TRIGGERING RULES FOR CONVERSATIONAL AGENTS IN

TRADING SITUATIONS

Grzegorz Dziczkowski, Arnaud Doniec and St´ephane Lecoeuche

Univ. Lille Nord de France, Ecole des Mines de Douai, IA, Douai, France

Keywords:

Conversational agents, Data mining, Web intelligence, Behavior analysis.

Abstract:

This paper describes a methodology to establish behavior rules for conversational agents on commercial web

sites. Our work is a contribution to a recent research ﬁeld: agent mining (Cao, 2009) which results from two

interrelated research area: Agent/Multi-agent system and Data Mining. The proposed methodology is based

on behavior analysis of e-commerce clients and customers’ segmentation. Our proposal has been applied on a

real commercial web site to construct the triggering rules of a virtual seller agent.

1 INTRODUCTION AND ISSUE

Electronic commerce (e-commerce) has become very

popular as the World Wide Web has grown, with many

websites offering on-line sales and e-commerce activ-

ity undergoing a signiﬁcant revolution.

However, on any commercial web sites, the conver-

sion rate (the ratio of visitors who convert casual con-

tent views to a commercial transaction) is very low.

Many reasons can be put forward to explain this ob-

servation. One of them is the lack of humanity in the

customer relationship.

The work described in this paper is a part of a

project which aims to study the implementation of

innovative, intelligent and automatic solutions to en-

hance on-line sales.

Project challenge is to build an intelligent agent which

is able to understand, to reason and to decide in an au-

tonomous way. The proposed agent has to be able to

interact with the visitors performing proposals of rel-

evant marketing messages at the right time and giving

useful advises. This virtual seller has to be a conver-

sational agent with the ability to:

• perceive and analyse the behavior of a new visitor,

• recognize an existing client,

• interact and communicate with clients through text and

using a human representation (avatar),

• understand the need of clients and decide upon which

action to take in collaboration with existing information

systems (CRM tools for instance).

This paper focuses on the triggering of the agent. In

other words, we try to answer the questions: when and

why should the virtual seller appear to a customer?

Our aim is to construct, for the agent, a set of behavior

rules which control the appearance and the beginning

of the discussion with the clients. We base our works

on an analysis of customers’ behaviors: we propose

to identify great categories of clients and to associate

some triggering rules to them.

2 LITERATURE REVIEW

By implementing new techniques to analyze more

precisely user’ actions, it becomes possible to extract

strong correlations between web site pages and typi-

cal behavior during a time period. The main difﬁculty

in the ﬁeld of Web Usage Mining is getting numerical

vectors describing the navigation of users.

The ﬁrst step to perform analysis of customer be-

havior is pattern discovery. A variety of machine

learning methods have been used for pattern discov-

ery. The approaches that most often appear in litera-

ture are: clustering, classiﬁcation, association discov-

ery, and sequential pattern discovery. We focus our

research on clustering methods. For this reason clus-

tering is the only approach described in more details

in this paper.

Generally clustering aims to divide a data set into

groups that are very different from each other and

whose members are very similar to each other. This

method has been used for grouping users with com-

mon browsing behavior (Srivastava et al., 2000). As

the customer may belong to more than one group

the clusters should be able to overlap. (Yan et al.,

1997) use the Leader algorithm to cluster user ses-

382

Dziczkowski G., Doniec A. and Lecoeuche S..

TRIGGERING RULES FOR CONVERSATIONAL AGENTS IN TRADING SITUATIONS.

DOI: 10.5220/0003183603820387

In Proceedings of the 3rd International Conference on Agents and Artiﬁcial Intelligence (ICAART-2011), pages 382-387

ISBN: 978-989-8425-40-9

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

sions. Each user session is represented by an n-

dimensional features vector, where n is the number

of Web pages visited during the last 30 minutes in the

session. The computation of weight is based on a dif-

ferent parameters (like the number of times the page

has been accessed, the amount of time the user spent

on the page). A partitioning clustering method is em-

ployed by (Cadez et al., 2000), which visualizes user

navigation paths in each cluster. In this system, users’

sessions are represented using categories of general

topics for Web pages. A number of predeﬁned cat-

egories are used as a bias, and URLs are assigned to

them, constructing the user sessions. The Expectation

Maximization (EM) algorithm, based on mixtures of

Markov chains is used for clustering user sessions.

