of certain groups of managers called universe job (Figure 1).
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Figure1: From the data warehouse to the Datamart
Given the extensive list of EM products and the diversity of its clientele and suppliers,
the firm opted for the construction of a Datamart instead of a data warehouse. In fact, a
datamart is somewhat a mormophized data warehouse. That’s to say, the steps required
for its creation are the same as with the data warehouse. It has just more queries and
functionalities.
To construct the datamart , we first collected the information in the transactional system,
an ERP (Enterprise Resource Planning: a computer system that integrates various pieces
of information across the entire organization) that the company uses mainly for editing
customer invoices and monitoring the state of stocks. At this point we would normally
use a power tool ETL (Extract-transform-load), which main function is to collect data
from various sources, and finally to homogenize the load at the data warehouse (DW) /
DataMart (DM).
The load of the data corresponds on average to 60-70% of the proposed design of a DW /
DM, but the problem is that ETL tools are expensive, and for this reason it was chosen
for the case of EM Company to make a manual entry on “Excel”. This process has its
advantages. For instance, it makes visible some hidden mistakes. Indeed, it was noted
some errors in the invoice totals as shown below:
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Figure 2: Example of errors in invoice totals
The data were then processed with Excel which allowed us to achieve the following
result: four sheets of Excel workbook including customers, products, bills and invoices
for the two activities of the company: Imports (EM1) and local manufacturing (EM2).
Figure 3: The database of EM Company
At this level, the team discovers that there has been incomplete or missing information
(See Figure 4), making exploration difficult because of the absence of the contacts details
of several customers (e.g., telephone, fax numbers or e-mail addresses).
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Figure 4: An example of EM Client file
Subsequently, the data was purified and corrected. The data entered in Excel must be
displayed in a way allowing it to be used with relevance and consistency and without
duplication. Here several solutions are possible including using Microsoft Access as an
intermediary which was the one applied. This choice is determined simply by the
performance and the simplicity of SQL (a programming language to manage data in a
relational database such as the Ms Access) queries in visual mode, and also thanks to its
use in the professional world to perform this step at a lower cost. Six steps were
necessary to implement the latter solution and obtain a Datamart.
Step 1: Import from Excel: the Excel file contains four sheets: Customers, products,
invoices for EM1 and EM2. From a new Access database, we first import the table
directly from Excel file (Figure 5)
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Figure 5: Importing data from Excel to Access
Step 2: Purification and data correction: For the success of this import, the data has to
be structured. After structuring, we get four tables: Products, clients, bills and invoices.
Step 3: Verification of data: this task is essential to check the inter-tables’
communication. It is performed by several iterations and validation with the team of the
EM company computer engineers and managers. With Access we ensure that all Excel
tables "communicate" each other by making joins between excel tables to detect possible
errors (Figure 6).
Figure 6: Example of Audit
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The software issues several error messages showing incompatibles entries such as:
Report the list of bills without required information (not entered), such
unnamed client,
Report the list of products without the required information;
Report the list of products used in the bills but not in the list of existing
products.
Report the customer list used in the bills but not in the list of existing
clients.
The last two points can be explained by typographical errors, differences in capital letters
and some spaces that are not apparent. At this level, there were duplication and errors,
and the same client will appear in the database under two different names. These include
for example;
Before Correction After Correction
CO.GE.PA
COGEPA
COGEPA
Remove any insignificant registration which result from calculated lines in
Excel tables, these values will be calculated directly from Cognos ( a software for
calculation)
Check by suppressing the names of clients and products that appear in the
bills but not in the client and product tables.
After purification, correction and verification of the data entries and queries, we obtained
the following sheet (figure 7)
Figure 7: Final result of the audit
In the last two columns, the customer names in the invoice correspond to the names
which exist in the list of client names. This proves that we attend the step of zero
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anomalies. In fact, we start with more than two thousand recordings with problems, and
after several iterations, the number of anomalies was restricted to ten then finally attends
zero defect.
Step 4: Importing data to "Cognos ", Creating OLAP cubes: OLAP ( On Line Analytic
Processing) is "the set of technologies that are based on a multi-dimensional
representation of data and allows analysts and decision makers to handle their data
analytics, interactive (sessions), fast and to see the company data from different angles
(dimensions) " (Grim, 2008(P5)).
OLAP and data warehouses are complementary. A data warehouse stores and manages
data. OLAP transforms data warehouse into strategic information. OLAP transforms data
to show their added value in the company, according to the skills of the user. In fact,
policymakers will use the advanced capabilities of OLAP, and can move from data
access to information, then to knowledge. To some extent, data warehousing and OLAP
are the two phases to transform abstract information about customers into interpretable
knowledge.
In the case of EM, a multidimensional cube was constructed via the software called
Cognos.
Step 5: Generation and manipulation of data cubes with "Cognos ":
The construction of the hypercube takes a few seconds. And takes place via two steps:
1- Formulate queries: After analyzing the data, we must finally produce the
results by arranging them so that they offer the best possible response to the
various requirements: clarity and fluidity, precision and conciseness.
