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Electronic Business: Customer Profiling -...
Transcript of Electronic Business: Customer Profiling -...
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
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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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Prof. E. Meier Véronique Herrmann
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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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Prof. E. Meier Véronique Herrmann
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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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Prof. E. Meier Véronique Herrmann
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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
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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.
Electronic Business Nadine Biegajlo
Prof. E. Meier Véronique Herrmann
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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
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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
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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”.
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Prof. E. Meier Véronique Herrmann
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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.
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Prof. E. Meier Véronique Herrmann
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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.
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Prof. E. Meier Véronique Herrmann
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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
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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.
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Prof. E. Meier Véronique Herrmann
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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
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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!
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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.
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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.
Electronic Business Nadine Biegajlo
Prof. E. Meier Véronique Herrmann
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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.
Electronic Business Nadine Biegajlo
Prof. E. Meier Véronique Herrmann
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Bibliography: • [G. Adomavicius, A. Tuzhilin: 2005] G. Adomavicius, A. Tuzhilin (2005):
Personalisation technologies, Communications of the ACM, Volume 48, No.10, 2005
• [Chaffey and al., 2006] Chaffey D. and al.: Internet Marketing, Strategy,
Implementation and Practice, 3rd edition, Prentice Hall, 2006.
• [H. Deitel, p. Deitlel, k. Steinbuhler: 2001] H. Deitel, p. Deitlel, k. Steinbuhler
(2001): E-Business and E-comerce for managers, Prentice Hall, 2001.
• [E. Eakin : (march 2004)] E. Eakin (march 2004): Study Finds a Nation of Polarized
Readers:http://query.nytimes.com/gst/fullpage.html?res=9F01EFD6103EF930A25750
C0A9629C8B63 accessed on April 29th 2008.
• [D. Jobber: 1998] D. Jobber, 1998, Principles and Practices of Marketing, McGraw-
Hill.
• [D. Hand, H. Mannila, P. Smyth: 2001] D. Hand, H. Mannila, P. Smyth (2001).
Principles of Data Mining. MIT Press, Cambridge, MA.
• [Y. Ichikawa, M. Nakamura, K. Hata, T. Nakagawa] Y. Ichikawa, M. Nakamura, K.
Hata, T. Nakagawa: Provision of Services According to Individual User Preferences
over a Cross-section of Sites Implemented with “Personalized-service Platform”.
• [Kotler and Armstrong: 2001] P. Kotler, G. Armstrong, (2001): Principles of
marketing, Prentice Hall.
• [Lendrevie, Lévy, Lindon: 2003] Lendrevie, Lévy, Lindon (2003) : Mercator, 7th
edition, Dalloz, 2003.
• [G. Linden, B. Smith, J. York: 2003] G. Linden, B. Smith, J. York (2003): Item-to-
Item Collaborative Filtering.
http://www.win.tue.nl/~laroyo/2L340/resources/Amazon-Recommendations.pdf
• [Jim Novo: unknown year] Jim Novo (unknown year): Comparing the Potential Value
of Customer Groups, available: http://www.jimnovo.com/Recency-Model.htm,
accessed on April 10th 2008.
• [S. Sandberg, D. Fasel: 2007] S. Sandberg, D. Fasel (2007): Managing Customer
Profiles for Effective CRM.
• [P. Schubert, 1999] P. Schubert, 1999, Virtuelle Transaktionsgemeinschaften im
Electronic Commerce: Management, Marketing und Sozia-le Umwelt, Lohmar - Köln:
Josef Eul Verlag.
Electronic Business Nadine Biegajlo
Prof. E. Meier Véronique Herrmann
- 32 -
• [Schubert, Petra: 2000] Schubert, Petra (2000): The Participatory Electronic Product
Catalog: Supporting Customer Collaboration in E-Commerce Applications, in:
Electronic Markets Journal, Vol. 10, No. 4, 2000, S. 229-236.
• [P. Schubert, U. Leimstoll: 2003] P. Schubert and U. Leimstoll, 2003, Extending ERP
systems in SMEs into personalized E-commerce applications.
• [Schubert and Koch: 2002] P. Schubert, Koch (2002): The power of personalisation:
customer collaboration and virtual communities. Eighth Americas conference on
information Systems.
• [S. Spiekermann, C. Parachiv: 2002] S. Spiekermann, C. Parachiv (2002): Motivating
Human-Agent Interaction: Transferring Insights from Behavioral Marketing to
Interface Design, in: Special Issue of the Journal of Research in Electronic Commerce.
• [J. Srivastava, R. Cooley, M. Deshpande, P. Tan: 2000] J. Srivastava, R. Cooley, M.
Deshpande, P. Tan, (2000): Web Usage Mining: Discovery and Applications of Usage
Patterns from Web Data
• [Sun Microsystems Inc. 2007 ] Sun Microsystems Inc. (2007): Vers un Profil Client
Unique, available: http://fr.sun.com/practice/software/soa/pdf/wp_scv_fr.pdf, accessed
on May 7th 2008.
• [R. Van Meteren and M. Van Someren (unknown year)] R. Van Meteren and M. Van
Someren: Using Content-Based Filtering for Recommendation
http://www.ics.forth.gr/~potamias/mlnia/paper_6.pdf, accessed on April 11th 2008.
• Le web mining et son application sur www.amazon.fr (2006)
http://www.oboulo.com/etude+cas+amazon+com, accessed on April 15th 2008.
• https://www.ntt-review.jp/archive/ntttechnical.php?contents=ntr200804le1.html
• http://wiki.media-culture.org.au/index.php/Amazon_-_Business_Model