An extension of partitioning clustering methods is

fuzzy clustering that allows the presence of ambigui-

ties in the data, by ’distributing’ each object from the

data set over the various clusters. Such a fuzzy clus-

tering method is proposed in (Joshi and Joshi, 2000)

for grouping user sessions, where each session in-

cludes URLs that represent a certain traversal path.

The Web site topology is used as a bias in comput-

ing the similarity between sessions. The site is mod-

elled using a tree, where each node corresponds to an

URL in the site, while each edge represents a hierar-

chical relation between URLs. The computation of

the similarity between sessions is based on the rela-

tive position in the site tree of the URLs included in

the sessions.

Model-based clustering methods have been also

used in (Paliouras et al., 2000). A probabilistic

method, a neural network Self-Organizing Maps, and

a conceptual clustering method, are exploited in order

to construct user community models (i.e. models for

groups of users with similar usage patterns). Com-

munity models are derived as characterizations of the

clusters and correspond to the interests of users’ com-

munities.

Despite of the variety of clustering methods that

have been used for Web usage mining, no work has

been done on the comparison of their performance.

The reason for this is the inherent difﬁculty in com-

paring clustering results, due to the lack of objective

criteria independent of the speciﬁc application (Pier-

rakos et al., 2003).

3 SELLER AGENT TRIGGER

In order to determine the onset triggering factors of

seller agent for each customer, we are interested in at

least 2 different aspects, i.e.:

• how can the agent characterize clients’ behaviors, and

• how will the agent’s appearance be performed.

The ﬁrst aspect listed above is in the ﬁeld of Web

Usage Mining. Research has focused on methods,

based on Web Mining and Machine Learning algo-

rithms, to automatically analyze different data (from

questionnaires, observation of sales areas, interviews

with clients and vendors, loyalty programs or internet

data such as web server logs) in order to obtain the

most relevant information.

To answer the second aspect, we will study the

seller agent trigger based on customer navigation

analysis.

It means that our system has to categorize a user

who is surﬁng on the commercial website. The seller

agent should evaluate user expectations and motiva-

tions from his apparent behavior and habits. The

source of data is limited to the data we can collect

(e.g. visited pages, request, connection time, the use

of site tools, etc.). The main problem is to determine

the kind of client’s behaviors we could regroup based

upon navigation information and how user’s classes

can be evaluated. The proposed methods also take

into account the visit context like the year period (va-

cation, sales period, etc.), the day of week or the hour.

By the expression creation of customers’ behavior

we mean the analysis of customers’ navigation traces

on the commercial website in order to detect the most

signiﬁcant and common proﬁles - clustering. The cre-

ation of the triggering rules set is based directly on the

customers’ behavior types detected.

To perform acceptable triggering rules of the agent

we should ﬁrst identify the web users, then create and

analyze the general customers’ behavior and at last

deﬁne the set of rules of virtual agent triggering.

We concentrate our research on two sorts of rules

of virtual seller agent trigger: the speciﬁc and the gen-

eral rules. The general rules are divided into direct

rules and transition rules.

As an example of speciﬁc rules, we can consider

the cases where a user enters on support pages of

ecommerce website, or when the client try to buy sev-

eral products of the same family with incoherent pa-

rameters (like for example a size of a duvet cover may

be different from size of a ﬂat sheet). In ﬁrst case we

can lunch the virtual seller in order to perform assis-

tance, in the second case the agent can suggest the

possibility of confusion of products features. These

rules could satisfy a customer but as they are too spe-

ciﬁc and detailed, the number of agent triggers based

on these rules will be the most of time low. Even if the

speciﬁc rules will have high precision it seems impos-

sible to predict all the situations they can be adapted,

it means that the recall will be low.

