Electronic Business: Customer Profiling - unifr.chdiuf.unifr.ch/main/is/.../eBiz_fs08/...customer_profiling_paper.pdf · Electronic Business Nadine Biegajlo ... Different Implications

Electronic Business:

Customer Profiling

Nadine Biegajlo

(Av. de Villardin 3, 1009 Pully, [email protected], 03-404-894)

Véronique Herrmann

(Rte de Corbaroche 28, 1723 Marly, [email protected], 02-305-514)

Date of Submission: May 15th 2008

Professor: A. Meier Assistant: D. Fasel

Electronic Business Nadine Biegajlo

Prof. E. Meier Véronique Herrmann

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TABLE OF CONTENTS

1. Introduction ___________________________________________________________ 1

2. The different steps of personalisation process _______________________________ 3

2.1. Modelling customer profiles ________________________________________________ 4

2.2. Data input_______________________________________________________________ 5

2.3. Data processing __________________________________________________________ 6

2.4. Information output _______________________________________________________ 6

3. Condition sine qua non for making effective customer profiles: to have an

integrated database ____________________________________________________ 8

4. Different Implications of customer profiling _______________________________ 10

4.1. To know the customers and understand their needs ____________________________ 10

4.2. To cluster the different customers in groups (segmentation)______________________ 10

4.3. To define a better targeting strategy and a better marketing campaign _____________ 11

4.4. To propose them adequate offers (personalisation) _____________________________ 12

4.5. To do product analysis____________________________________________________ 13

4.6. To find out the most valuable customers and the others _________________________ 13

4.7. To show the efficiency of the Web-shop ______________________________________ 15

4.8. To improve the profitability of the company___________________________________ 16

5. Illustration of the e-profiling process: The Amazon case _____________________ 17

The different steps of personalisation in the Amazon.com case: ______________________ 19

5.1. Modelling customer profiles and data input in the Amazon case: ____________ 19

5.2. Data processing ____________________________________________________ 20

5.3. Information output _________________________________________________ 21

6. Dangers of Customer Profiling __________________________________________ 26

6.1. For the customer ________________________________________________________ 26

6.2. For the company ________________________________________________________ 27

7. Conclusion ___________________________________________________________ 29

Bibliography:_____________________________________________________________ 31



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1. Introduction

The relation between a company and its customers has changed over time. Before, the supply

was quite limited and the customers bought what the companies presented, so standardised

products were dominant in the market. Today, the situation is quite different. Customers face

a large choice of products and services offered by many different companies which compete

all around the world. Furthermore, Internet has enhanced the supply and the transparency of

the market. Customers have now the power to get all the information they wish about

different products and to choose what they really want and if their needs are not fulfilled, they

can easily switch to another producer. As competition has increased over time between the

different producers, nowadays the challenge is to keep the customers. So companies are more

and more oriented to specific customer needs. These needs have become more and more

specific and volatile over time. So to keep their customers and satisfy them, companies now

face the challenge to propose them very specific and customized offers which exactly

correspond to their needs. They must constantly analyse the customer preferences, their

behaviour, to predict their willingness and their future needs. To fulfil this requirement

companies have to know very well their customers: Who are they? What are they buying?

Why? What are their preferences? Their behaviours? Etc. Thus by collecting different data,

they establish customer profiles which allow answering those different questions and

personalising the different offers. “The general term for stored customer information is ‘user

profile’ or in the context of electronic shopping ‘customer profile’” [Schubert and Koch:

2002, 1955]. In order to get a precise idea of the topic that we will discuss in this text, we

need to understand what a customer profile is. A customer profile can be defined as a

description of a customer. It includes several characteristics helpful for market segmentation

such as geographic and psychographic elements. You can also find in a customer profile

information about the average amount spend by the consumer on a web page, the articles he

buys, its buying patterns, etc. The advantage of these profiles is that you can compare them in

order to be able to offer to your customers the product that they will want before they thought

they will. You can personalise you offer to the customer in order to increase the sales

probability and to secure the loyalty of your customer. The next step after establishing

customers’ profiles is to do a profile segmentation that “allows consumer groups to be

classified in such way that they can be reached by the communication media” [D. Jobber:

1998, 180]. According to D. Jobber (1998) there are several segmentation variables:



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demographic, socio economic, geographic. They consider elements such as age, gender, social

class, or population distribution. Another interesting way to segment customers profile is to

consider a behavioural segmentation. In this case you consider for example the occasion of

purchase of the product, or the expected benefits that the customer can have about the article

he bought. You can also consider the usage made by the customer, for example heavy user vs.

light users, as well as the purchase behaviour of your customer. You can differentiate as well

the early users or those who have high levels of brand loyalty. The advantages of using such

variable can be for example that when you buy a CD in an internet site the site will

automatically offer you to buy other CD’s that other customers’ who bought the same CD as

you, did also bought. This is of course an undeniable element in order to increase sales. Your

profile can also give information to the seller about your loyalty to a certain brand, and

knowing that information he will propose product of the brand you like. So we can say that by

using profiling the degree of personalisation of the offer increases.

In order to understand more in detail all these elements and the different steps to formulate a

customer profile we will start our paper discussing the different steps for personalisation

according to a study of P. Schubert and U. Leimstoll. In the second part we will deal with the

different implication of customer profiling in order to really understand its importance in

nowadays sales. We will then focus on the dangers and negative aspects of this technique, and

we will end by an illustration of all this purposes with the amazon.com case that uses

customer profile as an essential tool for its sales.



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2. The different steps of personalisation process

Nowadays customer profiles are a core element in the personalisation process. Traditional

Medias have a quite limited influence concerning personalisation. The increasing importance

of internet in the relations business to consumer has opened the door of a wide range of

possibilities concerning personalisation. As the goal of this study is to understand the

profiling of customers over the web, we will concentrate in this part on the personalization

steps that we encounter in this distribution channel.

There are different definitions of personalization. Deitel et al. (2001) defines it as the

“information from tracking, mining and data analysis to customize a person’s interaction with

the company’s products, services, web site and employees”. For G. Adomavicius and A.

