IMPROVING THE CUSTOMER INTELLIGENCE WITH
CUSTOMER ENTERPRISE CUSTOMER MODEL
Domenico Consoli, Claudia Diamantini and Domenico Potena
Dipartimento di Ingegneria Informatica, Gestionale e dell’Automazione,
Universit´a Politecnica delle Marche, Via B. Bianche, 60131, Ancona, Italy
Keywords:
External Information, Customer Opinions, Customer-Centred Enterprise Information System, Customer Re-
lationship Management, Web 2.0, Text Mining.
Abstract:
Customer can be profitably considered as an enterprise strategic asset. So, it is very important to define a
bi-directional communication channel between the customer and the enterprise itself. In this work we propose
a model named Customer enterprise Customer (CeC), that continuously hears customer opinions about enter-
prise products/services and behaves accordingly. CeC exploits the inherent nature of the growing so-called
web 2.0, where users spontaneously join and spend time in sharing their reviews. The CeC model first collects
and analyses customer opinions, then reports dysfunctions about the product/service to competent offices for
giving feedback to customers, either by making the necessary improvements or by answering customers. The
proposed model crosses all internal business functions, from design to production, and it is placed on the top
of a customer-centred Enterprise Information System. In this work an overview of CeC model is given.
1 INTRODUCTION
Customer opinions constitute a gold mine for generat-
ing high value added information for making strategic
decisions (Hagel and Rayport, 1999; Fabris, 2007).
The gathering of opinions is classically performed
by ad-hoc interviews (often by phone or by email),
that are based on pre-structured questionnaires. This
approach, besides to be expensive, is limited. In
fact, a customer expresses her opinions by using nat-
ural language better than by answering to a structured
question (Bolasco et al., 2005). In order to overcome
this limit, an enterprise may take advantages of the
huge amount of information available over the Inter-
net in form of forum, blog, wiki, and other web 2.0
tools.
It is, in fact, evident as the coming of web 2.0
promoted the birth of a sharing business philosophy
and stimulated conversations and exchange among
people (Wenger and Snyder, 2000). Customers ex-
ploit web 2.0 tools for both expressing their opinions
about a product and suggesting solutions for improv-
ing it. The enterprises often encourage exchange of
opinions, by making available virtual communities,
e.g. Italian Nikon’s Camera forum, where people re-
view Nikon products (http://www.nital.it/forum/), the
blog on Benetton products (http://benettontalk.com),
and so on. Furthermore there are various web sites
that collect and make free available customer reviews
(Cho et al., 2002): epinions.com, cnet.com, com-
plaints.com, planetfeedback.com, ecomplaints.com,
ciao.it, dooyoo.it.
To capitalize customers opinions is very important
for an enterprise, both for the improvement of prod-
ucts and for the reinforcementof the customer loyalty.
The customer will be motivated to be loyal if the en-
terprise shows a strong attention to her needs and her
identity.
It is estimated that for each customer which makes
a complaint, there are 4 to 26 customers do not claim
even if they are dissatisfied. Each dissatisfied cus-
tomer, on average, expresses her mood to 10 people.
So, in the best case, behind each complaint there are
40 potential skeptical customers. Nevertheless, it is
interesting to note that if a customer who complained
sees the enterprise accepting the review and solving
the related problem, then her propensity to purchase
from the same company increases of 3 times, while
for the customer who has not claimed, the propensity
to purchase increases by 6 times (Lombardi, 2006).
In the last years the enterprise informative sys-
tem have been designed in a customer-centric way to
gather more accurate and detailed information about
customers. Enterprise information System (EIS) feels
323
Consoli D., Diamantini C. and Potena D. (2008).
IMPROVING THE CUSTOMER INTELLIGENCE WITH CUSTOMER ENTERPRISE CUSTOMER MODEL.
In Proceedings of the Tenth International Conference on Enterprise Information Systems, pages 323-326
DOI: 10.5220/0001733503230326
Copyright
c
SciTePress
Figure 1: The Customer-enterprise-Customer model.
the necessity to have a model that gives, in real-time,
the situation of the market and, in particular, that
makes a continuous monitoring of the customer opin-
ions.
In this paper a new model named Customer en-
terprise Customer (CeC) is proposed, that is aimed to
continuously hears customer opinions about the prod-
uct/service and behavesaccordingly. To this end, CeC
first collects and analyses customer opinions freely
available over Web 2.0 tools, then reports dysfunc-
tions about the product/service to competent offices
for the necessary improvements or for answering cus-
tomers.