For this reason we have to develop general rules

TRIGGERING RULES FOR CONVERSATIONAL AGENTS IN TRADING SITUATIONS

383

which should be more global. As we already de-

scribed above we will establish and analyze cus-

tomers’ behaviors. We predicted the set of behaviors’

types. As an example of general rules we can con-

sider the situation when customer changes his behav-

ior from one type to another type during his naviga-

tion. We assume that these changes may indicate that

the client gets lost, needs some help, or is confused,

we can also detect that a customer checks for more

details or compares the prices/products/products’ fea-

tures ... (conclusion - that the customer could be inter-

ested to talk with seller, even with the virtual seller).

Nevertheless, the change of customer’ behavior can

show that a client lost or earned his interest in prod-

ucts proposedon the website what also is an important

criterion to seller triggering. On the opposite side,

when the client follows a common type of naviga-

tion (behavior) we can assume that the client knows

exactly what he wants and he pursues the objective.

The general rules implementation requires the intro-

duction of client behavior clustering. If a rule refers

to one cluster we have a direct rule, and if a rule refers

to the changes of clusters during navigation we have

a transition rule.

As we can infer from this section, the most impor-

tant part in our research is the customers’ behavior

analysis.

4 PROPOSED METHODOLOGY

The proposed methodology to perform the set of vir-

tual seller triggering rules consist in 3 steps:

• feature selection (from raw logs ﬁles)

• performing the clustering (and assign commercials la-

bels to clusters)

• establish the set of triggering rules (speciﬁc and gener-

als rules)

The main idea of analysis the customers’ behavior

and transition of clusters during user navigation was

the limitation the customers’ sessions to 10, 15 and

20 actions. Each action corresponds to one page view

by the users. The session limitation means that for ex-

ample one user session with 40 actions was changed

on 3 sessions with 10, 15 and 20 actions. Then the

clustering was done separately for each limitation (10,

15, and 20). The choice of actions numbers in new

limited session was due to the two reasons. First it

was not possible to perform a pertinent clustering us-

ing less than 10 actions for user session because the

cluster was not enough signiﬁcant and differences be-

tween clusters were negligible. Second reason was

more commercial - the agent should not wait to much

time before appear in front of customer and only 10%

of sessions are longer than 20 actions.

After repeating this treatment for each session,

we performed the clustering using different features

for sessions’ limitation. The pertinent clusters (from

statistic point of view) was then analyzed with mar-

keting experts of our commercials partners in the goal

to establish the best clustering from marketing point

of view. During this state the commercial labels was

assigned to the clusters in the goal of prediction the

set of triggering rules.

At the end the speciﬁc and general rules was cre-

ated. The details of each step are described below.

During our research we were in possession of our

commercial partner’s data for the one entire year pe-

riod (120 Gb). For our learning base we use the sam-

ple of data of one month. We choose the month of

April due to the miss of any marketing actions. The

database for one month represents more than 300 000

of sessions with more then 10 actions performed. On

account of the scale of the database the treatment is

time consuming.

The logs ﬁles delivered from our partners were in

the form of NedStat logs ﬁles. The main difference

between this format and Extended logs ﬁles is that to

each client the unique Id is assigned (based on cook-

ies).

Before selecting the navigation features, the hier-

archy of web site was performed. We divided the site

on 7 different universes: store (the main universes

with for example products list), quick order (direct

purchases by entering catalogue reference), shopping

cart (purchase), sales, consulting (customers ques-

tions, FAQ), condition (terms of sale, shipping), vari-

ous (all others like for example home page). The uni-

verse store was divided on three levels of hierarchy:

section, subsection and sub subsection. Generally the

ﬁnal product page corresponds to sub subsection.

Example:

the final page of product:

Tablecloth XYZ

--> universe: Store; section: Table;

subsection: Tablecloth; sub subsection: Tablecloth XYZ

Based on this hierarchy, we selected 36 session

features witch describe customer navigation of our

commercial partners web site Table 1.