Tuzhilin (2005) “personalisation tailors certain offerings (such as content, services, product

recommendations, communications and e-commerce interactions) by providers (such as e-

commerce Web sites) to consumers (such as customers and visitors) based on knowledge

about them, with certain goal(s) in mind”. According to P. Schubert and U. Leimstoll,

personalization takes place after the login has been introduced. At this stage the customer can

be clearly identified, and its previous visits to our web site can be defined. Companies are

particularly interested in this topic because it is an important way to propose to the customer

tailor-made offers. The personalization procedure carried out by big multinational companies

is quite different than the one that can be applied by SME because large companies’ can

afford expensive software whereas small companies cannot. Furthermore small companies

have more difficulties to generate as much information as contained in the important

databases created by the big ones. According to P. Schubert and U. Leimstoll, the

personalization procedure takes places trough different steps. “The basic idea of

personalization is to learn something about the customers and to use this information to tailor

offer for services or information to the needs of the customer” [P. Schubert, U. Leimstoll

(2003), 209]. For these authors there are four steps of personalization: modeling customer

profiles, data input, data processing, and information output. These steps are summarized in

the Fig.1 and they represent the customer profile lifecycle. These elements will be explained

in this section.



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Figure 1. Customer profile lifecycle [Schubert and Leimstoll (2003)]

2.1. Modelling customer profiles

In this step the goal is to create customer profiles with the information that we have about the

customer. According to P. Schubert and U. Leimstoll (2003) customer profiles can be

composed of information that we asked directly to the client such as the identification profile.

This profile enables us to get some information about the user personal data. For example we

can ask him his user name, in order to be able to identify him when he will visit again our web

site. We can also have in this step the information concerning his IP-address, or payment

information. The explicit profiles are also composed of preference profiles, which concerns

the customer’s preferences, and the products that the customer usually buys by the internet

way. Examples of this kind of profiles can be the preference of the customer for science

fiction book, or R&B music. According to the same author, the customer can also be asked to

rate between several products in order to distinguish the ones he likes to the ones that he

dislikes. To do so we can use a 1 to 10 rating.

However not all the information that you find in customer profile has been obtained with the

customer awareness. There are the so called “implicit profiles” composed of element such as

transaction profiles. For Schubert (1999), this kind of element of the customer profile can be

composed of the “product purchased linked to product meta data” and its purpose is to obtain

complementary data about the different purchases, or the way of payment used by the

customer. The profile can also contain interaction elements such as the other products that the

consulted before making its choice. For this author, this “click stream” allows the company to

establish preference categories that can be used for other customers. The implicit profile uses

techniques such as the data mining: “the science of extracting useful information from large



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data sets or databases” [D. Hand, H. Mannila, P. Smyth (2001)]. Companies can also use web

mining which is the usage of data mining techniques in order to obtain the different patterns

followed by the customers on the Web.

As there are many elements that can compose a customer profile, it is important for the

company to establish first the information it wants to get from the customer profiles and the

purpose of that information in order to fit precisely with the product offered by the company

and the company needs. Moreover, “the products in the product catalog have to be annotated

using a chosen category with appropriate attributes. The annotation of products or information

objects is a prerequisite to the matching of preferences with specific purchase transactions or

interactions with the Web site” [P. Schubert and U. Leimstoll (2003) 210]. Due to the need of

adaptation to the company’s need, and the applications that are linked to this profile, it is had

to use one customer profile of one company in another.

2.2. Data input

After the creation of a customer profile, the next step of the personailsation profile lifecycle is

the Data Input. We have seen before that for P. Schubert and U. Leimstoll (2003), the

information about the customer to establish customer profiles can be obtained in two ways:

“Asking the customer” by using explicit profiles information, or “watching the customer” via

the utilisation of implicit profiles. So we can say that “There are different possibilities to

acquire information about the interests of a user: (1) user maintains profile (explicit

information input), (2) the system monitors the user in her browsing or shopping behaviour

and determines her interests from using information clustering techniques” [P. Schubert and

U. Leimstoll (2003) 211].

The “explicit information input” or “reactive approach” supposes to ask directly to the

customer to fill a preferences profile. As said before, this can be done by selecting customer

preferences in different lists. This the technique used for example by MSN when you create a

hotmail account, you are asked your preferences in a wide list of elements in order that they

will be able to send you on a regular basis promotional offers related to your favourite

subjects. Asking the customer explicit information can also be done by rating products, as

explained above.

“Recording customer activity” or “non reactive approach” differs from the previous approach

because here the customer is not aware that he is giving information to the company. An

example used by P. Schubert and U. Leismtoll (2003) to explain this situation is the usage by

Migros and Coop of the “membership card program” in order to have information about the



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products bought by the customer, at what frequency in which store etc. This kind of

information can then be used in different marketing fields such as geomarketing, promotions,

etc. On the Internet companies can track the different web paged visited by a customer,

however according to P. Schubert and U. Leimstoll (2003) this tracking technique seems to

have some technical limitation because the customer cannot be clearly identified. On the web,

a very important tool available for companies in order to create customer profiles is the

creation of “new categorisation schemes” [P. Schubert and U. Leimstoll (2003) 212]. This

supposes that “if specific products are simultaneously bought by a number of customers one

could suspect that they serve a similar purpose and that it would make sense for other clients

to know about the existence of the other books when buying one of the books from this

cluster” [P. Schubert and U. Leimstoll (2003) 212]. We will talk abut this situation in the last

part of our paper taking the example of Amazon that uses this kind of cross-selling technique.

2.3. Data processing

The third step of customer’s profile lifecycle is the data processing. This step very important

because “the data collected from watching the customer (transaction or browsing histories)

usually is not suitable to be used in information filtering algorithms directly. So different data

mining or web mining techniques are used to cluster and filter the data” [P. Schubert and U.

Leimstoll (2003) 212]. The customer will be classified in a group. By creating different

segments of customers the company is then more able to establish particular offers to the

different groups. “Opportunities for personalization range from customization of the

application interface to the customization of the product bundle itself (…). In addition to data

mining, data processing is also about interactively learning from past interactions” [P.

Schubert and U. Leimstoll (2003) 212]. However, the customer has to accept giving real data

about him, otherwise all the efforts done by the company will be useless. For Spiekerman and

Paraschiv (2002) “the main reason for demotivation is the missing ‘learning’ from user

interaction. Transactions that appear several times have to be simplified by features like the

automatic fill-in of parameters” [P. Schubert and U. Leimstoll (2003), 212] in order to keep

the visitor interested in our products.

2.4. Information output

The last step is the information output that consists in the combination of “customer profile

information and meta information of products or information objects. The goal of matching

methods is to select something for the customer based on his or her profile. In general, the



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selection can be about content (to be displayed), interaction (how to interact with the user) or

media usage/configuration (on which channel/using which media)” [P. Schubert and U.