Differing from classical operational CRM, CeC
model is not limited to Customer Area (e.g. mar-
keting, sales and post-sales), but it is introduced as a
frameworkthat crosses all internal business functions,
from marketing to design and production. So, we
placed our proposal on the top of the Enterprise Infor-
mation System. Implementing a CRM model the en-
terprise is able to answer a customer only if her ques-
tion is already in the CRM knowledge base. On the
other hand, CeC addresses the customer opinions to
competent offices analysing from time to time the un-
structured texts containing opinions themselves. This,
of course, introduces various issues to be consid-
ered, mainly regarding Natural Language Processing
and Mining. In this work we briefly discuss these
and other technical issues, concentrating on giving an
overview of the proposed model.
The CeC model forms part of enterprise models
introducing the concept of prosumer (Toffler, 1990),
where it is emphasized the relationship between pro-
ducer and customer. In this vision, the customer plays
an active role in the enterprise, in particular he/she
participates as a co-producer or as a consultant. Ex-
amples of co-producer are customers buying furniture
at the IKEA and then finishing the productive process
by assembling it at home. An example of consultant
prosumers is given in the Internet site of the “Fiat
500” (500 Wants You - www.fiat500.com), where each
visitor expressed her/his creative contribution to the
design of the new car.
The following Section is devoted to detail the CeC
model, briefly discussing its main parts. Section 3
ends the work.
2 THE CeC MODEL
In Fig. 1, we show the Customer - enterprise - Cus-
tomer model. The model is divided into three phases:
sensing, mapping and actuation. Sensing consists of
crawler agents that scan the web 2.0 tools (e.g. web
site, forum, blog and so on) for finding and gath-
ering opinions. Then, since customer expresses her
opinions in unstructured text, we have to prepare and
process opinions by text mining algorithms in order
to extract useful complaints. In the mapping phase,
complaints are mapped and routed to specific compe-
tence centers (CCs), that can be departments, inter-
nal experts, external consultants, and others groups of
people that are competent in the problem expressed
in the complaint. The mapping is described by the
Complaint to Competence Matrix (CCM), e.g.:
Complaints: unfocused photos, distorted images
CC: Optic Department;
Complaints: LCD monitor does not shown photos
CC: Electronic Department.
Finally, the goal of the actuation phase is to react to
stimuli, either by making changes to the product or by
answering to customers (messaging). In this second
case, the enterprise becomes, in a peer-to-peer web
2.0 vision, an actor as a customer.
ICEIS 2008 - International Conference on Enterprise Information Systems
324
2.1 Sensing
The sensing module is composed by a set of crawler
agents specialized in different protocols (http, https,
pop3, imap4, nntp), that are responsible to inspect
and retrieve information, respectively, from web sites,
blogs, chats, e-mails, newsgroups and so on. Each
agent can be configured with policies to extract only
texts (not structured, semi-structured or structured)
with advanced techniques of NLP (Natural Language
Processing). The various crawler agents are usually
coordinated by a crawler manager which defines poli-
cies shared by all agents (Boldi et al., 2004). Crawlers
could work in parallel exploiting the intrinsic nature
of the network.
The customer reviews may be referred to positive
or negative opinions. The CeC model has its principal
focus on negative opinions. So, in order to reduce the
searching space, intelligent agents can be exploited
(Chan, 2008; Jansen et al., 2006). Such agents im-
plement appropriate strategies and heuristics for ana-
lyzing only documents containing negative opinions.
In order to distinguish between positive and negative
opinions we can exploit techniques from the affective
computing field (Grefenstette et al., 2004), for the in-
dividuation of emotions into a text.
In our model, collected reviews populate the
Opinion Warehouse, that forms the corpus to be anal-
ysed by text mining algorithms in the next mapping
phase.
Some examples of customer reviews are:
Review n.1: The edges of the photograph
were faded;
Review n.2: The figures in the foreground
aren’t sharp.
2.2 Mapping of Complaints to
Competence Centers
The main part of this phase is the Text Mining. The
goal of the text mining part is the application of al-
gorithms for pre-processing and for classification of
customer opinions. Since opinions are written in Nat-
ural Language, we need specific pre-processing tech-
niques: elimination of stop-words (articles, conjunc-
tions, prepositions), division of the phrases into single
words, identification of different parts of speech POS
(nouns, verbs, adjectives), lemmatization, and numer-
ical representationof text data (Berry and Castellanos,
2007). In the pre-processing phase it is important the
use of the lexical ontology (e.g. WordNet). The ontol-
ogy containsa set of explicit assumptions that concern
the meaning of the words.