The set of triggering rules depends directly from

the navigation feature selection. For this reason, it

seems necessary to describe them more precisely:

”User ID” describes the id based on cookies, unique

id for a user; ”Session ID” designs the session id

during one day, each session is considerate as closed

after 30 minutes without any action; ”Purchase”

is Boolean value which shows if the customer al-

ready made a purchase during his action; ”Reduction

ICAART 2011 - 3rd International Conference on Agents and Artificial Intelligence

384

Table 1: All the session features possible to retrieve.

User ID Session ID

Day/Month/Year Hour of begin

Hour of end Purchase

Total amount Nb products bought

Nb references bought Reduction code

Knew customer Source of navigation

Total time Time universe (1-7)

Nb total pages seen Nb pages universe (1-7) seen

Nb universes changes Nb sections changes

Nb subsection changes Nb subsubsection changes

Nb of section seen Nb of subsection seen

Nb product pages seen Nb of same product seen

code” - Boolean value describes the presence of re-

duction code during the purchase; ”Knew customer”

describes whether the user has been recognized as a

client who has already made a purchase on the site;

”Source of navigation” describes whether the user is

entered into our commercial partner’s site voluntarily

by using for examplethe search engine, or was pushed

to visit the site by the mail company; ”Total time”

gives the length of a session; ”Total universe (1-7)”

represent 7 different features which describe the time

that a visitor spend on each universes; ”Nb total pages

seen” describes the number of all the pages visited

by the user during a session; ”Nb pages universe (1-

7) seen” represent 7 different features which describe

the number of pages visited by a user on each uni-

verse; ”Nb universes/section/subsection/sub subsec-

tion changes” - 4 features which describe the num-

ber of changes the user make during his navigation,

if for example the user switch the universe and then

came back to previous one the value of this feature

is equal to 2; ”Nb of section/subsection seen”- 2

features which describe the number of different sec-

tion or subsection seen during the user session; ”Nb

product pages seen” describes the number of product

pages seen in total; ”Nb of same product seen” de-

scribes the sum of product pages that have been seen

several times.

4.1 Customer Behavior Analysis

Once the above treatment was performed for users

sessions of our learning base (month of April -

1000000 sessions), the customer behavior analysis

can be implemented. At the beginning, the work cor-

responds to analyse the features obtained (data min-

ing techniques) in order to detect the most discrimi-

nated features. For customer behaviors detection we

decided to perform the clustering. Our approach con-

sist of the study of users comportment in two cases:

ﬁrst comportment can be assign to the cluster the user

belongs to (direct rules), second comportment is an-

alyzed in accordance with the users transition from a

cluster A to a cluster B during his navigation (transi-

tion rules). The speciﬁc rules depends on the charac-

teristic comportments of user navigation and not on

the clusters detected, they are build in empiric way

and will not be described in this paper.

In order to determine the changes in the user nav-

igation, we perform an analysis on client’s session

which assigns the customer to one of the classes

which were previously detected (cluster). In order to

choose the best clustering from commercial point of

view, our statistical results were discussed with our

commercial partners’ experts. In this way clusters for

each limitation get their commercial labels. The gen-

eral rules are based on the labels form and their tran-

sition (due to the change of user comportment during

navigation).

As it’s difﬁcult to calculate the performance of

clustering model the choice of ﬁnal clustering was

done by marketing experts based on statistic descrip-

tions of clusters. The data was pre-treated before

and normalized. The Ward clustering method was

implemented, the number of cluster was calculated

in accordance of cubic criterion cut-off parameters

(CCC < 3), and the cluster criterion chosen was Least

Squares.

The interesting results for customer behavior anal-

ysis on this stage are the features which are the most

discriminated for each session limitation, and the

number and parameters of clusters.

Below we will discuss our results of clustering which

was chosen depending on statistic and marketing cri-

terions.

User Session Limitation to 10 Actions

We obtain 6 clusters. The most discriminated char-

acteristics are: [table 2]

Table 2: Discriminated features for 10-actions sessions.