Leimstoll (2003), 213]. The two main methods to do so are, according to Schubert and

Leimstoll (2003), content based filtering, that uses information about the product, and

collaborative based filtering. “A content-based filtering system selects items based on the

correlation between the content of the items and the user’s preferences as opposed to a

collaborative filtering system that chooses items based on the correlation between people with

similar preferences” [R. Van Meteren and M. Van Someren (unknown year) 3]. The purpose

of these methods is to help the customer to find more easily what he is looking for, sometimes

even before knowing that this kind of product will interest him. Moreover, for Schubert and

Leimstoll (2003) the main purpose of content based filtering is to mark objects with meta

information. For example, we can say that “the user profile is represented with the same terms

and built up by analyzing the content of documents that the user found interesting. Which

documents the user found interesting can be determined by using either explicit or implicit

feedback. Explicit feedback requires the user to evaluate examined documents on a scale. In

implicit feedback the user’s interests are inferred by observing the user’s actions, which is

more convenient for the user but more difficult to implement” [R. Van Meteren and M. Van

Someren (unknown year) 3]. On the other hand, as shown in figure 2, collaborative filtering

focuses on the customer’s tastes and tries to match his preferences with the ones of another

customer in order to create a “group” with similar tastes that can after be transposed to other

customers that present the same basic characteristics. For Schubert and Leimstoll (2003) this

corresponds to “electronically support the principle on the ‘word-of-mouth’”. With these

techniques the customer profile can be used to create “sub-communities of customers with

similar taste ‘affinity groups’. By linking affinity groups with recorded purchase transactions

of a big numbers of customers a knowledge bases emerges which can be used for the

prognosis of future buying behaviour of individuals” [Schubert and Leimstoll: 2003, 214].

Figure 2. Collaborative Filtering: Building Affinity Groups [Schubert 2000]



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3. Condition sine qua non for making effective customer

profiles: to have an integrated database

Many companies collect efficiently a lot of data about their customers. Their demographic

data, their purchase behaviours, etc. They ask them frequently and register all the information

provided through many channels. These can be offline channels (like phone, direct mail, face-

to-face communication, fax, etc.) or online channels (websites email, etc.). However, the

companies omit too often to integrate these data in a unique database system. They are kept in

different database in different departments. Often they are not crossed and compared together.

So we see that the company receive information about customers through many sources and

this can lead to a problem if the company doesn’t have at its disposal an integrated database

system which regroups all the data collected via all the channels. Indeed, some relevant data

can be neglected. For example, if the customer phones the company to say that his address

will change soon because he will move on; or he sends a letter to say that he wants to

terminate his contract with the company, and these information cannot be taken into account

by the firm because the database system is not integrated (so perhaps there is a separated

database system for online communication), this lack of integration will harm the company

and the customer.

The customer won’t receive some information or offers by the firm because the new address

was not modified in the database, or he will receive some offers although he has told that he’s

not interested anymore. Thus the customer feels a lack of interest from the company about its

needs and its lifecycle. This will increase dissatisfaction and loss of loyal customers, and we

know that loyalty takes a large part in the profitability of the company. Indeed, according to

Sun Microsystems [Sun Microsystems Inc. 2007] “a 5% increase in the customer loyalty can

lead to a 75% increase in the profitability of the company”. So if the company loses some

loyal customers, this will have a huge negative impact in its profitability. Furthermore, if the

customer shares his bad experience with the company on the web, this will be seen by many

people all around the world and may very quickly tarnish the reputation of the company. The

customer profiling will thus become ineffective and inefficient because the information

contained in the database system are either incomplete or inaccurate. So all the actions

undertake by the company to contact and target its customer will remain ineffective. The

marketing campaigns will be costly and badly targeted. This represents a huge amount of

useless expenditures for the company. Thus this will damage the profitability and the



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existence of the company. Because the company doesn’t have a complete and updated

database system, it cannot know and anticipate exactly their needs so all the marketing

campaigns to increase the actions of the customers will remain ineffective because badly

targeted. The customer service centre will also feel this ineffectiveness. In fact, the employees

cannot help the customer in the right way because a lack of personal knowledge about this

customer due to scattered information about him.

Furthermore, another problem has to be solved when a company collects many data through

different channels: it’s the “normalization” of these data. In fact, all the different system

which collect the data use perhaps different terms to define the same information. For

example, for collecting some information about the sex of the customer (male or female), the

system 1 defines this in term of M or F; but the system 2 has also collected the same

information about the customer but it defines it as Man or Woman. Despite it’s the same

information, it will cause a real problem when the company will have to integrate all the data

gathered. To solve this problem, the company has to normalize all theses data, i.e. make them

consistent with each others in order to integrate them in the most effective way.

A good solution to achieve this goal is to use data warehouse systems which are “centralized

data repositories that extract the data out of different heterogeneous systems” [S. Sandberg, D.

Fasel: 2007]. As they say, “the data gets normalized before being entered into a repository”

[S. Sandberg, D. Fasel: 2007].

So we can deduct that without an integrated and a normalized database, a company can’t build

an effective relation with its customers (knowing them, classify them and propose them

adequate offers) so it cannot succeed in customer’s satisfaction and loyalty. Thus its

profitability and its future existence have to be called into question because nowadays in the

world of competition, all these consequences are not viable for a company. So to avoid all

these dangers, a company has to have an integrated database system to beneficiate of a

unique, completed and consistent customer profile for each customer.



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4. Different Implications of customer profiling

4.1. To know the customers and understand their needs

Establishing customer profiles enables the company to really know their customers. In fact, a

customer profile includes several different demographic and behavioural data like age, sex,

job, hobbies, buying behaviour, products purchase, frequency of purchasing, etc.

With excellent data mining tools which locate all their behaviours on the web, customers are

scrupulously analysed and all their actions are reported into database system and are added in

their customer profile.

Knowing the customers is very important today in a world of competition, and fast-moving

environment. Indeed, the lifecycle of the products are getting shorter over time so to remain

on the market, companies have to understand and to predict customers’ needs as exactly as

possible to satisfy them continuously and analyzing their behaviours based on customer

profiles allows to fulfil this goal.

Furthermore, knowing the customers allows to increase the likelihood of their loyalty to the

company and this is very important today notably with the internet media which enables to

easily compare the offers of different competitors by a simple click.

Therefore, companies, and more specifically companies operating in the internet channel,

have to build and maintain a strong relationship with its customers by constantly

demonstrating that they are their most valuable asset and that they take care of them and of

their expectations.