Table 1: An example of Complaint Competence Matrix.
Complaints
distorted unfocused LCD
images photos
Competence Mechanic Dep. 0.15 0.35 0.00
Centers Optic Dep. 0.75 0.05 0.00
Electronic Dep. 0.10 0.60 1.00
Classification model allows us to associate an
opinion with one or more complaints on the basis of
measures of semantic similarity. For instance, the
opinions of the previous example can be classified
as ”The photos are unfocused”. In order to design
the classification model, information about both prod-
ucts/services and the internal organization of the en-
terprise has to be taken into account. To this end can
be exploited a Business Ontology formed by two sub-
ontologies: Product Ontology and Enterprise Ontol-
ogy. The former defines in detail all products, their
components, how they are produced and the related
competence centers. The latter is a conceptualiza-
tion of the whole enterprise: functions, business pro-
cesses, competence centers, and so on.
The output of the data mining process are com-
plaints, that are used in input to the Complaint Cen-
ter Matrix (CCM), a model associating a complaint to
one or more Competence Centers (CCs) (see example
in tab. 1). In order to build the CCM, we can exploit
information contained in the Business Ontology. The
values of CCM elements represent the weight of a De-
partment/CC in facing the complaint inherent prob-
lem.
At this point, a message containing the compliant
and related reviews is automatically routed to indi-
viduated CCs. In the case of enterprise with a lot of
CCs, we can improve the effectiveness of the routing
by sending messages only to most competent centers,
that is CCs with CCM values over a given threshold.
In the example of Fig. 1, we see that the Optic De-
partment is the most competent center for the problem
regarding the “distorted images”.
2.3 Actuation
The actuation phase is the less automatic one, cause
the decision making process is mainly performed by
people. In this phase CCs exploit the complaint mes-
sages for the improvement of products/services or for
answering customers. In both cases, the enterprise
give a (indirect or direct) feedback to the customer.
As a matter of fact, the improvementof a product is an
indirect message, communicating that the enterprise
acknowledged the customer complaints. On the other
hand, the enterprise may direct answer customer over
IMPROVING THE CUSTOMER INTELLIGENCE WITH CUSTOMER ENTERPRISE CUSTOMER MODEL
325
the same channel used by the customer for express-
ing her opinions. For instance, if opinions was found
over a public forum, the enterprise participates in the
forum itself as any other user.
The knowledge of the web sites dealing with en-
terprise products and services, can be also exploited
for improving the effectiveness of communications
towards the market. As a matter of fact, the message
is addressed to interested people.
Furthermore, we introduce a feedback line from
CCs and CCM, that allows managers to dynamically
adjust the weights of the CCM, improving the effec-
tiveness of the mapping and the routing (dotted line in
Fig. 1).
3 CONCLUSIONS
In this work we propose the CeC model, a customer-
centred enterprise information system model, aimed
to exploit customer opinions for enriching the enter-
prise internal knowledge.
The main characteristics of this model is that the
enterprise just limits itself to observe the web and,
in particular, communities of customers discussing
about enterprise products or services. Our model ex-
ploits the inherent nature of the growing so-called
web 2.0 tools, where users spontaneously join and
spend time in sharing their reviews. The CeC model is
aimed to find, collect and analyse opinions, to react to
stimuli and to send feedback to customers. So, while
the enterprise plays a passive role in the discussions,
it becomes participative when it send return messages
to market, either by making changes to products (in-
direct messaging) or by direct answering customers
by using the same web 2.0 tool.
The core of CeC is formed by the Sensing and
the Mapping phases, where customer opinions are se-
lected and analysed by tools derived from NLP and
Text Mining areas. In these phases, after an appro-
priate pre-processing, unstructured opinions are clas-
sified as complaint classes, and then routed to centers
most competent to respond to the complaint.
The CeC model is part of a more wide projet,
that, at this moment, is at an embryonic state. We
implemented only some crawler agents and designed
the opinions warehouse for collect customer opinions
about photo cameras and holiday villages domains. In
next works we want to follow two main directions. In
the former, we will use the collected warehouse for
mapping phase, studying Text Mining algorithms and
techniques most suitable for the specific problem. To
this end, it will be needed to also design the business
ontology.
In the latter direction, we will investigate tech-
niques for design intelligent crawlers that are able
to distinguish both useless from informative sources,
and negative from positive customers reviews.
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