Source of navigation Nb pages universe store seen

Nb of subsection seen Nb pages universe shopping cart seen

Nb subsection changes Nb pages universe various seen

Purchase Nb pages universe quick order seen

Hour of begin Nb of section seen

Nb sub subsecion changes

The example of description of cluster from statis-

tic and marketing point of view is as follows:

Statistic Features. Cluster Id: 6; Frequency of clus-

ter = 23357 (12.6%); Root-mean-square standard de-

viation = 0.13; Source of navigation: Mail = 0.88; Nb

pages universe store seen = 8.62; Nb of subsection

seen = 7.14; Nb pages universe shopping cart seen =

TRIGGERING RULES FOR CONVERSATIONAL AGENTS IN TRADING SITUATIONS

385

0.03; Nb subsection changes = 1.53; Nb pages uni-

verse various seen = 1.33; Purchase: non = 1; Nb

pages universe quick order seen = 0.01; Hour of begin

= 53511; Nb of section seen = 2.16; Nb sub subsec-

tion changes = 1.21

Marketing Label. The cluster refers to sessions in

which the gateway is not directly accessible but with

an e-mail that pushes the speciﬁc store section to a

customer. The client is at least a lead which gave his

opt-in (level of commitment higher than if entry from

search engine). Of the ﬁrst 10 pages no product has

been moved to shopping cart. At this point, a dialog

with a virtual seller could be desirable.

User Session Limitation to 15 Actions

We obtain 8 clusters. The most discriminated char-

acteristics are [table 3]:

Table 3: Discriminated features for 15-actions sessions

Source of navigation Nb pages universe store seen

Nb sub subsecion changes Nb pages universe various seen

Nb of section seen Nb product pages seen

Nb of subsection seen Nb universes changes

Purchase

The example of description of cluster from statis-

tic and marketing point of view is as follows:

Statistic Features. Cluster Id: 8; Frequency of clus-

ter = 1327 (1.2%); Root-mean-square standard devi-

ation = 0.14; Source of navigation: Mail = 0.08; Nb

pages universe store seen = 6.03; Nb sub subsection

changes = 1.17; Nb pages universe various seen =

6.09; Nb of section seen = 1.60; Nb product pages

seen = 2.61; Nb of subsection seen = 2.11; Nb uni-

verses changes = 1.1; Purchase: non = 0

Marketing Label. The cluster refers to sessions in

which customer already purchase products and vali-

dated his command. The other features are not sig-

niﬁcant and it’s the only cluster where client already

purchase his product. Despite the presence of pur-

chase the client still navigates on web site or just ﬁn-

ishes the validation step. If the customer will stay an

a website for few actions there is possibility of seller

agent trigger.

User Session Limitation to 20 Actions

We obtain 8 clusters. The most discriminated char-

acteristics are: [table 4]

Table 4: Discriminated features for 20-actions sessions.

Nb of subsection seen Nb pages universe store seen

Source of navigation Nb subsubsecion changes

Nb pages universe various seen Nb universes changes

Nb product pages seen Nb of section seen

Purchase Nb pages universe shopping cart seen

The example of description of cluster from statis-

tic and marketing point of view is as follows:

Statistic Features. Cluster Id: 2; Frequency of clus-

ter = 25335 (27.6%); Root-mean-square standard de-

viation = 0.11; Nb of subsection seen = 8.55; Nb

pages universe store seen = 18.5; Source of naviga-

tion: Mail = 0; Nb sub subsection changes = 0.7;

Nb pages universe various seen = 1.36; Nb universes

changes = 1.32; Nb product pages seen = 3.79; Nb

of section seen = 1.46; Purchase: non = 1; Nb pages

universe shopping cart seen = 0.11

Marketing Label. The cluster refers to sessions in

which the customer doesn’t have a precise idea of

product he want to purchase. Client often changes the

subsections of web site hierarchy and do not visit the

product pages of the same family. The navigation is

based on store part of web site but like there are only

few products’ pages visited and the section is usually

changed it seems that the customer checks different

family of product and gets the general idea of internet

shop offer. The client did not indicate interest of the

speciﬁc products, or family of products.