4.2. To cluster the different customers in groups (segmentation)

Once the company has different customer profiles, it can classify them into several

homogeneous groups. Why does it do that? Because generally customers don’t have the same

characteristics, and don’t share the same wants and needs. They have different habits and

preferences. So if the company doesn’t bring together customers by groups, it would be

difficult for it to respond to a heterogeneous group and to conduct an efficient marketing

campaign.

We segment customers into groups which are more or less homogeneous (same life styles,

same demographic data, same preferences and purchase behaviour) to better respond to their

wants and to differ from some competitors. All these groups are different to each other and

can be chosen by the firm for a specific marketing action.



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The segmentation allows also to find out new market opportunities. In fact, by grouping

people, a company can detect not only real needs but also unfulfilled and implicit (potential)

needs, so the company, by detecting that, can have a competitive advantage against other

competitors who have not seen the new trend.

Another reason to segment customers is that it allows to define in which group the company

will compete because a company generally can’t respond to all the customers’ needs. So (as

we will see later) it has to make priorities and choose the most valuable segment (the most

valuable customers) and concentrate its strengths on this to better respond to this segment.

The segmentation has to conduct to different homogeneous groups which have been defined

by specific criteria which are relevant for the company. These criteria can be demographic,

geographic, socio-economic, or they can be based on customer’s personality or life styles, or

based on behaviour (e.g. segmentation according to the user status (new user, potential user,

regular user, occasional user, etc) and its loyalty to the firm; segmentation based on the

volume purchase; or on the consumer modes; or based on their profitability, etc.) [Lendrevie,

Lévy, Lindon: 2003]. So there are a lot of potential criteria and the company has to select the

most relevant for it.

4.3. To define a better targeting strategy and a better marketing

campaign

Based on the two precedent implications, we can also say that establishing customer profiles

allow to better define a strategy and a marketing campaign.

In fact, by knowing the customers’ preferences and behaviours and by classifying them into

homogeneous groups, the company exactly knows the wants and the needs of each customer

group and can model its strategy and target its marketing campaign to better satisfy them.

Considering that, we can say that the marketing campaign will be more effective and efficient.

Thus, the specific campaign will address those who are the most likely to be interested in

because it was modelled to completely respond to their preferences and needs, so the success

on the campaign is very probable.

Furthermore, targeting the right customer with the right words will avoid some important

costs due to ineffective campaign based on feelings and assumptions rather than rational and

strategic analysis. If the company has no idea about the characteristics of the public it speaks

to, it’s impossible to obtain satisfaction from customers.

There are different reasons a company cannot reach its target. For example, it can propose a

supply that doesn’t fulfil their needs or their preferences so the supply itself isn’t adequate; or



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the supply could be in accordance with their wants, but the way it is presented to the

customers don’t reach them because of a lack of knowledge about their preferred

communication media (e.g. the company sends an email to its customer to present its new

supply but its target doesn’t use very often the internet channel (or the contrary), so even the

campaign is in accordance with the customer’s wants, it remains ineffective).

Thus the firm has to be careful of the way customers prefer to receive information. This is

also mentioned in the customer profiles. We see that analysing customer profiles allow to

avoid many costs in the marketing campaign and rendering it more effective.

4.4. To propose them adequate offers (personalisation)

One of the implications of customer profiling is that it allows to practise mass customization,

and even in extreme cases personalisation.

Why is it so important to do this? Today customers are dealing with a considerable offer in

the market. The internet has enhanced this offer by considering foreign products. So even the

customers have more choice than before, it is also more difficult for them to find the perfect

and adequate product they need.

This has two consequences: first, it is more difficult for the companies to put their products

forward, so their products are less likely to be bought by the customers; second the decision

process for the customers is more complex and complicated because they have to deal with a

lot of products in the market and don’t really know which is the best product for them.

To promote their products companies have to invest a lot of money by improving their offer

to distinguish themselves against the competitors (like marketing campaigns, R&D, etc) but

they also have understood that helping the customers in a way to improve and facilitate the

decision process when they want to buy a product will be beneficial not only for the

customers but also for the company itself. In that way, customer profiles are a crucial tool to

improve the decision process and to satisfy the customer in its purchase.

Having at its disposal customer profiles is very beneficial for the company because by this, it

can know its customers and thus can propose them directly adequate offers. Internet has

reinforced this trend because the information given to the customers can be tailored at very

low costs.

The company can practise mass customization, so it delivers the same information to a group

of customers with a common interest, so to a segment of customers. Or in a more extreme

case, it can do personalisation by delivering personalised information for each individual

customer. In the two cases we speak about individualisation which is “based on the



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intelligence collected about site visitors and then stored in a database and subsequently used

to target and personalise communications to customers” [Chaffey and al.: 2006].

By giving (conscientiously or not) information collected after in its customer profile, the

customer becomes (even he doesn’t really realize this) an active partner in the strategy of the

company.

Of course, it is implicit that to achieve mass customization or personalisation, the company

must have sufficient information about its customers. The more the company wants to provide

personalisation, the more it must have pertinent and detailed information about its customers,

like not only demographic data (which is alone not sufficient to practise personalisation), but

also other more relevant data like their specific interests notably available throughout a

purchase history etc.

4.5. To do product analysis

The analysis of the customer behaviour on the internet and the establishment of customer

profiling provides to the company also many information about their purchases. In fact, by

doing data mining analysis, the company can do a product analysis, i.e. who buy this product,

in which frequency, in which quantity, etc. Knowing this type of information allows the

company to find out what type of customers the product is likely to interest the most and

therefore, the company can better target its customers by proposing them an adapted offer. By

doing such analysis, the company can also find out which products are the most successful

and which are the less, so it can adapt its marketing campaign and focus its investment on the

most profitable products. An illustration of this will be made further in the Amazon case.

Concerning products, another advantage can be found by establishing customer profiles:

improving the product plan. As S. Sandberg and D. Fasel (2007) mention, “the company can

use the customer profile into its market research for product development in order to better

understand and fulfil the market needs”. This is very important and especially nowadays in

the world of competition. The company has to be able to predict the future and potential

customers’ needs in order to maintain its position in the market.

4.6. To find out the most valuable customers and the others

Another advantage to have customer profiles in the company is that it allows to spot the most

valuable customers by analysing their behavioural data. The idea is that if you can compare

the potential value of each customer, you can allocate more resources and investments to

higher value customer groups so you can concentrate your efforts on them.