2- The summary: "Cognos" also illustrates the hypercube as 3D graph to help
managers in the result interpretations (Figure 8).
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Figure 8: 3D graph
According to the graph above, it is clear that hyper and supermarkets are the most
profitable clients, then come wholesalers and pharmacies during the period from January
2009 until November 2009. The details of the margins (in TND) per category are the
following:
Hyper and super markets: 12 000;
wholesalers: 6000;
Pharmacies: 2500;
Individuals: 500;
Gyms: 250.
The results note the large proportion margins out of supermarkets compared to
wholesalers and pharmacies. For EM Company, the first target (as a paramedical space)
was pharmacies, but the result of this low profitability for pharmacies comparing to
hyper and supermarkets raises questions about targeting and marketing choices in the
company.
Step 6: Implementation of CKM: after learning from this experience, EM Company
decides to develop a complete CKM system going to better monitor its customer loyalty
and conquer new markets. The management was thrilled by the results of the Excel and
Access application and how the firm’s marketing orientations were out of the market
reality. The conviction about the usefulness and the great add-value a CKM can bring to
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the firm guided to the unanimous decision to adopt a complete CKM system starting
from gathering data to categorizing and establishing possible links among the various
groups of data and then to synthesizing these data and links to generate knowledge
embedded and hidden within these pieces of information. EM decided to start with the
open source "vTiger" waiting for the professional CKM system to be put in place.
“vTiger” is an integrated application management easily loaded from the Internet and
which allows the management of e-mails, inventory, sales, sales records, creating /
editing invoices, employee management, and the creation of interfaces with visual tools
to summarize business activities.
EM aims to use the generated reports and these technological solutions to achieve its
marketing, financial and organizational goals. The EM management plans to double the
loyalty rate of its clientele within two years and to know deeper the profiles of its
customers to serve them with customized services. The firm plans also to archive its
knowledge about customers to capitalize on it with additional data processing in the
future and hence ensures and takes benefit from knowledge cross-fertilization across
periods.
Readings:
Broadbent, M., Weill, P., O’Brien, T.,and Neo, B.S. (1996). Firm Context and Patterns of
IT Infrastructure Capability. In Proceedings of the Seventeenth International Conference
on Information Systems. New York.
Broadbent. M, Weill. P and Clair. D.S (1999): “The implications of information
technology infrastructure for business process redesign”, MIS Quarterly.
Cinquin, L., Lalande, P.A. and Moreau, N. (2002), eCRM projet : Customer Relation and
internet, Editions Eyrolles.
Crosby, L.A. and Johnson, S.L . (2001), “Technology: Friend or Foe to Customer
Relationship”, Marketing Management, Vol. 10, N° 4 pp. 10-13.
Day, and Van Den Bulte G.S, C. (2002), "Superiority in Customer Relationship
Management: Consequences for performance and competitive advantage.",
the Wharton School, University of Pennsylvania, pp 1-49.
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Firestone, J.M. (2001) « keys issues in knowledge management» Knowledge
management and innovation, journal of the KMCI, Volume 1 N°3, 15 Avril 2001.
Gibbert. M, Leibold, M, Probest. G (2002), “Five Styles of Customer Knowledge
Management, and How Smart Companies Use Them to Create Value”; European
Management Journal Vol. 20, No. 5, pp. 459–469.
Gold A.H., Malhotra A., Segars A.H., (2001) “Knowledge Management: An
Organizational Capabilities Perspective”, Journal of Management Information
Systems, vol. 18, n° 1, p. 185-214.
Grabner-Kraeuter, S. and Moedritscher, G. (2002), Alternative
Approaches Toward Measuring CRM performance, 6th Research Conference
on Relationship Marketing and Customer Relationship Management, Atlanta, 1-16.
Grim, Y (2008), “OLAP: les fondamentaux”, accessible at www.developpez.com
Lin. Y, Su. H.Y, Chien. S (2005): “A knowledge-enabled procedure for customer
relationship management”; Industrial Marketing Management.
Malhotra Y., (2000) « From information management to knowledge management »;
Beyond 1 the ‘hi-tech hide bound’s systems, knowledge management journals.
Nonaka, I. (1994). « A Dynamic Theory of Organizational Knowledge Creation ».
Organization Science. 5:1. 14-37 Customer Knowledge Management
Garcia-Murillo, Martha A. and Annabi, Hala, Customer Knowledge Management (2002).
Journal of the Operational Research Society, Vol. 53, No. 8, 2002. Available at
SSRN: http://ssrn.com/abstract=1328602
Ramon C. Barquin, (2001) « what is knowledge management? » .Journal of the KMCI,
Volume one, no.two, January 15.
Skyrme D., (1999) «Knowledge management: Making sense of an oxymoron
».Management insight No.2, 1999.
Su. C.T et Chen. Y.H, Sha. D.Y (2005): “Linking innovative product development with
customer knowledge: a data-mining approach”; Technovation. pp 1–12.
Swan J., Scarbrough H. and Preston J. (1999) “Knowledge management the next Fad to
forget people?” Proceedings of the 7th European Conference on information Systems
(ECIS’99), Copenhagen, Denmark. P 668-678.
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