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.

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