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How can you evaluate the potential value of the customers based on behavioural data? One

method (among others) is to show the Recency Frequency Monetary value of the customers:

the RFM analysis. Recency is “the number of days that have gone by since a customer

completed an action (purchase, log-in, download, etc.)” [Chaffey and al.: 2006]. Some people

say that recency is “the most important variable in predicting the likelihood of a customer to

repeat an action” [Jim Novo: unknown year]. They say that the more recently a customer has

done something (e.g. purchase, log-in in a website, etc.), the more likely they will do this

again. Furthermore, the more a customer repeats an action, the more it will be active when a

promotion is made for this action.

So considering these two implications, we can deduct that the more a customer is recent, the

more it has a high potential value for the company because it will be more likely to contribute

to the profit of the company.

How implement the recency? First, the company must identify the groups it wants to evaluate.

Then, it must decide which activity is the most relevant for the analysis (e.g. if the main

activity of the company is to sell products, it will probably be best to choose purchases than

log-in). After, it reports all purchases of each group. Then the company must take a frame

time to conduct its analysis (e.g. 90 days), and after some calculations (total number of

customer in each group who have made a purchase during these 90 days / total number of

customer of each group) the firm can discover what percentage of each group who has

purchase something has made one purchase or more during these last 90 days. The group with

the higher percentage is more “recent” so has the higher potential value for the company.

Based on this method, you can extract very interesting dimensions. For example, if you do

this process by firstly group the customer by product they buy first, the firm can find out

which product leads to new customer with high potential value; or if you group them by

which part of the website they visit the more frequently, the result can give it which part of

the website generates the most important customers, etc. Frequency is defined as “the number

of times an action is completed in a period of a customer action, e.g. purchase, visit, e-mail

response, etc, e.g. 5 purchases per year, 5 visits per month, etc” [Chaffey and al.: 2006].

Monetary value of purchase is the total amount paid by the customer for example during a

certain period. The customers who have a high monetary value generally have a higher loyalty

and a high potential value in the future for the company.

More generally, the higher is the value of the three dimensions, the higher the value of the

customer is.



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We can see that with this method a company can analyse its data not only in terms of

purchase history, but also for visit or log-in frequency to a website and for assessing the

response rates to online communications.

Thus analysing behavioural data leads the company to better evaluate their customers and to

concentrate its activities on the most valuable customers because these are the most likely to

make the company more profitable now and in the future.

Understanding the categories of customers allows to better target the right customers with the

right methods. For example, if the firm knows that the group of customers X buys a lot of

products during a year, and the group Y buys less, the company will rather concentrate their

efforts of promotion to the group Y, etc.

So knowing the potential value of the customers based on data given in customer profiles

enables the company to better target its efforts and to improve its marketing strategy.

4.7. To show the efficiency of the Web-shop

By analyzing the customer behaviour in their websites, companies can see all their actions

they do in the website.

With web analytics, they can see what each customer is looking for in the website, which part

of the website he visits, how long and at which frequency. This allows the company to exactly

know which parts of its websites are the most consulted and which parts the company should

improve to attract more customers in their website and to keep them as long as possible to

have a better probability that they consult the whole site so that they buy some articles.

Thus by analyzing this, the company can build and modify its website according to the

customer’s preferences and needs.

This is a real advantage because actually, with the huge number of company’s websites, the

customer has become more demanding and volatile in his choice and he doesn’t want any

more to spend a lot of time trying to discover in the website some information that are not

immediately available. So if the website doesn’t suit him, he will be very encouraged to

consult another website which could more respond to its expectations, but that website could

be probably a competitor so at the result the company will lose the chance to gain a potential

new customer.



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4.8. To improve the profitability of the company

All these favourable implications allow the company to increase the satisfaction of its

customers because they feel that their specific needs are very well considered and that the

company tries to satisfy them personally by proposing them specific offers.

This satisfaction contributes to the loyalty of the customer so their life cycle increases. This

increases the profit of the company and ensures the durability of the company.

Thus despite customer profiling necessitates a lot of investment in money and time, this is

very profitable in the long term.



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5. Illustration of the e-profiling process: The Amazon case

Amazon is an American company founded in 1994 by Jeff Bezos. It was one of the first big

companies to sell goods on internet. The firm first sold books and quickly diversified its offer

into many other lines of business: music, video, electronics, toys, pharmaceuticals and online

auctions, etc. “Amazon.com attracts almost 10 million visitors a month, has annual sales for

more than $1 billion, and is growing fast” [Kotler and Armstrong: 2001, 490]. The advantages

offered by the electronic business were enormous at the time of its foundation. Internet

represented a new distribution channel that allowed sellers to charge lower prices and to get

higher margins than in the traditional bookstores. Moreover the creation of a web site such as

amazon.com allows you to track customer behaviour and to adapt your offer to your different

customers. Marketing channels have been modified due to the growth of online marketing.

The major change is the “disintermediation” process that means “that more and more, product

and service producers are bypassing intermediaries and going directly to final buyers, or that

radically new types of channel intermediaries are emerging to displace traditional ones”

[Kotler and Armstrong: 2001, 441]. Amazon’s case will be the second type of

disintermediation. According to Kotler and Armstrong (2001) “Amazon.com doesn’t

eliminates the retail channel- it’s actually a new type of retailer that increases consumers’

channel choices rather than reducing them. Still, disintermediation has occurred as

Amazon.com and superstores’ own Web site are displacing traditional brick-and-mortar

retailers (…). Thus if Amazon.com weren’t giving buyers greater convenience, selection, and

value, it wouldn’t be able to lure customers away from traditional retailers” [Kotler and

Armstrong: 2001, 442]. However, internet also opens the door to new opportunities from the

producer side. In order to help the customer in his buying process the seller has now new tools

to adapt his offer to a particular customer. As we have seen before personalisation in one of

them, and is one of the most important sales tool that a web seller such as Amazon can use in

order to secure the loyalty of its customers. According to the article “Le web mining et son

application sur www.amazon.fr” (2006), the different data that Amazon collects during the

different visits of their customers are a very important element in the firm’s strategy and are

used by 3 different purposes. From one side the collected information helps management to

personalise and optimise its e-mails. It also helps merchandising to recommendations to the

customers such as “the client that bought this also bought that”, or to prevent you about new

products linked to those that you have bought previously on the site. The last sector that uses