4.2 The Set of Triggering Rules

During the step of clustering, we ﬁnd that in differ-

ent session’s limitations (10, 15, and 20) we have the

corresponding cluster (if a customer doesn’t change

his behavior during navigation). We assign the direct

and transition rules with the help of commercial ex-

perts which has a strong knowing how in the ﬁeld of

sales. The rules were assigned by analyzing all selling

scenarios which are interesting from marketing view.

The general form of the set of triggering rules is as

follows:

∈ c

∧C

∈ c

∧C

∈ c

) −→ Ta{Id, K, R}

where, C

, C

- customer proﬁle at 10, 15

and 20 actions; c

, c

- existing cluster for each

limitation (i=6, j= 8, k=8); Ta - triggering action; Id -

Id User; K - context of triggering rules, R - the set of

user navigation features.

The virtual agent triggering can be seen as a

condition-action rule: if a criterion then a trigger-

ing action (which answer questions when and why).

The antecedent describes the user navigation on ses-

sion limitation and the consequent describes the pres-

ence of triggering rules. The triggering rule parame-

ters are: Id user for identify the client for which the

virtual seller will appear immediately, and parameters

which are transmitted to our project partners to per-

form the correct dialogue between client and virtual

seller. These parameters are represented by the con-

text of triggering (see below)and the set of users’ nav-

igation features which contains the value of features

ICAART 2011 - 3rd International Conference on Agents and Artificial Intelligence

386

from Table 1 and the description of all section, sub-

section and sub subsection visited.

We present an example of a direct and a transition

rule.

Case 1.

Context. A rule concerns the cluster 8 of session lim-

itation to 15 actions. It refers to a customer who al-

ready validated an order before the onset of virtual

seller. The case was described in section 4.1

Trigger. The customer has already ordered but still

browsing on the site. If a customer will not left the

site in a few seconds, the virtual agent should appear

to push new products based on recommender system

and propose to back to shopping cart to add some

goods.

Case 2.

Context. A rule concerns the transition of cluster

3 to 7 to 2. During two ﬁrst session limitation (10

and 15) the customer exhibits the behavior of a client

who knows his taste, who checks for precise products

of particular families, who spends time on the

similar products’ pages. The last session limitation

is different and describes a general navigation on the

store without main idea of purchase (section 4.1).

The client arrived spontaneously on the site and has

a priori a clear idea of the desired product (many of

the same family product pages seen). After about 6

minutes, he stops to check product descriptions and

seems to disperse in the section of the highest level

Trigger. There is a risk that the customer will leave

the site. The virtual agent should appear immediately

to struck up a discussion about the consulted products.

5 CONCLUSIONS

The presented work was done for one month without

signiﬁcant sales and marketing campaigns. The ﬁrst

analysis of clusters performed for sale period show

strong differences. The future work includes the anal-

ysis of number of year period for which the clustering

will be implemented in ﬁnal system.

This paper deals with the design of behaviors for a vir-

tual seller agent. Such agent should mimic the com-

portment of human vendor in real shop. More pre-

cisely, our work focuses on the way to deﬁne rules

for the triggering of the discussion between the agent

and the client. The general steps presented in this

paper are: navigation features selections, clustering

compatible with marketing assumptions, and design

of triggering rules.

We manage the selection of 36 customer naviga-

tion features. The adaptation for a new e-commerce

site is feasible in a relatively short time because they

are based on navigation patterns and not on URL’s.

We also can assume that the format of e-commerce

shops remains in the similar form. We succeed in es-

tablish the ﬁnal clustering model for each user ses-

sion limitation of our approach. The choice of ﬁ-

nal clustering model depended on the quality of clus-

ters’ commercial labels assigned. At the end the set

of triggering rules was performed based most of all

on clusters labels. We also implemented the super-

vised classiﬁcation on our data and we obtained good

and promising results which can evaluate the step of

navigation feature selection for clustering part. We

didn’t ﬁnd any related work on automatic triggering

of agent for e-commerce site. The main contribution

of this paper is the methodology used to design the set

of triggering rules which mainly depends of customer

behavior and his navigation on the web site.

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