- 18 -

this information collected about the customer is advertising, because as you know more about

the customer you can personalise and make it more attractive according to his points of

interests that you have previously recorded. As said by A. Weigend, the director of the Data

mining at Amazon “Most people are way more predictable than they believe. If they are in a

certain situation, they will react in a certain way. If you follow customers over time, you

discover strong regularities, for example, in their information-foraging behavior. Additionally,

short-term human behavior often has indicators that make it much more predictable than long-

term behavior” [Le web mining et son application sur www.amazon.fr: (2006)]. It is for this

reason that customers’ profiles are an essential tool for a web site such as Amazon. Amazon

is today one of the world’s leader shops on the internet not only because it was one of the first

movers in this sector, but also because since its creation it has created a strong competitive

advantage towards its competitors. As we have said before the internet is a more transparent

channel that allows customer s to easily find information about different products. This allows

comparing easily prices for example among different websites. Amazon.com has taken

advantage of this tool and has made cost leadership one of its main strategies. However price

is not the only element that encourages customers to buy product. The second important tool

from amazon.com is the usage of customer profile that allow Amazon to recognise its

different visitors and to propose them a particular offer that they wont find in other e-shops.

The last important tool used by Amazon is the particular attention to niche markets.

“Amazon.com focuses on outstanding customer service as a niche but not the whole market

because each niche has its own demand and requirement” [http://wiki.media-

culture.org.au/index.php/Amazon_-_Business_Model]. These tree elements together give to

Amazon an outstanding comparative advantage towards its competitors.

As we can observe in figure 3, in order to differentiate the customer Amazon uses different

elements to get information about him and its behaviors. In this example we will focus, on

this customer differentiation and more precisely in the importance of customer profiles for a

company such as Amazon. We will orient the theoretical part explained above in order to

implement amazon’s strategy in the customer profile lifecycle.



- 19 -

Figure 3. Amazon business model [Le web mining et

son application sur www.amazon.fr : (2006)]

The different steps of personalisation in the Amazon.com case:

5.1. Modelling customer profiles and data input in the Amazon case:

As we have seen before, this step is the first step of the personalisation process that allows

establishing customers’ profiles. We have seen that the needed information to establish such

customers profiles can be obtained with the awareness and consent of the customer, or in

some cases without he really notices we are tracking him in order to establish his customer

profile. In the case of a company such as Amazon, this step required first that the company

decides on which elements it wanted to retain in its customer profiles in order to do a

segmentation of if different types of customers that could help them in the future to retain its

clients.

In the case that the company asks direct data to the customer, we can be asked in the site of

amazon.com information such as your name or your e-mail address, in order they can send

you a confirmation of your order as well as promotional offers that match with your

preferences. In order to know which these last ones are, the company asks us to log in to be

able to identify the customer, and to choose in a category of information that we are interested

in such as new products, or special offers. The site can also use information concerning your

phone number, the card number that you used to pay, the e-mail addresses of your “Amazon

friends”, as well as the different comments that you have made on their site. The different

ratings of the product are also recorded in this step. For example books or DVD’s can be rated

by each customer and this information is then shown to other customers. However this is only



- 20 -

part of the information used by Amazon to establish our customer profile. There is also

information concerning “implicit profiles” that is automatically collected by amazon.com,

without the customer being really aware of it. This kind of information is for example the IP

address that links your computer to internet. However this cannot be enough to identify your

customer due to the fact that the same IP address can be used by different customers. The site

also registers automatically information about the different articles you bought in the past in

order to create lists such as the best sold product or “Just like you” that proposes other articles

that were bought by customers that show the same profile as yours. They also register your

participation to the “zShops” that you have visited. The site also collects information about

the number of pages you have visited, and by using cookies is able to know who the visitor is

and what pages he has visited before. An example of a cookie used by Amazon can be:

“Session-id: 103-5522513-6507amazon.com/01650098176 29346719 1552735424

29345431*” [Le web mining et son application sur www.amazon.fr : (2006)]. This Web

mining techniques can be of two different types: on one side the Web usage mining

techniques that is “the application of data mining techniques to discover usage patterns from

Web data, in order to understand and better serve the needs of Web-based applications” [J.

Srivastava, R. Cooley, M. Deshpande, P. Tan: 2000, 1]. On the other side the web content

mining technique allows to analyze the different contents of the web pages. By the “click

stream” you can establish then different categories that have been used for other customers.

All these information collected during this step builds large amounts of information that are

stored in databases and used with Web mining techniques in order to be used afterwards in a

commercial purpose.

5.2. Data processing

As we have seen previously on this study, the data obtained from the customer in the previous

stages of the customer life cycle needs to be “processed” in order to be used by the

companies. This can be done as said previously by data mining techniques or in the case of an

internet company such as Amazon by, web mining techniques. This technique helps to

establish different categories of customers that help to establish a customer profile and to

create a real relationship with the customer. For a company such as Amazon that faces many

competitors it is important that once they acquire new clients they are able to retain them. It is

well know in this sector such as in many other distribution sectors that to keep a customer is

cheaper than to acquire new ones, so we can say that in the case of Amazon, the web mining

techniques are a very important tool to keep the company competitive in its market. As we



- 21 -

have seen before there are different techniques of web mining, and the most interesting in the

Amazon case is the “Web usage mining [that] consists of three phases, namely pre-

processing, pattern discovery, and pattern analysis” [J. Srivastava, R. Cooley, M. Deshpande,

P. Tan: 2000, 3] in order to get information about the different operations done by the visitor

on a web page. After analysing the different information we get to the last step of the

customer profile lifecycle, the information output.

5.3. Information output

In this step we combine information about the customer and about the different products in

order to suggest the appropriate offer to the customer. The two main methods to do so are

content based filtering, that uses information about the product, and collaborative based

filtering that establish correlations between people that have the same preferences.

“Information filtering systems that personalize web sites often use a collaborative approach to

filtering. Amazon.com for example uses the GroupLens system [Resnick et al. 1994] to make

recommendations about books and videos” [R. Van Meteren and M. Van Someren: 8]. This

system uses ratings from the different customers that are then used to sell this product to other

customers. “Content-based filtering performs profiling by extracting feature values (vectors

expressing interests by, for example, applying weights to keywords) from content used in the

past and recommending content with similar feature values (Figure. 4). These methods

assume that metadata, such as keywords or genre data, will be provided with the content” [Y.

Ichikawa, M. Nakamura, K. Hata, T. Nakagawa]. However, as said previously Amazon

prefers to use collaborative based filtering that according to Y. Ichikawa, M. Nakamura, K.

Hata, T. Nakagawa “a profile is created by evaluating content used by the user in the past,

and recommendations are made by evaluating users with similar profiles, and hence similar

interests (Figure. 5). The content itself is not used for profiling, so a recommendation service

can be provided without preparing metadata. This method can also provide a larger number of

serendipitous recommendations, so it is currently the most commonly used. A well-known

example of this approach is the ‘Customers who bought this item also bought…’ section

displayed on Amazon product pages”.



- 22 -

Figure.4. Content-based filtering [Y. Ichikawa, M.

Nakamura, K. Hata, T. Nakagawa].

Figure.5. Collaborative filtering. [Y. Ichikawa, M.

Nakamura, K. Hata, T. Nakagawa].

“At Amazon.com, we use recommendation algorithms to personalize the online store for each

customer. The store radically changes based on customer interests, showing programming

titles to a software engineer and baby toys to a new mother” [G. Linden, B. Smith, J. York,

(2003): 1]. However even if Amazon is more oriented towards collaborative filtering, G.

Linden, B. Smith, J. York (2003) call the method used by Amazon “item-to-item collaborative

filtering”. This method implies that “unlike traditional collaborative filtering, our algorithm’s

online computation scales independently of the number of customers and number of items in

the product catalog. Our algorithm produces recommendations in real time, scales to massive

data sets, and generates high quality recommendations” [G. Linden, B. Smith, J. York,

(2003): 1].

So Amazon doesn’t need really to know other information about its customers like

demographic data, it take a less interest in that. It tries rather to guess the customers’ interests

by analyzing their behaviour on the website.



- 23 -

How does this technique work concretely? “Rather than matching the user to similar

customers, item-to-item collaborative filtering matches each of the user’s purchased and rated

items to similar items, then combines those similar items into a recommendation list” [G.

Linden, B. Smith, J. York: (2003)].

To determine the most-similar match for a given item, the algorithm builds a similar-items

table by finding items that customers tend to purchase together.

The following iterative algorithm is used to calculate the similarity between a product and the

other products:

• “For each item in product catalog, I1

• For each customer C who purchased I1

• For each item I2 purchased by customer C

• Record that a customer purchased I1 and I2

• For each item I2

• Compute the similarity between I1 and I2” [G. Linden, B. Smith, J. York (2003)].

So Amazon focuses more on the products than on the customer characteristics to make

personalized recommendations. The company looks for each product I1 bought by some

customers if they also have bought another product I2, and if there is a significant correlation

between the two products, i.e. if customers who has bought I1 have in many cases also bought

the product I2, they propose to all customers who will buy or look at the products I1 a

recommendation to buy the product I2 because apparently, there is a link, a positive

correlation between those two products. The challenge is to find out the rules which express a

correlation between one product purchased or rated and other products.

So to make recommendations for each customer, Amazon doesn’t focus on the customer’s

similar characteristics but focus more on the product characteristics.

We see that they don’t need to really know who its customers are and they don’t need to ask

them directly specific data because all the recommendations they do are based more on

implicit (behavioural) data than on explicit data. “Given a similar-items table, the algorithm

finds items similar to each of the user’s purchases and ratings, aggregates those items, and

then recommends the most popular or correlated items.” [G. Linden, B. Smith, J. York,

(2003)].

It’s only in that way that they can find out the customers interests. And behavioural data are

more focused on the products than on the customer itself to make customized offers. So

demographic data like name, age, sex, etc. are in most cases only used by the company to

deliver the products in the right way.



- 24 -

An example of this kind of method used by Amazon is the study done by Valdis Krebs that

establishes how the recommendation system works. In Figure 6 we can see the most sold

books talking about American politics in 2004. This author “was able to determine what other

titles buyers had purchased at the same time. Following the links between titles, Mr. Krebs

ended up with a list of 66 books. His map showing how the titles are connected by buyers

reveals a readership -- or at least a book buyership -- as fiercely polarized as the national

electorate is said to be” [E. Eakin : (march 2004)]. The different blue points represent different

books more oriented to the liberal political side, whereas the right side there are the red points

that shows us the more conservative books. Between both sides we see grey point that

represent the books with more moderate political opinion or that don’t support one party nor

another. Valdis Krebs “found, buyers of liberal books buy only other liberal books, while

buyers of conservative books buy only other conservative books” E. Eakin : (march 2004)].

Figure. 6. Book network derived from “people who bought… also bought…data. [Valdis

Krebs 2004, orgnet.com]

Other methods than item-to-item collaborating filtering, like traditional collaborative filtering,

cluster models and search-based models, are used by companies to segment and propose

customized offers to the customers, but as we see they don’t work for the case of Amazon

company either because they are impractical on large data sets, or they don’t provide some



- 25 -

pertinent data for the company, or they are not adapted for huge purchases and ratings. Thus

they are not suitable for the Amazon case.

The item-to-item collaborative filtering method doesn’t encounter this sort of problems. As

the authors say, “the key to item-to-item collaborative filtering’s scalability and performance

is that it creates the expensive similar-items table offline. The algorithm’s online component

— looking up similar items for the user’s purchases and ratings — scales independently of the

catalog size or the total number of customers; it is dependent only on how many titles the user

has purchased or rated. Thus, the algorithm is fast even for extremely large data sets. Because

the algorithm recommends highly correlated similar items, recommendation quality is

excellent. Unlike traditional collaborative filtering, the algorithm also performs well with

limited user data, producing high-quality recommendations based on as few as two or three

items.” [G. Linden, B. Smith, J. York (2003)]. So this technique, according to Amazon, is the

best for achieving the company’s purpose.



- 26 -

6. Dangers of Customer Profiling Customer profiling is very profitable for many things. However, it can represent a real danger

for the customer and for the company if it is badly used or if the data it contained are badly

exploited.

6.1. For the customer

The main danger for the customers is the non respect of the protection of the personal data.

By asking some personal data (name, address, phone, mode of payment, etc.) and by tracking

and analyzing the customer behaviour in their website, the company collects a lot of personal

information about their different customers. In fact, this is obligatory for establishing

customer profiles.

The risk for the customer is that the company doesn’t respect the protection of these data by

divulgating personal data to other companies either intentionally or not because it hasn’t a

sufficient protection against these sort of usurpation.

The company can also use customer data in another way it is generally planned.

But generally and in most cases, the company has special clauses in which it engages itself to

not divulgate customer data and to respect the privacy of their customers. These clauses are

based in national and international laws as for example the Swiss federal law concerning the

protection of data (1992) or the Directive 95/46/EC for the European level.

Unfortunately, sometimes the company isn’t able to protect these data and other companies

can obtain some information about personal data. So another company can obtain data about

the customers (e.g. email addresses, phone numbers, names, etc.) and the other company

cannot control that.

Thus the customer will after receive an email by an unknown company and he won’t

understand how this firm has obtained its email address. This can irritate the customer and he

will become more suspicious next time when he will be asked to give some data in a website.

Furthermore and linked to the data protection problem, another danger for the customer is the

hidden profile. In fact, today with all the technologies used, huge amounts of data can be

collected by different companies via many different ways. Especially in the web, all

customers’ actions are scrupulously gathered and analyzed. All their behaviours in the internet

are tracked without the netsurfer’s agreement but also without the netsurfer’s being aware of

it). So the customer (or netsurfer) doesn’t manage anymore to control all the information



- 27 -

gathered by companies. Even the customer doesn’t know which information have been

collected about him and for what purpose they have been collected.

Legally, the customer has the right all the time to ask the company what information the firm

has collected about him, and can ask to modify or delete some data.

Despite in the legal aspect, each company has to be able to convey the information concerning

each customer if a customer asks it for that, in many cases the companies can’t fulfil this

requirement because they are juggle with too many information so it’s very difficult to exactly

transmit these information to the customer. Furthermore, some companies transmit sometimes

some data collected to other companies, so a very complex network has been created over

time. Therefore, we see that all these information are out of control of the customer and of the

company and this can be very problematic in term of data protection for the customer.

6.2. For the company

One risk for the company to establish customer profiles is that the data contained in these

profiles are not exact or not updated. In fact, the data themselves can be wrong because

customers have entered false information, or because there was a technical problem with the

data base system.

The data can also be misinterpreted by the company. For example, in a web shop site, if the

company proposes some specific articles based on the previous purchases of the customer

because it deducts that the previous purchases represents the interests of this customer, it will

perhaps miss the target because the customer has in fact not purchased this article for himself

but for a friend who is not a member of the website company and who has used the password

of his friend to order an article. For example the customer bought a book for his mother about

cooking, so the company thought that he loves cooking and it will propose him after some

books about this topic but the customer doesn’t care about cooking. So the customer will be

irritated for this offer.

The data can also be not updated by the company, because either the customer has forgotten

to modify his profile (e.g. his new address, etc) or because the company doesn’t take into

consideration the life cycle of their customers. For example, the company has proposed a

special offer for children (e.g. toys or books for children) to a specific customer because in its

profile it is mentioned that he has little children but in fact the profile was not updated for

years and now the children are no more children but teenagers!



- 28 -

All these wrong data represent a danger for the company because its actions will be not

addressed to the right segment of people or to the right person. So even its marketing

campaign was excellent, it will remain inefficient because the right target was missing.

The customers will be unsatisfied because the proposed offer is not adapted for their needs,

and the reputation of the company will be damaged. The company will lose its credibility and

thus many customers.

Another danger for the company is to overexploit the collected data. In fact, because of

knowing the customers, their personal data and their behaviour, the risk for the company is to

contact them too often with specific and personalised offers and inundate them with messages

coming from different channels (phone, mail, or information directly on the website of the

company). This could irritate the customer, he could see these actions as a breach of the right

to privacy because he will feel too tracked, too analysed by the company. He will think that he

could not do anything without being scrupulously analyzed by the company and he couldn’t

feel free to see something in the website without receiving a proposition from the company.

So he will perceive that as a breach of the right to privacy and of liberty. This could lead him

to deregister to this website.

So the company must be careful of not being too intrusive in its communication with its

customers and don’t overexploit the information about different customers. One solution to

find a happy medium in communication is to use the different tools of permission marketing

which means that the company asks the customer, when he’s registering in the website to

become a real customer of the company, if he wants to receive some information about the

company’s products or news. This avoids to inundate and irritate the customer with too much

information and offers. By asking this sort of information, the customer feels that the

company is serious and that it considers him with respect. So this tends to increase the

customer’s trust and loyalty to the company.



- 29 -

7. Conclusion This seminar has provided us a really comprehensive view to customer profiling. We saw that

the process to build an efficient data base is complex and not as simple as we can think. An

important infrastructure has to be built in the company to support that process. More

specifically, the system must integrate all the data gathered through different channels and

this represents a big challenge for the company but is crucial for the efficiency of the

customer profiles’ goal.

Making the profiles of the customers has many positive effects as well for the customer as for

the company itself. This process has become essential especially nowadays because today the

customers live in affluence, there is too many choices regarding the products offer in the

market, and the customer often has some difficulties to distinguish what is the best product for

him which corresponds exactly to his needs. The difference today is not in the product quality

because the quality is more or less the same in the developed countries, so to bring out its

products a company has not other choice than to guide/push the customer to its product, so it

has to advise the customer as best as possible. To implement this strategy and to deliver the

adequate offer to each customer (or each group of customers), the company has to know them

and this is possible only by doing customer profiles. So the company wants to facilitate the

decision process by immediately providing the most customized offer to the customer.

But the company has to be careful to not advice the customer too much, otherwise the

customer could feel this as a pressure coming from the company which absolutely wants that

the customer buys its products, and he could feel that he’s no more free to decide himself

what product is best for him. So he could be irritated and unsatisfied. This can be painful for

him and for the company which risk losing a loyal customer.

Another aspect that the company has to take into account is the respect of the personal data

protection which represents one of the biggest dangers of customer profile. Thus a company

has to find a happy medium, a balance between too much and too less and this represents a

big challenge but is determinant if the company wants to keep its customers satisfied.

Considering this, customer profiles represent some great advantages for both the company and

the customer. Moreover, this can constitute the success of a company. Amazon is a good and

representative example of this strategy, because it is a company which has built all its

business on customer profiling and now the company is known internationally for this

competence.



- 30 -

Therefore, customer profiles can provide many important benefits for the company and the

customer but to be really efficient, it has to be well managed, integrated and carefully done

with the respect of the customer privacy.



- 31 -

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Documents

Electronic Business: Customer Profiling - unifr.chdiuf.unifr.ch/main/is/.../eBiz_fs08/...customer_profiling_paper.pdf · Electronic Business Nadine Biegajlo ... Different Implications